Chemometrics and data mining in chemistry - IS MUNI

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MASARYK UNIVERSITY

Faculty of Science

Department of Chemistry

FILIPPO AMATO

Chemometrics and data mining in chemistry

DOCTORAL THESIS

Supervisor: Prof. RNDr. Josef Havel, Dr.Sc.

Brno, 2014

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Bibliographic Identification

Author’s name Filippo AMATO

Supervisor Prof. RNDr. Josef Havel, DrSc, DrHc.

Thesis title Chemometrics and data mining in Chemistry

Study program Chemistry

Study domain Analytical Chemistry

Year of defence 2015

Key words Chemometrics, Artificial Intelligence, Soft-

Modeling, Matrix-Assisted Laser Desorption

Ionisation Mass Spectrometry, Isotopic Pattern

Modeling, Artificial Neural Networks, Orthogonal

Projection to Latent Structures Discriminant

Analysis

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Declaration

I hereby declare on my Honour that I worked on thes thesis independently, except

where otherwise stated or fully acknowledged the literature adapted from other sources.

Brno, December 2014 Filippo Amato

Filippo Amato, Masaryk University, 2014

All Rights Reserved, Masaryk University

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ACKNOWLEDGEMENTS

I would like to sincerely thank my Ph.D. supervisor Prof. Josef Havel, Dr.Sc., Dr.Hc.

for his kind support and valuable advices during my studies. He has guided me through

the difficult path of Ph.D. and I am very grateful for this.

I am sincerely grateful to my family and especially to my parents, for their support

during my studies.

My sincere thank to my colleagues and friends: Anton Salykin, Oleksandra Lemesko,

Nagender Reddy Panyala, Annapurna Pamreddy, Jan Houška, Lenka Kolářová,

Kateřina Šutorová, Vlasta Štěpánová, Lubomír Prokeš, Ravi Mawale, Mayuri Vila

Ausekar and Lenka Elečková for their nice friendship.

Un ringraziamento speciale va al mio caro prof. Vincenzo Romano. Il suo ricordo non

mi abbandona mai, cosi come i suoi preziosissimi insegnamenti, consigli e moniti.

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Abstract

The Thesis deals with chemometrics and with "Data evaluation" in Chemical Sciences.

Methods such as: principal component analysis (PCA), non-linear principal component

analysis (NLPCA), factor analysis (FA), orthogonal projection to latent structures

discriminant analysis (OPLS-DA), experimental design (ED), multiple linear regression

(MLR), hierarchical clustering, multi-layer feed-forward artificial neural networks

(MLFF ANNs) and autoassociative artificial neural networks (AA ANN) have been

used in solving several problems:

1. Mass spectrometric data obtained from experiments of laser ablation synthesis

(LAS) of various inorganic materials (Au-C, Au-Se, Au-Ag-Te, etc.) have been

evaluated via the modeling of isotopic patterns and in some cases the problem of

signal deconvolution has been solved using an algorithm based on least-squares

optimization.

2. Combined ED-ANNs approach for the evaluation of kinetic data for the purpose

of kinetic constants determination and multicomponent kinetic analysis has been

proposed and published.

3. Mass spectrometric fingerprints of whole-mammalian cell-lines have been

evaluated by statistical methods and OPLS-DA technique. Original criterion for

the recognition of possible markers of subtle cell changes from the results of

OPLS-DA applied to MALDI-TOF-MS data has been developed.

4. A new general definition of "additive effect" of two or more drugs in multi-drug

mixtures has been developed as well as a robust and validated chemometric

method based on the combination of ED and ANNs to search for the optimal

composition of multi-drug mixtures showing the maximum synergistic effect.

The developed method overcomes the drawbacks, limitations and errors present

in current techniques such as Combination Index and Isobologram ones.

5. A review concerning the application of ANNs in medical diagnosis has been

elaborated and published (as Editorial).

6. A review concerning past, present and future application of coordination

compounds in anti-cancer therapy was submitted.

7. Several other papers dealing with applications of chemometrics and artificial

intelligence in proteomics are in progress.

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Abstrakt

Doktorská distertace se zabývá Chemometrií a "vyhodnocením dat" v chemických

vědách. Metody, jako jsou: analýza hlavních komponent (PCA), nelineární analýzy

hlavních komponent (NLPCA), faktorová analýza (FA), ortogonální projekce na

latentní struktury diskriminační analýza (OPS-DA), experimentální design (ED),

vícenásobné lineární regrese (MLR), hierarchické shlukování, vícevrstvé dopředné

neuronové sítě (MLFF ANNs) a autoassociativní umělé neuronové sítě (AAANN) byly

použity při řešení několika problémů:

1. Hmotnostní spektrometrie - údaje získané z pokusů s laserovou ablační syntézou

(LAS) různých anorganických materiálů (Au-C, Au-Se, Au-Ag-Te, atd.) byly

vyhodnoceny pomocí modelování izotopových obálek a v některých případech byl

problém dekonvoluce signálu vyřešen použitím algoritmu na bázi optimalizace

s využitím metody nejmenších čtverců

2. Byla navržena a publikována metoda kombinace metody plánování pokusů a

umělých neuronových sítí ( ED-ANN) jako nový přístup k vyhodnocení kinetických

dat pro účely stanovení kinetických konstant a provícesložkovou kinetickou analýzu..

3. „Hmotnostně spektrometrické Fingerprints “ (otisky) celých-savčích buněčných linií

byly vyhodnoceny pomocí statistických metod a OPLS-DA techniky.

4. Byla vyvinuta nová obecná definice "aditivního účinku" dvou nebo více léčiv v

multi-komponentní směsi léčiv a kombinací ED a ANN byl nalezen algoritmus pro

hledání optimálního složení takových směsí s maximálním synergickým efektem.

Vyvinutá metoda překonává nevýhody, omezení a chyby, které jsou ve stávajících

technikách.

5. Bylo publikováno REVIEW týkající se aplikací ANN v lékařské diagnostice a

předložena metodika těchto aplikací.(publikováno jako Editorial).

6. Review týkající se minulosti, současné a budoucí aplikace koordinačních sloučenin v

léčbě rakoviny bylo submitováno.

7. Několik dalších prací, které se zabývají aplikacemi chemometrie a umělé inteligence

v proteomice jsou ve vývoji.

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Content

1. INTRODUCTION 10

1.1. Chemometrics 10

1.2. Mass Spectrometry 11

2. AIMS OF THE THESIS 12

3. RESULTS AND DISCUSSION 13

3.1. Artificial neural neworks combined with experimental

design: A "soft" approach for chemical kinetics 14

3.2. REVIEW, Artificial neural networks in medical

diagnosis (Editorial) 15

3.3 Development and validation of a general approach to predict and

quantify the synergism of anti-cancer drugs using experimental

design and artificial neural networks 17

3.4. Laser desorption ionisation quadrupole ion trap time-of-flight mass

spectrometry of titanium-carbon thin films 21

3.5. Laser ablation synthesis of new gold carbides. From gold-diamond

nano-composite as a precursor to gold-doped diamonds.

Time-of-flight mass spectrometric study 23

3.6. MALDI QIT TOF mass spectrometry of novel shape-persistent

macrocycles - (submitted) 26

3.7. Remotely Sensed Soil Data Analysis Using Artificial Neural

Networks. A case study of El-Fayoum Depression,

Egypt - (submitted) 30

3.8. Laser ablation synthesis of new gold selenides from gold-selenium

mixtures and nano-composites. laser desorption ionisation

time-of-flight mass spectrometry - (submitted) 31

3.9. REVIEW, Coordination compounds contra cancer: past, present and

future trends 35

3.10. Cell authentication by whole cell MALDI-TOF-MS:

development and validation of experimental

protocol - (to be submitted) 37

3.11. Whole-cell mass spectrometry reveals minute changes in

hESCs and clusters morphologically uniform, yet

different cell populations - (to be submitted) 40

3.12. Estimation of cell-line cross-contamination level by mass

spectrometric fingerprinting of whole mammalian cell lines

coupled with artificial neural networks - (to be submitted) 43

3.13. Computer programs and codes 48

3.13.1 Programs written in C/C++ 48

Program norm 50

Program txttopat 52

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Program denorm 55

Program res2txt 57

Program cancel_rows 59

Program normalizza 61

Program rounding 64

Program transform 66

Program transpose 67

Program triple 70

Program autoscaling 71

Program ann 74

Program anntest 78

Subroutine random_matrix_double.h 79

3.13.2. MATLAB functions 81

FUNCTION MS_prepro.m: 82

MS_prepro subroutines 84

FUNCTION MS_stat.m 85

FUNCTION ann_corr_VER_1_2.m (subroutines

not given) 89

FUNCTION massfit.m 92

FUNCTION massfit_norm.m 93

4. PAPERS 94

1. Artificial neural neworks combined with experimental design:

A "soft" approach for chemical kinetics 95

2. REVIEW, Artificial neural networks in medical diagnosis (Editorial) 102

3. Development and validation of a general approach to predict and

quantify the synergism of anti-cancer drugs using experimental

design and artificial neural networks 114

4. Laser desorption ionisation quadrupole ion trap time-of-flight

mass spectrometry of titanium-carbon thin films 124

5. Laser ablation synthesis of new gold carbides. From gold-diamond

nano-composite as a precursor to gold-doped diamonds.

Time-of-flight mass spectrometric study 131

6. MALDI QIT TOF mass spectrometry of novel shape-persistent

macrocycles 139

7. Remotely Sensed Soil Data Analysis Using Artificial Neural

Networks. A case study of El-Fayoum Depression, Egypt 156

8. Laser ablation synthesis of new gold selenides from gold-selenium

mixtures and nano-composites. laser desorption ionisation

time-of-flight mass spectrometry 176

9. REVIEW, Coordination compounds contra cancer: past, present

and future trends 183

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5. CONCLUSIONS 221

How the aims of the Thesis were fulfilled 224

6. REFERENCES 225

7. LIST OF ABBREVIATIONS 226

8. APPENDIX 227

8.1. List of publications 227

8.2. Other papers not included in Ph.D. thesis 227

8.3. Presentations to international and domestic conferences 228

8.4. Seminaries 230

8.5. Collaborations 231

8.6. Participation in projects 231

8.7. Others 232

8.8. Curriculum Vitae 233

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1. INTRODUCTION

In the digital era, the amount of data available to the researcher increases continuously.

Chemometrics is the branch of science which deals with the mathematical treatment of

large data sets in order to extract information useful for the study that is being carried

out. Chemometric treatment of data involves various steps or possibilities such as data

mining, feature extraction, feature selection, data modeling and reduction of data

dimensionality.

Among various modern instrumental methods, mass spectrometry represents one of the

most outstanding and advanced applied ones especially in the field of material and life

sciences. The extraction of information from large amount of mass spectrometric data is

nowadays a challenging task.

1.1. Chemometrics

With the term "Chemometrics" we usually refer to the ensemble of mathematical and

statistical techniques applied in science to (i) design or select optimal experimental

procedures and (ii) extract the maximum relevant information from experimental data.[1]

The "chemometrics revolution" has been possible thanks to the development of high-

speed digital computers. The use of chemometrics for data visualization, modeling and

interpretation significantly facilitates the handling and treatment of large experimental

data sets.[2]

Modern research is rapidly becoming more interdisciplinary and, therefore, the

information coming from various sources and fields should be used for a more complete

understanding of the studied system.Also a global view about information coming from

different sources is needed. The availability of large or very large data sets produced by

each technique represents both a great opportunity and a challenge for the modern

researcher. The opportunity lies in the enormous content of information hidden in the

available data, while the challenge is the extraction of meaningful information required

to reach the desired objective.

The most used method for data compression and visualization is principal components

analysis (PCA).[1–3]

It assumes that the variance in the data matrix is due to a number of

underlying "factors" being a linear combination of the original variables. By

decomposition of the data matrix, PCA allows the representation of both the "objects"

and "variables" in a space with dimensionality lower than the original one. In this way,

the hidden data structure (clusters) can be revealed. Several other methods are available

to perform classification, patter recognition, signal processing and data modeling tasks.

Among the most common ones, it is worth mentioning artificial neural networks

(ANNs), orthogonal projection to latent structures discriminant analysis (OPLS-DA),

hierarchical clustering, fast Fourier transform and wavelet denoising. An overview of

the main chemometric methods and details about the algorithms can be found

elsewhere.[4]

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1.2. Mass Spectrometry

Mass spectrometry is an instrumental analytical technique that allows highly sensitive

determination of molecular masses of ions generated in the gas phase. The separation of

ions is achieved exploiting the differences in m/z ratio.

Mass spectrometric techniques are widely used in science for the characterization of

inorganic and organic compunds, materials, tissues, complex biological mixtures, etc.

Mass spectrometry allows determination of the molecular weight of all the generated

ions and, if isotopic resolution is achieved, the composition of each ion can be

determined by isotopic pattern modeling. Other ways to obtain structural information is

to perform collision-induced dissociation (CID) or tandem MS/MS studies. Such

methods are particularly useful in proteomics for the identification of proteins and

peptides.[5]

Structural information about solid materials can be obtained by laser

desorption ionisation (LDI) and/or laser ablation (LA) mass spectrometry.[6]

Details

about the various mass spectrometric techniques and their applications can be found

elsewhere.[7]

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2. AIMS OF THE THESIS

The application of mass spectrometry in material and life sciences leads to the

availability of large data sets for which the interpretation is not always straightforward.

Therefore, suitable mathematical techniques are needed for the extraction of relevant

information from data. The identification of species generated by laser ablation

synthesis and the mass spectrometric fingerprinting of complex biological mixtures are

among the major fields in which the use of chemometrics and artificial intelligence

methods is receiving increasing attention.

The aims of the Thesis are the following:

1. To develop and validate new, rigorous and general approach for the

quantification of drug synergism coupling artificial neural networks and

experimental design.

2. Study the laser ablation synthesis of new inorganic clusters from various

precursors.

3. Application of isotopic pattern modeling to determine the stoichiometry of

clusters generated via laser ablation.

4. Work out a new, robust and reliable mass spectrometric method for mammalian

cell lines fingerprinting.

5. Use mass spectrometric fingerprints to follow tiny changes induced in stem cells

by various factors with consequent chemometric data evaluation. For example,

use of orthogonal projection to latent structures discriminant analysis (OPLS-

DA) to recognize the changes occurring in stem cells.

6. Develop a ANN-based procedure for rapid and reliable estimation of cell-lines

cross-contamination.

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3. RESULTS AND DISCUSSION

The Thesis is composed of 32 items:

9 papers

3 individual chapters

14 programs written in C/C++

6 MATLAB functions

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3.1. Artificial neural neworks combined with experimental design: A "soft"

approach for chemical kinetics

The highly non-linear kinetic behaviour of chemical systems make the rigorous

computation of rate constants and or the prediciton of optimal reaction conditions

difficult and in some cases even impossible. Therefore, "hard-modeling" methods are

usually found to be inefficient in comparison to "soft" ones.

In this work the applicability of artificial neural networks (ANNs) coupled with

experimental design (ED) in chemical kinetics, their power, robustness and limitations

are investigated through a series of model examples.

Four cases have been studied: (i) consecutive reactions, (ii) cyclic reaction pathways,

(iii) multicomponent kinetic analysis and (iv) optimization of reaction conditions. In

addition, the effect of various levels of random errors on ANN's performance was

studied.

In conclusion it was found that: (i) ANNs are able to model with sufficient precision

any kind of kinetic curve, (ii) the ED-ANNs approach for the prediction of a kinetic

constant is general and can be applied to data concerning whichever reaction path, (iii)

the application of the ED-ANNs approach to multicomponent kinetic data can be done

without a prior knowledge about the chemical reactions involved and (iv) ANNs can be

applied in the optimization of chemical processes without requiring any knowledge

about the processes involved and if ED-ANNs approach is used, the number of

experiments to be performed can be greatly reduced.

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3.2. REVIEW, Artificial neural networks in medical diagnosis (Editorial)

An extensive amount of information is currently available to clinical specialists, ranging

from details of clinical symptoms to various types of biochemical data and outputs of

imaging devices. Each type of data provides information that must be evaluated and

assigned to a particular pathology during the diagnostic process. To streamline the

diagnostic process in a daily routine and avoid misdiagnosis, artificial intelligence

methods (especially computer aided diagnosis and artificial neural networks) can

be employed. These adaptive learning algorithms can handle diverse types of medical

data and integrate them into categorized outputs. In this paper, we briefly review and

discuss the philosophy, capabilities, and limitations of artificial neural networks in

medical diagnosis through selected examples.

Figure 1. Overview of the main applications of artificial neural networks in medicine.

Figure 2. Details of input and output items concerning ANNs-based diagnosis (ANN

architecture is often hidden and it is indicated here as a black box).

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Figure 3. Diagram of fundamental steps in ANNs-based medical diagnosis. Building of

the database and “learning” represents the left half (green) and its application for the

diagnosis is the right part (blue).

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3.3 Development and validation of a general approach to predict and quantify the

synergism of anti-cancer drugs using experimental design and artificial neural

networks

The combination of two or more drugs using multidrug mixtures is a trend in the

treatment of cancer. The goal is to search for a synergistic effect and thereby reduce the

required dose and inhibit the development of resistance. An advanced model-free

approach for data exploration and analysis, based on artificial neural networks (ANN)

and experimental design is proposed to predict and quantify the synergism of drugs. The

proposed method non-linearly correlates the concentrations of drugs with the

cytotoxicity of the mixture, providing the possibility of choosing the optimal drug

combination that gives the maximum synergism. The use of ANN allows for the

prediction of the cytotoxicity of each combination of drugs in the chosen concentration

interval. The method was validated by preparing and experimentally testing the

combinations with the predicted highest synergistic effect. In all cases, the data

predicted by the network were experimentally confirmed.

The definition of 'additive effect' of drugs has been generalized and extended to

combination n drugs (n ≥ 2). The NAAE (non-algebraic additive effect) has been defined

as:

NAAE=∑i=1

n

ai−1

100 [∑i≠ j

ai a j ]+1

1002 [ ∑i≠ j≠l

a i a j al ]−.. .+(−1)k−1 1

100n−1 [∏ai ]

(1)

where ai (with ai ≥ 0) are the individual percentage of mortality values (number of dead

cells with respect to the controls) and Cn,k{a1, a2, …,an} are the simple combinations

without repetition of the cytotoxicity values of the n drugs taken k at a time (with k ≥ 2).

Using the definition of NAAE, the 'net multi-drug effect index' (NMDEI) was defined as:

NMDEI=Eexp .−NAAE (2)

where Eexp. is the experimental antiproliferative effect of a given drug combination.

The proposed ED–ANN approach consists of the following steps:

1. Set-up of the ED.

2. Experimental determination of the cytotoxicity of the drugs alone and those

of the multidrug mixtures prepared according to the chosen ED.

3. Training and verification of the artificial neural network.

4. Prediction of the response using the network, according to a suitable grid

that covers the entire working space.

5. Calculation of the NAAE for each point of the grid.

6. Calculation and plot as a function of the drug concentrations of NMDEI

(NMDEI surface).

7. Testing the obtained results.

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The method was applied to several binary mixtures of cisplatin (CDDP) and Cu(1,10-

orthophenanthroline)(H2O)2(ClO4)2 (1), [Cu(1,10-orthophenanthroline)2(H2O)](ClO4)2

(2), or [Cu(1,10-orthophenanthroline)2(imidazolidine-2-thione)](ClO4)2 (C1). The

cytotoxicity of the two drugs, alone and in combination, against human acute T-

lymphoblastic leukemia cells (CCRF-CEM) was determined. For all systems, a

synergistic effect was found for selected combinations.

The chosen experimental designs and the results of ANN training and verification are

given in Fig. 1.

Figure 1. Experimental design (■training set,☆validation set and ○test set) for the

systems (a)1-CDDP, (c) 2-CDDP and (e) C1-CDDP; comparison between

experimental (■training set, ☆validation set and ○test set) and calculated mortality

values for the systems (b) 1-CDDP, (d) 2-CDDP and (f) C1-CDDP.

Example of obtained cytotoxicity response surface is given in Figure 2.

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Figure 2. Response surface with experimental data (■training set, ☆validation set and

○test set) (a) and contour plot of cytotoxicity iso-values (b) for the system 1-CDDP.

Dose-response curves of individual drugs predicted by ANN are shown in Figure 3.

Figure 3. Calculated (-●-) and experimental (■) dose–response data for (a) 1, (b) 2, (c)

C1 and (d) CDDP against the CCRF-CEM cell line.

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From the cytotoxicity response surface and values of NAAE computed for each point of

the grid (40×40) the NMDEI surface was obtained (Fig. 4a) and from the contour plot,

the drug combination with the highest synergic effect was determined (Fig. 4b).

Figure 4. NMDEI surface with experimental data (■training set,☆validation set and

○test set) (a) and contour plot of NMDEI iso-values (b) for the system 1-CDDP.

The predicted values of cytotoxicity for the optimal drug combinations found were

confirmed experimentally (Fig. 1 and 4, circles).

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3.4. Laser desorption ionisation quadrupole ion trap time-of-flight mass

spectrometry of titanium-carbon thin films

Titanium-carbon (Ti-C) ceramic thin films (abbreviated as n-TiC/a-C:H) are very

important for industrial applications. However, their chemical structure is still not

completely resolved. The aim of this study was to determine the chemical composition

of such n-TiC/a-C:H layers prepared by balanced magnetron sputtering under various

experimental conditions. The mass spectrometric analysis of Ti-C thin films was carried

out via laser desorption ionisation (LDI) using a quadrupole ion trap and reflectron

time-of-flight analyser. The stoichiometry of clusters formed via laser ablation was

determined, and the relative abundances of species for which the isotopic patterns

overlaps were estimated using a least-squares program written in house.

Ti-C films were found to be composites of (i) pure and hydrogenated TiC, (ii) titanium

oxycarbides, and (iii) titanium oxides of various degrees of hydrogenation (all

embedded in an amorphous and/or diamond-like carbon matrix). Hydrogenated titanium

oxycarbide was the main component of the surface layer, whereas deeper layers were

composed primarily of TiC and titanium oxides (also embedded in the carbon matrix).

Mass spectrometry was proved useful for elucidating the chemical structure of the hard

ceramic-like Ti-C layers produced by magnetron sputtering. The Ti-C layers were found

to be complex composites of various chemical entities. Knowledge of the resolved

structure could accelerate further development of these kinds of materials.

Figure 1. Agreement of experimental and theoretical isotopic envelopes. Conditions

include reflectron negative-ion mode and laser energy 120 a.u. (a) Pattern at m/z ~96.9.

The model assumes the formation of the species [TiO3H]– (68.9%) and [Ti2H3]

–

(31.1%). (b) Pattern at m/z ~121.0. The model assumes the formation of [TiC4H9O]–

(50.3%), [Ti2CH3O]– (9.5%), [Ti2C2H4]

– (21.9%), and [Ti2C2H3]

– (18.4%) species.

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Figure 2. Magnification of the mass spectrum of sample B in the range m/z 800–3500,

and laser energy at 120 a.u.

Figure 3. Agreement between experimental and theoretical isotopic patterns for the ion

at m/z ~852.4 suggests the following species: (a) [Ti8C29O7H10]– (38.3 ± 0.6%) and

[Ti9C29O4H9]– (61.8 ± 0.5%); (b) [Ti8C25O10H8]

– (11 ± 1%), [Ti8C25O10H9]

– (43 ± 3%),

and [Ti8C25O10H10]– (44 ± 1%).

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3.5. Laser ablation synthesis of new gold carbides. From gold-diamond nano-

composite as a precursor to gold-doped diamonds. time-of-flight mass

spectrometric study

Most transition metals form carbides, which are extremely hard, refractory and resistant

to corrosion, wear and heat. In addition, they often show other valuable properties in

combination with toughness, such as low thermal expansion, electrical conductivity and

abrasiveness. Therefore, they have great importance in industry and, for example, they

are excellent for coating drills and other machining tools. The aim of this work was (i)

to systematically apply laser desorption ionisation time-of-flight mass spectrometry

(LDI TOF MS) and LDI quadrupole ion trap TOF MS (LDI QIT TOF MS) to study the

LAS of gold carbides from suitable mixtures of gold nano-particles (NG) and various

carbonaceous materials and (ii) to study the possibility of LAS for the generation of

new gold carbides from nano-composite formed using NG and nano-diamonds (ND) as

precursors.

The NG-ND nano-composite was characterized by TEM and SEM analysis (Fig. 1).

Figure 1. TEM images of ND (a), NG (b), and ND-NG nano-composite (c). EDX

image of the ND-NG nano-composite (d). Colours indicate the signal of carbon (green)

and gold (blue).

Examples of recorded mass spectra showing signals corresponding to gold carbide

clusters are given in Fig. 2 and 3.

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Figure 2. Part of the mass spectrum obtained via LAS of ND-NG nano-composite

showing the formation of Au2+, Au3

+ and gold carbides with stoichiometry Au2Cn

+

(n=1–16) in the m/z 390–600 range. Conditions: AXIMA CFR MALDI TOF mass

spectrometer, reflectron positive ion mode. Mass spectrum is normalized to 500 mV.

(The formation of AuC18+ cluster is also confirmed from the analysis of the same

sample using a MALDI Autoflex TOF mass spectrometer).

Figure 3. Part of the mass spectrum showing the formation of Au3

+, Au4

+ and Au3Cn

+

(n=1–10) species in the m/z 590–800 range. Conditions: AXIMA CFR MALDI TOF

mass spectrometer, reflectron positive ion mode. Mass spectrum is normalized to 150

mV.

Overview of detected AumCn+ clusters is given in Table 1. Species with carbon content

corresponding to a possible diamond structure are highlighted. Hypothetical structures

of possible gold-doped diamond clusters are given in Fig. 5.

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Figure 5. Optimized structures of endohedral Au@C14, Au@C18 and Au@C22

supramolecular complexes calculated using Hyperchem™.

A simple procedure for the preparation of ND-NG nano-composite was developed using

NG and ND; the formation of AuCn+ (n = 1-11, 18), Au2Cn

+ (n = 1-16) and Au3Cn

+ (n =

1-10) clusters during LAS of the nano-composite was proved. Structures of gold

carbides are proposed and discussed. Diamond-containing AumCn+ (m = 1-3, n = 10, 14,

18, 22 ) clusters might not be carbides but endohedral supramolecular complexes (i.e.,

'gold-doped' diamonds).

Knowledge about generated clusters might inspire synthesis of new Au-C materials with

specific properties.

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3.6. MALDI QIT TOF mass spectrometry of novel shape-persistent macrocycles

Shape-persistent macrocycles (SPMs) represent innovative molecular building blocks

for the development of highly organized supramolecular architectures with application

in nanotechnology, chemistry, catalysis and optoelectronics. Systematic mass

spectrometric characterization of SPMs and their collision-activated decay is not

available to date.

In this work, a series of alkoxy-decorated SPMs (Fig. 1) was characterized by matrix-

assisted laser desorption quadrupole ion trap time-of-flight mass spectrometry (MALDI

QIT TOF MS) and collision-induced dissociation (CID).

Fig. 1: Overview of studied shape-persistent macrocycles.

The laser excitation of SPMs leads to the formation of stable cation radicals which show

characteristic fragmentation patterns (Fig. 2).

Fig. 2: Collision-induced fragmentation pattern of molecular cation radical of

compound 5. The m/z values reported refer to the monoisotopic peak. The mass loss

occurring in each MSn step is indicated above the arrow. The value 169.1 corresponds to

the loss of two C6H13 units and the gain of one proton.

All the product ions formed were identified.

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In order to study the regioselectivity of the CID, the macrocycle 10 (Fig. 3) was

synthesized and characterized (Fig. 4).

Fig. 5: Structure of 10 (R = C6H13, R1 = C10H21).

Fig. 44: Overview of CID pattern of 10. Losses corresponding to hexyls or decyls are

indicated by red and blue arrows, respectively. Loss of 84.1, 85.1, 140.2 and 141.2 Da

corresponds to C6H12, C6H13, C10H20 and C10H21, respectively.

From the evaluation of experimental findings the general fragmentation mechanism of

SPMs was proposed (Fig. 5).

28

Fig. 5: Proposed scheme of possible SPMs fragmentation mechanism. The

fragmentation of the molecular ion (a) leads to the formation of the product ions

labelled: b, c, e, f, h and i, respectively.

Photoelectrons generated during MALDI process and full-ring conjugation were found

fundamental for the formation of molecular cation radicals and their stabilization,

respectively. Formation of supramolecular aggregates of SPMs by π-π stacking was

proven (Fig. 6).

29

Fig. 6: Structures of: 2a dimer (I), 2a trimer (II), 7a dimer (III) and adduct of 2a and

7a (IV) optimized via force field minimization.

It was found that upon laser irradiation, SPMs significantly enhance the ionisation of

fullerene in negative ion mode (Fig. 7). This can be explained as due to the electron-

deficient character of fullerene.

Fig. 7: Effect of the SPM 5 on the ionisation of fullerene in reflectron negative ion

mode. On average, 5 leads to ionisation enhancement of about 10 times (lines

connecting points do not have physical meaning).

Concluding, alkoxy-decorated SPMs represent promising electron-donating building

blocks that can be exploited in electronics and optoelectronics for the development of

robust and highly efficient laser-activated supramolecular switches.

30

3.7. Remotely Sensed Soil Data Analysis Using Artificial Neural Networks. A case

study of El-Fayoum Depression, Egypt

Abstract: Earth observation and monitoring of soil quality, long term changes of soil

characteristics and deterioration processes such as degradation or desertification are

among the most important objectives of remote sensing. The georeferenciation of such

information contribute to the development and progress of the Digital Earth project in

the framework of information globalization process. Earth observation and soil quality

monitoring via remote sensing are mostly based on the use of satellite spectral data.

Advanced techniques are available to predict the soil or land use/cover categories from

satellite imagery data. Artificial Neural Networks (ANNs) are among the most widely

used tools for modeling and prediction purposes in various fields of science. The

assessment of satellite image quality and suitability for analysing the soil conditions

(e.g., soil classification, land use/cover estimation, etc.) is fundamental. In this paper, a

methodology for the preliminary data exploration and subsequent application of ANNs

in remote sensing is presented. It consists of preliminary explorative data analysis and

of ANNs application. The first stage is achieved via: (i) elimination of outliers, (ii) data

pre-processing and (iii) the determination of the number of distinguishable soil

“classes” via Eigenvalues Analysis (EA) and Principal Components Analysis (PCA).

The next stage of ANNs use consists of: (i) building the training database, (ii)

optimization of ANN architecture and database cleaning and (iii) training and

verification of the network. Application of the proposed methodology is shown.

31

3.8. Laser ablation synthesis of new gold selenides from gold-selenium mixtures

and nano-composites. laser desorption ionisation time-of-flight mass spectrometry

Only a limited number of gold selenides is known. Some of them find important

applications in industry, medicine and electronics. Laser ablation synthesis (LAS) has

high potential for the fast and efficient generation of new compounds.

Gold selenide clusters were generated via LAS using mixtures of gold and selenium as

precursors and detected as singly-charged negative ions by time of flight mass

spectrometry. The stoichiometry of generated clusters was determined via computer

modeling of isotopic envelopes while the structure of selected AumSen clusters was

computed via DFT optimization.

In total, 67 gold selenide clusters up to Au21Se11- were generated and their stoichiometry

determined.

Nano-composites prepared from mixtures of gold or gold nano-particles (GNP) and

selenium are suitable precursors for the generation of novel gold selenides via laser

ablation synthesis. Knowledge about the generation of such species might facilitate

development of novel high-tech materials.

Selenium powder was suspended in acetonitrile. Colloidal solution of gold nano-

particles (GNP) was prepared in water from auric acid using gallic acid as a reducing

agent. Powdered selenium was added to the GNP suspension (GNP: 3 mmol expressed

as Au) in molar ratio GNP:Se = 1:10 (Se: 2.4 mg) or 10:1 (Se: 0.3 mg) (precursors I and

II, respectively). Precursor III was prepared by mixing and sonicating the aqueous

solution of auric acid (4 ml, 0.75 mM) with selenium powder (2.4 mg) in molar excess

(Au:Se equal to 1:10) and precursor IV by grinding in agate mortar selenium powder

and commercial gold nano-powder and then resuspending the mixture in acetonitrile.

Suspension of each precursor (1 μL) was pipetted onto a sample plate and dried in an air

stream at room temperature. The products were subject to laser ablation and the

generated species were detected by TOF MS.

The interaction of selenium with auric acid was investigated by UV-vis

spectrophotometry (Fig. 1) and it was found that the intensity of the absorption band

corresponding to AuCl4- diminished with time. A complete explanation of the processes

occurring in solution is beyond the scopes of the present work.

32

Fig. 1: Overview of UV-vis spectra of HAuCl4-Se mixture in molar ratio 1:10 recorded

every 30 minutes. Changes in absorbance at 220 and 300 nm vs. reaction time are given

in the inset.SEM and backscattered SEM images of precursor II (a, b) and III (c, d).

The laser ablation of the precursors I-IV leads to the formation of several series of gold

selenide clusters (Fig. 2, 3).

Fig. 2: Example of mass spectrum concerning laser ablation of precursor I. Signals

corresponding to Sen- (n = 1-5) predominate. Magnification of the m/z range 400-1000

is given in the inset (signal-to-noise ratio for signal corresponding to AuSe6- cluster was

~150). Conditions: AXIMA CFR, linear negative ion mode, laser energy 140 a.u.

33

Fig. 3: Example of mass spectrum concerning laser ablation of precursor IV showing

signals corresponding to high-mass AumSen- clusters. Conditions: AXIMA Resonance,

reflectron negative ion mode, laser energy 130 a.u.

In total the following species were generated and detected: Aum- (m = 1-5); Sen

- (n = 1-

7); AuSen- (n = 2-10); Au2Sen

- (n = 1-8); Au3Sen

- (n = 1-8); Au4Sen

- (n = 1-7); Au5Sen

-

(n = 4-7); Au6Sen- (n = 4-6); Au7Sen

- (n = 5-7); Au8Sen

- (n = 5-6); Au9Sen

- (n = 5-6);

Au10Sen- (n = 6-7); Au11Sen

- (n = 6-7); Au12Sen

- (n = 7-8); Au13Sen

- (n = 7-8); Au2Se8

-;

Au15Sen- (n = 8,9); Au16Se9

-; Au17Sen

- (n = 9,10); Au18Sen

- (n = 9,10); Au9Se10

-;

Au20Sen- (n = 10,11); and Au21Sen

- (n = 10,11).

The DFT study of selected AumSen- series was performed. For each stoichiometry, the

energy of several possible isomers was computed. Overview of selected structures is

given in Fig. 4.

Fig. 4: Examples of DFT-optimized AumSen- structures for series with m = 1-4.

Formula, symmetry point group, spin, calculated bond energy in eV, examples of bond

lengths in pm and angles in degrees are given. The stability of given structures is

confirmed by no imaginary normal mode frequency in each case. If more stable

structures for the same stoichiometry were found the lowest energy one is shown.

34

Concluding, the Au-Se nano-composites prepared were found to be suitable precursors

for laser ablation synthesis of new AumSen clusters. In this work, synthesis of 67 new

gold selenide clusters was achieved in the gas phase. DFT-optimized cluster structures

show great diversity and many new structural patterns as compared to published

AumSenk-

entities stabilized by alkali metals or found in gold-selenium minerals. This

indicates that the species synthesized in this work could represent new chemical entities.

Laser ablation synthesis represents a powerful and fast method for the generation of

novel nano-structured Au-Se molecular motifs for application in nano-technology.

35

3.9. REVIEW, Coordination compounds contra cancer: past, present and future

trends

Extensive research has been done to apply various compounds in anticancer therapy.

Complexes of various metal ions such as platinum, ruthenium, gold or copper have been

synthesised and tested with the aim to develop effective and safe drugs. Various reviews

have been published on the use of metal complexes as anticancer agents pointing out the

most relevant examples of platinum- or non-platinum-based compounds. In vitro and in

vivo tests summarized the anticancer activity of the most active compounds and give a

general overview. However, up-to-date reviews giving a wide overview of the different

metal ions used against cancer and, providing at the same time, an outline of the

chemical and biological problems as well as the various strategies adopted are not

available. In addition to the overview of historical milestones, the aim of this work is to

provide:(i) an overview of the various branches of the current research, (ii) an outline of

the design and the rationale behind the synthesis of new metal complexes to be used

against cancer, (iii) a summary of the bio-chemical reactivity and physicochemical

properties of metal complexes, (iv) an outlook about the future of the metalcomplexes

in anticancer therapy.

Fig. 1. Chronological and historical overview of the anticancer metal and metalloid

complexes that have been approved or entered the clinical practice.

36

Fig. 9. Overview of main targets affected by drugs containing either platinum or

ruthenium or gold or copper as a metal ion.

Fig. 17. Biological target processes of Cu(II) complexes with antitumor activity.

Pages from 37 to 93 are not public.

94

4. PAPERS

1. Artificial neural neworks combined with experimental design: A "soft" approach

for chemical kinetics

2. REVIEW, Artificial neural networks in medical diagnosis (Editorial)

3. Development and validation of a general approach to predict and quantify the

synergism of anti-cancer drugs using experimental design and artificial neural

networks

4. Laser desorption ionisation quadrupole ion trap time-of-flight mass spectrometry

of titanium-carbon thin films

5. Laser ablation synthesis of new gold carbides. From gold-diamond nano-

composite as a precursor to gold-doped diamonds. Time-of-flight mass

spectrometric study

6. MALDI QIT TOF mass spectrometry of novel shape-persistent macrocycles

7. Remotely Sensed Soil Data Analysis Using Artificial Neural Networks. A case


8. Laser ablation synthesis of new gold selenides from gold-selenium mixtures and

nano-composites. laser desorption ionisation time-of-flight mass spectrometry

9. REVIEW, Coordination compounds contra cancer: past, present and future

trends

139

MALDI QIT TOF mass spectrometry of novel shape-persistent macrocycles

Rapid Commun. Mass Spectrom.

Submitted on 12 - November - 2014

Filippo Amato,1 Bhimrao Vaijnath Phulwale,

1,2 Ctibor Mazal,

1,2 Josef Havel

1,3,4*

1Department of Chemistry, Faculty of Science, Masaryk University, Kotlářská 2, 611 37

Brno, Czech Republic 2CEITEC – Central European Institute of Technology, Masaryk University, Kamenice 5,

625 00 Brno-Bohunice, Czech Republic 3Department of Physical Electronics, Faculty of Science, Masaryk University, Kotlářská

2, 61137 Brno, Czech Republic 4CEPLANT, R&D Center for Low-Cost Plasma and Nanotechnology Surface

Modifications, Masaryk University, Kotlářská 2, 61137 Brno, Czech Republic

RATIONALE: Shape-persistent macrocycles (SPMs) represent innovative molecular

building blocks for the development of highly organized supramolecular architectures

with application in nanotechnology, chemistry, catalysis and optoelectronics. Systematic

mass spectrometric characterization of SPMs and their collision-activated decay is not

available to date.

METHODS: Characterization of alkoxy-decorated SPMs was done by matrix-assisted

laser desorption quadrupole ion trap time-of-flight mass spectrometry (MALDI QIT

TOF MS) and collision-induced dissociation (CID).

RESULTS: Laser excitation of SPMs leads to the formation of stable cation radicals

which show characteristic fragmentation patterns. All the product ions formed were

identified. Photoelectrons generated during MALDI process and full-ring conjugation

were found fundamental for the formation of molecular cation radicals and their

stabilization, respectively. Formation of supramolecular aggregates of SPMs by π-π

stacking was proven. Upon laser irradiation, SPMs enhance significantly the ionisation

of fullerene in negative ion mode.

CONCLUSIONS: Alkoxy-decorated SPMs represent promising electron-donating

building blocks that can be exploited in electronics and optoelectronics for the

development of robust and highly efficient laser-activated supramolecular switches.

*Correspondence to: J. Havel, Department of Chemistry, Faculty of Science, Masaryk

University, Kamenice 5/A14, 62500 Brno, Czech Republic.

E-mail: [email protected]

INTRODUCTION

Shape-persistent macrocycles are organic molecules characterized by a rigid, non-

collapsing ring with a cavity of fixed size. The ring can be either fully or partially

conjugated. Physico-chemical properties of SPMs can be altered by derivatization with

140

suitable substituents on the ring structure.[8]

The SPMs have wide application as building blocks for the “bottom-up” design and

development of highly organised supramolecular architectures,[9–14]

nano-materials,[13–

15] nano-sized molecular devices,

[16] receptors,

[17] selective catalysts, chelators,

[18]

discotic liquid crystals[19,20]

and metal organic frameworks. Mass spectrometric

investigation of the fragmentation pattern of SPMs has not been reported to date.

The aim of this work is to study the collision-induced fragmentation pathways of a

series of recently synthesized shape-persistent macrocycles such as 1 - 7 (Fig. 1) by

MALDI QIT TOF MS, search for self-assembled supramolecular aggregates, and study

the effect of fullerene as MALDI matrix on the ionisation of SPMs.

Fig. 1: Overview of studied shape-persistent macrocycles.

EXPERIMENTAL

Chemicals

The α-cyano-4-hydroxycinnamic acid (CHCA) and 2,5-dihydroxybenzoic acid (DHB)

were purchased from Sigma-Aldrich (Steinheim, Germany). Red phosphorus was

purchased from Riedel de Haën (Hannover, Germany). Fullerene C60 and acetonitrile

(purity for isotachophoresis) were purchased from Merck (Darmstadt, Germany).

Helium and argon (purity ≥99.998 %) were from SIAD S.p.A. (Braòany, Czech

Republic). All other reagents were of analytical grade purity. Water was doubly distilled

from the quartz apparatus by Heraeus Quartzschmelze (Hanau, Germany).

Synthesis of SPMs

141

Shape-persistence macrocycles (SPMs) were synthesized starting from the

commercially available phenanthrene-9,10-dione, which was brominated to get 3,6-

dibromophenanthrene-9,10-dione.[21]

Reduction of the dione and subsequent alkylation

have been done by a reported procedures with various alkylating agents.[22–25]

The Pd-

catalyzed Sonogashira cross coupling reaction between an alkylated compound and 2-

methylbut-3-yn-2-ol followed by deprotection under basic condition gave 9,10-

bis(alkoxy)-3,6-diethynylphenanthrenes.[20]

Those diyne precursors were either used in

further Sonogashira coupling reactions with 9,10-bis(alkoxy)-3,6-diiodophenanthrenes

yielding phenanthrylene/acetylene SPMs 1, 3, and 4 or underwent to a one-pot

cyclization under palladium-mediated oxidative coupling reaction[26]

yielding

arylene/butadiyne macrocycles 2 and 7. Modification of the butadiyne linkers by a

reaction with Na2S[27]

gave arylene/thienylene macrocycle 5. The partially conjugated

SPM 6 has been synthesized according to the literature.[23]

All the products were

characterized by standard techniques. Full details of synthetical procedures can be found

elsewhere.[28]

Instrumentation

Mass spectra were recorded on AXIMA Resonance mass spectrometer from Kratos

Analytical (Manchester, UK) using quadrupole ion trap and reflectron time-of-flight

analyser. A nitrogen laser (337 nm) and delayed extraction were used. The laser energy

was expressed in arbitrary units (a.u.) from 0 to 180. The laser power at 180 a.u. was 6

mW. The diameter of the irradiated spot was approximately 150 µm. Mass spectra were

recorded in both positive and negative ion modes. However, the intensity of signals in

positive ion mode were about 5 times higher and with higher signal-to-noise ratio.

Therefore, mass spectra recorded in positive ion mode were preferred. External

calibration was done using red phosphorus clusters.[29]

Mass spectra were recorded

using a laser repetition rate equal to 5 Hz and with a pulse time width of 3 ns. Each

mass spectrum was obtained by accumulation of spectra from at least 500 laser shots.

Software and computation

Theoretical isotopic patterns were calculated using Launchpad software (Kompact v.

2.9.3, 2011) from Kratos Analytical Ltd. Eigenvalues analysis was performed using

STATISTICA v. 6 (StatSoft Inc., USA). Molecular structures were calculated via force

field optimization using the program Avogadro.[30]

Sample preparation

The SPMs were dissolved or suspended in acetonitrile. Saturated matrix solutions were

prepared in acetonitrile while fullerene was dissolved in toluene. MALDI analysis was

carried out using the “thin layer preparation” method.[31]

A volume equal to 1 μL of

matrix solution was spotted onto the target and allowed to dry at room temperature and

then, 1 μL of sample solution was deposited on the dry matrix layer and dried again.

The target was introduced into the mass spectrometer and mass spectra were recorded

after the pressure had dropped below 10-4

Torr.

142

RESULTS AND DISCUSSION

Various matrices were tested and the best mass spectra in terms of signal intensity and

resolution were obtained with either α-cyano-4-hydroxycinnamic acid (CHCA) or

dihydroxybenzoic acid (DHB). Mass spectra show that both matrices often lead to

partial fragmentation of the compounds. However, such phenomenon was reduced by

using laser energy only slightly above the threshold value. The CID pattern of each

macrocycle was studied by MSn using argon as collision gas. The structure of detected

fragments was determined by modeling of the isotopic patterns.

MALDI QIT TOF MS characterization of SPMs

CID-MS/MS of fully-conjugated macrocycles decorated with alkoxy units

The mass spectrum of 1 was recorded using CHCA as matrix. The molecular peak was

detected as cation radical. The CID fragmentation pattern shows signals at m/z 1285.0

[M]+, 1003.7 [M-(C10H21)2+H]

+, 863.5 [M-(C10H21)3+2H]

+ and 722.5 [M-

(C10H21)4+2H]+. The available range of collision energy does not lead to further

fragmentation of the ion detected at m/z 722.5. Such ion contains two methoxy groups

as substituents on the phenanthrene unit. However, the loss of methyl groups does not

occur and this might provide useful indication about the possible mechanism of SPMs

fragmentation (see section „Proposed fragmentation mechanism of SPMs“). The

analysis of the CID pattern shows that the loss of a couple of alkyl side chains is always

accompanied by the addition of one proton and the resulting ion is always detected as

singly charged species.

The compounds 2a, 2b and 2c form a series of fully-conjugated SPMs having

triangular shape and alkoxy side chains of different length. In this series the ring

backbone is always the same, but 2a, 2b and 2c have CH3O-, C6H13O- and C10H21O- as

alkoxy side chains, respectively. The molecular peak was always detected as cation

radical when using either DHB or CHCA as matrix. The mass spectrum of 2a

recorded using CHCA as matrix shows signals at m/z 852.2 [M]+, 1704.5 [M2]

+ and

2556.7 [M3]+. In addition, a low-intensity signal was detected at m/z 1420.4 and

explained as due to an obvious by-product of the synthesis, the macrocycle 9 (Fig. 2),

confirmed also by NMR analysis. Several attempts have been made to separate the

compound 9 from 2a by column chromatography, however, successful separation has

not been achieved. The collision-induced fragmentation of the molecular ion of 2a leads

to three successive losses equal to 15.0 Da. This could be explained as due to successive

loss of CH3- units.

The formation of supramolecular adducts of 2a was confirmed by MSn analysis,

modeling of the isotopic envelopes and eigenvalues analysis of NMR spectra (c.f.

section “Supramolecular aggregates of SPMs”).

143

Fig. 2: Compound 9 detected by MALDI-MS and also confirmed by NMR as a by-

product of the synthesis of 2a.

The mass spectrum of 2b recorded using DHB as matrix shows signals at m/z

1272.4 [M]+, 1188.4 [M-C6H13+H]

+ and 1103.3 [M-(C6H13)2+H]

+. The intensity of the

signal detected at m/z 1188.4 is lower with respect to that of the signals recorded at m/z

1272.4 and 1103.3. The collision-induced dissociation of the molecular ion leads to the

formation of fragments detected at m/z 1019.3 [M-(C6H13)3+2H]+, 934.2 [M-

(C6H13)4+2H]+, 850.2 [M-(C6H13)5+3H]

+ and 765.0 [M-(C6H13)6+3H]

+. The MS

n

analysis of each of the product ions resulted in the loss of the remaining side chains that

decorate the ring backbone. The difference between monoisotopic peaks of two

consecutive signals in the CID pattern are: 84.0 (1272.4-1188.4), 85.1 (1188.4-1103.3),

84.1 (1103.3-1019.3), 85.1 (1019.3-934.2), 84.2 (934.2-850.2) and 85.2 (850.2-765.0).

These differences can be explained as due to alternate loss of C6H12 (84.0 Da) and

C6H13 (85.0 Da) units, respectively. Such results can be interpreted according to the

general scheme depicted in Fig. 3. The precursor ion a looses the first side chain

retaining a proton leading to b which then looses the second side chain forming c. The

positive charge is most probably delocalized on the fully-conjugated ring. The signal

intensity of the ions with even number of side chains was higher than that one for the

ions with odd number of side chains. This indicates that the former are more stable than

the last ones.

Fig. 3: General scheme of fragmentation pathway of alkoxy-decorated SPMs (positive

charge is omitted).

An analogy of this kind of fragmentation can be found in GC-MS of o-

alkoxyanisols where longer alkyls were lost in the first step also with the gain of proton

leaving the guaiacol radical cation, mostly as a base peak, which then lost methyl in the

subsequent step.[32]

The mass spectrum of 2c recorded using DHB matrix shows signals at m/z

1609.0 [M]+, 1327.7 [M-(C10H21)2+H]

+, 1046.4 [M-(C10H21)4+2H]

+, and 765.1 [M-

(C10H21)6+3H]+. The difference between the monoisotopic peaks of two consecutive

signals is constant and equal to 281.3 Da which corresponds to the loss of two side

144

chains and gain of one proton as observed for 2b. Each one of the detected ions was

fragmented by CID confirming the constant loss of 281.3 Da. The intensity of the

signals for the ions with odd number of side chains is rather low as compared with that

one of signals for the ions with even number of side chains. The fragmentation pathway

is in agreement with the scheme shown in Fig. 3.

The mass spectrum of 3 was recorded using CHCA as matrix. The molecular ion

was detected as cation radical at m/z 1342.9 [M]+. The fragmentation of the molecular

ion proceeds first by a loss of two alkyl side chains (C10H21) accompanied by the

addition of one proton. The resulting ion is detected at m/z 1061.6 [M-(C10H21)2+H]+.

The further fragmentation (MS3) of such ion leads to two fragments detected at m/z

921.4 [M-(C10H21)3+2H]+ and 780.2 [M-(C10H21)4+2H]

+. It is worth to mention that

during the MS2 analysis of the molecular cation radical no fragments were detected in

the m/z range 1065-1340. This indicates that either the cation radical looses the first two

side chains at the same time or most probably that the ion resulting from the loss of only

one side chain has short lifetime and it fragments rapidly leading to the ion detected at

m/z 1061.6. The fragmentation pattern is in agreement with the general scheme reported

in Fig. 3. In mass spectrum of 3 other signals are present at m/z 1366.9, 1226.7, 1085.6,

945.4 and 804.2. Such signals cannot be explained as due to fragments of 3. In addition,

they were not detected in the CID spectrum of the molecular cation radical. Modeling of

the isotopic patterns and further CID study proved that in mass spectrum of 3 the signals

corresponding to another SPM were present. This SPM was identified as compound 4

present in the sample of 3 as synthesis by-product. The molecular ion of 4 was detected

as cation radical at m/z 1366.9 [M]+. This one decays according to the scheme depicted

in Fig. 3 giving rise to fragments detected at m/z 1266.7 [M-C10H21+H]+, 1085.6 [M-

(C10H21)2+H]+, 945.4 [M-(C10H21)3+2H]

+ and 804.2 [M-(C10H21)4+2H]

+.

The compound 5 is analogous to 1, but in contrast to this one, in 5 the

phenanthroquinone units are separated from each other by thiophene units and the ring

backbone is decorated with hexyloxy chains in place of the decyloxy ones. The mass

spectrum of 5 was recorded using DHB as matrix. The molecular ion and the dimer

were detected as cation radicals at m/z 1374.7 [M]+ and 2749.4 [M2]

+, respectively. The

mass spectra obtained from the MSn analysis of 5 are shown in Fig. 4. The product ions

were detected successively at m/z 1205.6 [M-(C6H12)2+H]+

(MS2), 1036.4 [M-

145

(C6H12)4+2H]+ (MS

3) and 867.2 [M-(C6H12)6+3H]

+ (MS

4). The MS

n analysis shows that

each precursor ion leads to the formation of only one product ion. Also in this case, the

fragmentation proceeds according to the scheme given in Fig. 3.

Fig. 4: Collision-induced fragmentation pattern of molecular cation radical of

compound 5. The m/z values reported refer to the monoisotopic peak. The mass loss

occurring in each MSn step is indicated above the arrow. The value 169.1 corresponds to

the loss of two C6H13 units and the gain of one proton.

The compounds 7a and 7b have square-shaped cavity. The alkoxy side chains

for 7a and 7b are CH3O- and C6H13O-, respectively. The mass spectrum of 7a recorded

using CHCA as matrix shows the signal of the base peak at m/z 1136.64 [M]+. The mass

spectrum of 7b recorded using CHCA as matrix shows signals at m/z 1696.76 [M]+,

1611.7 [M-C6H13]+, and 1527.6 [M-(C6H13)2+H]

+. The CID of the [M]

+ ion leads to

fragments detected at m/z 1611.7 [M-C6H13]+, 1527.6 [M-(C6H13)2+H]

+, 1357.5 [M-

(C6H13)4+2H]+, 1188.4 [M-(C6H13)6+3H]

+,

and 1019.1 [M-(C6H13)8+4H]+. The

difference between the monoisotopic peaks of two consecutive fragments is constant

and equal to 169.2 Da. The observed CID pattern shows that the loss of the alkyl side

chains leads to the same unit as reported in Fig. 3 c. However, in contrast to what was

observed for 2b, the fragmentation occurs with alternate losses of 85.0 and 84.0 Da,

respectively. This means that for each phenanthroquinone unit of the macrocycle the

loss of the first side chain (C6H13-) is not accompanied with the gain of one proton

while this occurs during the loss of the second side chain. The observed fragmentation

order might probably be explained by considering the different geometry of 7b with

respect to 2b and its possible influence either kinetics or thermodynamics on the

possible fragmentation pathways.

As discussed above, the intensity of the signals for ions with odd number of side

chains was found always rather low (10-20 times) as compared with that one of signals

for ions with even number of side chains.

From our results it is found that longer alkyl side chains are lost more easily

from the SPM cation radical.

CID-MS/MS of partially-conjugated SPM

Compound 6 is a partially-conjugated macrocycle in which bicyclopentane units

are present in the ring backbone preventing thus its full conjugation. Such molecule was

found quite difficult to ionise using both DHB and CHCA matrices. As a consequence,

the study of collision-induced fragmentation pattern was not possible. The inertia of the

compound 6 towards ionisation is compatible with the proposed ionization and

fragmentation mechanisms which involve the stabilization of the cation radical via

delocalization on the ring backbone.

Proof of fragmentation regioselectivity

146

As described above, each fragmentation step occurs with the loss of a couple of alkyl

chains that decorate the SPMs backbone and gain of one proton (Fig. 3). It was

supposed that the couple of alkyl chains is lost from the same phenanthrene unit in two

successive steps. In order to prove this assumption, SPM 10 which contains side chains

of different length (hexyloxy and decyloxy) in the same molecule was synthesized (Fig.

5).

Fig. 5: Structure of 10 (R = C6H13, R1 = C10H21).

The CID pattern of 10 (Fig. 6) clearly shows that the loss of a couple of alkyl side

chains occurs always from the same phenanthrene unit. The signal corresponding to the

molecular ion was detected at m/z 1385.1. It fragmented according to three possible

pathways: (i) sequential loss of decyls (m/z 1244.9 and 1103.7, respectively) followed

by sequential loss of four hexyls (m/z 1019.6, 934.5, 850.4 and 765.3, respectively), (ii)

sequential loss of four hexyls (m/z 1301.0, 1215.9, 1131.8 and 1046.7, respectively)

followed by sequential loss of two decyls (m/z 906.5 and 765.3, respectively) or (iii)

sequential loss of two hexyls (m/z 11301.0 and 1251.9, respectively) followed by the

sequential loss of another two decyls (m/z 1075.7 and 934.5, respectively) and finally

followed by the sequential loss of the last two hexyls (m/z 850.4 and 765.3,

respectively). For the loss of each couple of chains one proton is retained in agreement

with the scheme in Fig. 3. No product ions formed by sequential loss of one decyl

followed by loss of one hexyl chain were detected. Therefore, the fragmentation is

regioselective indicating that the sequential loss of one couple of alkyl side chains

occurs always from the same phenanthrene unit.

147

Fig. 6: Overview of CID pattern of 10. Losses corresponding to hexyls or decyls are

indicated by red and blue arrows, respectively. Loss of 84.1, 85.1, 140.2 and 141.2 Da

corresponds to C6H12, C6H13, C10H20 and C10H21, respectively.

Proposed mechanism of SPMs fragmentation

For fully-conjugated SPMs (except 7b), the first product ion results from the loss

of one alkyl side chain (R) and the simultaneous addition of one proton. It was observed

that such loss does not occur when R is a methyl group indicating that a -hydrogen is

needed. The second loss observed corresponds to another alkyl chain. Such series of

losses is repeated until all chains that decorate the ring backbone are lost. As discussed

above, the relative abundance of the first product ion was 20 - 30 % lower than that of

the second one. This indicates that each couple of alkyl chains is lost under the same

conditions. Various possible CID pathways have been considered to explain the

experimental findings. A scheme of proposed general mechanism for the fragmentation

of a macrocycle with triangular shape is given in Fig. 7. It results from CID study that

the same fragmentation pathway holds for all other conjugated SPMs. The molecular

ion is always detected as cation radical and the charge is most probably generated on the

oxygen atom (Fig. 7 a). Such ion looses one alkyl chain as neutral alkene while the

proton in position is transferred to the oxygen atom yielding the first product ion (Fig.

7 b) which looses one alkyl chain as a radical. This loss gives the second product ion

(Fig. 7 c). Conjugation across the macrocycle ring allows positive charge to reach

another phenanthrene unit (Fig. 7 d) where the aforementioned series of losses takes

place again leading to the product ions e and f, respectively. The alternate losses of

olefin and alkyl radical occurring on the last phenanthrene unit are leading to the

formation of the product ions h and i, respectively.

148

The experimental CID pattern cannot be fully explained without supposing the

loss of an alkyl side chain as a radical from e. Such loss violates the so-called „even-

electron rule“ which is, however, quite often not followed anyway.[33]

The feasibility of

the even-electron rule violating fragmentation is supported by the formation of an

intramolecular hydrogen bond (see section: „Proof of fragmentation regioselectivity“),

which has been demonstrated to favour the loss of radical rather than a closed-shell

unit.[34]

Fig. 7: Proposed scheme of possible SPMs fragmentation mechanism. The

fragmentation of the molecular ion (a) leads to the formation of the product ions

labelled: b, c, e, f, h and i, respectively.

Supramolecular aggregates of SPMs

Among all the studied SPMs, self-aggregation was observed for 2a and 7a. This

might be explained by the fact that these compounds are decorated with short alkoxy

side chains (CH3O-) which facilitate the π-π stacking of the macrocycles as the main

intermolecular interaction to form short tubular supramolecular architectures.

149

The supramolecular species formed were detected in mass spectra and their structures

were confirmed by MSn analysis. Signals corresponding to the supramolecular

aggregates of 2a were detected in mass spectra at m/z 1704.5 [M2]+ and 2556.8 [M3]

+,

respectively (Fig. 8). The dimer of 7a was detected at m/z 2272.7. A mixed 1.1 adduct of

the compounds 2a and 7a were also detected at m/z 1988.6.

Fig. 8: Mass spectrum showing the signals corresponding to self-aggregates of 2a.

Together with the molecular cation radical (m/z 852.2) also the dimer and trimer of 2a

are detected (m/z 1704.5 and 2556.8, respectively). The asterisk indicates the signal

corresponding to 9 (synthesis by-product).

Structures of detected supramolecular aggregates have been calculated via molecular

mechanics optimization minimizing force field using Avogadro software.[30]

Computed

structures (Fig. 9) indicate that studied SPMs decorated with methyl groups interact via

π-π stacking leading to the formation of self-assembled tubular supramolecular

architectures.

150

Fig. 9: Structures of: 2a dimer (I), 2a trimer (II), 7a dimer (III) and adduct of 2a and

7a (IV) optimized via force field minimization.

The possible formation of supramolecular aggregates of studied SPMs was also

investigated by NMR analysis of 2a as well as of 7a dissolved in chloroform-d at

concentrations equal to 0.44, 0.88, 1.76, 3.6 and 7.05 mM. In 1H NMR spectra of the

both macrocycles, the phenanthrene hydrogens signals were up-field shifted with

increasing concentration. The shift in peak positions clearly indicated the self-

aggregation. The number of independent NMR-active species was estimated by

eigenvalues analysis (EA) of NMR data and it was found to be equal to three in

agreement with the number of aggregates detected in mass spectra.

Fullerene as matrix for mass spectrometry of SPMs

The effect of fullerene on the ionisation of SPMs was investigated. For all SPMs it was

found that fullerene behaves as „hot“ matrix enhancing thermal fragmentation.

151

In contrast to DHB and CHCA matrices, fullerene usually allows the clear detection of

signal corresponding to the ring backbone as, for example, shown in Fig. 10.

Fig. 10: Comparison of mass spectrum of compound 2c recorded using CHCA (a) or

C60 as matrix (b). Signal detected at m/z 764.9 corresponds to the ring backbone.

Significant enhancement of signals corresponding to molecular cation radical and

complete suppression of fragmentation were observed in mass spectra of SPMs

decorated with methyl groups when irradiated in presence of fullerene.

The observed behaviour of SPMs upon laser irradiation in presence of fullerene could

also be explained considering that while SPMs are electron-rich molecules, the fullerene

is electron-deficient. Therefore, C60 might behave as electron-acceptor promoting the

formation of the SPMs cation radical. According to such hypothesis, the ionisation of

C60 in presence of SPMs should then be easier. In order to check this hypothesis, mass

spectra of C60 mixed with various SPMs were recorded in reflectron negative ion mode.

Example of fullerene ionisation enhancement obtained using 5 is given in Fig. 11.

Fig. 11: Effect of the SPM 5 on the ionisation of fullerene in reflectron negative ion

mode. In average, 5 leads to ionisation enhancement of about 10 times (lines connecting

points do not have physical meaning).

Role of photo-electrons

152

It has been reported that the use of non-metallic surface as MALDI target for proteins

and peptides enhances the yield of positive ions.[35]

However, in our case when SPMs

samples were spotted onto a thin layer of parafilm M®[36]

attached to the metal target,

rather strong suppression of the ion yield was observed. This indicates that in contrast to

peptides and proteins, direct photoionisation of SPMs might be excluded as possible

ionisation mechanism. Therefore, ionisation routes involving interaction of

photoelectrons generated by action of the laser pulse on the metallic target surface with

either the matrix or the analytes seem to be more probable here.

CONCLUSIONS

Via mass spectrometric study of 12 recently synthesized alkoxy-decorated shape-

persistent macrocycles, the possible fragmentation pathways have been established. It

was found that the alkoxy side chain does not affect the observed CID patterns while

photoelectrons generated during the MALDI process and full-ring conjugation have

been found to be important factors needed for the generation of molecular cation

radicals and ion stabilization, respectively.

Fully-conjugated SPMs have been proven to be excellent precursors for the

generation of highly-stabilized cation radicals. Decoration of SPMs with methyl groups

allows: (i) formation of supramolecular self-aggregates and (ii) suppression of

fragmentation while preserving their electron-donating character towards electron-

deficient motifs such as fullerene. Therefore, they represent promising electron-

donating motifs that can be exploited in electronics and optoelectronics for the

development of robust and highly efficient laser-activated supramolecular switches.

Acknowledgements

Support from Ministry of Education, Youth and Sports of the Czech Republic (Projects

MSM, 0021622411, 0021627501, the Czech Science Foundation (Projects No.

104/08/0229, 202/07/1669) and the Grant Agency of Czech Republic (Project No. 13-

05082-S) are greatly acknowledged. This research has been also supported by

CEPLANT, the project R&D center for low-cost plasma and nanotechnology surface

modifications (Project CZ.1.05/2.1.00/03.0086) and by CEITEC, the European

Community’s 7FPs (Project CZ.1.05/1.1.00/02.0068) from the European Regional

Development Fund.

153

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156

Int. J. Geo Info.

Submitted on 19 - August - 2014

ISPRS Int. J. Geo-Inf. 2014, 3, 1-x manuscripts; doi:10.3390/ijgi30x000x

ISPRS International Journal of

Geo-Information ISSN 2220-9964

www.mdpi.com/journal/ijgi/

Article

Remotely Sensed Soil Data Analysis Using Artificial Neural Networks. A case


Abstract: Earth observation and monitoring of soil quality, long term

changes of soil characteristics and deterioration processes such as

degradation or desertification are among the most important objectives of

remote sensing. The georeferenciation of such information contribute to the

development and progress of Digital Earth project in the framework of

information globalization process. Earth observation and soil quality

monitoring via remote sensing are mostly based on the use of satellite

spectral data. Advanced techniques are available to predict the soil or land

use/cover categories from satellite imagery data. Artificial Neural Networks

(ANNs) are among the most widely used tools for modeling and prediction

purposes in various field of science. The assessment of satellite images

quality and suitability for analysing the soil conditions (e.g., soil

classification, land use/cover estimation, etc.) is fundamental. In this paper,

methodology for the preliminary data exploration and subsequent

application of ANNs in remote sensing is presented. It consists of

preliminary explorative data analysis and of ANNs application. The first

stage is achieved via: (i) elimination of outliers, (ii) data pre-processing and

(iii) the determination of the number of distinguishable soil “classes” via

Eigenvalues Analysis (EA) and Principal Components Analysis (PCA). The

next stage of ANNs use consists of: (i) building the training database, (ii)

optimization of ANN architecture and database cleaning and (iii) training

and verification of the network. Application of the proposed methodology is

shown.

Keywords: Remote Sensing; Soil Classification; Desertification; Land

use/cover; Eigenvalues Analysis; Principal Components Analysis; Artificial

Neural Networks

OPEN ACCESS

157

1. Introduction

The concept of globalization is a technological unification of the world leading to a

global information society. Such process requires several steps such as the design and

development of well-organized infrastructures able to solve global and regional

problems [1]. To do this, the informatization and georeferenciation of world’s

knowledge is required. This is the task of „Digital Earth“ project. In this framework,

remote sensing and earth observation technologies contribute significantly to the

progress of Digital Earth. Among the various tasks and objectives of remote sensing

sciences is the monitoring of soil characteristics, deterioration and use (e.g., cultivation

or urbanization). Soil degradation and desertification is a phenomenon that affects many

countries, especially those with arid and semiarid regions. Common degradation

processes include water stress, soil salinization, forest fires, overgrazing and water

erosion, etc. Such processes are often monitored using approaches based on various

biophysical and socioeconomic indicators. The evaluation of such indicators has

recently been reported [2]. Other approaches are based on the estimation of the

vegetation cover fraction and/or the class of the soil by remote sensing using satellite

imagery data (usually reflectance spectra). Scheme of the reflectance data acquisition

procedure using a satellite is given in Figure 1.

Figure 1. Scheme of remote sensing principle. A fraction of sunlight reflected or

scattered by the soil is detected at selected wavelengths by the satellite.

Remote sensing estimation of soil class and land use/cover is a complex process that

involves several steps. First, the image of the studied area is recorded by the satellite.

The quality of satellite image is given by parameters such as spatial, spectral,

radiometric and temporal resolution. The spatial resolution is related to the dimensions

of the pixels in a raster image. The pixel corresponds usually to area with dimensions

ranging from 1 to 1 000 meters. The spectral resolution refer to the wavelength width of

the detected frequency bands. The radiometric resolution indicates the ability of an

imaging system to discriminate slight differences in energy of the detected radiation.

The temporal resolution in the frequency of flyover by the satellite.

158

Over the studied area, m sampling points are chosen either randomly or using a grid.

Soil samples are then collected from the sampling points and classified according to the

results of chemical and morphological analysis (Figure 2).

Figure 2. Example of random distribution of 15 sampling points over the studied area.

From each sampling point a soil sample is collected. The class of the collected soil

samples is determined by means of chemical and morphological analysis.

Table of reflectance spectra of the collected soil samples and their corresponding class

constitutes the “database”. The reflectance spectrum of a soil sample depends mostly on

the soil chemical composition. Therefore, the reflectance spectra of soils belonging to

different classes are expected to be different. Unfortunately, the mathematical law

expressing the relation between the reflectance spectrum and the chemical composition

of the soil is not known. However, the data in the database contain information that can

be used to build an empirical model of the spectrum-soil relationship. The process that

leads to obtain such empirical relation is called “modeling”. The model allows to predict

the class of the soil using the reflectance data for arbitrary pixels of the satellite image

in places where no sampling and chemical analysis were done. In this way, a map

representing the distribution of the various soil classes over the studied area is obtained.

Various mathematical methods are available for the modeling. The choice of the method

depends upon the complexity of the data to be modeled. Non-linear methods offer the

flexibility required to model complex data structures. Methods based on artificial neural

networks are among the most powerful and widespread [3, 4]. The applications of

ANNs in science and technology is quite extensive in various branches of chemistry,

physics, biology, social sciences and economy and they cover classification, modeling,

pattern recognition purposes etc. For example, ANNs are used in chemical kinetics [5],

prediction of the behavior of industrial reactors [6], modeling kinetics of drug release

[7], prediction of optimal composition of multidrug mixtures [8], optimization of

electrophoretic methods [9], classification of agricultural products such as onion

varieties [10], and even insect species determination [11, 12]. Application of ANNs in

medical diagnosis has recently been reviewed [13]. In general, very diverse data such as

classification of biological objects, chemical kinetic data, or even clinical parameters

can be handled in essentially the same way. Selected examples of ANNs applications in

remote sensing are: water quality monitoring [14], estimation of evapotranspiration

159

[15], derivation of ocean color products [16], mapping fractional snow cover [17],

prediction of soil organic matter [18], spatial assessment of air temperature [19],

mapping contrasting tillage practices [20], classification of soil texture [21] prediction

of productive fossil localities [22], sub-pixel mapping and sub-pixel sharpening [23],

etc. Often, ANNs are used even when some basic conditions for their use are not

fulfilled.

The aim of this work is to present the general philosophy and fundamental

methodological steps that should be followed namely in the evaluation of satellite

spectral data using ANNs for soil classification purposes. In particular, importance of

the use of preliminary data exploration by EA and PCA is stressed. The proposed

methodology is exemplified to the evaluation of satellite data concerning El-Fayoum

area in Egypt and the results of each step are commented.

2. The study area

El-Fayoum depression is a Governorate located at 90 km southwest of Cairo (Figure

3) and characterized by temperate climatic conditions. The total area of the depression is

6068.70 km2; the land use in this area includes only 1849.64 km

2 (i.e. 30.48 % of the

total area). The agricultural land in the depression is 1609.34 km2. El-Fayoum is

connected to the Nile Valley by the Hawara area, where a canal, called Bahr Yousef, is

transporting the Nile water. The depression is distinguished by its long history,

extending back millions of years, having the importance of emerging Ancient Egyptian,

Greek, Roman, Coptic and Islamic eras. It is the only Egyptian Governorate, where a

salt lake (i.e., Qaroun Lake), vegetation and desert in their diverse features and unique

combination exist. The climatic data of El-Fayoum district indicate that the total

rainfalls does not exceed 7.2 mm/year and the mean minimum and maximum annual

temperatures are 14.5 and 31.0 °C, respectively. The evaporation rates are coinciding

with temperatures, where the lowest evaporation rate (1.9 mm/day) was recorded in

January while the highest one (7.3 mm/day) was recorded in June [24]. According to the

aridity index classes [25] the depression is classified as territory under arid climatic

condition.

The depression has a particular nature, differing from the Delta and Upper Egypt and

from the Oases, as well. The differences are not limited to agriculture, they extend to

geographical and topographical features, as the environment vary between agricultural,

desert and coastal areas. The El-Fayoum depression has been formed as a result of basin

subsidence, relative to the Nile River, permitting it to break through and to flood the

area. This led to the formation of a thick fertile alluvium [26]. The main identified

landforms in El-Fayoum depression are fans, recent and old lake terraces, depressions,

plains, and basins [27]. With the present regime of practiced flooding and rising of

water level in the Qaroun Lake, surrounding arable land would be in danger by

salinization and water logging.

Figure 3. Location map of the El-Fayoum Depression (a) and satellite image (Land sat

ETM 2011) (b).

160

3. Satellite data

In this study, satellite Digital Number (DN) values of the pixels representing soil

sampling sites were used out of different spectral bands which include the seven

LANDSAT images (bands 1-5, 7) and three SPOT ones (bands 1, 2 and 3).

Wavelengths and spatial resolution of different spectral bands are shown in Table 1.

Table 1. Wavelengths and spatial resolution of satellite spectral bands used.

Spectral Band Wavelength (μm) Resolution (m)

LANDSAT 1 0.45-0.52 30

2 0.52-0.60 30

3 0.63-0.69 30

4 0.77-0.90 30

5 1.55-1.75 30

7 2.09-2.35 30

SPOT 1 (green) 0.50-0.59 10

2 (red) 0.61-0.68 10

3 (NIR) 0.79-0.89 10

4. Software and computation

All calculations were performed on a standard PC x86 running Microsoft Windows

XP Home Edition as operating system. Statistical data processing and analysis were

performed using STATISTICA 10 (StatSoft. Inc. 1984-2011, USA). ANNs computation

was carried out using Trajan Neural Network Simulator, Release 3.0 D (Trajan Software

Ltd. 1996–1998, UK).

5. Preliminary data analysis

As outlined in the Introduction, the database is used to build an empirical model by

which the class of a soil can be predicted correctly from reflectance data. This can be

achieved only if the data in the database contain the information which is able to

distinguish samples belonging to different soil classes. Preliminary data analysis by EA

161

allows estimate the number of distinguishable soil classes while PCA enables data

compression and visualization.

5.1. Organization of experimental data

For each of the m soil samples collected over the studied area, the value of

reflectance intensity is recorded at n wavelengths by the satellite. Data are organized in

a matrix X with dimensions m×n. Therefore, the elements of the i-th row of the matrix

X represent the reflectance spectrum of the i-th soil sample. The further discussion

concerns the analysis of the data in the matrix X.

5.2. Data pre-processing

Pre-processing of data in the matrix X consists of: (i) data inspection to search for

missing values and (ii) mathematical transformation of data. Various methods such as

data smoothing [28] or iterative algorithms [29, 30] are available to replace missing

values with suitable estimate. Common data transformation includes either column

centering or standardization or normalization [31, 32]. In this work, data were

autoscaled (i.e., centering around column mean and scaled by column standard

deviation).

5.3. Eigenvalues analysis

Let us considering a matrix X with m rows and n columns containing error-free data.

The column standardized matrix Z is calculated as:

(1)

where 1m is a vector of length m with all elements equal to one, is the transpose of

the vector which elements are the column mean values of X and is the diagonal

matrix with dimension n×n in which the main diagonal elements are equal to the

column standard deviations of data in matrix X. The matrix Z is used to compute the

variance-covariance matrix D as:

(2)

The matrix D is symmetric and with dimension m×m. Linear algebra ensures that the

variance-covariance matrix is also positive semi-definite and this implies that all its

eigenvalues are non-negative. The first step in the analysis of matrix D is the calculation

of its eigenvalues λi (i=1,…, m). This can be done using various approaches [37]

. On the

principal diagonal of matrix D are the column variances of the original matrix Z.

Therefore, the trace of D is equal to the total column variance of Z:

162

(3)

A property of variance-covariance matrix and its eigenvalues is that:

(4)

and, considering Equation (3):

(5)

Therefore, the percent of the total variance „explained“ by the i-th eigenvalue is

expressed as:

(6)

The number of non-zero eigenvalues of a matrix is called „rank“ r. In general,

considering a matrix with dimension m×n:

(7)

For the matrix D, the non-zero eigenvalues are only the first r while, the remaining

ones are all equal to zero. The rank of D is equal to that of the matrix X.

The number of non-zero eigenvalues (or rank) is interpreted as the number of

„factors“ responsible for variance in the data. In the case of reflectance data of soils, the

rank r of the data matrix X can be interpreted as the number of „distinguishable“ soil

classes.

Up to now a matrix X with error-free data has been considered. However, every

measured quantity (such as reflectance values) is subject to measurement error. The

consequence is that errors contribute to the overall variance in data (σ2). Therefore, all

eigenvalues of the matrix D result non-zero and its „apparent“ rank is equal to m. The

m-r eigenvalues represent variance due to measurement errors. The aim of eigenvalues

analysis is to estimate the true rank r of the matrix D. Several criteria are available for

this purpose [37,38]

. Simple method for the estimation of the true rank of the matrix D

recommended in this work is the use of the so-called scree-plot [39]

. First of all, the n

eigenvalues λi of the matrix D are calculated and ordered according to their magnitude.

The scree-plot is obtained by plotting the magnitude of eigenvalues λi against the

corresponding value of i (section “7. Examples”). As the value of i increases, the

variance explained by the corresponding i-th eigenvalue decreases. After, the tangents

to the two branches of the scree-plot are drawn and the value of r is found as the integer

number closest to the intersection point coordinate at the x axis (section “7.

Examples”). The knowledge of r value is of fundamental importance for the next

application of modeling techniques (such as ANNs).

163

5.4. Principal Components Analysis

The PCA is a technique of exploratory analysis and dimensionality reduction of

multivariate data. The eigenvectors and the corresponding eigenvalues of X are obtained

by matrix factorization.

To each eigenvalue an eigenvector is associated which is related to the percent of

total variance explained by that eigenvalue. The eigenvectors are called “principal

components” (PC) and represent successive orthogonal directions of maximum variance

in data. Therefore, the eigenvectors define a new coordinate system (principal factor

space) in which both the variables and the samples can be represented. Using the scree-

plot or other suitable procedure [37]

, the rank r of matrix D is estimated. Then, the data

can be represented in a reduced r-dimensional factor space.

In general, the data matrix X can be decomposed into eigenvalues and eigenvectors

by several methods. Among these, the singular value decomposition (SVD) represents

one of the more robust and accurate. The SVD leads to the factorization of the matrix X

as:

(8)

where U is the matrix of normed scores, W is the diagonal matrix of eigenvalues

square roots, and V is the matrix of loadings [37,39]

.

The matrix E = W2 is the diagonal matrix of eigenvalues. The trace of E represents

the total variance in data. The importance of the k-th principal component is expressed

as percent of explained variance (% vark) using the k-th eigenvalue (λk):

(9)

The elements of the k-th column of the matrix VT represent the coordinates of the

variables (satellite spectral bands) on the k-th principal component (loadings).

Analogously, the elements of the j-th column of the matrix U represent the coordinates

of the soil samples on the j-th principal component (scores). By plotting the columns of

U, the distribution of the variables in the reduced r-dimensional principal factor space is

visualized (loadings plot). In the same way, the distribution of the samples in the

reduced factor space is obtained by plotting the columns of the matrix VT (scores plot).

From such plot, clusters of “similar” samples can be visualized.

6. Artificial Neural Networks

ANNs are mathematical tools that mimic the structure and function of human brain.

They are able to perform “learning”, “generalization” and “prediction” tasks. For this

reason, ANNs belong to so called methods of artificial intelligence (AI). The network

“learns” from a series of “examples” that form the “training database”. An “example”

164

is given by a vector X of inputs and a vector

of outputs. In case of soil classification from satellite data, the

vectors Xi and Yi contain the reflectance intensities and the class of the i-th sample,

respectively. The objective of the “learning” is to model the unknown relation f

between the vectors Xi,p and Yi,q (Equation (10)):

(10)

Because of their inherent non-linear nature, ANNs are able to model complex

relationships among data. However, PCA remains fundamental to visualize the

underlying structure of the data and to get an idea of possible ANNs outcome. Beside

the widespread use of multilayer feed-forward neural networks, several other networks

including Bayesian, stochastic, recurrent, or fuzzy ones are available. A review of

various classes of neural networks can be found elsewhere [40,41]

.

6.1. Mathematical background of ANNs

The basic processing unit of a neural network is called “neuron” (or “node”). The

neurons are organized in layers and each neuron in a layer is connected with each

neuron in the next layer through a weighted connection. The value of the weight wij

indicates the strength of the connection between the i-th and the j-th neuron. A neural

network is formed by the “input” layer, one or more “hidden” layers, and the “output”

layer. The number of hidden layers and that of neurons therein (z) depends on the

complexity of the relation f to be modeled (Equation (10)). Therefore, in the first step

the network architecture must be optimized. The general scheme of a typical three-

layered ANN architecture is given in Figure 4.

Figure 4. Example of three-layered ANN architecture with one hidden layer. The

symbol wij represents the weight of the connection between the i-th and j-th neuron.

The data (xi) received by the input layer are transferred to neurons in the first hidden

layer where they are mathematically processed by calculating their weighted sum and

adding a “bias” term (θj) according to Equation (11):

165

(11)

where p and q are defined as stated above. The resulting value of netj is transformed

using a suitable mathematical function (transfer function) and transferred to neurons in

the next layer. Various transfer functions are available [41]

but the most commonly used

is the sigmoid one (Equation (12)):

(12)

At the beginning, random values within the interval [-1, 1] are assigned to all the

connection weights wij. The “learning” is achieved by iterative alteration of the

connection weights values (wij) according to a given mathematical rule (training

algorithm). Various algorithms are available [41,42]

. The most common training

algorithm is back-propagation (BP) which searches for the values of the weights wij that

minimize the sum-of-squared residuals (E) calculated as:

(13)

Where yij and yij* represent the actual and network j-th output.

The weight change at the k-th epoch on the neurons in a given layer is calculated as:

(14)

where η is a positive constant called “learning rate”, μ is the “momentum” term and

Δwijk-1

is the change of the weight wij from the (k-1)-th epoch. The learning rate controls

the speed of the learning while the momentum term stabilizes the process avoiding local

minima. Details can be found in monographs [41]

. A nice and detailed introduction to

ANNs can be found elsewhere [43]

.

6.2. Optimization of network architecture

Optimized network architecture can be obtained using the function given by

Equation (13) as criterion. A widely used approach is to plot the value of E (Equation

(13)) as a function of the number z of nodes in the hidden layer (section “7.

Examples”). The value of E decreases as that of z increases. However, after an optimal

value of z the change in the value of E becomes rather poor.

Usually, the optimal value of z is found from the coordinate of the intersection point

of the tangents to the two branches of the plot. Before proceeding with optimization of

ANN architecture it is advisable to check data in matrix X for the presence of possible

outliers using proper statistical tests [39]

. The effect of outliers on ANN performance has

been reported elsewhere [44]

. Methods of outlier detection using ANNs have also been

166

described [45]

.

6.3. The verification of the network

The training process is carried out using the optimal network architecture found until

a proper minimum value of E is reached. The “generalization” ability of the network is

checked in the so-called “verification” procedure using data not used in the training. A

common approach is to use cross-verification by selecting randomly one or more rows

of the matrix X for verification and to use the remaining ones for the training. This

process is repeated until each row of X has been used for verification at least once.

Modern ANNs softwares allow to perform training and verification simultaneously.

After successful training and verification, the network can be used to classify new

samples.

6.4. Structure of the training database

As stated above, a suitable training database is used to perform ANN training. Such

database is a table (or matrix) of data concerning samples of soil for which the class is

known. Each row of the matrix refers to one soil sample. The first n elements of the row

are satellite data while the last element is the output (soil class). ANNs require a

“sufficient” number of samples for each class, however, such number depends from the

complexity of the problem and general rule is not available.

6.5. Data pre-processing before ANN analysis

Data pre-processing is a recommended step before using ANNs. Such step involves

mathematical data transformation. Usually, data are scaled within the interval [0, 1].

When the matrix X contains missing data, various procedures can be applied such as

substitution by data smoothing [46]

or removal of the corresponding row and column.

7. Examples

Satellite reflectance data concerning 100 locations randomly chosen in El-Fayoum area

(Figure 5) were recorded at 9 wavelengths. The locations were grouped into

„vegetation“ (V), „lake“ (L) and „urban“ (U) classes. The following examples concern

the use of reflectance data for land use/cover estimation.

Figure 5. Distribution of 100 locations over the El-Fayoum area (Egypt) for which

satellite reflectance data were recorded.

167

7.1. Distinguishability of “vegetation” and “lake” classes

In this example, reflectance data concerning only .“Vegetation” (V) and “Lake” (L)

classes were used. Data were organized in a matrix X with 76 rows and 9 columns.

The eigenvalues of the matrix X were calculated and the number of non-zero

eigenvalues (also called structural eigenvalues) was estimated from the scree-plot as

shown in Figure 6. As can be seen, two eigenvalues explain 87.54% of the data

variance.

Figure 6. Scree-plot obtained for the reflectance data of soil samples collected in El-

Fayoum Egyptian region. The method of the tangents gives two structural eigenvalues.

The percentage of variance in data explained by each eigenvalue is given.

7.1.2. Principal Components Analysis

The eigenvectors and the corresponding eigenvalues of the matrix X were computed

by SVD. The distribution of the variables and samples in the bi-dimensional principal

factor space is given by the loadings and scores plots, respectively. In the loadings plot

(Figure 7), two groups of variables are highlighted in ellipses. The smaller is the

168

distance among variables in the loadings plot the higher is their correlation. For

example, the small distance between variables SPOT (B2) and LANDSAT (B4) in

Figure 7 means that they highly correlated. Therefore, the spectral bands SPOT (B2)

and LANDSAT (B4) are “equivalent” for the distinguishability of V and L classes. The

loadings plot is a mean to visualize the extent of correlation among the variables.

Figure 7. Representation of the satellite spectral bands (variables) in the reduced bi-

dimensional principal factor space. Highly correlated spectral bands are highlighted in

ellipses.

The distribution of the samples in reduced bi-dimensional principal factor space is

shown in the scores plot (Figure 8). Clearly, two well-separated clusters of samples are

visualized. This result is in agreement with the finding of EA (two structural

eigenvalues were found).

7.1.3. Classification using ANNs

From the matrix X the so-called ”complete” matrix F was obtained by adding to X a

further column containing the class (V, or L) of each sample. The matrix F was used as

training database. In the first step, data were pre-processed by autoscaling

(standardization to zero mean and unit standard deviation). In the second step, the

optimal neural network architecture was searched for. A network with only one neuron

in the hidden layer was found able to classify correctly the samples. This result is due to

the fact that clusters in Figure 8 can be separated by drawing a line between them.

Therefore, the network performs a simple linear discrimination. The generalization

ability of the network was checked by choosing randomly six or more samples at a time

to perform cross-verification. It was found that the trained network was always able to

predict correctly the class of the sample (100% of correct classification).

Figure 8. Representation of samples in the reduced bi-dimensional principal factor

space. Samples belonging to class V are well-separated from those belonging to class V

(dotted ellipse).

169

7.2. Distinguishability of “vegetation”, “lake” and “urban” classes

This example is an extension of the previous one to include also the class “urban”

(U) beside the classes V and L. Data were organized in a matrix X with dimensions

100×9. The complete matrix F was obtained by adding to X a further column containing

the class (V, L or U) of each sample.

7.2.1. Eigenvalues analysis

As the first step the eigenvalues of X were calculated. The number of structural

eigenvalues was estimated from the scree-plot as shown in Figure 9. In this case, the

determination of the number of structural eigenvalues is not straightforward as in the

previous case. We know that data were collected for three classes of soil. However,

although the first two eigenvalues are quite different from each other, the difference

between the second and the third eigenvalue is quite small. This indicates that probably

two of the three soil classes are barely distinguishable.

Figure 9. Scree-plot obtained from reflectance data concerning “vegetation”, “lake” and

“urban” classes (El-Fayoum Egyptian region). The number of structural eigenvalues is

most probably equal to two.

170

7.2.2. Principal Components Analysis

The matrix X was decomposed using SVD algorithm. The loadings plot (Figure 10)

shows the distribution of the spectral bands used in the reduced bi-dimensional principal

factor space. The spectral bands LANDSAT (B1-5, 7) are highly correlated.

Figure 10. Loadings plot representing the distribution of the satellite spectral bands

(variables) in the reduced bi-dimensional principal factor space. PC1 and PC2 are the

first and the second principal component, respectively.

The scores plot is shown in Figure 11. Only samples belonging to the L class can be

clearly distinguished from the others. This finding is in agreement with the results of

EA. As can be seen, there is a partial overlap of the clusters for the classes U and V

(highlighted by ellipse in Figure 11). This might means that several samples represent

“green” urban zones. In the same way, the samples belonging to the class L highlighted

by red arrows in Figure 11 might be either “outliers” or samples representing “lake”

areas where vegetation is abundant. The sample highlighted by the dotted circle in

Figure 11 is quite far from the other samples belonging to the V class. However, this

does not necessarily mean that the sample in the dotted circle represents an outlier. Its

position in the scores plot indicates that it might represent an urban area where

vegetation is also present (e.g., a park in a city).

From the results of PCA it is clear that the reflectance data used contain enough

information to discriminate clearly the class “lake” from both “urban” and “vegetation”

ones. However, data do not allow complete distinguishability of pure urban and “green”

urban areas by PCA. Therefore, we can expect that after removal of the outliers from

the database, ANNs can distinguish samples from class L from those belonging to either

U or V classes.

Figure 11. Distribution of samples in the reduced bi-dimensional factor space given by

the first and second principal components (PC1 and PC2, respectively). The partial

overlap of cluster for samples belonging to the U class with that for samples belonging

171

to the V class is highlighted by the black ellipse. The arrows indicate possible outliers.

Doubtful case is highlighted with dotted circle.

7.2.3. Classification using ANNs

The training database was cleaned from seven outliers as described in section 6.2.

(“Optimization of network architecture”). The plot of E vs. number of neurons in the

first hidden layer (z) is shown in Figure 12. The coordinate of the intersection point of

the two tangents on the x axis indicates that the minimum number of neurons in the

hidden layer to model the data is equal to three. Here a network with four neurons in the

first hidden layer was used.

Figure 12. Plot of the sum-of-squared residuals (E) as a function of the number z of

neurons in the first hidden layer of the network. The optimal value of z is equal to three.

The “generalization” ability of the trained network was checked by cross-

verification using one sample at a time for verification. Such process was repeated until

all samples were used at least once. Although PCA was not able to clearly discriminate

class U and V, the percentages of correctly classified samples using ANNs are: 100%

(class L), 92% (class V) and 84% (U).

172

8. Conclusions

Remote sensing using satellite spectral data enables efficient monitoring of soil

quality in large extent. ANNs represent powerful tool to estimate soil classes and the

assessment of the suitability of satellite spectral data is a fundamental step.

On the basis of real satellite spectral data, methodology for the purposes of soil

classification and land use/cover estimation by artificial neural networks was developed

pointing out the main issues that are often overlooked. Preliminary data screening by

means of eigenvalues analysis and principal components analysis were proven to be

powerful means to assess in advance the number of distinguishable soil classes and

which spectral bands are the most suitable for soil classification. Information provided

by developed procedure for preliminary data screening was shown to be an important

prerequisite to improve soil class and land use/cover estimation from remotely sensed

data using artificial neural networks.

Concluding, developed procedure for preliminary screening and subsequent ANNs

analysis of remotely sensed data provides affordable results contributing to the

objectives of Digital Earth.

Acknowledgements

The authors acknowledge the support of the Egyptian National Authority for Remote

Sensing and Space Sciences (NARSS), who provided the processed satellite images of

study area. Sincere appreciation goes to the Egyptian Academy of Scientific Research

and Technology, funding the project “Establishment of Egyptian Land Resources

Database” through which soil data sets were extracted. Authors also acknowledge the

EU-FP7 program; funding SUDSOE (Grant agreement no: 295031) which provided the

platform for author collaboration. Support from Ministry of Education, Youth and

Sports of the Czech Republic (Projects MSM, 0021622411, 0021627501, the Czech

Science Foundation (Projects No. 104/08/0229, 202/07/1669) and the Grant Agency of

Czech Republic (Project No. 13-05082-S) are greatly acknowledged. This research has

been also supported by CEPLANT, the project R&D center for low-cost plasma and

nanotechnology surface modifications CZ.1.05/2.1.00/03.0086 funding by European

Regional Development Fund.

Conflicts of Interest

The authors declare no conflict of interest.

173

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Flight Mass Spectrometry

Chem. Eur. J.

Submitted on 24 - September - 2014

Filippo Amato,[a] Lubomír Prokeš,[a,b,c] Eladia Maria Peña-Méndez, José Elias Conde,[d]

Milan Alberti,[a,b,c] Pavel Kubáček,[a] and Josef Havel*[a,b,c]

Abstract: Method for the rapid construction of new

chemical motifs might accelerate the development of nano-

science. In this work the synthesis of new chemical entities

via laser ablation is systematically demonstrated using

mixtures of gold and selenium. The generated species were

detected by time-of-flight mass spectrometry and for

selected of them the structure was investigated via DFT

optimization. In total, 67 new gold selenide clusters were

synthesized. Chemical species generated in the gas-phase

might inspire new routes for synthesis novel compounds in

solid-state.

Introduction

Gold selenium minerals such as fischesserite

(Ag3AuSe2), kurilite ((Au,Ag)2(Te,Se,S)2) and petrovskite

((AuAg)(S,Se)) are found in the nature.[1,2] Other gold

selenides were synthesized in solution or in melts[3–6] or

by passing hydrogen selenide into an aqueous solutions

of gold salts.[3,4,7] Auric selenide (Au2Se3) is black and

amorphous solid that is decomposed by heat.[3,8] It is

formed by reaction of hydrogen selenide in aqueous

solution of gold trichloride. Light must be excluded

otherwise the precipitate is mixed with elemental gold.[3]

Aurous selenide (Au2Se) is unstable black compound

generated by action of potassium aurous salts on

solution of hydrogen selenide protected from air.[3,7] Two

modifications of gold selenide, α- and β-AuSe, were

described in Au-Se phase diagrams[9,10] and by X-ray.[11]

Both contain gold atoms in two different lattice sites. It

was explained by a mixed valence compound

AuIAuIIISe2 with both linear (AuISe2) and square-planar

(AuIIISe4) units in the structure.[12–14] Alternatively, EHTB

calculations[12] and X-ray absorption spectroscopy[15]

suggest that in both modifications all atoms of gold are

monovalent.

In some gold ores, native gold is intimately inter-grown

with selenium minerals and this one is positively

correlated with gold because both of them can be

transported via [Au(HS,HSe)2]- and [Au(HSe)2]

-

complexes during deposit formation.[2] Gold polyselenide

complexes like [Au2(Se2)(Se3)]2-, [Au2(Se2)(Se4)]

2-,

[Au2Se2(Se4)2]2- or inorganic cryptate [NaAu12Se8]

3- were

also synthesized.[16–18] Interaction of selenium dihydride

with small gold clusters (Aun) yields Aun-SeH2

complexes with stable gold-selenium bond.[19]

By reaction of gold trichloride with selenium, gold(III) is

reduced to elemental gold in solutions heated nearly to

boiling point.[3,20] Few studies of reductive

electrodeposition of selenium atomic layers from

selenous acid solutions on gold surfaces have been

reported, showing the existence of chemisorbed

selenium and square ring structures attributed to Se8

molecules. Some other dimer, trimer, and chain-like

selenium structures were also observed.[21,22] Selenium

clusters generated via laser ablation (LA) of selenium or

detected in selenium vapours have been reported.[23–31]

The structure of selenium clusters has been extensively

studied.[30,32–35] Laser desorption ionisation time-of-flight

mass spectrometry (LDI TOF MS) is powerful and useful

technique to generate and study clusters formed during

the LA of various metal chalcogenides[36–44] and metal

pnictides.[45–47]

The aim of this work is to generate new gold selenides

via LAS using either mixtures of elemental selenium and

GNP or selenium and auric acid as precursors.

Results and Discussion

Preparation of precursors

[a] Mgr. F. Amato, Ing. L. Prokeš, Dr. M. Alberti, Prof. P.

Kubáček, Prof. J. Havel

Department of Chemistry, Faculty of Science, Masaryk

University, Kamenice 5/A14, 625 00 Brno, Czech

Republic

E-mail: [email protected]

[b] Ing. L. Prokeš, Dr. M. Alberti, Prof. J. Havel

Department of Physical Electronics, Faculty of Science,

Masaryk University, Kotlářská 2, 611 37 Brno, Czech

Republic

[c] Ing. L. Prokeš, Dr. M. Alberti, Prof. J. Havel

CEPLANT, R&D Center for Low-Cost Plasma and

Nanotechnology Surface Modifications, Masaryk

University, Kotlářská 2, 611 37 Brno, Czech Republic

[d] Prof. E. M. Peña-Méndez, Prof. J. E. Conde

Department of Chemistry, Faculty of Science, University

of La Laguna, Campus de Anchieta, 38071 La Laguna,

Tenerife, Spain

Supporting information for this article is available on the WWW under http://dx.doi.org/10.1002/chem.2014xxxxx

177

Selenium powder was suspended in acetonitrile.

Colloidal solution of GNP was prepared in water from

auric acid using gallic acid as reducing agent as

described in[48] but without using poly-(N-vinyl-2-

pyrrolidone). Powdered selenium was added to the GNP

suspension (GNP: 3 mmol expressed as Au) in molar

ratio GNP:Se = 1:10 (Se: 2.4 mg) or 10:1 (Se: 0.3 mg)

(precursors I and II, respectively). Precursor III was

prepared by mixing and sonicating the aqueous solution

of auric acid (4 ml, 0.75 mM) with selenium powder (2.4

mg) in molar excess (Au:Se equal to 1:10) and

precursor IV by grinding in agate mortar selenium

powder and commercial gold nano-powder and then

resuspending the mixture in acetonitrile. Suspension of

each precursor (1 μL) was pipetted onto a sample plate

and dried in air stream at room temperature. The

products were subject to laser ablation and the

generated species were detected by TOF MS.

SEM analysis

SEM analysis of prepared precursors was done. In case

of precursor I and II, GNP (size ≈ 40-50 nm)

chemisorbed on the surface of selenium crystals were

observed (Figure 1 a, b). From the SEM analysis of

precursor III it was found that selenium crystals were

covered by a thin deposit (Figure 1 c, d). This might be

the result of some reaction occurring between auric acid

and selenium. As mentioned in the Introduction, it is

known that metallic gold can be obtained by reducing

auric acid with selenium in hot solution. Here, precursor

III was prepared at 25 °C. In order to get more insight

about the nature of the observed deposit and processes

accurring in auric acid-selenium mixture, kinetic study

was performed by UV-vis spectrophotometry.

It follows from SEM analysis that precursors I-III

represent a kind of Au-Se nano-composites.

Figure 1. SEM and backscattered SEM images of precursor II (a, b)

and III (c, d).

Interaction of selenium with auric acid in solution

The interaction was studied following the reaction

kinetics of the HAuCl4-Se mixture in molar ratio

~1:10 by UV-vis absorption spectrophotometry. The

concentration of auric acid and selenium in the

mixture were 1 mM and 1 gL-1, respectively while the

pH was equal to 2.5. The reaction mixture was

sonicated and aliquots were collected every 30

minutes, filtered on G4 glass filter, diluted 15 times

and transferred to a quartz cuvette with 1 cm path

length. Absorbance spectra are shown in Figure 2.

Intensity of the light-yellow colour of the solution, due

to AuCl4- species, diminished with time. This means

that auric acid was involved in a chemical reaction.

However, no absorption bands corresponding to

GNP plasmons were observed. We can suggest that

GNP and/or other reaction products are strongly

adsorbed on the surface of selenium crystals.

Complete elucidation of the chemical reaction

occurring in solution of auric acid and selenium is

beyond the scope of the present work.

In spite of this, precursor II was also evaluated for

LAS of AumSen clusters.

Figure 2. Overview of UV-vis spectra of HAuCl4-Se mixture in

molar ratio 1:10 recorded every 30 minutes. Changes in

absorbance at 220 and 300 nm vs. reaction time are given in the

inset.SEM and backscattered SEM images of precursor II (a, b)

and III (c, d).

Mass spectrometric study

The use of precursors I-IV for LAS of AumSen

clusters was studied. Mass spectra were recorded

using either reflectron or linear mass analyser in both

positive and negative ion modes. It was found that

the intensity of signals detected in negative ion mode

were at least two times higher than those detected in

positive ion mode. In addition, no significant

differences where observed between mass spectra

recorded in linear and reflectron modes. In the next

part of this work mostly results concerning negative

ion mode will be given.

The maximum resolution achieved using AXIMA

178

CFR mass spectrometer was ~500. This value was

not sufficient to obtain signals isotopically resolved.

However, this was achieved on AXIMA Resonance

mass spectrometer where the resolution was in the

6-10 000 range. The same signals were recorded

using both instruments.

Selenium

Laser ablation of selenium powder leads to the

formation of selenium clusters. In this study, Sen- (n

= 1-7) clusters were observed in negative ion mode

in agreement with the literature.[23–27]

Gold nano-particles - selenium composite

(precursor I)

Mass spectra were recorded at different values of

laser energy and signals corresponding to Sen- (n =

1-7) clusters predominate over those corresponding

to AumSen- ones. This is expected because precursor

I was prepared under molar excess of selenium. The

signal corresponding to Au- was detected with low

intensity even when laser energy above 100 a.u was

used. Signals corresponding to gold selenide

clusters with general formula AumSen- were detected

at m/z values higher than ~ 400 but with rather low

intensity. Example of mass spectrum recorded for

the laser ablation of precursor I is given in Fig. 3.

Figure 3. Example of mass spectrum concerning laser ablation

of precursor I. Signals corresponding to Sen- (n = 1-5)

predominate. Magnification of the m/z range 400-1000 is given

in the inset (signal-to-noise ratio for signal corresponding to

AuSe6- cluster was ~150). Conditions: AXIMA CFR, linear

negative ion mode, laser energy 140 a.u.

Auric acid - selenium mixture (precursor II)

In this precursor the Au:Se molar ratio is equal to 10

and recorded mass spectra were found richer in

terms of signals corresponding to AumSen- clusters.

The effect of laser energy was studied and the

threshold energy was found to be ~ 90 a.u. Example

of mass spectrum showing the effect of laser energy

on signals corresponding to AumSen- clusters

detected is given in Fig. 4.

Figure 4. Effect of laser energy on mass spectra in the 300-900

m/z range obtained for the laser ablation of precursor II.

Conditions: AXIMA CFR, reflectron negative ion mode. Mass

spectra are normalized.

When laser energy higher than 130 a.u. was used,

the intensity of signals recorded in the m/z range

300-1000 decreased. This is most probably due to

laser-induced clusters decomposition. In addition to

the formation of Aum- (m = 1-5) and Sen

- (n = 1-7)

species the formation of the following series of gold

selenide clusters was observed: AuSen- (n = 2-10);

Au2Sen- (n = 1-8); Au3Sen

- (n = 1-8); Au4Sen- (n = 1-

7); Au5Sen- (n = 4-7); Au6Sen

- (n = 4-6); Au7Sen- (n =

5-7); Au8Sen- (n = 5-6); Au9Sen

- (n = 5-6); Au10Sen- (n

= 6-7); Au11Sen- (n = 6-7); Au12Sen

- (n = 7-8);

Au13Sen- (n = 7-8); Au2Se8

-; Au15Sen- (n = 8,9);

Au16Se9-; Au17Sen

- (n = 9,10); Au18Sen- (n = 9,10);

Au9Se10-; Au20Sen

- (n = 10,11); and Au21Sen- (n =

10,11).

Gold nano-particles – selenium composite

(precursor III)

Laser ablation of precursor III prepared from auric

acid under molar excess of selenium yields the same

results as found for the laser ablation of precursor II.

Example of comparison between mass spectra

recorded in reflectron positive and negative ion

modes is given in Fig. 5. Intensity of signals

corresponding to AuSe3+ and Au2Se5

+ clusters is

almost negligible in comparison to corresponding

signals detected in negative ion mode.

179

Figure 5. Comparison of mass spectra concerning laser ablation

of precursor III in reflectron positive (a) and reflectron negative

(b) ion mode. Conditions: AXIMA CFR, reflectron, laser energy

110 a.u. Mass spectra are normalized.

Gold nano-particles - selenium composte

(precursor IV)

The species synthesised by ablation of precursor IV

were the same as those obtained from precursor II

and III but with lower signal intensity. Example of

mass spectrum recorded in the m/z range 2000-4500

is given in Fig. 6, while comparison between

experimental and model isotopic patterns concerning

the high-mass Au10Se6- and Au14Se8

- clusters is

given in Fig. 7.

Figure 6. Example of mass spectrum concerning laser ablation

of precursor IV showing signals corresponding to high-mass

AumSen- clusters. Conditions: AXIMA Resonance, reflectron

negative ion mode, laser energy 130 a.u.

Figure 7. Comparison between experimental (abbreviated as

„EXP.“ in the figure) and model isotopic patterns concerning the

species Au10Se6- and Au14Se8

-. Conditions: AXIMA Resonance,

reflectron negative ion mode, laser energy 130 a.u.

Overview of stoichiometry of all Aum, Sen and AumSen

generated clusters is given in Table 1 (Supporting

Information) and graphically in Fig. 8. Interestingly,

most of the AumSen clusters show m:n ratio ≤ 2:1.

Figure 8. Overview of stoichiometry concerning Aum, Sen and AumSen clusters synthesised via laser ablation of selenium-gold

mixtures and nano-composites. The lines concerning 1:1 and

2:1 stoichiometric ratio of gold and selenium are given (in black

and red, respectively).

Structure of AumSen clusters

The structure of gold and selenium clusters is well

known.[49,33] In addition to minerals, the structures of

only few gold selenides were published.[16–18,5,6]

Some of such species were also generated in this

work as mono-charged species and even if they

posses the same stoichiometry, their structure might

be different.

On the basis of analogy between selenium and sulfur

and from comparison with published structures of

gold sulphides[50–55] many structures of AumSen

clusters can be suggested.

180

Generally, the structure of most of the clusters

generated in this work is unknown. Therefore, the

structure of at least some of the clusters was

computed via DFT optimization (Fig. 9).

Figure 9. Examples of DFT-optimized AumSen- structures for

series with m = 1-4. Formula, symmetry point group, spin,

calculated bond energy in eV, examples of bond lengths in pm

and angles in degrees are given. The stability of given structures

is confirmed by no imaginary normal mode frequency in each

case. If more stable structures for the same stoichiometry were

found the lowest energy one is shown.

For the AuSen (n = 4-8) series the energy lowest

arrangement seems to be a cycle with more

conformational energy minima for n = 5-8. In the

clusters with more than two Au atoms those atoms

often tends to be mutually in proximity; the

separation of the nuclei is in many cases around 300

pm, hence significantly greater than the closest

contact in AuSe crystals (270 pm [11]). Except for

small clusters, the structural differences between the

monoanion and the corresponding neutral and

monocationic moieties are mostly within few percent

of bond lengths and angles. We have to note here

that Fig. 9 shows optimized structures of only for 26

AumSen- clusters, while the total number of gold-

selenium species is 67. More calculation are in

progress with the aim to generalize structural

features of AumSen clusters.

Conclusions

The Au-Se nano-composites prepared were found to be

suitable precursors for laser ablation synthesis of new

AumSen clusters. In this work, synthesis of 67 new gold

selenide clusters was achieved in gas phase. DFT-

optimized cluster structures show great diversity and

many new structural patterns as compared to published

AumSenk- entities stabilized by alkali metals or found in

gold-selenium minerals. This indicates that the species

synthesized in this work could represent new chemical

entities. Laser ablation synthesis represents powerful

and fast method for the generation of novel nano-

structured Au-Se molecular motifs for application in

nano-technology.

Experimental Section

Chemicals

Selenium powder (CAS: 7782-49-2), gold nano-powder (size: <

100 nm) was purchased from Sigma-Aldrich (St. Louis, U.S.A.).

Auric and gallic acids were purchased from Sigma-Aldrich

(Steinheim, Germany). The red phosphorus obtained from

Riedel de Haën (Hannover, Germany) was purified via

sublimation in nitrogen atmosphere. Acetonitrile (purity for

isotachophoresis) was purchased from Merck (Darmstadt,

Germany). Double distilled water was obtained from a quartz

apparatus from Heraeus Quarzschmelze (Hanau, Germany). All

other reagents were of analytical grade purity.

Instrumentation

Scanning Electron Microscopy

Scanning Electron Microscope MIRA3 from TESCAN (Brno,

Czech Republic) was used to characterize the precursors.

Mass spectrometry

Mass spectra were recorded using AXIMA Resonance mass

spectrometer from Kratos Analytical (Manchester, UK) equipped

with reflectron mass analyser and quadrupole ion trap (QIT). In

addition, AXIMA CFR mass spectrometer from Kratos Analytical

(Manchester, UK) was also used. In both instruments nitrogen

laser (337 nm) was employed. Details can be found

elsewhere.[56,57] Mass spectra were recorded in both positive

and negative reflectron ion modes using a laser repetition rate

equal to 10 Hz and a pulse time width of 3 ns. Mass

spectrometric analysis was carried out by averaging mass

spectra from at least 1000 laser shots. External mass

calibration using red phosphorus clusters was applied in both

ionisation modes.[58]

UV-vis spectrophotometry

UV-vis absorption spectra were recorded using Cary 5000 UV-

vis-NIR spectrophotometer from Agilent (Santa Clara, CA,

USA).

Software and computation

Determination of clusters stoichiometry was done comparing

experimental isotopic patterns with theoretical ones calculated

181

using Launchpad software (Kompact Version 2.9.3, 2011) from

Kratos Analytical Ltd. (Manchester, UK). Examples of possible

cluster structures were computed via DFT optimization using

ADF® 2013 molecular modeling suite.[59,60] The OPBE

exchange-correlation functional,[61] scalar relativistic ZORA

geometry optimization[62–64] and the all electron Slater-type

basis set QZ4P [65] were used. For each structure, analytical

normal mode frequencies were calculated.

Acknowledgements

Support from Grant Agency of the Czech Republic, project

no.13-05082S is acknowledged. This research has been also

supported by the project R&D Center for Low-Cost Plasma and

Nanotechnology Surface Modifications CZ.1.05/2.1.00/03.0086

funding by European Regional Development Fund. E. M. Peña-

Méndez and J. E. Conde thank to University of La Laguna for

support. Shimadzu Prague and Shimadzu Austria, namely Drs.

T. Petřík and R. Kaubek are greatly acknowledged. Drs. O.

Belgacem, M. Openshaw and R. Castangia from Kratos

Analytical (Manchester, U.K.) are kindly acknowledged for

valuable technical support.

Keywords: gold selenide clusters • nano-composite •

laser ablation synthesis • laser desorption ionisation •

time-of-flight mass spectrometry

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REVIEW

Coordination compounds contra cancer: past, present and future trends

J. Appl. Biomed.

Submitted on 24 - October - 2014

Filippo Amato1, Federica Trudu

2, Tiziana Pivetta

2, E. M. Peña-Méndez

3, Josef

Havel1*

1Department of Chemistry, Faculty of Science, Masaryk University, Kamenice

5/A14, 625 00 Brno, Czech Republic; 2Department of Chemical and Geological

Sciences, University of Cagliari, 09042 Monserrato (CA), Italy; 3Department of

Chemistry, Area of Analytical Chemistry, Faculty of Science, University of La

Laguna, 38071 La Laguna, Tenerife, Spain.

Received

Revised

Published on line

Summary

Extensive research has been done to apply various compounds in anticancer therapy.

Complexes of various metal ions such as platinum, ruthenium, gold or copper have

been synthesised and tested with the aim to develop effective and safe drugs. Various

reviews have been published on the use of metal complexes as anticancer agents

pointing out the most relevant examples of platinum- or non-platinum-based

compounds. In vitro and in vivo tests summarized the anticancer activity of the most

active compounds and give a general overview. However, however up-to-date

reviews giving a wide overview of the different metal ions used against cancer and

providing at the same time an outline of the chemical and biological problems as

well as the various strategies adopted are not available. In addition to the overview of

historical milestones, the aim of this work is to provide:(i) an overview of the various

branches of the current research, (ii) an outline of the design and the rationale behind

the synthesis of new metal complexes to be used against cancer, (iii) a summary of

the bio-chemical reactivity and physico-chemical properties of metal complexes, (iv)

an outlook about the future of the metal complexes in anticancer therapy.

Key words: cancer, metal complexes, cisplatin, platinum, copper

*Josef Havel, 1Department of Chemistry, Faculty of Science, Masaryk

University, Kamenice 5/A14, 625 00 Brno, Czech Republic

[email protected]

+420 549494114

+420 549492494

INTRODUCTION

With the term "cancer" a series of diseases mainly characterized by unregulated

cell’s growth are defined. The cancer can develop in an organ and spread to other

mailto:[email protected]

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organs in a specific way or in the entire body in a non-specific way. Due to a high

incidence, cancer is nowadays among the first causes of death, especially in the

developed world. An extensive research has been done on various classes of

compounds, spacing from organic molecules often inspired from natural molecules,

to organometallic and inorganic compounds to study their possible application in

anticancer therapy. The fortuitous discovery of the cytotoxic properties of

diamminedichloroplatinum(II) (cisplatin) in 1968, opened the route for the use of

metal complexes in cancer therapy (Arnesano et al. 2011). Cisplatin is still widely

used in clinical practice because it is effective against a broad spectrum of cancers

and in particular against very deadly kinds of tumour. Despite its great efficacy, it

causes from moderate to very severe side-effects. Moreover, in some patients the

continual administration of cisplatin is prevented by the onset of drug resistance

(Desoize 2004).

In the last 15 years, lot of efforts have been devoted to the development of more

effective and less toxic drugs. Various new trans-platinum(II) and platinum(IV)

complexes have been synthesized and some of them have been selected for clinical

trials (L R Kelland et al. 1999). However, not all types of cancer are sensitive to

platinum-based drugs and the problem of side-effects remains still unsolved.

Therefore, the use of less toxic metal ions such as ruthenium, gold or copper in place

of platinum appears to be a new and promising inquiry (Clarke 2002, Marzano et al.

2009, Nobili et al. 2010, Tiekink 200). Various reviews have been published on the

use of metal complexes as anticancer agents, with the intent to give an overview of

the proposed approaches concerning the application of these systems in clinical

practice (Babu et al. 2013, Beija et al. 2012, Bertrand and Leroux 2012, Boulikas et

al. 2007, Bruijnincx and Sadler 2008, Cao-Milán and Liz-Marzán 2014, Esteban-

Fernández et al. 2010, Maldonado et al. 2013, V Milacic et al. 2008, Mjos and Orvig

2014, Muhammad and Guo 2014, Petrelli et al. 2014, Sukumar et al. 2013, Tisato et

al. 2010, Todd and Lippard 2009, Vilmar and Sørensen 2009, N. X. Wang and von

Recum 2011). The majority of the available reviews point out the most relevant

examples of platinum- or non-platinum-based compounds, eventually focusing on

one particular metal ion or making a compendium on two or more metal ions. The

results of in vitro and in vivo tests are listed to summarize the anticancer activity of

the most active compounds and give a general overview of the state of art in this

field. To the best of our knowledge, up-to-date reviews giving a wide overview of

the different metal ions used against cancer providing at the same time an outline of

the chemical and biological issues as well as of the various strategies adopted are not

available.

This review is addressed to researchers interested in the treatment of cancer and

working in fields such as chemistry, biology, biochemistry and medicine. The aim of

this work is to review the various branches of current research outlining at the same

time: i) the rationale that drives the synthesis of new metal complexes as possible

anticancer drugs; ii) the different basic strategies developed by several authors for an

efficient application of metal complexes in antitumor therapy; iii) the chemical

reactivity and properties of the metal compounds to be exploited for medical

applications; iv) the biological and chemical basis of the action mechanism, when

known; v) the main target organs; vi) the related side effects, pharmacodynamics and

pharmacokinetics of selected metal-complexes. In the work, an overview concerning

the development of the most important metal-based drugs entered the clinical

practice (Fig. 1) is given and discussed. In addition, the achievements obtained using

185

complexes of platinum, ruthenium, gold, copper, and other metal and metalloid with

potential anti-cancer activity are commented.

PLATINUM-BASED COMPLEXES AGAINST CANCER

Platinum(II) complexes

Cisplatin and transplatin

Diamminedichloroplatinum(II) is a complex with square planar geometry and two

possible geometrical isomers, cis and trans, called cisplatin and transplatin,

respectively (Fig. 2).

Cisplatin is currently used in clinical practice as anticancer agent and it is

administered intravenously. Once in the blood stream, cisplatin interacts with several

proteins such as human serum albumin (HSA), haemoglobin (Hb) and, in less extent,

transferrin (Tf) (Rudnev et al. 2005). The 95 % of injected cisplatin is bound to

proteins even 24 hours after the administration (Sooriyaarachchi et al. 2011). From

the body's circulatory system, the metal complex is widely distributed into body

fluids and tissues. The highest concentrations are observed in kidneys (0.4–2.9 μg/g),

liver (0.5–3.7 μg/g wet weight) and prostate (1.6–3.6 μg/g). Minor concentration

levels can be found in muscles, bladder, testes, pancreas and spleen (Stewart et al.

1982). The concentration of the drug is generally lower in tumors than in the organ in

which the tumor is located, with the exception of intracerebral tumors. Diffusion into

the central nervous system (CNS) does not occur readily, and the concentration

levels of cisplatin are low in CNS, but significant in intracerebral tumour tissue and

in oedematous brain tissue adjacent to the tumour (McEvoy 1992, NDIS 1985).

Cisplatin enters the cells via passive diffusion and also with protein-mediated

transport systems as the human organic cation transporter (hOCT2) and the copper

transport protein (Ctr1) (Burger et al. 2010, Ishida et al. 2002, Song et al. 2005).

Once cisplatin enters the cell, the lower chloride concentration, in comparison to that

of the blood promotes the hydrolysis of the complex. One of the two chloride ligands

is displaced by a water molecule to form the [PtCl(H2O)(NH3)2]+ species. The water

molecule is easily removed allowing the binding of platinum ion to DNA bases,

mainly in the N7 position of the guanine and also to the N7 of adenine and the N3 of

cytosine, forming the monofunctional adduct [PtCl(DNA)(NH3)2]+. The second

chloride ligand can be displaced by a water molecule to form the adduct

[Pt(H2O)(DNA)(NH3)2]2+

. Both these last two species may re-interact with DNA by

crosslinking forming a bifunctional adduct (Alderden et al. 2006) as shown in Fig. 3.

Several modes of cisplatin crosslinking with DNA have been proposed (Trzaska

2005). All of them interfere with cell mitosis. The main adducts with DNA are 1,2-

intrastrand cross-links with two adjacent guanines (1,2-d(GpG)) and 1,2-intrastrand

cross-links with an adenine and an adjacent guanine (1,2-d(ApG)) (Jamieson and

Lippard 1999). The adducts 1,2-d(GpG) are supposed to be responsible for the

cytotoxic activity of the drug (Todd and Lippard 2009). Minor adducts are 1,3-

intrastrand cross-links formed with nonadjacent guanines and interstrand adducts.

Transplatin, the trans isomer of the diamminedichloroplatinum(II), shows lower

cytotoxic activity than cisplatin. The different behaviour of the two isomers could be

explained considering the related DNA adducts. In fact, while interstrand cross-links

between guanine and cytosine are formed by both isomers, the 1,2-intrastrand cross-

links are prevented by the geometry in the case of transplatin (Bernal-Méndez 1997).

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In addition, the conversion of the monofunctional adducts into the bifunctional ones

occurs particularly slowly for transplatin. In fact, after 24 hours, the majority of

DNA adducts formed by transplatin are still monofunctional (Bernal-Méndez 1997).

Then, from the comparison of the adducts and the related cytotoxic activities, it

follows that the higher efficacy of cisplatin is due to the formation of the 1,2-

intrastrand bifunctional adduct and to the fast conversion of the monofunctional

adduct into the bifunctional one.

One of the major limitations of the use of cisplatin in clinical practice is the inherited

or acquired drug resistance. In fact, after the first treatments, almost any kind of

cancer, in particular the most lethal ones (i.e., breast, ovarian (Eckstein 2011),

prostate (Dhar et al. 2008) testes ((Rixe et al. 1996) and non-small cell lung cancer

(NSCLC) (Michels et al. 2013) become non responsive or less responsive to

cisplatin. Therefore, higher doses are required to reach the same efficacy achieved in

the first treatments. But higher doses lead to more or more severe side effects. In

order to defeat or at least to reduce the onset of the resistance, the mechanisms

responsible for the cellular resistance have been widely studied. The resistance

appears to be connected with the action mechanism of the drug, i.e. the get into the

cell and the DNA interaction. Then, a reduced cisplatin influx and a decreased

intracellular accumulation of the drug, a higher deactivation due to a more efficient

cellular detoxification system, an increased capacity of DNA repair, and an increased

cisplatin efflux (Andrews and Howell 1990, Scanlon et al. 1989) have been proposed

as mechanisms to explain the resistance to cisplatin. Another important drawback of

the cisplatin therapy is the onset of several side-effects. Some of them are probably

due to the reactivity of the cisplatin mono-aqua complex. Then, to avoid hydrolysis,

cisplatin used for intravenous infusions is stabilized in solutions of sodium chloride.

However, after the infusion part of cisplatin undergoes hydrolysis leading to the

formation of the mono-aqua complex which can be found in the blood (Verschraagen

et al. 2002).

Other cis-Pt complexes

Because of the cisplatin success in clinical therapy, various new cis-Pt(II) complexes

have been synthesized by substitution of either chloro or ammonia ligands with

different motifs. Up to now, only carboplatin and oxaliplatin (cit., Platinum drugs

approved for clinical practice), have shown better performance than cisplatin against

some types of cancers and their use has been approved worldwide.

The promising compound named picoplatin (cis-amine-dichloro-(2-methylpyridine)-

Pt(II)) (Fig. 4) has been developed by the Institute of Cancer Research (123 Old

Brompton Road, London SW7 3RP, United Kingdom) in collaboration with Johnson

Matthey (5th Floor, 25 Farringdon St, London, EC4A 4AB, United Kingdom). It has

been introduced by Poniard Pharmaceuticals for the treatment of patients with solid

tumours and its clinical trials started in 1997 (L. Kelland 2007a, Wheate et al. 2010).

Picoplatin presents a marked steric bulk around the platinum ion that reduces its

inactivation by thiol-containing species. Its cytotoxic activity is due to the interaction

with DNA that leads mainly to the formation of intrastrand adducts. Picoplatin was

found to be active against cisplatin- and oxaliplatin-resistant cell lines (L. Kelland

2007b). When used as single agent its main dose-limiting side effect is

myelosuppression. Unfortunately, phase III trials did not confirm the previous

promising results in the treatment of small-cell lung cancer and new trials are not

currently planned (Hamilton and Olszewski 2013, Lopez-Chavez and Sandler 2012).

187

Recently, cis-Pt(II) complexes has been reconsidered and used as scaffolds for

biologically active ligands. One example is the complex cis-[Pt(NH3)2(L)Cl] (L = 3-

aza-5H-phenanthridin-6-one) which contains a poly(ADP-ribose) polymerase

(PARP-1) inhibitor as ligand (B. Wang et al. 2014). PARP-1 is a non-histone nuclear

protein that is involved in DNA replication, damage repair, and transcriptional

regulation. After DNA damage, the activity of PARP-1 increases stimulating the

response of DNA-damage repairing proteins. This platinum complex exhibits

increased activity and enhanced solubility with respect to those of the free inhibitor.

Other cis-Pt(II) compounds conjugated with side-directing molecules have been

introduced with the aim to increase their selectivity. Analogues of cisplatin,

carboplatin and oxaliplatin have been prepared with estrogens-like compounds in

order to increase selectivity towards breast, ovarian and uterus tumours where tissues

are estrogens-dependent (Descôteaux et al. 2003, 2008, Saha et al. 2012). The

estrogens-derived ligands present high affinity for the estrogens receptor α (ERα) and

the cytotoxic activity of the complexes results improved. This effect is significantly

enhanced by using cisplatin- and carboplatin-derived complexes. In fact, the cisplatin

analogues show a better tumour regression than cisplatin alone against human breast

cancer estrogens-receptor positive xenograft model in mice (Van Themsche et al.

2009). However, the substitution of cisplatin ligands has not extended significantly

the range of applicability of platinum-based drugs against cancers unresponsive to

cisplatin. It has been suggested that this might be due to the use of only one structural

motif (Adoración Gómez Quiroga 2012). For this reason, the research focused on

platinum complexes with different geometry (e.g., trans-Pt(II) complexes) and

oxidation state (e.g., Pt(IV) complexes).

Other trans-Pt complexes

Beside the poor chemotherapeutic efficacy of transplatin, other trans-Pt(II)

compounds with substituents different from ammonia have shown cytotoxic activity

equal or higher than that of cisplatin. The cytotoxic effect induced by such

complexes might be due to the formation of monofunctional adducts and interstrand

crosslinking with DNA as well as to the interactions with proteins. The biological

activity of these complexes appears related to the steric hindrance of the substituents

that slow down the displacement of the chlorides (Marzano et al. 2010). Planar

amines such as pyridine, quinoline, isoquinoline and thiazole were firstly used as

substituents (Aris and Farrell 2009). The first trans-platinum complex with marked

antitumor efficacy in vivo was the trans-ammine-(cyclohexylamine)-dichloro-

dihydroxo-Pt(IV) (known as JM335) (Fig. 5a) which combines the trans geometry

with the higher oxidation state of the metal ion (L. R. Kelland et al. 1994). The

substituents are chosen in order to modulate the solubility and stability in aqueous

media. For example, the use of ligands containing carboxylic groups leads to trans-

platinum complexes stable towards hydrolysis but water soluble and able to

accumulate in cisplatin-resistant cell lines (Adoración Gomez Quiroga et al. 2006).

Complexes containing iminoethers or aliphatic amines in place of planar ones were

found active in cisplatin-resistant cell lines (Aris and Farrell 2009). Various trans-

[Pt(amine)2(amidine)2]Cl2 compounds with four N-ligands bound to the platinum

core have been synthesized (Fig. 5b) and tested with relevant results against a large

panel of human cancer cell lines (Marzano et al. 2010). In addition, it has been

observed that the size of the substituents and the cytotoxicity of the resulting

molecule are directly correlated. The cyclohexyl derivative appears, in fact, as the

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most active. In vivo tests against Lewis lung carcinoma (LLC) show a reduction in

tumour size similar to that achieved using cisplatin (68 and 72 %, respectively) but

without the side effects typical of this last one (Marzano et al. 2010). Recently, the

class of trans-Pt(II) compounds has been extended to complexes containing

iminothioether ligands with general formula trans-[Pt{N(H)=C(SEt)R}2Cl2] (R =

Me, Et, Ph, CH2Ph) (Fig. 5c). The most active compound of such series is the trans-

[Pt{N(H)=C(SEt)CH2Ph}2Cl2] that shows higher cytotoxic activity than cisplatin

against a broad panel of human solid tumour cell lines (Sgarbossa et al. 2013). The

aforementioned complexes are active against cisplatin-resistant and Multi Drug

Resistance (MDR) cell lines. The MDR phenomenon occurs when the acquired

resistance to a specific drug causes resistance to other drugs, even not chemically

related.

Another interesting example is the family of trans-Pt(II) complexes with

sulphonamide ligands. This class of compounds shows antitumor activity against

cisplatin-resistant cell lines of cervix adenocarcinoma, ovarian carcinoma and ductal

breast epithelial tumour (Pérez et al. 2014). Such compounds are able to block cells

in growth 1 phase (G1) by interacting with DNA.

Platinum(IV) complexes

Complexes of Pt(IV) are thermodynamically stable, kinetically inert and

diamagnetic. The metal ion is hexa-coordinated and its complexes have octahedral

geometry. The biological properties of such complexes can be finely tuned because

of the six coordination sites available.

Despite some contradictory results (Khokhar et al. 1993, Talman et al. 1998), it is

now widely accepted that the antitumor activity of Pt(IV) complexes is due to their

reduction to Pt(II) analogues. In fact, it was experimentally observed that: i) DNA-

binding activity of Pt(IV) complexes is increased in presence of some intracellular

reductants (S. Choi et al. 1998); ii) Pt(IV) complexes are reduced by various

biomolecules present in blood and cells; iii) Pt(IV) complexes present lower

chemical reactivity in comparison to their Pt(II) counterparts. From these findings, it

is clear that Pt(IV) compounds have to undergo reduction in order to exert their

activity. Normally, such process occurs thanks to biomolecules such as glutathione

(GSH), methionine, cysteine, metallothioneins, serum albumin, ascorbate, DNA

nucleobases, nucleotides and their analogues. Depending on the reduction potential

of the Pt(IV) complex, the reduction may occur into the bloodstream instead that

within the cells giving rise to side-reactions that lead to systemic toxicity (Hall and

Hambley 2002). The reduction rate of the Pt(IV) complexes increases by using bulky

equatorial and axial electron-withdrawing ligands (S. Choi et al. 1998). Besides, the

reduction rate might be also influenced from the low kinetics of axial ligand

dissociation in Pt(IV) complexes (E. Wong and Giandomenico 1999).

The cellular uptake of Pt(IV) complexes is higher than that of Pt(II) ones. This has

been correlated with their higher lipophilicity which facilitates their passive diffusion

inside the cell. This could be also the reason for their efficacy against some cisplatin

resistant cancer cell lines (Hall and Hambley 2002). However, the in vivo reduction

of the Pt(IV) complex is accompanied by the loss of the axial ligands and the

resulting compound may show a lower activity due to decreased lipophilicity (Hall

and Hambley 2002).

189

Satraplatin (bis-acetate-amine-dichloro-cyclohexylamine-Pt(IV)) (Fig. 6a), also

known as JM216, is a drug for oral administration developed by the Institute of

Cancer Research (123 Old Brompton Road, London SW7 3RP, United Kingdom) in

collaboration with Johnson Matthey (5th Floor, 25 Farringdon St, London, EC4A

4AB, United Kingdom) (Wheate et al. 2010). It contains two acetate ligands that

increase the lipophilicity of the compound. In the bloodstream it is reduced with

consequent loss of the two acetate ligands forming its main metabolite labelled

JM118. This compound unwinds DNA and induces apoptosis. Unlike cisplatin and

carboplatin, the DNA adducts formed by satraplatin are not detected by DNA

mismatch repair proteins (Fink et al. 1996). Satraplatin is active against cancers that

show acquired resistance to cisplatin, thanks to its higher influx and reduced DNA-

repair (Fokkema et al. 2002, Kelland 2007). It has been investigated in clinical trials

for the treatment of advanced prostate cancer where other previous chemotherapy

approaches failed. In addition, it has been proposed for the treatment of non-small

cell lung, and squamous head and neck cancers (D. Y. Q. Wong and Ang 2012). To

the best of our knowledge no new clinical trials have started and the development of

the drug has been abandoned (Hamilton and Olszewski 2013).

Besides satraplatin, other two Pt(IV) complexes have been tested in clinical trials:

tetraplatin and iproplatin. The first one, [Pt(trans-1,2-diaminocyclohexane)Cl4], also

known as ormaplatin (Fig. 6b), shows a good cytotoxic activity both in vitro and in

vivo but the severe neurotoxicity observed in phase I trials has stopped its

development (Boulikas et al. 2007). The latter compound,

[Pt(isopropylamine)2(OH)2(Cl)2] (Fig. 6c), has been extensively studied in phase I,

II, and III trials for the treatment of a wide range of cancers. Its development was

abandoned because its efficacy was found to be not higher than that of cisplatin and

some cases of toxic death were reported (Clavel et al. 1988). A different strategy is

the synthesis of Pt(IV) complexes containing ligands which already present

biological activity. In this case, the complex acts as a scaffold. In fact, the ligand is

transported by the complex inside the cell where it is released thanks to the reduction

of the Pt(IV) to Pt(II). Once released within the cell, the ligand exerts the required

activity. By proper selection of the ligand this strategy allows to exploit various

biological processes such as inhibition of enzymes, expressions of proteins and

signal transduction (D. Y. Q. Wong and Ang 2012). One interesting example is a

complex (Fig. 6d) containing ethacrynic acid prepared and tested in 2005 (Ang et al.

2005). Ethacrynic acid is an inhibitor of glutathione-S-transferase (GST) which

catalyses the reaction of cisplatin with GSH. Overexpression of GST enzyme has

been reported in cisplatin-resistant cell lines and the GST inhibitors have been used

in combination with other drugs in anticancer therapy. The GST inhibition induced

by the Pt(IV) complex containing ethacrynic acid is more effective than that induced

by the ethacrynic acid alone. In addition, it shows cytotoxic activity higher than that

of cisplatin in the first 24 hours and equal to that of cisplatin after 72 hours. The high

GST inhibition shown by the complex has been attributed to its covalent binding to

the enzyme and subsequent binding of ethacrynate ligands at the active sites (Parker

et al. 2011).

Polynuclear platinum complexes

Polynuclear platinum complexes containing aliphatic amines as bridging linkers have

been designed with the intent to overcome drug resistance. They react rapidly with

190

DNA forming long-range interstrand and intrastrand cross-links (Wang and Guo

2008, Wong and Giandomenico 1999). Some of them have been found to be active

against cisplatin-resistant cells.

Among the tested polynuclear platinum compounds, the most studied one is

dichloro-hexamine-bis(μ-1,6-hexane-1,6-diamine)-tri-Pt(II), known as BBR3464

(Fig. 7a).

This molecule is a multiply charged (+4) trinuclear complex that interacts with DNA

by forming 1,4-intrastrand and 1,4-interstrand cross-links that cannot be repaired by

the excision repair mechanism. The cytotoxic activity of the drug is due to the long

life of the intra- and interstrand cross-links (Kasparkova et al. 2002). The compound

BBR3464 shows complete lack of cross-resistance against cisplatin-resistant cell

lines (Perego et al. 1999). However, its development was abandoned because it was

found not active in phase II trials (Wheate et al. 2010). Its lack of activity is probably

due to the binding with plasma proteins which results in drug deactivation.

Among trinuclear Pt(II) complexes, the [{trans-Pt(NH3)2(NH2(CH2)6(NH3+)}2-µ-

{trans-Pt(NH3)2(NH2(CH2)6NH2)2}]8+

(Fig. 7b) shows a peculiar interaction with

DNA. In fact, it binds via hydrogen bond to the oxygen atoms of the phosphate

groups on the DNA backbone. The complex has been presented as the first example

of a non-covalent platinum compound with cytotoxicity equivalent to that of cisplatin

(Komeda et al. 2006).

Polynuclear complexes with rigid bridging ligands such as aromatic compounds and

with more flexible linkers like 4,4-methylenedianiline have been developed

(Mlcouskova et al. 2012, Olivova et al. 2012, Zerzankova et al. 2010).

The substitution of chloride ligands with alkylcarboxylates leads to a class of

polynuclear complexes with increased stability and reduced ability to bind plasma

proteins with respect to BBR3464. Among these compounds, CT-47463 (Fig. 7c)

presents a marked cytotoxic activity against cisplatin-resistant ovarian and squamous

cell carcinoma, and osteosarcoma human cell lines with IC50 (i.e., drug concentration

required for 50% inhibition of cell growth) of 0.003, 0.77 and 0.041 µM (Gatti et al.

2010). The CT-47463 inhibits tumour growth in platinum-resistant human ovarian

carcinoma xenograft by 80%. Analogues of CT-47463 are currently under

development by the Cell Therapeutics Inc. (Barry and Sadler 2013a).

Platinum drugs approved for clinical practice

Despite the huge number of platinum complexes synthesized up to now, less than 30

have reached the human experimentation. Among them, only cisplatin, carboplatin,

and oxaliplatin have been approved worldwide for clinical use. Nedaplatin,

heptaplatin, and lobaplatin have been approved only in Japan, China, and South

Korea, respectively. Other drugs previously tested have evidenced severe side-effects

or lack of activity in phase I or II trials and their development has been abandoned

(Wheate et al. 2010). Recently, satraplatin and picoplatin development has been

stopped as well (Hamilton and Olszewski 2013).

The main platinum-based drugs approved for human therapy are briefly described

here. Their advantages as well as their major limitations in clinical therapy with

respect to cisplatin are highlighted and commented.

Carboplatin

191

Carboplatin (cis-diamine(1,1-cyclobutanedicarboxylato)-Pt(II)) (Fig. 8a) was

discovered at Michigan State University, and developed at the Institute of Cancer

Research in London. In 1989, Bristol-Myers Squibb (345 Park Avenue, New York

10154, USA) gained approval from Food and Drug Administration (FDA) for the

treatment of ovarian cancer with carboplatin, under the brand name Paraplatin. From

October 2004, generic versions of the drug became available worldwide. Carboplatin

is the first cisplatin derivative used in clinical therapy. In this complex, the metal ion

is coordinated by a bidentate dicarboxylic ligand. Being the carboxylate a leaving

group more stable than chloride, carboplatin exhibits lower reactivity and slower

DNA binding kinetics. Within the cellular environment it forms the same DNA

adducts formed by cisplatin, but with a product profile noticeably different

(Blommaert et al. 1995). The major carboplatin adduct identified is the cis-

[Pt(NH3)2(dG)2] (40 %). Minor products are 1,2-d(GpG) (30 %), 1,2-d(ApG) (16 %)

and a small number of interstrand cross-links (3 – 4 %), together with

monofunctional adducts. Being less reactive than cisplatin, carboplatin results also

less toxic towards kidneys and gastrointestinal tract (Kelland 2007). The dose-

limiting toxicity (i.e., side-effects that are severe enough to prevent the use of higher

drug doses) is due to myelosuppression (Calvert et al. 1982). Despite many studies

have shown that carboplatin and cisplatin present the same cytotoxic activity,

carboplatin has successfully replaced cisplatin in the treatment of some kinds of

cancers, such as advanced, metastasized or recurrent non-small cell lung and

advanced or recurrent ovarian cancer (www.cancer.gov). In fact, shorter infusion

periods are required and the whole treatment is cheaper. Moreover, the lower toxicity

allows the use of higher doses and the prolongation of treatment time (Lebwohl and

Canetta 1998). Unfortunately, carboplatin does not overcome the problem of drug

resistance as cross-resistance occurs with cisplatin (Desoize 2004).

Oxaliplatin

Oxaliplatin (1R,2R-diaminocyclohexane-oxalate Pt(II)) (Fig. 8b) was prepared in

1976 at Nagoya City University by Professor Yoshinori Kidani, who has been

granted the U.S. Patent 4,169,846 in 1979. Oxaliplatin was licensed by Debiopharm

(Lausanne, Switzerland) and developed as drug for treatment of advanced colorectal

cancer. In 1994 the drug was licensed to Sanofi-Aventis. Its use was approved in

1996 in Europe and in 2002 by FDA. Oxaliplatin is now used for the treatment of

cancers resistant to other platinum drugs and in particular against colorectal cancer. It

is typically administered with fluorouracil and leucovorin in a combination known as

FOLFOX (Desoize 2004, Maindrault-Goebel et al. 1999). In oxaliplatin, the metal

ion is coordinated by 1,2-diaminocyclohexane and oxalate ligands. The cytotoxic

activity of this drug arises from the inhibition of DNA synthesis in cancer cells. In

fact, it forms both inter- and intrastrand cross links in DNA, preventing DNA

replication and transcription, and triggering cell death (Graham et al. 2004).

Oxaliplatin does not show cross-resistance with other platinum drugs because it

binds DNA differently than cisplatin and the resulting adducts are not recognized by

the DNA mismatch repair proteins (Fink et al. 1996). Also, it seems that no

interaction with the copper transporter CTR1 occurs and this prevents its efflux

outside the cell in some kinds of cisplatin-resistant cancers (Holzer et al. 2006).

Oxaliplatin is less toxic than cisplatin but its dose-limiting toxicity is associated with

a not predictable occurrence of sensory neuropathy.

http://en.wikipedia.org/wiki/Leucovorin

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Nedaplatin

Nedaplatin (diamine(1,2-(O,O')-2-hydroxyacetato)-Pt(II)) (Fig. 8c) is a second-

generation cisplatin analogue. This drug was developed in 1983 by Shionogi

Pharmaceutical Company (1-8, Doshomachi 3-chome, Chuo-ku, Osaka 541-0045,

Japan) with the aim to avoid the nephrotoxicity and gastrotoxicity of cisplatin while

maintaining the same efficacy (Mabuchi and Kimura 2011). It presents two ammonia

and a glycolate ligand which forms a five-membered ring with the platinum ion. The

water solubility of nedaplatin is ten times higher than that of cisplatin. It is actually

less nephrotoxic than cisplatin and carboplatin ((Alberto et al. 2009, Kuwahara et al.

2009), presenting anticancer activity comparable to that of cisplatin (Alberto et al.

2009, Kawai et al. 2005). Nedaplatin interacts with DNA forming mainly interstrand

cross-links. It reacts with GSH and metallothioneins in minor extent because of the

presence of the five-membered ring which prevents the binding to the platinum core.

Nedaplatin can cause thrombocytopenia and also nephrotoxicity in absence of a pre-

and post-treatment hydration. Its efficacy is not higher than that of cisplatin, but it

has been proved to be less toxic for kidneys, gastrointestinal tract and nervous

system. It is currently registered in Japan for the treatment of head and neck,

testicular, lung, ovarian, cervical, and non-small-cell lung cancer (Wheate et al.

2010). Clinical trials are ongoing for the use of nedaplatin in different schedules, in

particular in combination with other drugs (Gong et al. 2009, Kurita et al. 2010,

Oshita et al. 2004), against non-small-cell lung, cervical, oesophageal, testicular,

head and neck cancers.

Lobaplatin

Lobaplatin (1,2-diaminomethyl-cyclobutane-lactate-Pt(II)) (Fig. 8d) was developed

at first by ASTA Pharma AG (Frankfurt am Main, Germany) and subsequently by

Zentaris AG (Weismüller Strasse 45 Frankfurt, 60314, Germany). This last company

has been acquired by Aeterna Zentaris Inc. (1405 du Parc-Technologique Blvd.

Québec, Canada). In lobaplatin, the Pt(II) ion is coordinated by the nitrogen atoms of

a 1,2-diaminomethyl-cyclobutane and by one molecule of lactic acid. According to

the producer (Zentaris), lobaplatin is involved in the inhibition of the DNA and RNA

polymerase. It is also able to bind DNA, in particular at guanine residues, forming

mainly intrastrand cross-links. Lobaplatin shows in vitro cytotoxic activity against a

wide range of tumour cell lines and also in some cisplatin and carboplatin resistant

ones (McKeage 2001). In clinical trials the dose-limiting side-effect was found to be

thrombocytopenia (Gietema et al. 1993). Lobaplatin has been approved in China for

the treatment of chronic myelogenous leukemia, inoperable metastatic small-cell

lung, and breast cancer. Phase II clinical trials have been also completed in other

countries such as USA, Australia, Europe, Brazil, and South Africa for the treatment

of breast, oesophageal, lung, and ovarian cancers, and chronic myelogenous

leukemia (Boulikas et al. 2007). Despite the lack of cross-resistance established in

vitro, in a clinical trial lobaplatin has shown no activity against a cisplatin-resistant

form of ovarian cancer (Kavanagh et al. 1995).

Heptaplatin

Heptaplatin (2-(1-methylethyl)-1,3-dioxolane-4,5-dimethanamine-

N,N'][propanedioato-O,O']-Pt(II)) (Fig. 8e) has been proposed by SK Chemicals

(310 Pangyo-ro, Bundang-gu, Seongnam-si, Gyeonggi-do 463-400, Korea) for the

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treatment of gastric cancers and has gained approval for clinical therapy in South

Korea. It is a Pt(II) square planar complex with malonato ligand as leaving group and

a dimethanamine-1,3-dioxolane derivative. It has been designed to have higher

antitumor activity and lower toxicity with respect to cisplatin. It shows activity in

vitro and in human tumour xenografts against different types of cisplatin-resistant

tumours (Kim et al. 1995). The activity of heptaplatin on cisplatin-resistant cell lines

is partially due to a major resistance to deactivation by metallothioneins (Choi et al.

2004). Nephrotoxicity, hepatotoxicity, and myelosuppression are its dose-limiting

side-effects. The toxicity of heptaplatin has been confirmed to be lower than that of

cisplatin as supposed by its developers but it does not present a higher cytotoxic

activity, at least in advanced gastric adenocarcinoma and small-cell lung cancer (Kim

et al. 1999, Zang et al. 1999). Currently, heptaplatin is used for the treatment of

advanced gastric and lung cancers (Graf et al. 2012). Its approval for clinical therapy

depends mainly on its lower toxicity profile.

NON PLATINUM COMPLEXES AGAINST CANCER

As mentioned above, despite the extensive research done in the field of platinum-

based drugs only a limited number of them has been approved for clinical practice.

Therefore, the possibility to replace platinum with other metal ions has been studied

in order to obtain new complexes with low systemic toxicity and active against

cancers unresponsive to cisplatin. The rationale behind this approach is that

complexes based on other metals may act on different targets with respect to DNA,

involving for example metastatic processes, proteins, reactive oxygen species (ROS)

or mitochondria (Fig. 9). In addition, exogenous complexes of metal ions that are

essential or that mimic essential ones, may exploit the natural biological pathways

for their transport and storage. Being then regulated by the homeostatic systems,

these complexes might present a low systemic toxicity.

Ruthenium

Ruthenium(II) forms either penta- or hexa-coordinated complexes, while

ruthenium(III) only hexa-coordinated ones. Both of them show antitumor activity,

but some studies suggest that the activity of Ru(III) is probably due to the in vivo

reduction to Ru(II). Such process seems to be facilitated in tumour tissues because of

their low oxygen content (Clarke 2002). Generally, ruthenium(II) compounds show

systemic toxicity lower than that of platinum ones. Ruthenium belongs to the same

group of iron and their ions in the oxidation state II are chemically similar. In fact,

Ru(II) is bound by iron chaperones such as transferrin and albumin. In this way, the

circulation of the free complex in the bloodstream is prevented (Allardyce et al.

2005). This mechanism is also responsible for the high selectivity shown by the

ruthenium compounds. Cancer cells replicate faster than normal ones and then they

require an higher iron uptake. As a consequence, in cancer cells the number of

transferrin receptors is higher than in normal ones. For this reason, ruthenium

compounds bound to transferrin are mainly delivered to cancer cells.

Complexes of Ru(II) with arene ligands are the most studied anticancer compounds

of this metal. They have been extensively studied because of the tunability of their

properties and amphiphilicity. In fact, in these complexes the metal ion represents the

hydrophilic part while the arene ligand the hydrophobic one. Similarly to cisplatin,

the chosen leaving groups are generally chlorides that can be displaced inside the cell

by water molecules. The stability of the resulting aqua-complex is pH-dependent. In

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fact, it loses one proton in basic environment, forming the corresponding hydroxo

complex which is less reactive. It is important to highlight that the ruthenium-arene

bond is too strong to be broken under physiological conditions (Süss-Fink 2010).

Cytotoxic activity of ruthenium complexes is due to their interaction with DNA and

with other biological targets influencing different cellular mechanisms (Fig. 10).

Targeting DNA

The DNA is the target of a family of ruthenium compounds with general formula

[Ru(η6-arene)(N,N’)X]

+ (X = Cl

- or I

-, N,N’ = ethylenediamine or N-

ethylethylenediamine). Monofunctional and bifunctional adducts with DNA are

formed by reaction with guanine nucleobases. Such Ru(II) compounds show

cytotoxic activity similar to that of carboplatin but lower than that of cisplatin against

human ovarian cancer cell line A2780 (Morris et al. 2001). In addition, these

compounds do not present cross-resistance with cisplatin neither in vitro nor in vivo

(Aird et al. 2002). The most interesting compound of the family mentioned above is

[Ru(biphenyl)(en)Cl]PF6, known as RM175 (Fig. 11a). This compound binds DNA

either by intercalation through the aromatic ligand or by covalent binding with the

metal ion (Hayward et al. 2005). By using anionic acetylacetonate (acac) derivatives

in place of ethylenediamine ones, the extent of DNA binding is higher and both the

rate and the extent of hydrolysis are enhanced. The hydrolysis appears to be a

fundamental step for the activation of the complex. In fact, the compound [Ru(η6-p-

cym)(acac)Cl] (p-cym = para-cymene) (Fig. 11b) can undergo to rapid hydrolysis

leading to the loss of the chloride ligand. The resulting species is then able to bind in

the same extent guanine and adenine (Fernández et al. 2004).

Besides Ru(II), also Ru(III) complexes with indazole motifs as ligands show

interesting antitumor properties due to DNA interaction. One of the most studied

Ru(III) complexes is the [Ru(HIn)2Cl4](H2In) (HIn = indazole) (KP1019) (Fig. 11c).

This reacts with DNA and induces apoptosis via the intrinsic mitochondrial pathway

(Hartinger et al. 2006). It accumulates mainly in cell nucleus (55 % after two hours

exposition) (Pongratz et al. 2004). Although the target of this compound is the same

as that of platinum-based drugs, the induced DNA lesions are different. The complex

KP1019 shows biological activity against tumour cell lines overexpressing several

multidrug-resistance-associated proteins (multidrug resistance protein 1 MRP1,

breast cancer resistance protein BCRP, and lung resistance protein LRP) (Hartinger

et al. 2006). In phase I trial no dose-limiting toxicity has been observed (Dittrich et

al. 2005). The lower occurrence of side effects is probably due to the ability of

KP1019 to rapidly bind transferrin in blood, maintaining its cytotoxic activity against

tumour cells (Kratz et al. 1994).

Recently, it has been proposed the conjugation of a ruthenium complex with an

inhibitor of poly(ADP-ribose) polymerase-1 (Wang et al. 2014). This ruthenium

complex shows DNA-binding activity and an PARP-1 inhibition higher than those of

the free inhibitor.

Targeting proteins

The family of Ru(II) complexes with arene ligands and water-soluble phosphines, in

particular 1,3,5-triaza-7-phospha-adamantane (PTA), show interesting activity

against metastasis. The most active complex is [Ru(η6-p-cym)(PTA)Cl2] (RAPTA-C)

(Fig. 12a). Its cytotoxic activity appears to be due to interactions with proteins

(Dorcier et al. 2005). In fact, it is able to inhibit cathepsine B, a cysteine peptidase

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overexpressed in some tumours, probably involved in metastasis, angiogenesis, and

tumour progression (Casini et al. 2008). The RAPTA-C complex shows a pH-

dependent selectivity between cancer and normal cells (Vock et al. 2008).

The use of N-methyl-PTA (mPTA) as ligand in water-soluble ruthenium complexes

containing chloride and cyclopentadiene (Cp) motifs was found to improve the

interaction with super-coiled DNA (Romerosa et al. 2006). To evaluate the possible

use as drug of [RuCpCl(mPTA)2](OSO2CF3)2 (Fig. 12b), its hydrolysis has been

studied using mass spectrometry (MS) and UV-Vis spectrophotometry (Peña-Méndez

et al. 2009).

It was found that the complex forms hydrated species and undergoes to exchange of

chloride, mPTA and Cp ligands with water, hydroxyl or counter ions following the

hydrolysis scheme given in Fig. 13 (Peña-Méndez et al. 2009).

In order to prevent occurrence of hydrolysis, it has been suggested to dissolve the

complex in isotonic 0.15 M NaCl solution. Following this strategy,

[RuCpCl(mPTA)2](BF4) has been synthesized but its biological activity has not been

evaluated (González et al. 2009).

The [Ru(2-phenyl-pyridine)(NCMe)2phen]PF6 (RDC-11) (Fig. 14a) belongs to a

family of organometallic Ru(II) compounds (RDC) (Fernandez et al. 1999). It

presents an atypical mechanism of cytotoxic activity. In fact, RDC-11 induces the

apoptosis of tumour cells by activating the pro-apoptotic protein CHOP

(CCAAT/Enhancer-Binding Protein Homologous Protein), a transcription factor that

influences the endoplasmic reticulum stress (Meng et al. 2009). Moreover, RDC-11

is able to arrest cells in G1 phase in RDM4, TK6 and A172 tumour cell lines (the

first two derive from lymphoblastoma and the last from glioblastoma). It is also less

sensitive to inactivation of the cellular tumour antigen p53 function and

overexpression of the ATP7B proteins (Gaiddon et al. 2005). The p53 protein

regulates the cell cycle and thus acts as tumour suppressor, preventing the

development of cancer. The ATP7B proteins are part of the P-type ATPase family, a

group of proteins that transport metals inside and outside the cells. It is known that

these two mechanisms are involved in cisplatin-resistance.

An interesting example of ruthenium complex that shows cytotoxic activity due to

interaction with non-classical targets is trans-[Ru(HIm)(DMSO)Cl4](H2Im) (Him =

imidazole; DMSO = dimethylsulfoxide) labelled as NAMI-A (Fig. 14b).

Surprisingly, despite the poor activity shown in vitro and the absence of in vivo

activity against primary tumour growth, it displays a significant anti-metastasis

activity. In vivo it is stored into the kidneys, liver, and tissues with high collagen

percentage with which it binds leaving its activity unaltered. In fact, NAMI-A is

particularly active in lung metastasis, because of their high content of collagen (Sava

et al. 2003). It is able to interfere with the cellular cycle, promoting the passage to the

G1 phase and decreasing the percentage of cells in synthesis phase (S phase) (Gava

et al. 2006). Moreover, it presents anti-angiogenic activity due to the inhibition of

angiogenesis induced by the VEGF (Ott and Gust 2007). The mechanism of action of

NAMI-A is supposed to be based on the inhibition of some tumour cell invasion

processes as the reduction of gelatinase activity, the inhibition of cell crossing of

endothelial barriers, and changes on cell shape and F-actin-dependent cytoskeleton

organization (Gava et al. 2006).

For their important features, both KP1019 and NAMI-A are currently under clinical

evaluation (Hartinger et al. 2006, Rademaker-Lakhai et al. 2004). The first one has

been used in phase I clinical trial against different solid tumours while the second

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one is tested in combination with gemcitabine for the treatment of metastatic non-

small cell lung cancer.

Gold

The study of gold complexes as antitumor drugs started when it was observed that

patients treated with auranofin (Fig. 15a) for rheumatoid arthritis showed lower

malignancy rates (Fries et al. 1985). This drug is a complex of Au(I) with a

phosphine ligand that acts as thioredoxin reductase (TR) inhibitor. The TR is a class

of seleno-cysteine enzymes that catalyse the reduction of thioredoxins which are

ubiquitous redox proteins containing a redox-active disulphide bond in the active site

(Holmgren 1989). Thioredoxins are involved in several biological processes,

including ROS reduction (Nordberg and Arner 2001). Elevated concentrations of TR

have been found in human tumour cells and they have been associated with tumour

proliferation (Bruijnincx and Sadler 2008). The inhibition of TR leads to anti-

mitochondrial effects that cause apoptosis (Gromer and Arscott 1998). In addition,

TR inhibition is also probably responsible for the side-effects observed during the

treatment with auranofin (Ott and Gust 2007).

Complexes of Au(III) have also been extensively studied because its chemistry is

similar to that of Pt(II). In fact, Au(III) is isoelectronic with Pt(II) and forms

complexes with square planar geometry. However, under physiological conditions

Au(III) is rapidly hydrolysed and reduced to Au(I) (Wang and Guo 2008). Therefore,

the stabilization of Au(III) complexes by using suitable ligands is needed. Chelating

nitrogen donors such as phen, 2,2’-bipyridine (bipy), 2,6-bis(2-pyridyl)-pyridine

(terpy) and ethylenediamine (en) have been proposed for the purpose (Marcon et al.

2002). Despite of their stability, Au(III) complexes with phen and bipy derivatives

show cytotoxic activity comparable to that of their ligands. On the contrary, the

cytotoxicity of [Au(en)2]Cl3 (Fig. 15b) was found to be due to the presence of the

gold centre (Tiekink 2002). The cytotoxicity of Au(III) complexes containing

terpyridine derivatives as ligands (Fig. 15c) is mainly due to DNA intercalation. Such

complexes are also stable towards reduction by GSH and represent the first example

of Au(III) complexes interacting with DNA (Wang and Guo 2008).

The cytotoxic activity of Au(III) complexes with porphyrin ligands might be due to

their action on mitochondria. As a consequence, apoptosis is induced by caspase-

dependent and caspase-independent pathways (Y. Wang et al. 2005). The leader

compound [Au(TPP)]Cl (H2TPP = tetraphenyl-porphyrin) (Fig. 15d) shows IC50

values on the μM order towards several cell lines, including human cervix epitheloid

(HeLa) and hepatocellular (HepG2) carcinoma. It presents a similar activity also

against cisplatin-resistant and MDR cell lines (Che et al. 2003). This compound

shows tumour inhibition of about 80 % (Sun et al. 2007) in cisplatin-resistant

nasopharyngeal carcinomas (NPC) cells implanted into mice.

Several Au(III)-thiocarbamates complexes with general formula [Au(dtc)X2] (X =

Cl, Br; dtc = N,N-dimethyl-dithiocarbamate, ethyl-sarcosine-dithiocarbamate) (Fig.

15e) inhibit the proteasome both in vitro and in vivo (Milacic et al. 2006). The main

function of the proteasome is to degrade unnecessary or damaged proteins by

proteolysis. This process is essential for cell cycle, regulation of gene expression and

response to oxidative stress. Proteasome inhibitors are effective against tumour cells

because they induce apoptosis by perturbing the regulated degradation of pro-growth

cell cycle proteins (Orlowski 1999). The Au(III)-thiocarbamates compounds are

equally or more active than cisplatin against various human tumour cell lines and

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show low cross-resistance with cisplatin. The complexes show 50 % reduction of

tumour growth in human breast cancer MDA-MB-231 and no toxic side-effects in

vivo (Milacic et al. 2006).

Recently, Au(III)-peptide-dithiocarbamate complexes with general formula

[AuX2(pdtc)] (X=Cl, Br; pdtc = oligopeptide-dithiocarbamate) (Fig. 15f) have been

synthesized using di-, tri-, tetra- and pentapeptides (Kouodom et al. 2012). The

incorporation of peptides makes the complex recognizable by intracellular peptide

transporters (PEPTs). Therefore, cellular uptake is enhanced, and side-effects

reduced. The most promising results have been obtained with the tripeptide

derivatives, such as H-Sar-Aib2-O(t-Bu) and H-D,L-Pro-Aib2-O(t-Bu) (Sar =

sarcosine (N-methylglycine); Aib = α-aminoisobutyrric acid (2-methylalanine), that

show IC50 values lower than that of cisplatin in vitro. They also show no cross-

resistance with cisplatin, confirming a different cytotoxic mechanism. The study of

in vivo activity and mechanism started recently (Kouodom et al. 2012).

The variety of cytotoxic mechanisms reported for gold complexes (Fig. 16) expands

the possibility to find out new compounds able to overcome platinum-drug

resistance. Nevertheless, the application of gold complexes in clinical practice still

requires an extensive evaluation of their chemical and pharmaceutical properties

such as hydrolysis equilibria, cellular uptake, biodistribution, and pharmacokinetics.

Copper

Copper is an essential metal ion present as cofactor in several enzymes. It is involved

in haemoglobin formation, xenobiotics and carbohydrates metabolism,

catecholamine biosynthesis, cross-linking of collagen, elastin, and hair keratin.

Copper ion is also engaged in antioxidant defence mechanism. In fact, copper-

dependent enzymes, such as cytochrome C oxidase, superoxide dismutase,

ferroxidases, monoamine oxidase, and dopamine β-monooxygenase, are deputed to

reduce ROS or molecular oxygen. It worth to mention that in the efflux of cisplatin

outside the cells, copper-efflux transporters ATP7A and ATP7B, and some multidrug

efflux pumps belonging to the ABC superfamily (P-glycoprotein (Pgp, ABCB1) and

multidrug resistance protein 2 (MRP2, ABCC2)) are involved (Leslie et al. 2005,

Samimi et al. 2004).

Copper has been selected for the synthesis of antitumor drugs under the hypothesis

that complexes with endogenous metal ions might give lower systemic toxicity.

Several Cu(II) complexes with a variety of N-, S-, or O- containing ligands have

been designed, synthesised and tested as antitumor drugs. The different biological

actions of these complexes, in comparison to that of cisplatin, suggest that different

mechanisms for their antitumor activity are involved (Fig. 17). However, such

mechanisms are not completely clarified to date (Santini et al. 2014).

Copper(II) is also an angiogenesis promoter because it enhances the formation of

new blood vessels from pre-existing ones. In cancer cells, this physiological process

may eventually trigger the transition from a benign to malignant state of tumours.

Further studies are required to completely clarify the role of copper in angiogenesis

processes.

Copper(II) complexes are supposed to act by triggering cell apoptosis or inhibiting

enzymes (Cox and Nelson 2008, Tripathi et al. 2007). In fact, the expression of

Tyrosine-protein kinase CSK is inhibited by complexes of Cu(II) with pyridine-2-

carbohidrazide derivatives.

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Copper(II) chelate of salicylaldoxime induces cell cycle arrest and apoptosis, perhaps

involving the inhibition of the topoisomerase II enzyme (Jayaraju and Kondaki

2001). Topoisomerases (Topo) are ubiquitous enzymes able to break and reseal the

DNA polyphosphate backbone, preventing the overwinding or underwinding of the

double helix. The disruption of Topo activity leads to single and double stranded

breaks in DNA, inducing apoptosis. Among Topo-I inhibitors, the

[Cu(phen)L](NO3)2 ( L = 2,4,6 tri-(1H-pyrazol-1-yl)-pyrimidine) is a Cu(II) complex

containing a ligand derived from barbituric acid and pyrazole (Tabassum et al. 2014).

This complex inhibits Topo-I at 10 µM concentration. Another family of Topo-I

inhibitors with general formula [Cu(N)L]Cl ( N = phen, bipy or 5,5’-dimethyl-2,2’-

bipyridine; L = doubly deprotonated 5-(triphenyl-phosphonium-methyl)-

salicylaldehyde-benzoyl-hydrazone) show a good cytotoxic activity against human

lung carcinoma (A549) and prostate adenocarcinoma (PC-3) cell lines (Chew et al.

2014). The most active compound of this family is the one containing phen. It shows

an IC50 value of 3.2 µM against PC-3 cell line and starts to inhibit Topo I at 40 µM.

A metalloprotease activity (Shrivastava et al. 2002) is evidenced by the complex 2,6-

bis-(benzimidazo-2-yl)-pyridine copper(II) chloride. Proteasome inhibition resulting

in apoptosis is instead observed when Cu(II) binary complexes containing neutral or

anionic molecules such as phen, 8-hydroxyquinolinate, pyrrolidine dithiocarbamate,

or (pyridine-2-ylmethylamino)-methyl phenolate are used. Evaluation of proteasome

inhibition shows that both the complexes and the copper ion inhibit the enzyme in

the same extent while the free ligands have no activity. Then, the complex behaves as

carrier of the metal ion through the cell membrane. This is achieved by tuning the

lipophilicity of the complexes by suitable ligands (Hindo et al. 2009).

Copper complexes with thiosemicarbazone ligands, which possess antitumor activity

and are used in clinical practice, inhibit enzymatic activity and induce cell apoptosis

(Tisato et al. 2010).

The cytotoxic properties of copper complexes with phen as ligand have been firstly

reported by Sigman (Sigman et al. 1979). The complex with two phen ligands is able

to cleave DNA by binding to the deoxyribose units and thus acting as a chemical

nuclease. It has been tested against a great number of cancer cell lines, both solid and

hematologic (Cai et al. 2007, Pivetta et al. 2012). Consequently, many other copper

complexes with phen, phen-derivatives or structurally related compounds such as

bipy have been studied. Modulation of the cytotoxic activity of [Cu(phen)2]2+

species

with insertion of substituted imidazolidine-2-thione ligands (Fig. 18a) has been

evaluated against acute T-lymphoblastic leukaemia (CCRF-CEM), acute B-

lymphoblastic leukaemia (CCRF-SB), lung squamous carcinoma (K-MES-1), and

prostate carcinoma (DU-145). Correlation between the dipole moment of the

complexes and the cytotoxic activity has been found (Pivetta et al. 2012). Complexes

with high dipole moment result more active against haematological tumour cell lines,

while less polar complexes show higher activity against solid tumour ones.

The use of serinol bridge (called Clip) in position 2 or 3 to link two phenanthroline

(phen) units has led to the preparation of a new class of compounds (Pitié,

Donnadieu, et al. 1998). The copper(II) complexes obtained by reaction with 2- and

3-Clip-phen show a 2 to 60-fold increased ability to cleave DNA in comparison to

phen complexes (Pitié, Sudres, et al. 1998). Further studies have shown that the

optimal length of the bridge to achieve optimal DNA cleavage activity corresponds

to three methylene units (Pitié et al. 2003). Compounds with a functionalized serinol

bridge have also been prepared by using a conjugate of 3-Clip-phen (Fig. 18b) with a

cisplatin derivative (De Hoog et al. 2007).

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A class of Cu(II) complexes with general formula [Cu(N-N)(A-A)]NO3, where N-N

is phen or bipy and A-A is either a nitrogen-oxygen or oxygen-oxygen donor ligand,

has been developed and registered as Casiopeinas® (Trejo-Solís et al. 2012). Some of

them show antitumor activity both in vitro and in vivo (Carvallo-Chaigneau 2008).

Apoptosis and autophagy have been proposed as mechanisms to explain their

cytotoxic activity. In particular, such complexes interact with cellular mitochondria,

inhibiting the oxidative phosphorylation and respiration. They also exhibit high DNA

binding and nuclease activity towards plasmid, genomic, and internucleosomal DNA

(Mar n-Hernández et al. 2003). Recent findings show that the most active compound

of the series (Cas-III) causes the accumulation of ROS in rat C6 glioma cells,

activating the apoptotic and autophagic pathway by activation of the c-Jun N-

terminal kinase (JNK) (Trejo-Solís et al. 2012). Other complexes of Cu(II) with

Schiff bases and 2-amino-2-thiazoline show important anti-inflammatory,

antibacterial, and anticancer activity against various cell lines (Chaviara et al. 2005).

The development and use of Cu(I) complexes as antitumor agents is limited by their

low stability and their tendency to be easily oxidized. Some complexes, such as

[Cu(N,N′-disubstituted thioureas)Cl] and [Cu(1,3,5-triaza-7-phosphaadamantane)4]+

exhibit moderate cytotoxicity against various human cell lines (Porchia et al. 2009).

In analogy with gold, Cu(I)-phosphine complexes have been synthesized and

evaluated as antitumor compounds ((Marzano et al. 2006, Płotek et al. 2013, Porchia

et al. 2013, Santini et al. 201). Some mixed Cu(I) complexes of triazolylborate and

alkyl- or aryl-phosphines have been found to be effective against A549

adenocarcinoma cisplatin resistant cells (Marzano et al. 2006).

Cobalt

The Co-ASS (Fig. 19a) is a cobalt(II) complex containing an acetylsalicylic acid

(ASA) derivative which inhibits the cyclooxygenase enzymes COX-1 and COX-2.

Recently, it was found that the regular use of aspirin reduces cancer incidence (Algra

and Rothwell 2012). The COXs inhibition induced by Co-ASS complex is more

efficient than that induced by ASA alone and this probably determines the resulting

higher cytotoxic properties, in particular against breast cancer cell lines (Bruijnincx

and Sadler 2008, Ott et al. 2005). The use of COXs inhibitors is suggested in

combinational therapies with other antitumor drugs (Ott and Gust 200).

Cobalt(III) complexes have been developed as prodrugs which exploit the hypoxic

environment in tumours to release highly cytotoxic ligands. In fact, in hypoxic

environment Co(III) complexes can be reduced to Co(II) with subsequent release of

one neutral ligand. This strategy has been widely exploited for the release of

compounds such as DNA alkylators and a matrix metalloproteinase inhibitor (Ahn et

al. 2006, Failes et al. 2007, Lu et al. 2011).

Rhodium

Many complexes of rhodium have been synthesized but most of them show severe

nephrotoxicity and thus no further studies were done (Katsaros and Anagnostopoulou

2002). Recently, it has been suggested that rhodium(III) complexes that are inert

towards substitution, may show low systemic toxicity (Geldmacher et al. 2012).

Some interesting rhodium complexes are [Rh(2-(2’-hydroxy-5’-methylphenyl)-

benzotriazole)2(H2O)2]Cl that shows promising activity against human breast cancer

(MDA-MB231) and human ovarian cancer (OVCAR-8) cell lines (El-Asmy et al.

2014) and also a series of rhodium(I)-N-heterocyclic carbene complexes with CO as

200

secondary ligand, that shows marked antiproliferative effects together with moderate

inhibitory activity of thioredoxin reductase and efficient binding to biomolecules

(e.g., DNA, albumin). With the use of this complex, modifications in the

mitochondrial membrane potential and DNA fragmentation were observed in wild-

type and daunorubicin- or vincristine-resistant Nalm-6 leukemia cells (Oehninger et

al. 2013).

Despite the encouraging results, in order to define the activity spectrum, selectivity

and systemic toxicity, in vivo studies still need to be performed (Zhong et al. 2014).

Titanium

The cis-[Ti(CH3CH2O)2(bzac)2] (bzac = 1-phenylbutane-1,3-dionato) complex was

the first non-platinum compound tested in clinical trials but it was found that it has

no antitumor activity. Afterwards, complexes with arene ligands have been tested.

The titanocene dichloride (Fig. 19b) binds DNA via the phosphate backbone,

inducing apoptosis (Meléndez 2002). It shows in vitro cytotoxic activity against a

broad spectrum of cancers, in particular human stomach and colon adenocarcinomas,

and has been tested in Phase I and II trials. Its development has been abandoned

because it shows, as others Ti(IV) complexes, poor antitumor activity, probably due

to low water solubility and high deactivation by plasma proteins and nephrotoxicity

as dose-limiting toxicity (Ott and Gust 2007).

Arsenic

Arsenic trioxide (As2O3) represent important metalloid compound for cancer

treatment. It has been used in leukaemia treatment with high rate of survival (Wang

2001). It has been approved by FDA under the name of trisenox and it is the most

active single agent for acute promyelocytic leukaemia treatment (Barry and Sadler

2013). However, the antitumor mechanism is still unclear and cardiotoxicity has been

reported as side-effect (Desoize 2004). The compound darinaparsin (S-

dimethylarsino-glutathione) (Fig. 19c) has been approved for clinical therapy in the

treatment of peripheral T-cell lymphoma and other arsenic compounds are currently

under clinical trials (Barry and Sadler 2013).

FINAL REMARKS

From all the results presented in literature in the field of metal complexes as

anticancer agents, it appears necessary to choose a more constructive and rational

approach. In fact, preparing continuously the same complexes with different metal

ions has been proven to be not always useful, having each metal ion its own

characteristics. The continued study to optimize the design and potency of a

particular metal complex used as anticancer agents, sometimes led to undesirable

results. Then to overcome these problems, the design of a metal complex with

potential antitumor properties, should now focus not only on achieving a good

cytotoxic activity or only on the study of its interactions with the biomolecules, but

on the simultaneous evaluation of all the involved parameters. In particular, it must

be clearly understood i) the proper strategy for the possible use of the metal complex,

ii) the selection of the target, and iii) the interactions of the drug with biomolecules.

The proper strategy

201

Two different strategies can be exploited for the use of a metal complex, and it is

fundamental to choose in advance the strategy most suitable to the needs. A metal

complex, in fact, might be used as active agent if it presents cytotoxic activity, or as a

carrier of organic ligands that present independent biological activity or as a carrier

for metal ions that present some biological activity.

The selection of the target

For the proper design of a metal complex, the desired target should be considered

and identified before its synthesis. In this way, a molecule with features suitable to

interact with the chosen target may be prepared, and the antitumor activity might be

exerted according to the required mechanism. The traditional approach in this field

are still now founded mainly on the interaction of the metal complex with DNA.

Unfortunately, the continuous research for compounds able to form more stable

DNA-adducts, by intercalation, groove binding or electrostatic interactions, does not

always lead to a better drug. Instead, other types of targets may play a key role and

should be considered. In fact, the antitumor activity can be obtained also through

reactions with proteins, disruption of mitochondrial processes or through inhibition

of angiogenesis or metastatic routes.

Drug and biomolecules

In parallel with the choice of the proper strategy and the required target, the

interaction of the drug with the biomolecules naturally present in the body, should be

estimated or at least hypothesized. In fact, the resulting biochemical reactions may

eventually trigger the onset of side effects. Moreover, the metal complex, due to its

structural characteristics, might be susceptible to mechanisms that can cause the drug

resistance, deactivation for transformation or loss of the functional groups, for

hydrolysis or redox reactions.

CONCLUSIONS

Cancer continues to have a strong impact on our life, while the improvements

accomplished so far in its treatment, do not assure the necessary recovering and

healing of all the patients.

The limited achieved success is supposed to be determined by the high complexity of

the human biological system, that should be considered as a whole by modern

approaches. In addition, the pharmaceutical companies, being the principal funders

for research and clinical trials, exert a relevant influence in this field. As a

consequence, companies choice which molecules have to be developed according to

their economic interests. One example among all is the case of dichloroacetate

(DCA) compound. This drug had been approved for the treatment of mitochondrial

diseases and lactic acidosis in children. In 2007, it was proven to be able to reactivate

mitochondrial function, triggering apoptosis in cancer cells without affecting the

normal ones (Bonnet et al. 2007). In spite of these promising results, pharmaceutics

do not show interest in DCA development because this drug cannot be patented.

Historically, the search of new antitumor compounds has been based mostly on a

trial-and-error procedure, quite often following the so-called “structural working

motifs” approach. This has led to the synthesis and testing of a huge number of metal

complexes but the number of those approved for clinical therapy is rather small. This

approach seems now to be expensive, time-consuming, and poorly efficient.

202

Nowadays, the use of platinum complexes as antitumor agents appears to be

exhausted and the attention is moving towards new metal ions. However, besides the

promising obtained results, the application of new metal-based compounds in cancer

treatment appears far. In fact, preclinical tests and clarity about their mechanisms of

action, often totally different from that of platinum-based drugs, are needed.

In the last three years (2012-2014) the drugs that have obtained FDA approval for

oncology are primarily monoclonal antibody and kinase inhibitors. This fact shows

that the development of new metal complexes has come to a break since the approval

of oxaliplatin in 2002, despite some of them are still in clinical trials.

In conclusion, it seems that the variety shown by cancer has led to a multiplicity of

therapeutic approaches. But, far from finding the perfect molecule against all kinds

of cancer, the research is focusing on a drug treatment for every mutated form of

cancer.

ACKNOWLEDGEMENTS

Federica Trudu gratefully acknowledges the Masaryk University and its Department

of Chemistry, for the kind hospitality and for funding her stay in Brno, and to the

Sardinia Regional Government, for the financial support of her PhD scholarship

(P.O.R. Sardegna F.S.E. Operational Programme of the Autonomous Region of

Sardinia, European Social Fund 2007-2013 - Axis IV Human Resources, Objective

l.3, Line of Activity l.3.1.). CEPLANT, the project R&D center for low-cost plasma

and nanotechnology surface modifications CZ.1.05/2.1.00/03.0086 funding

by European Regional Development Fund, is gratefully acknowledged.

203

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Text to the figures

Fig. 1. Chronological and historical overview of the anticancer metal and metalloid

complexes that have been approved or entered the clinical practice.

Fig. 2. Structures of the cis (a) and trans (b) isomers of

diamminedichloroplatinum(II) called cisplatin and transplatin, respectively.

Fig. 3. Scheme of the reaction pathway leading to the formation of adducts between

cisplatin (a) and DNA. One chloride ligand is displaced by water to form the aquo-

complex [PtCl(H2O)(NH3)2]+ (b) which interacts with DNA forming the

monofunctional adduct [PtCl(DNA)(NH3)2]+ (c). This last might exchange the

chloride ligand with one molecule of water forming the hydrated monofunctional

adduct [Pt(H2O)(DNA)(NH3)2]2+

(d). Both the monofunctional adduct (c) and its

hydrated form (d) lead to the formation of the bifunctional adduct

[Pt(H2O)(DNA)(NH3)2]2+

(e).

Fig. 4. Molecule of picoplatin (cis-amine-dichloro-(2-methylpyridine)-Pt(II)),

developed by the Institute of Cancer Research in collaboration with Johnson

Matthey.

Fig. 5. Structures of trans-ammine-(cyclohexylamine)-dichloro-dihydroxo-Pt(IV)

(JM335) (a), trans-[Pt(amine)2(amidine)2]Cl2 complex (b) and trans-

[Pt{N(H)=C(SEt)R}2Cl2] (R = Me, Et, Ph, CH2Ph) complexes (c).

Fig. 6. Structures of satraplatin (a), tetraplatin (or ormaplatin) (b), iproplatin (c) and

Pt(IV) complex containing ethacrynic acid as ligand (d).

Fig. 7. Structures of BBR3464 (a), [{trans-Pt(NH3)2(NH2(CH2)6(NH3+)}2-µ-{trans-

Pt(NH3)2(NH2(CH2)6NH2)2}]8+

(b) and CT-47463 (c).

Fig. 8. Overview of platinum-based drugs used in clinical practice: carboplatin (a),

oxaliplatin (b), nedaplatin (c), lobaplatin (d) and heptaplatin (e).

Fig. 9. Overview of main targets affected by drugs containing either platinum or

ruthenium or gold or copper as metal ion.

Fig. 10. Overview of Ru(II) and Ru(III) leading compounds and their main action

mechanisms. The acronyms CHOP and VEGF refer to CCAAT/enhancer-binding

protein homologous protein and vascular endothelial growth factor, respectively.

Fig. 11. Structures of main Ru(II) and Ru(III) complexes: RM175 (a), [Ru(η6-p-

cym)(acac)Cl] (p-cym = para-cymene) (b) and KP1019 (c).

Fig. 12. Structures of the Ru(II) complexes active against tumour RAPTA-C (a) and

[RuCpCl(mPTA)2](OSO2CF3)2 (b).

Fig. 13. Scheme of [RuCpCl(mPTA)2](OSO2CF3)2 hydrolysis as determined by mass

spectrometric and spectrophotometric investigation.

214

Fig. 14. Structures of the Ru(II) complexes RDC-11 (a) and NAMI-A (b).

Fig. 15. Structures of selected antitumor gold complexes: auranofin (a), [Au(en)2]Cl3

(b), Au(III) complex with substituted terpyridine ligand (c), [Au(TPP)]Cl (H2TPP =

tetraphenyl-porphyrin) (d), [Au(dtc)X2] (X = Cl, Br; dtc = N,N-dimethyl-

dithiocarbamate, ethyl-sarcosine-dithiocarbamate) (e) and [AuX2(pdtc)] (X=Cl, Br;

pdtc = oligopeptide-dithiocarbamate, PEP-N is a di-, tri-, tetra- or penta-peptide) (f).

Fig. 16. Overview of main ligand families used in Au(I) and Au(III) antitumor

complexes and principal biological targets.

Fig. 17. Biological target processes of Cu(II) complexes with antitumor activity.

Fig. 18. Structures of some Cu(II)-phen complexes: [Cu(phen)2(imidazolidine-2-

thione)]2+

(R1, R

2 = H, Me or Et) (a) and [Cu(phen)2]

2+ with serinol bridge. Charges

are omitted.

Fig. 19. Structure of the Co-ASS complex (a), titanocene dichloride (b) and

darinaparsin (S-dimethylarsino-glutathione) (c).

215

Figure 1

Figure 2

Figure3

216

Figure 4

Figure 5

Figure 6

Figure 7

217

Figure 8

Figure 9

Figure 10

218

Figure 11

Figure 12

Figure 13

219

Figure 14

Figure 15

Figure 16

220

Figure 17

Figure 18

Figure 19

221

5. CONCLUSIONS

The conclusions achieved in individual papers are briefly summarized here:

The applicability of artificial neural networks combined with experimental

design in chemical kinetics for the prediciton of rate constant values, optimization of

reaction yield or multicomponent kinetic determination has been studied through

selected theoretical examples. The proposed ED-ANNs approach was proven to be

of general applicability in chemical kinetics. Multicomponent kinetic analysis and

reaction yield optimization can be performed successfully without any knowledge

about the chemical reactions involved.

From an extensive review of the literature, the general philosophy,

possibilities, limitations and fundamental steps of artificial neural networks

applications in medical diagnosis have been evaluated. The analysis of selected

examples concerning cardiovascular diseases, cancer and diabetes shows that ANNs

have been proven suitable for satisfactory and reliable medical diagnosis. They allow

reduced likelihood of overlooking relevant information. Methods of artificial

intelligence in medicine should be considered only as a powerful tool to facilitate the

final decision of a clinician who is utimately responsible for the critical evaluation of

ANN's output.

The concept of drug synergism has been revised. New and general definition

of "additive effect" of drug mixtures has been worked out and a rigorous "model-

free" method based on artificial neural networks combined with experimental design

for the evaluation of cytotoxicity data has been developed and validated. The method

allows the accurate prediction and quantificationof the extent of drug synergism

through the computation of the "net multi-drug effect index" (NMDEI). Unlike

traditional methods, the use of ANN allows us to evaluate the cytotoxicity of all the

possible combinations on the entire space defined by the chosen concentration

intervals.

Laser desorption ionisation was applied to the study of the chemical structure

of thin titanium-carbon layers produced by balanced magnetron sputtering in

argon/acetylene plasma. It was found that the layer's surface was quite

inhomogeneous and consisted of a mixture of titanium carbide, hydrogenated

titanium carbides, oxycarbides and oxides embedded in an amorphous and/or

diamond-like carbon matrix. The general stoichiometry of the most abundant

titanium oxycarbides was Ti8(9)CnOp:H. The deeper layers were found to be

composed by TiC and TiO2. Mass spectrometric study of thin titanium-carbon films

has proven that even a low level of oxygen impurities results in a significan change

of the chemical structure of the synthesised layers.

Laser ablation synthesis of novel gold carbide clusters from mixtures of gold

nano-particles (NG) and various carbonaceous materials has been studied by laser

desorption ionisation and quadrupole-ion-trap time-of-flight mass spectrometry. A

novel and simple method to prepare nano-composite of gold nano-particles and nano-

diamonds (ND) has been developed. The NG-ND nano-composite has been found to

be suitable precursor for the generation of gold carbide clusters. The AuCn+ (n = 1-

222

11, 18), Au2Cn+ (n = 1-16) and Au3Cn

+ (n = 1-10) clusters were generated and

detected via TOF-MS. The structure of diamond (C10, C14, C18, C22)-containing

AumCn+ clusters was suggested to be either that of AumCn

+ carbides or Au@Cn

+ (n =

10, 18, 22), Au2@Cn+ (n = 10, 14) and Au3@C10

+ endohedral supramolecular

complexes. Knowledge about the generation of Au-C clusters might be useful as a

motivation to synthesize new Au-C materials with specific properties.

The fragmentation pathway and mechanism of 12 recently synthesized

alkoxy-decorated shape-persistent macrocycles has been studied via MALDI TOF

MS. The photoelectrons generated during laser irradiation and full-ring conjugation

were found to play a fundamental role for the generation of molecular cation radicals

and ion stabilization, respectively. Formation of supramolecular aggregates of

methoxy-decorated SPMs via π- π stacking has been proven. Fully-conjugated SPMs

were found to enhance the ionisation of electron-deficient motifs such as fullerene;

therefore, they represent promising electron-donating motifs to be exploited in

electronics and optoelectronics for the development of highly efficient laser-activated

supramolecular switches.

Using real satellite spectral data concerning the El-Fayoum depression

(Egypt), a general methodology for soil classification and/or land use/cover

estimation using ANNs was worked out. The fundamental role of preliminary data

screening by eigenvalues analysis and principal components analysis for the

evaluation of the number of distinguishable "classes" without any a priori knowledge

was highlighted. Concluding, the developed procedure for preliminary data screening

is of fundamental importance for the subsequent ANNs analysis of remotely sensed

data.

Novel gold selenide clusters have been generated via LAS of various gold-

selenium precursors. Such precursors were found to be nano-composite. Their

ablation yields Aum- (m = 1-5), Sen

- (n = 1-7) and several series of AumSen

- clusters

up to Au21Se11-. In total, 67 gold selenide clusters were generated and their

stoichiometry determined via modeling of the isotopic pattern. Nano-composites

prepared from mixtures of gold or gold nano-particles (GNP) and selenium are

suitable precursors for the generation of novel gold selenides via laser ablation

synthesis. Knowledge about the generation of such species might facilitate

development of novel high-tech materials.

From a review of the literature concerning coordination compounds contra

cancer it can be concluded that the cancer variety has led to the development of

several therapeutic apporaches. But, far from finding the perfect molecule against all

kinds of cancer, the research is fucusing on a drug for every mutated form of cancer.

A general, specific, robust and highly reproducible method for MALDI TOF

mass spectrometric fingerprinting of whole-mammalian cell-lines has been

developed. The effect of critical factors influencing the the quality and

reproducibility of cell fingerprints has been evaluated with advanced chemometric

approaches. Robustness and reproducibility of the developed protocol has been

evaluated by riigorous statistical analysis. The overall reproducibility of cell

223

fingerprints was found to be higher than 95% on average. The method has been

proven robust with respect to technical, biological, inter-operator, inter-laboratory

and inter-instrumental variability. Results are opening the way for the

implementation of a database of standard cell-lines for fast routine checking of cell-

lines authenticity.

Advanced chemometric evaluation of highly-reproducible MALDI TOF mass

spectrometric fingerprints of human embryonic stem cells was developed and applied

to the selection of markers responsible for subtle changes in cultured cell-lines. The

new procedure has been found able to distinguish tiny changes induced in cultured

cells by: (i) passaging, (ii) growing time and (iii) treatment with retinoic acid.

Concluding, a simple, robust and unbiased cell authentication tool revealing

spontaneous alterations in genetically and morphologically uniform cell populations

has been developed and optimized.

A methodology for the estimation of mammalian cell-lines cross

contamination using whole-cell MALDI TOF mass spectrometric fingerprints and

ANNs has been worked out. The method has been applied to two cases: (i) hESCs

contaminated by MEFs and (ii) hESCs contaminated by mESCs. Results indicate that

the proposed method is able to reveal cross-contamination of mammalian cell-lines

below 1% and in particular, down to 0.1%. Concluding, the developed procedure

opens the possibility of fast and robust routine checking of cell lines identity and

estimation of cross-contamination level.

Concluding, it has been demonstrated on different kind of chemical data concerning

kinetics, mass spectrometry, pharmacology, medicine, proteomics and material

science that chemometrics with suitable mathematical methods facilitate or enable or

even makes possible to extract important but often hidden chemical information from

large and/or complex data sets.

224

How the aims of the Thesis were fulfilled

The aims of the Thesis, as formulated on page 12 were fullfilled in the following

way:

1. New, rigorous and general approach for the quantification of drug synergism

coupling artificial neural networks and experimental design has been

developed (1 paper published).

2. Laser ablation synthesis of novel Au-C, Ag-Te, Au-Ag-Te and Au-Se clusters

has been performed and the stoichiometry of generated clusters has been

determined via isotopic pattern modeling (3 papers published, 1 to be

submitted).

3. Isotopic pattern modeling has been successfully applied to the determination

of the stoichiometry concerning ions generated via laser ablation of thin

films, nano-composite and shape-persistent macrocycles (4 papers published,

1 submitted, 1 to be submitted).

4. New, robust and reliable mass spectrometric method for mammalian cell

lines fingerprinting has been developed (1 paper to be submitted).

5. Tiny changes induced in human embryonic stem cells by various factors were

followed by a novel approach based on OPLS-DA to evaluate MALDI TOF

mass spectrometric fingerprints (1 paper to be submitted).

6. Workflow for rapid and reliable estimation of cell-lines cross-contamination

has been developed and successfully applied to real examples (1 paper to be

submitted).

225

6. REFERENCES

[1] B. G. M. Vandeginste, D. L. Massart, L. M. C. Buydens, S. de Jong, P. J. Lewi,

J. Smeyers-Verbeke. Handbook of Chemometrics and Qualimetrics, Part B.

Elsevier, Amsterdam, 1998.


[3] M. Daszykowski, B. Walczak. Use and abuse of chemometrics in

chromatography. TrAC Trends Anal. Chem. 2006, 25, 1081.

[4] S. D. Brown, R. Tauler, B. Walczak. Comprehensive Chemometrics, Four-

Volume Set: Chemical and Biochemical Data Analysis. Elsevier Science, 2009.


MALDI MS (Eds: F. Hillenkamp, J. Peter-Katalinić). Wiley-VCH Verlag GmbH

& Co. KGaA, 2007, pp. 83–108.



226

7. LIST OF ABBREVIATIONS

Artificial neural network(s) ANN, ANNs

Human acute T-lymphoblastic leukemia cells CCRF-CEM

Collision-induced dissociation CID

Eigenvalues analysis EA

Experimental design ED

Energy-dispersive X-rays analysis EDX

Human embryonic stem cells ESCs

Laser ablation LA

Laser desorption ionisation LDI

Matrix-assisted laser desorption ionisation MALDI

Mouse embryonic fibroblast(s) MEF, MEFs

Mouse embryonic stem cells mESCs

Mass spectrometry MS

Non-algebraic additive effect NAAE

Nano-diamonds ND

Nano-gold NG

Net multi-drug effect index NMDEI

Orthogonal projection to latent structures discriminant analysis OPLS-DA

Principal components analysis PCA

Quadrupole ion trap QIT

Root mean squared error RMSE

Scanning electron microscopy SEM

Shape-persistent macrocycles SPMs

Short tandem repeats STR

Transmission electron microscopy TEM

Time-of-flight TOF

227

8. APPENDIX

8.1. List of publications

1. Filippo Amato, José Luis González-Hernández, Josef Havel, Artificial

Neural Networks combined with Experimental Design: a “soft” approach

for chemical kinetics, Talanta, 93, (2012), 72-78, ISSN: 0039-9140. IF:

3.498.

2. Filippo Amato, Alberto López, Eladia Maria Peña-Méndez, Petr Vaňhara,

Aleš Hampl, Artificial neural networks in medical diagnosis, J. Appl.

Biomed., 11, (2013), 47-58, DOI 10.2478/v10136-012-0031-x, ISSN: 1214-

0287. IF: 0.978.

3. Tiziana Pivetta, Francesco Isaia, Federica Trudu, Alessandra Pani, Matteo

Manca, Daniela Perra, Filippo Amato, Josef Havel - Development and

validation of a general approach to predict and quantify the synergism of

anti-cancer drugs by using Experimental Design and Artificial Neural

Networks - Talanta, 2013, 115, 84-93, DOI: 10.1016/j.talanta.2013.04.031.

IF: 3.498.

4. Filippo Amato, Nagender Reddy Panyala, Petr Vašina, Pavel Souček, Josef

Havel - Laser Desorption Ionisation Quadrupole Ion Trap Time-of-Flight

Mass Spectrometry of Titanium-Carbon Thin Films – Rapid. Commun.

Mass Spectrom., 2013, 27, 1196-1202, DOI: 10.1002/rcm.6564. IF: 2.509.

5. Josef Havel, Eladia Maria Peña-Méndez, Filippo Amato, Nagender Reddy

Panyala, Vilma Buršíková – Laser ablation synthesis of new gold carbides.

From gold-diamond nano-composite as a precursor to gold-doped

diamonds. Time-of-flight mass spectrometric study - Rapid Commun. Mass

Spectrom. 2013, 28, 297-304, DOI: 10.1002/rcm.6783. IF: 2.509.

8.2. Other papers not included in Ph.D. thesis

1. Ravi Madhukar Mawale, Filippo Amato, Milan Alberti, Josef Havel –

Generation of AupAgqTer clusters via laser ablation synthesis using Au-Ag-

Te nano-composite as precursor. Quadrupole ion trap time-of-flight mass

spectrometry – Rapid Commun. Mass Spectrom., 2014, 28, 1601-1628, DOI:

10.1002/rcm, 6936, IF: 2.509.

228

2. Ravi Madhukar Mawale, Filippo Amato, Milan Alberti, Josef Havel -

Generation of new AgmTen clusters via laser ablation synthesis using Ag-Te

nano-composite as precursor. Quadrupole ion trap time-of-flight mass

spectrometry - Rapid Commun. Mass Spectrom., 2014, 28, 1-6, DOI:

10.1002/rcm.7070, IF: 2.509.

8.3. Presentations to international and domestic conferences

1. Filippo Amato, Josef Havel, Abd-Alla Gad, Ahmed El-Zeiny - Remotely

sensed soil data analysis using artificial neural networks. A case study of

El-Fayoum depression, Egypt - International conference on research and

innovation for sustainable soil management (ICRISSM, 2014), Hurghada

(Egypt), 27th-29-th November 2014. (ORAL presentation).

2. Filippo Amato, Lukáš Kučera, Petr Vaňhara, Aleš Hampl, Josef Havel -

MALDI-TOF fingerprinting of the whole mammalian cell-lines -

OrganoNET Conference 2014, 19-th - 20-th June 2014, Brno (Czech

republic). (POSTER presentation).

3. Filippo Amato, Bhimrao Vaijnath Phulwale, Ctibor Mazal, Josef Havel –

Fragmentation pattern of shape-persistent macrocycles: a mass

spectrometric study - European Symposium on Atomic Spectrometry (ESAS

2014) & 15th Czech - Slovak Spectroscopic Conference, 16-21 March 2014,

Prague (Czech Republic). (POSTER presentation).

4. Anton Salykin, Filippo Amato, Petr Dvořák, Vladimír Rotrekl, Josef Havel –

Identification of key regulatory metabolites in hESCs by MALDI-TOF-MS

- European Symposium on Atomic Spectrometry (ESAS 2014) & 15th Czech

- Slovak Spectroscopic Conference, 16-21 March 2014, Prague (Czech

Republic). (POSTER presentation).

5. Lubomír Prokeš, Eladia Maria Peña-Méndez, Filippo Amato, Milan Alberti,

Pavel Kubáček, Josef Havel - Generation of novel AumSen clusters via laser

ablation synthesis. Laser desorption ionisation time-of-flight mass

spectrometry – International Symposium on Metal Complexes (ISMEC

2014), Pavia (Italy), 8-12 June 2014, (ORAL communication). Acta of the

International Symposia on Metal Complexes – ISMEC Acta, Volume 4 - June

8-th – 12-th 2014, ISSN: 2239-2459 – Pavia (Italy). (ORAL presentation).

6. Filippo Amato, Ravi M. Mawale, Milan Alberti, Josef Havel - Generation of

mixed AgpTer clusters via laser ablation synthesis using novel Ag-Te nano-

composite as precursor. Time-of-flight mass spectrometric study – 11-th

International Conference on Nanosciences & Nanotechnologies (NN14), 8-11

229

July 2014, Thessaloniki (Greece). (POSTER presentation).


Panyala, Vilma Buršíková – Gold-diamond nanocomposite as a precurser

for laser ablation synthesis of gold carbides – Acta of the International

Symposia on Metal Complexes (ISMEC 2013), Burgos (Spain), 16 – 20 June

2013, page 76, ISSN: 2239-2459. (ORAL presentation).

8. Petr Vaňhara, Lukáš Kučera, Filippo Amato, Eladia Maria Peña-Méndez,

Milan Ešner, Josef Havel, Aleš Hampl - Whole-cell mass spectrometry

profiling combined with artificial intelligence as a novel tool for

fingerprinting of hESCs - In ISSCR 11th Annual Meeting, Boston, MA, USA,

12.-15.6.2013. 2013. (POSTER presentation).

9. Filippo Amato, Dana Skácelová, Tiziana Pivetta, Josef Havel, - Adsorption

of cisplatin on plasma treated surfaces: a mass spectrometric study - Book

of extended abstracts of the conference “Potential and Applications of

Surface Nanotreatment of Polymers and lass” (PASNP ), Hustopeče

(Czech Republic), 15 - 17 October 2012, page 28, ISBN: 978-80-210-5979-5.

(ORAL presentation).

10. Matteo Manca, Tiziana Pivetta, Franco Isaia, Federica Trudu, Daniela Perra,

Spiga F, Filippo Amato, Josef Havel, Alessandra Pani - Synergistic

cytotoxicity of copper(II) complexes in combination with cisplatin: an

application of artificial neural networks and experimental design. XII FISV

Congress, Roma (Italy), 24 – 27 September 2012. (POSTER presentation).

11. Filippo Amato, Eladia Maria Peña-Méndez, Tiziana Pivetta, Nagender

Reddy Panyala, Josef Havel – Supramolecular complexes formation.

Possibilities of mass spectrometric studies - Book of abstracts of the 4-th

congress of the European Association for Chemical and Molecular Sciences

(4-th EuCheMS) , Prague (Czech Republic), 26 – 30 August 2012, page

s1110, ISSN: 1803-2389. (POSTER presentation).


Manca, Daniela Perra, Filippo Amato, Josef Havel - Synergistic effects of

copper(II) complexes and cisplatin: an application of artificial neural

networks and experimental design - International Symposia on Metal

Complexes (ISMEC 2012) - Lisbon (Portugal), 18 – 22 June 2012, page 126,

ISSN: 2239-2459. (ORAL presentation).


Reddy Panyala, Josef Havel - MALDI and SALDI TOF mass spectrometry -

230

fast and efficient way to search for supramolecular complex formation and

new drug carriers - International Symposia on Metal Complexes (ISMEC

2012) - Lisbon (Portugal), 18 – 22 June 2012, page 138, ISSN: 2239-2459.


14. Filippo Amato, Vilma Buršíková, Jan Janča, Eladia Maria Peña-Méndez,

Josef Havel – Diamond-Like Carbon (DLC) chemical vapor deposition

technology: Characterization of DLC nano-layers and Artificial Neural

Networks for process modelling – Book of abstracts of the conference

“Trends in NanoTechnology” (TNT2011) (BI-3058/2011), Tenerife – Canary

Islands (Spain), 21-25 November 2011. (POSTER presentation).


Josef Havel – Artificial Neural Networks for Diamond Like Carbon

deposition: process modelling and prediction of optimal conditions – Book


Surface Nanotreatment of Polymers and lass” (PASNP ), Blansko (Czech

Republic), 17-19 October 2011, page 23. (ORAL presentation).

16. Filippo Amato, José Luis González, Josef Havel – Artificial Neural

Networks in Chemical Kinetics – Collected abstracts of the XXII

International Symposium on Metal Complexes (ISMEC 2011), Giardini

Naxos (Italy), 13-16 June 2011, page 7, ISSN: 2239-2459. (ORAL

presentation).

8.4. Seminaries

1. Filippo Amato, "International conference on nanosciences and

nanotechnologies (NN14)", 30 October 2014, Department of Chemistry,

Masaryk University, Brno (Czech Republic).

2. Josef Havel, Filippo Amato, course: “Applications of artificial neural

networks in science”, 9 – 11 April 2013, National Authority for Remote

Sensing and Space Sciences (NARSS), Cairo (Egypt).

3. Filippo Amato, “Mathematical background of artificial neural networks”,

9-th April 2013, National Authority for Remote Sensing and Space Sciences

(NARSS), Cairo (Egypt).

4. Filippo Amato, "Experimental Design and Artificial Neural Networks",

14-th March 2012, Department of Analytical and Geological Sciences,

University of Cagliari, Cagliari (Italy).

231

5. Filippo Amato, “Artificial Neural Networks: a tool for Science”, 23-rd

February 2012, Department of Chemistry, Masaryk University, Brno (Czech

Republic).

6. Filippo Amato, “Applications of ANNs in chemical kinetics” for the course

“Aplicaciones de redes neuronales (ANN) en ciencia”, 2a edition, 7-9 June

2011, University of Salamanca, Salamanca (Spain).

8.5. Collaborations

Masaryk University

1. Department of Histology and Embryology, Masaryk University, Brno (Czech

Republic)

2. Department of Physical Electronics, Masaryk University, Brno (Czech

Republic)

Foreign

3. Department of Chemistry, University of Girona, Girona (Spain)

4. Department of Chemistry, University of La Laguna, Tenerife (Spain)

5. Department of Physical Chemistry, University of Salamanca, Salamanca

(Spain)

6. Dipartimento di Scienze Chimiche e Geologiche, University of Cagliari,

Cagliari (Italy)

7. National Authority for Remote Sensing and Space Sciences (NARSS), Cairo

(Egypt)

8.6. Participation in projects

1. R&D Center for Low-Cost Plasma and Nanotechnology Surface

Modifications, Masaryk University, Kotlářská 2, 611 37 Brno, Czech

Republic, Project no. CZ.1.05/2.1.00/03.0086.

2. SUDSOE, “Characterization and sustainable use of Egyptian degraded soils”,

FP7 Grant, Project no. 295031, FP7-INCO-2011-6. EU project 7th

frame.

3. “Bioanalytical Cell and Tissue Authentication using Physical Chemistry

Methods and Artificial Intelligence”, AMU, MUNI/M/0041/2013.

4. GAČR P106, 13-050828, Analysis and application of plasma assisted

processes for the fabrication of amorphous chalcogenide thin films.

5. HistoPARK – „Centrum analýz a modelování tkání a orgánů“ -

CZ.1.07/2.3.00/20.0185.

232

8.7. Others

1. Filippo Amato, Giuseppe Alfano, Diana Amorello, Vincenzo Romano,

Roberto Zingales – The complex formation equilibria between Cd(II),

Pb(II) and iminodiacetic acid (IDA) in NaClO4 0,93 Mw at 25°C –

Collected abstracts of the XXI Spanish-Italian Congress on Thermodynamics

of Metal Complexes, Bilbao (Spain) – 7-11 June 2010, page 30.

2. Filippo Amato, Diana Amorello, Vincenzo Romano, Roberto Zingales – The

complex formation equilibria between Zn(II) and iminodiacetic acid (IDA)

in NaClO4 0,93 Mw at 25°C – Collected abstracts of the XX Spanish-Italian

Congress on Thermodynamics of Metal Complexes; XXXVI Annual

Congress of the “ ruppo di Termodinamica dei Complessi”, Tirrenia (Pisa) –

7-11 June 2009, page 38.

3. Filippo Amato, Diana Amorello, Vincenzo Romano, Roberto Zingales –

Unusual oxidation states of cations in aqueous solutions – Collected

abstracts of the XIX Spanish-Italian Congress on the Thermodynamics of

Metal Complexes; XXXV Annual Congress of the “ ruppo di

Termodinamica dei Complessi”, Baeza (Jaén) – 9-13 June 2008, page 54.

4. Filippo Amato, Diana Amorello, Vincenzo Romano, Roberto Zingales – La

semicella Zn(Hg)/Zn++

ed alcune sue applicazioni analitiche – Collected

abstracts of the XX National Congress of Analytical Chemistry, San Martino

al Cimino (VT), 16-20 September 2007, page 244.


possible use of the half cell Zn(Hg)/Zn++

in ionic equilibria studies –

Collected abstracts of the XVIII Italian-Spanish Congress on

Thermodynamics of Metal Complexes, Cagliari (S. Margherita di Pula) – 5-9

June 2007, page 8.

233

8.8. Curriculum Vitae

Person identification

Mgr. Filippo Amato

Born: 06/05/1984, Benevento (BN), Italy.

Address: Department of Chemistry, Faculty of Science, Masaryk University,

Kamenice 5/A14, 625 00 Brno, Czech Republic.

Education and Academic Qualifications

2010-Present Ph.D. student, Masaryk University, Brno

2006-2008 Masters in Chemistry, University of Palermo, Palermo, Italy

2003-2006 Bachelors in Chemistry, University of Palermo, Palermo, Italy

Courses attended

1. Mass spectrometry of biomolecules

2. Capillary electrophoresis

3. Chemical equilibria and kinetics analysis

4. Separation mathods A

5. Separation methods B

6. Bioanalytics II - laboratory medicine

7. Scientific knowledge and thinking

8. Optimization of Experiments using Experimental Design and Artificial

Neural Networks in Science

9. Czech language course for foreigners III

10. Czech language course for foreigners IV

Affiliations and membership

1. Member of the research group Nanomaterials in Analytical Chemistry and

Biomedicine (NQAB-Nanomateriales en Química Analítica y Biomedicina),

established at University of La Laguna, being coordinator of the group Mrs.

Eladia María Peña Méndez, PhD. (Department of Analytical Chemistry,

Nutrition and Food Science, Faculty of Chemistry).

Awards

2. Awarded the prize of the Dean 2012, Faculty of Science, Masaryk University,

Brno, Czech Republic.

234

3. Awarded the prize of the Department of Chemistry 2013, Faculty of Science,

Masaryk University, Brno, Czech Republic.

Publications

1. Filippo Amato, José Luis González-Hernández, Josef Havel, Artificial

Neural Networks combined with Experimental Design: a “soft” approach

for chemical kinetics, Talanta, 93, (2012), 72-78, ISSN: 0039-9140. IF:

3.498.

2. Filippo Amato, Alberto López, Eladia Maria Peña-Méndez, Petr Vaňhara,

Aleš Hampl, Artificial neural networks in medical diagnosis, J. Appl.

Biomed., 11, (2013), 47-58, DOI 10.2478/v10136-012-0031-x, ISSN: 1214-

0287. IF: 0.978.


Manca, Daniela Perra, Filippo Amato, Josef Havel - Development and

validation of a general approach to predict and quantify the synergism of

anti-cancer drugs by using Experimental Design and Artificial Neural

Networks - Talanta, 2013, 115, 84-93, DOI: 10.1016/j.talanta.2013.04.031.

IF: 3.498.

4. Filippo Amato, Nagender Reddy Panyala, Petr Vašina, Pavel Souček, Josef

Havel - Laser Desorption Ionisation Quadrupole Ion Trap Time-of-Flight

Mass Spectrometry of Titanium-Carbon Thin Films – Rapid. Commun.

Mass Spectrom., 2013, 27, 1196-1202, DOI: 10.1002/rcm.6564. IF: 2.509.


Panyala, Vilma Buršíková – Laser ablation synthesis of new gold carbides.

From gold-diamond nano-composite as a precursor to gold-doped

diamonds. Time-of-flight mass spectrometric study - Rapid Commun. Mass

Spectrom. 2013, 28, 297-304, DOI: 10.1002/rcm.6783. IF: 2.509.

Other papers not included in Ph.D. thesis

1. Ravi Madhukar Mawale, Filippo Amato, Milan Alberti, Josef Havel –

Generation of AupAgqTer clusters via laser ablation synthesis using Au-Ag-

Te nano-composite as precursor. Quadrupole ion trap time-of-flight mass

spectrometry – Rapid Commun. Mass Spectrom., 2014, 28, 1601-1628, DOI:

10.1002/rcm, 6936, IF: 2.509.

235

2. Ravi Madhukar Mawale, Filippo Amato, Milan Alberti, Josef Havel -

Generation of new AgmTen clusters via laser ablation synthesis using Ag-Te

nano-composite as precursor. Quadrupole ion trap time-of-flight mass

spectrometry - Rapid Commun. Mass Spectrom., 2014, 28, 1-6, DOI:

10.1002/rcm.7070, IF: 2.509.

Presentations to international and domestic conferences

1. Filippo Amato, Josef Havel, Abd-Alla Gad, Ahmed El-Zeiny - Remotely

sensed soil data analysis using artificial neural networks. A case study of

El-Fayoum depression, Egypt - International conference on research and

innovation for sustainable soil management (ICRISSM, 2014), Hurghada

(Egypt), 27th-29-th November 2014. (ORAL presentation).

2. Filippo Amato, Lukáš Kučera, Petr Vaňhara, Aleš Hampl, Josef Havel -

MALDI-TOF fingerprinting of the whole mammalian cell-lines -

OrganoNET Conference 2014, 19-th - 20-th June 2014, Brno (Czech

republic). (POSTER presentation).

3. Filippo Amato, Bhimrao Vaijnath Phulwale, Ctibor Mazal, Josef Havel –

Fragmentation pattern of shape-persistent macrocycles: a mass

spectrometric study - European Symposium on Atomic Spectrometry (ESAS

2014) & 15th Czech - Slovak Spectroscopic Conference, 16-21 March 2014,

Prague (Czech Republic). (POSTER presentation).

4. Anton Salykin, Filippo Amato, Petr Dvořák, Vladimír Rotrekl, Josef Havel –

Identification of key regulatory metabolites in hESCs by MALDI-TOF-MS

- European Symposium on Atomic Spectrometry (ESAS 2014) & 15th Czech

- Slovak Spectroscopic Conference, 16-21 March 2014, Prague (Czech

Republic). (POSTER presentation).

5. Lubomír Prokeš, Eladia Maria Peña-Méndez, Filippo Amato, Milan Alberti,

Pavel Kubáček, Josef Havel - Generation of novel AumSen clusters via laser

ablation synthesis. Laser desorption ionisation time-of-flight mass

spectrometry – International Symposium on Metal Complexes (ISMEC

2014), Pavia (Italy), 8-12 June 2014, (ORAL communication). Acta of the

International Symposia on Metal Complexes – ISMEC Acta, Volume 4 - June

8-th – 12-th 2014, ISSN: 2239-2459 – Pavia (Italy). (ORAL presentation).

6. Filippo Amato, Ravi M. Mawale, Milan Alberti, Josef Havel - Generation of

mixed AgpTer clusters via laser ablation synthesis using novel Ag-Te nano-

236

composite as precursor. Time-of-flight mass spectrometric study – 11-th

International Conference on Nanosciences & Nanotechnologies (NN14), 8-11

July 2014, Thessaloniki (Greece). (POSTER presentation).


Panyala, Vilma Buršíková – Gold-diamond nanocomposite as a precurser

for laser ablation synthesis of gold carbides – Acta of the International

Symposia on Metal Complexes (ISMEC 2013), Burgos (Spain), 16 – 20 June

2013, page 76, ISSN: 2239-2459. (ORAL presentation).

8. Petr Vaňhara, Lukáš Kučera, Filippo Amato, Eladia Maria Peña-Méndez,

Milan Ešner, Josef Havel, Aleš Hampl - Whole-cell mass spectrometry

profiling combined with artificial intelligence as a novel tool for

fingerprinting of hESCs - In ISSCR 11th Annual Meeting, Boston, MA, USA,

12.-15.6.2013. 2013. (POSTER presentation).

9. Filippo Amato, Dana Skácelová, Tiziana Pivetta, Josef Havel, - Adsorption

of cisplatin on plasma treated surfaces: a mass spectrometric study - Book


Surface Nanotreatment of Polymers and lass” (PASNP ), Hustopeče

(Czech Republic), 15 - 17 October 2012, page 28, ISBN: 978-80-210-5979-5.


10. Matteo Manca, Tiziana Pivetta, Franco Isaia, Federica Trudu, Daniela Perra,

Spiga F, Filippo Amato, Josef Havel, Alessandra Pani - Synergistic

cytotoxicity of copper(II) complexes in combination with cisplatin: an

application of artificial neural networks and experimental design. XII FISV

Congress, Roma (Italy), 24 – 27 September 2012. (POSTER presentation).


Reddy Panyala, Josef Havel – Supramolecular complexes formation.

Possibilities of mass spectrometric studies - Book of abstracts of the 4-th

congress of the European Association for Chemical and Molecular Sciences

(4-th EuCheMS) , Prague (Czech Republic), 26 – 30 August 2012, page

s1110, ISSN: 1803-2389. (POSTER presentation).


Manca, Daniela Perra, Filippo Amato, Josef Havel - Synergistic effects of

copper(II) complexes and cisplatin: an application of artificial neural

networks and experimental design - International Symposia on Metal

Complexes (ISMEC 2012) - Lisbon (Portugal), 18 – 22 June 2012, page 126,

ISSN: 2239-2459. (ORAL presentation).

237


Reddy Panyala, Josef Havel - MALDI and SALDI TOF mass spectrometry -

fast and efficient way to search for supramolecular complex formation and

new drug carriers - International Symposia on Metal Complexes (ISMEC

2012) - Lisbon (Portugal), 18 – 22 June 2012, page 138, ISSN: 2239-2459.



Josef Havel – Diamond-Like Carbon (DLC) chemical vapor deposition

technology: Characterization of DLC nano-layers and Artificial Neural

Networks for process modelling – Book of abstracts of the conference

“Trends in NanoTechnology” (TNT2011) (BI-3058/2011), Tenerife – Canary

Islands (Spain), 21-25 November 2011. (POSTER presentation).


Josef Havel – Artificial Neural Networks for Diamond Like Carbon

deposition: process modelling and prediction of optimal conditions – Book


Surface Nanotreatment of Polymers and lass” (PASNP ), Blansko (Czech

Republic), 17-19 October 2011, page 23. (ORAL presentation).

16. Filippo Amato, José Luis González, Josef Havel – Artificial Neural

Networks in Chemical Kinetics – Collected abstracts of the XXII

International Symposium on Metal Complexes (ISMEC 2011), Giardini

Naxos (Italy), 13-16 June 2011, page 7, ISSN: 2239-2459. (ORAL

presentation).

Seminaries

1. Filippo Amato, "International conference on nanosciences and

nanotechnologies (NN14)", 30 October 2014, Department of Chemistry,

Masaryk University, Brno (Czech Republic).

2. Josef Havel, Filippo Amato, course: “Applications of artificial neural

networks in science”, 9 – 11 April 2013, National Authority for Remote

Sensing and Space Sciences (NARSS), Cairo (Egypt).

3. Filippo Amato, “Mathematical background of artificial neural networks”,

9-th April 2013, National Authority for Remote Sensing and Space Sciences

(NARSS), Cairo (Egypt).

4. Filippo Amato, "Experimental Design and Artificial Neural Networks", 14-

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th March 2012, Department of Analytical and Geological Sciences,

University of Cagliari, Cagliari (Italy).

5. Filippo Amato, “Artificial Neural Networks: a tool for Science”, 23-rd

February 2012, Department of Chemistry, Masaryk University, Brno (Czech

Republic).

6. Filippo Amato, “Applications of ANNs in chemical kinetics” for the course

“Aplicaciones de redes neuronales (ANN) en ciencia”, 2a edition, 7-9 June

2011, University of Salamanca, Salamanca (Spain).

11.5. Collaborations

Masaryk University

1. Department of Histology and Embryology, Masaryk University, Brno (Czech

Republic)

2. Department of Physical Electronics, Masaryk University, Brno (Czech

Republic)

Foreign

1. Department of Chemistry, University of Girona, Girona (Spain)

2. Department of Chemistry, University of La Laguna, Tenerife (Spain)

3. Department of Physical Chemistry, University of Salamanca, Salamanca

(Spain)

4. Dipartimento di Scienze Chimiche e Geologiche, University of Cagliari,

Cagliari (Italy)

5. National Authority for Remote Sensing and Space Sciences (NARSS), Cairo

(Egypt)

Participation in projects

1. R&D Center for Low-Cost Plasma and Nanotechnology Surface

Modifications, Masaryk University, Kotlářská 2, 611 37 Brno, Czech

Republic, Project no. CZ.1.05/2.1.00/03.0086.

2. SUDSOE, “Characterization and sustainable use of Egyptian degraded soils”,

FP7 Grant, Project no. 295031, FP7-INCO-2011-6. EU project 7th

frame.

3. “Bioanalytical Cell and Tissue Authentication using Physical Chemistry

Methods and Artificial Intelligence”, AMU, MUNI/M/0041/2013.

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4. GAČR P106, 13-050828, Analysis and application of plasma assisted

processes for the fabrication of amorphous chalcogenide thin films.

5. HistoPARK – „Centrum analýz a modelování tkání a orgánů“ -

CZ.1.07/2.3.00/20.0185.

Others

1. Filippo Amato, Giuseppe Alfano, Diana Amorello, Vincenzo Romano,

Roberto Zingales – The complex formation equilibria between Cd(II),

Pb(II) and iminodiacetic acid (IDA) in NaClO4 0,93 Mw at 25°C –

Collected abstracts of the XXI Spanish-Italian Congress on Thermodynamics

of Metal Complexes, Bilbao (Spain) – 7-11 June 2010, page 30.


complex formation equilibria between Zn(II) and iminodiacetic acid (IDA)

in NaClO4 0,93 Mw at 25°C – Collected abstracts of the XX Spanish-Italian

Congress on Thermodynamics of Metal Complexes; XXXVI Annual

Congress of the “ ruppo di Termodinamica dei Complessi”, Tirrenia (Pisa) –

7-11 June 2009, page 38.

3. Filippo Amato, Diana Amorello, Vincenzo Romano, Roberto Zingales –

Unusual oxidation states of cations in aqueous solutions – Collected

abstracts of the XIX Spanish-Italian Congress on the Thermodynamics of

Metal Complexes; XXXV Annual Congress of the “ ruppo di

Termodinamica dei Complessi”, Baeza (Jaén) – 9-13 June 2008, page 54.

4. Filippo Amato, Diana Amorello, Vincenzo Romano, Roberto Zingales – La

semicella Zn(Hg)/Zn++

ed alcune sue applicazioni analitiche – Collected

abstracts of the XX National Congress of Analytical Chemistry, San Martino

al Cimino (VT), 16-20 September 2007, page 244.


possible use of the half cell Zn(Hg)/Zn++

in ionic equilibria studies –

Collected abstracts of the XVIII Italian-Spanish Congress on

Thermodynamics of Metal Complexes, Cagliari (S. Margherita di Pula) – 5-9

June 2007, page 8.

Chemometrics and data mining in chemistry - IS MUNI

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