Author's Accepted Manuscript

Exploration of crystal simulation potential byfluconazole isomorphism and its applicationin improvement of pharmaceutical properties

Amitha Thakur, Dinesh Kumar, Rajesh Thippar-aboina, Nalini R Shastri

PII: S0022-0248(14)00582-XDOI: http://dx.doi.org/10.1016/j.jcrysgro.2014.08.007Reference: CRYS22387

To appear in: Journal of Crystal Growth

Received date: 24 June 2014Revised date: 5 August 2014Accepted date: 8 August 2014

Cite this article as: Amitha Thakur, Dinesh Kumar, Rajesh Thipparaboina,Nalini R Shastri, Exploration of crystal simulation potential by fluconazoleisomorphism and its application in improvement of pharmaceutical proper-ties, Journal of Crystal Growth, http://dx.doi.org/10.1016/j.jcrysgro.2014.08.007

This is a PDF file of an unedited manuscript that has been accepted forpublication. As a service to our customers we are providing this early version ofthe manuscript. The manuscript will undergo copyediting, typesetting, andreview of the resulting galley proof before it is published in its final citable form.Please note that during the production process errors may be discovered whichcould affect the content, and all legal disclaimers that apply to the journalpertain.

www.elsevier.com/locate/jcrysgro

1

Exploration of crystal simulation potential by fluconazole isomorphism

and its application in improvement of pharmaceutical properties

Amitha Thakura, Dinesh Kumar

a, Rajesh Thipparaboina

a, Nalini R Shastri

a,*

aNational Institute of Pharmaceutical Education & Research, Hyderabad, India

*Corresponding author. Nalini R Shastri

Tel. +91-040-23423749

Fax. +91-040-23073751

E-mail: nalini@niperhyd.ac.in, svcphod@yahoo.co.in

Address: Department of Pharmaceutics, NIPER (National Institute of Pharmaceutical

Education & Research), Balanagar, Hyderabad, India, Pin Code – 500037

2

ABSTRACT

Control of crystal morphology during crystallization is a paramount challenge in

pharmaceutical processing. Hence, there is need to introduce computational methods for

morphology prediction to manage production cost of drugs and improve related

pharmaceutical and biopharmaceutical properties. Layer docking approach with molecular

dynamics opens a new avenue for crystal habit prediction in presence of solvent. In the

present study, attempts were made to correlate predicted and experimental crystal habits of

fluconazole considering solvent interactions using layer docking approach. Simulated

results from layer docking approach with methanol as solvent gave two dominant facets (0

1 1) and (1 0 1) with a surface area 22.43% and 19.82% respectively, which were in

agreement with the experimental results. Experimentally grown modified crystal habit of

fluconazole in methanol showed enhanced dissolution rate (p < 0.05) when compared to

plain drug. This was attributed to the increased surface area on the specified facets caused

by interactions with the solvent. Furthermore, Differential Scanning Calorimetry, Fourier

Transform Infrared (FTIR) Spectroscopy and powder X-ray Diffraction of recrystallized

samples confirmed only a habit change and absence of any polymorphs, hydrates or

solvates. Flow and compressibility of fluconazole recrystallized in methanol was

significantly improved when compared to plain drug. This study demonstrates a

methodical approach using computational tools for prediction and modification of crystal

habit, to enhance dissolution of poorly soluble drugs, for future pharmaceutical

applications.

3

Keywords: A1. Computer Simulation; A1. Crystal Morphology; A1. Recrystallization;

A1. Solubility

1. Introduction

Different crystal forms of drug possess different planes and thus differ not only in their

specific surface, but also in their surface free energy thereby leading to differences in their

physicochemical properties [1]. Variation in the solid-state properties of the crystallized

material is of practical relevance in many industrial processes since, it affects solid–liquid

separation characteristics, packaging, handling, drying, storage behaviour and end-use

properties of the crystallized material [2, 3]. Similarly, use of different solvents and

crystallization conditions may alter the polymorphic state and habit [4]. External

morphology of a crystal is called the crystal habit or isomorphism. Crystal habit or

isomorphs may or may not be altered with polymorphic transformation [5]. Iso-diametric

(equant/cubic), plate, tabular, columnar, blade and acicular (needle) are some of the

commonly found habits for pharmaceuticals. Iso-diametric crystal habit exhibits good flow

and compressibility properties than other habits and hence is generally preferred for

processing and manufacturing of solid dosage forms [5, 6]. In many reported studies,

crystal engineering strategies have been used to generate crystals of desired architecture

with some degree of success [7, 8]. In recent times, technological advancement in

4

computational methodology has hastened research areas related to crystal engineering to

design crystals of desired morphology [9].

BFDH (Bravais–Friedel–Donnay–Harker), growth morphology [10] and equilibrium

morphology models are generally used to predict crystal morphology, however, the results

obtained from these models are often not in full agreement with the experimental results

[11] as these models do not consider the impact of solvents during crystallization process

[12-14]. Schmidt and Ulrich used layer docking method to predict crystal habit in

presence of solvent, and reported good agreement with experimental habit [15]. Tedesco et

al, used a qualitative method to determine how solvents interact with different faces of

crystal and modify the habit [16, 17]. Molecular dynamics is a powerful tool to investigate

the effect of solvent on crystal morphology based on the current findings of some authors

who have reported successful crystal prediction for drugs, explosives and photoactive

micro-crystals like alpha cyclodextrin hexahydrate [18], HMX [13], RDX [19],

ginsenoside [20] and hydrocortisone methanol solvate [21].

Fluconazole, a triazole anti-fungal drug is primarily indicated for candidiasis and

cryptococcal meningitis [22]. It has poor dissolution rate owing to its limited solubility in

water. Various formulation approaches to improve the dissolution rate of fluconazole

includes solid dispersions [23] and microspheres [24]. Of all the methods, crystal habit

modification is considered as a most simple, viable and economic option to improve the

dissolution rate. Hence, due to its poor physico-chemical properties, fluconazole was

selected as a model drug. The objective of the present study was to carry out simulation

using layer docking approach and select an isometric crystal habit based on aspect ratio of

simulated crystals for further crystallization studies. This approach saves time by reducing

5

laboratory experiments and cost of crystallization of new products. In the reported study,

molecular dynamics simulations on fluconazole were carried out with acetone, ethyl

acetate and methanol to know the interactions between solvent and crystal faces. The

solvent with aspect ratio close to one, i.e., methanol was chosen for experimental

crystallization. The laboratory generated crystals were evaluated for various physico-

chemical properties and compared with the plain drug.

2. Experimental methodology

2.1 Materials

Fluconazole was received as a gift sample from Chandra Life Sciences Pvt. Ltd.

(Hyderabad, India). Acetone, methanol and ethyl acetate were purchased from SD Fine

chemicals Ltd (Hyderabad, India). In-house ultrapure water from Millipore was used for

all the experiments. All other chemicals used were of analytical grade.

2.2 Solubility studies

Solvents were selected in increasing order of polarity and dielectric constants. Preliminary

trials were carried out to determine the solubility of drug in different solvents

(supplementary Table 1). Fluconazole (50 mg) was taken in different screw capped glass

vials and the selected solvents; dimethyl formamide (DMF), dimethyl sulfoxide (DMSO),

acetone, ethyl acetate (EA), methanol, chloroform, dichloromethane (DCM), isopropyl

alcohol (IPA), petroleum ether and n-hexane were added in 0.1-0.5 mL increments. After

each addition of the solvent, the mixture was vortexed for 5 min and visually checked for

any undissolved parts of the sample. The total volume added to obtain a visually clear

6

solution was noted as the saturation point [25]. The experiment was carried in triplicate to

confirm the reproducibility of results.

2.3 Computer Simulation Details and Theory

Crystal structure of Fluconazole anhydrate (IVUQOF) was taken from CSD (Cambridge

Structural Database) as this is the only form used in pharmaceutical industry as reported by

Caira et al [26]. Crystal dimensions were defined in terms of length, height, and width as

a, b, and c and angles between them as α, β, and γ. Fluconazole, triclinic P-1 crystal

containing 2 molecules per unit cell with cell parameters as follows: a 7.4992Å, b

7.7869Å, c 11.9817Å, α 84.947°, β 84.625°, and γ 75.894° (fig 1). All molecular dynamics

and morphology predictions were performed using COMPASS (condensed phase

optimized molecular potentials for atomistic simulation studies) force field. Geometry

optimization was done by forcite algorithm with COMPASS force field. The general

procedure for molecular dynamics was based on the literature available so far [27].

Material studio software package was used to run the calculations (Materials Studio 6.1.,

Accelrys Inc., San Diego, CA).

2.3.1 Methods for crystal morphology prediction in vacuum

Selection of appropriate force field and description of charge set is very essential in

modeling of morphology. Initially, three models were used to predict crystal morphology

in vacuum. First, BFDH model was used to list possible growth faces and their growth

rates regardless of its low accuracy [20]. Second, attachment energy or growth morphology

model was used to calculate attachment energy with the faces obtained from BFDH model

7

[10]. Third, equilibrium morphology model was used to determine surface energies for all

relevant crystal faces at absolute zero (or 0 K) temperature [28].

2.3.2 Crystal habit prediction with effect of solvent

Layer docking approach scrutinizes the effect of additives on the individual crystal faces,

which are cleaved from a pure crystal. If the additive has inadequate interaction on specific

face, then the growth rate of that face will be higher. This eventually results in smaller

surface area or total disappearance of this face when compared to other faces [29-31].

From the crystallographic information file, initially, the unit cell was constructed and

optimized. Smart minimiser was used to perform minimization on the unit cell. Crystal

habit in vacuum was predicted by attachment energy model which gave information

containing multiplicities, inter-planar distances, facet areas and attachment energy. These

morphologically important faces lattice parameters were used for amorphous cell

construction and cleaved parallel to the (h k l) plane at a depth of four unit cell. Crystal

structure layer was constructed as a periodic superstructure of 3 × 2 unit cells. Next, the

crystal structure layer was optimized by molecular mechanics and dynamics [13].

Methanol, acetone and ethyl acetate with dielectric constant of 33, 20.7, 6.02 and density

of 0.791, 0.791, 0.897 g/mL respectively were chosen as solvents for this simulation study.

Subsequently, amorphous tool was used to construct solvent layer containing 300

methanol molecules by using lattice parameters of the faces obtained from attachment

energy model. Amorphous cell was minimized by smart minimizer using Newton method

at 10,000 iterations at medium quality. In the next step, NVE (N= constant number of

particles, V= constant volume, E = constant energy), NPT (P = constant pressure, T=

constant temperature) were performed for equilibration. This solvent layer was adsorbed

8

onto the crystal surface layer with vacuum slab of 50 A0 above the solvent layer to

eliminate the effect of free boundaries. Constraints were fixed for crystal structure layer

and were not allowed to relax during simulation, while solvent molecules were allowed to

move. Molecular dynamic simulation was performed using Nose algorithm, and Andersen

as temperature control method [32].

Energy minimization was carried out for the interfacial layer. In the next step, molecular

dynamic simulation was carried out using NVT ensemble for 10 ps at a time step of 1 fs.

Again, the layer was minimized and the potential energy (Etotal) was obtained as a sum of

crystal structure layer and solvent layer. Energy of the crystal structure layer and solvent

layer was denoted as Esurface and Eamorphous, respectively. Similarly, simulations were also

performed using 50 acetone and 190 ethyl acetate molecules separately. For the

equilibration stage, the time step for the molecular dynamics simulation was 1 fs with a

period of 60 ps. The Columbic and Vander Waals interactions were calculated by

employing the standard Ewald summation [33]. After equilibration stage, production stage

was performed. Modified attachment energy was then calculated by the formula which was

used to correct the vacuum attachment energy [34, 35].

Mod Eatt = Etotal - (Esurface + Eamorphous)

The aspect ratio of the crystal habit generated and % total facet area of each face was

calculated from the modified attachment energy.

9

2.4 Re-crystallization experiments

From the molecular dynamics simulation studies, aspect ratio close to one was obtained

with methanol and ethyl acetate; hence these two solvents were selected for crystallization

study. Solvent was taken in screw capped glass vial and maintained below its boiling

temperature. Drug was added to the solvent till saturation was reached and the solution

was allowed to cool for supersaturation. The rate of evaporation was controlled by using

inverted funnel. After 24 h, the crystals were harvested, air dried and stored in a dessicator

until further characterization.

2.5 Characterization of crystals

2.5.1 Solid state characterization

Inverted microscope (Nikone TiU) operating with NIE software was used to observe the

crystal habit at different magnifications. Aspect ratio (defined as the ratio of length to

width) and particle sizes were determined (n= 50). Mettler Toledo DSC system operating

with STARe software was used for DSC analysis. Indium was used for calibration. The

sample cell was purged with dry nitrogen at a flow rate of 60 mL/min. Accurately weighed

samples (5–10 mg) were placed in aluminium crimped pans with a pin hole and scanned at

a heating rate of 10 °C/min over a temperature range of 25–200 °C. PerkinElmer IR

spectrophotometer was used to obtain FTIR spectra. Accurately weighed 5 mg of samples

were mixed thoroughly with 100 mg of potassium bromide IR powder (1-2% w/w

10

sample/alkali halide) and compressed under vacuum at a pressure of 12 psi for 3 min. The

resultant pellet was affixed in a suitable holder and the FTIR spectrum was recorded from

4000 to 625 cm–1. p-XRD of fluconazole samples were measured using X-ray powder

diffraction using Ni filtered Cu Kα radiation (wavelength = 1.5406 Å). The data were

recorded over a 2θ angular range of 2° to 50° at step time of 0.030 steps/0.5 s. All p-XRD

measurements were done at ambient temperature.

2.5.2 Flowability and Hausners ratio

Flowability of samples was measured in terms of angle of repose using fixed funnel

method (n=6) [36]. For measurement of tapped and poured density, accurately weighed

amount of powder was taken in 10 mL measuring cylinder. Poured density was calculated

from mass and volume of cylinder. The cylinder was tapped until there was no change in

volume (100 taps with 10 min interval) and the volume was measured again to give tapped

density. The packing ability during tapping was determined by Carr’s index [36].

Hausner’s ratio which reflects the compactability of crystals was calculated from the given

formula [36]

Hausner’s ratio = tapped density/poured density

2.5.3 In-vitro dissolution studies

In-vitro release of plain drug and fluconazole recrystallised in methanol was monitored in

900 mL of 0.1 N HCl at 37 ± 0.5 0C using USP type II dissolution apparatus (paddle type)

at 50 rpm [37]. Aliquots were removed at predetermined times and were replenished

immediately with the same volume of fresh media. The aliquots, following suitable

11

dilution, were assayed using a validated spectroscopic method at a λ max of 261 nm (UV

instrument, JASCO V- 650.

3. Results and discussion

3.1 Solubility studies

Fluconazole shows high solubility in methanol, acetone, ethyl acetate, dimethyl

formamide, dimethyl sulfoxide and < 12 mg/mL solubility in petroleum ether and n-

hexane (supplementary table 1). Based on drug solubility methanol, ethyl acetate and

acetone was selected for habit simulation studies. From the results of habit simulation

studies, methanol and ethyl acetate were selected for further crystallization. Despite high

solubility of fluconazole in DMSO and DMF, these solvents were not used for

crystallization study because of its high boiling point which leads to difficulty in

crystallization.

3.2 Fluconazole crystal morphology prediction in vacuum

Vacuum morphology generated by BFDH model (fig 2) gave eight important faces

(comprised nearly 100% surface area) which were of high morphological importance and

were taken into consideration. Results show that crystal faces consists of (0 0 1), (0 1 0), (1

0 0), (0 1 1), (1 0 1), (0 1 -1), (1 0 -1), (1 1 0) planes of which (0 0 1) and (0 1 0) faces are

more dominant with a facet area of 42.96% and 21.68% respectively when compared to

other faces (Table 1). (1 0 0) and (1 1 0) are the other two major facets with surface of near

20.04% and 7.03 % respectively. Aspect ratio generated from BFDH model (2.19) and

experimental aspect ratio when recrystallized from methanol (1.43) was not in agreement.

12

This is due to the fact that BFDH theory is merely based on geometry consideration and

does not take into account any chemical or intermolecular interactions [38]. Hence,

growth morphology model which considers non-bonded interaction energy was studied.

According to growth morphology model, lowest attachment energy of particular face has

significant effect on crystal morphology. The unique exhibiting faces (0 0 1), (0 1 0), (1 0

0), (0 1 1), (1 0 1) were generated by growth morphology model (Table 1). The (0 0 1)

face was morphologically more crucial with percentage area of about 46.76%. (0 1 0) and

(1 0 0) facets were the other largest facets with surface areas of 26.73% and 12.95%

respectively. A third model; equilibrium morphology model, was also studied as this

models determines minimum surface energies for all relevant crystal faces at 0 K and

surface morphology was compared to other models. According to growth morphology

model, lower the surface free energy, higher is the morphological importance of the face

(table 1). The calculated aspect ratios from growth morphology and equilibrium

morphology model were 2.82 and 1.76 respectively.

Since all the three models were performed in vacuum without considering the effect of

solvent, temperature and super saturation, the simulated and experimental results were not

in consensus. Hence, it deemed necessary to include the solvent molecules to simulate the

experimental conditions. Modeling in presence of solvent which takes into account

molecular anisotropy and surface relaxation and thereby generating modified attachment

energy for crystal habit generation was employed which can be more precise than earlier

methods. Layer docking approach was used to study the impact of solvent on the crystal

habit. During crystallization, solvents have great impact on crystal morphology. Solvents

affect crystal morphology by adsorption on specific faces and modifying the crystal habit.

13

For all the calculations, COMPASS, which is a powerful ab-initio forcefield for atomistic

simulation studies as chosen because it gives precise prediction of structural information

and it also gives information about physical properties of molecule [39].

3.3 Layer docking model

Interactions between solvent and crystal face has a significant impact on growth rate of

crystal. In contrary to attachment energy model, there was significant decrease in (0 0 1),

(0 1 0) faces and a significant mount in (1 1 0) and (1 0 1) faces because of interaction of

polar surfaces with polar solvent. The (1 1 0) and (1 0 1) faces have a facet area of 0% and

0.91% in attachment energy model while in modified attachment energy model, it has total

facet area of 15.83% and 19.82% respectively because of interaction of the solvent with (1

1 0) and (1 0 1) faces (fig 3). The ultimate crystal morphology depends on modified

attachment energy (fig 4) in which the total facet area of (0 0 1) was diminished

remarkably from 46.76% to 22.43% because of less interaction with (0 0 1) face.

3.4 Crystallization experiments and comparison of layer docking morphology with

experimental morphology

Nearly isodiametric shaped habit with aspect ratio of 1.63 and 1.69 was obtained by layer

docking method for methanol and ethyl acetate systems respectively. Hence, these solvents

were selected for further crystallization studies. Nearly isodiametric shape crystal with

mean aspect ratio 1.43 ± 0.12 was obtained when methanol was used as solvent for

crystallization. There was no remarkable difference between experimental and simulated

habits of fluconazole (p < 0.05) which was also confirmed by microscopy (fig 4).

However, fluconazole when crystallized using ethyl acetate gave non uniform crystals

14

which were not reproducible and hence, further studies were not carried out with this

solvent.

3.5 Solid state characterization

DSC thermograms of fluconazole plain drug gave a single melting endotherm at 141 0C,

while the thermogram of fluconazole recrystallised from methanol also showed a single

endothermic peak at 140.10 0C corresponding to melting point of the pure drug

(supplementary fig 1). The onset of melting and ∆H at for plain drug was 137.36 0C and -

104.62 J/g, and 138.24 0C and -115.44 J/g for fluconazole recrystallized from methanol.

The above results clearly indicated that no polymorphic transitions had occurred which

was further confirmed by p-XRD studies. Presence of solvate was also ruled out as no

solvent peak was observed.

FTIR of fluconazole plain drug and recrystallized sample in methanol (supplementary fig

2) showed no difference in spectra and were super imposable. Similarly, the IR spectra did

not show any extra solvate peak at 3600 cm-1 corresponding to methanol solvate,

confirming the absence of pseudo-polymorphism. P-XRD also confirmed the absence of

polymorphism. Characteristics peaks of fluconazole were observed at 2θ values 9.8, 16.2,

20.0, 25.1 and 28.9, which was also seen in crystals grown from methanol (supplementary

fig 3). P-XRD pattern of methanol modified habit was also compared with simulated p-

XRD of fluconazole and its reported polymorphs, which proved that only habit

modification was occurred without any polymorphic transition (Supplementary fig 4-7 of

reported fluconazole polymorphs). Inference from DSC, FTIR and p-XRD thus ruled out

the possibility of solvate formation and confirmed that there was no change in internal

15

structure; i.e. no polymorphic transitions had occurred. The above data clearly indicated

only habit modification had occurred during crystallization of fluconazole from methanol.

3.6 Density and flowability

Fluconazole crystallized from methanol showed good flow property when compared to

plain drug based on the lower angle of repose. Similarly a Hausner’s ratio value of 0.97

indicated that the recrystallized drug has better flow properties, since Hausner’s ratio of

>1.23 indicates poor flow and <1.23, good flow. The improved packing ability and

enhanced compressibility of the recrystallized sample when compared to plain drug was

also confirmed by smaller value of Carr’s index as shown in table 2. A Carr’s index < 15

indicates good flow and compressibility [40]. The results clearly indicate that the methanol

generated habit showed improved tabletting properties when compared to plain drug.

3.7 Dissolution rate

Amount of drug dissolved depends on the facet area and number of groups exposed to the

dissolution medium. At 30 min, plain drug showed only 47% release while methanol

crystals showed 85% release (fig 5). After 1h, plain drug released only 68% of drug, while

methanol crystals showed 96% release. The polarity and non polarity of all important faces

was taken into consideration for explaining the significant increase (p < 0.05) in

dissolution of modified fluconazole habit (supplementary table 2). In crystal slices, (0 0 1)

face shows two non-polar groups; -C-H and -C-F (fig 6a) while (0 1 0) face shows

abundance of -C-H non-polar group (Fig 6b). The (1 0 0) face shows abundance of non-

polar group (two -C-H and one -C-F) (fig 6c) whereas, (0 1 1) face shows abundance of

polar groups due to the presence of oxygen and two -C-N groups (azoles are more polar) in

16

addition to two non-polar -C-H group (fig 6d). The (1 0 1) face as seen in fig 6e is

relatively more polar due to -C-N and C-F group when compared to the two -C-H. The (0

1 -1) face shows abundance of two non- polar -C-H groups (fig 6f), when compared to (1 0

-1) face which shows abundance of more polar groups; two –C-N (fig 6g) while (1 1 0)

face shows abundance of non-polar groups (one -C-H and one -C-F) (fig 6h).

From the crystal slices, it can be clearly understood that the order of polarity of faces is {0

1 1} > {1 0 1} and their facet area is 22.43%, 19.82% respectively. This might be due to

the interaction of a polar solvent like methanol with polar groups on the facet which

ultimately resulted in faces with high morphological importance giving rise to high relative

surface to volume ratio (1.12). This increase in relative surface to volume ratio was hence

responsible for the enhanced dissolution. The dissolution rate with different particles size

ranges was studied and no significant difference in the dissolution rate (supplementary fig

8) was observed. As there was no effect of particle size on dissolution rate, the

improvement in dissolution rate was attributed due to crystal habit modification. In

addition, each crystal face had different surface groups exposed to the solvent due to their

structural arrangement (supplementary table 2) which led to differences in the interaction

of solvent with different faces thereby modifying the crystal habit. The resultant modified

crystal habit with different solvent interaction potential was also responsible for the two

fold increase in dissolution rate of recrystallized sample (statistically significant p < 0.05)

than the untreated drug.

17

4. Conclusion

Crystal habits predicted by BFDH, growth morphology and equilibrium morphology were

not in agreement with the experimental results. Hence, crystal habit prediction was done in

presence of solvent, which gave modified attachment energies with improved correlation

with experimental habit. Effect of solvent on different faces is different due to variable

surface structural chemistry. Through this approach, crystal habit, in-turn surface

chemistry was modified to favor important pharmaceutical properties like dissolution and

packing ability. Our methodology was successfully applied to fluconazole. The same

approach can be used for different pharmaceuticals to design crystal architectures with

elegant pharmaceutical and biopharmaceutical attributes.

Acknowledgement

The authors acknowledge financial support from the National Institute of Pharmaceutical

Education & Research (NIPER), Hyderabad, India, and Indian Institute of Chemical

Technology (IICT), Hyderabad, India.

18

References

[1] H. Garekani, F. Sadeghi, A. Badiee, S. Mostafa, A.R. Rajabi-Siahboomi, A. Rajabi-Siahboomi,

Crystal habit modifications of ibuprofen and their physicomechanical characteristics, Drug Dev.

Ind. Pharm., 27 (2001) 803-809.

[2] S.K. Poornachary, P.S. Chow, R.B. Tan, Effect of solution speciation of impurities on α-glycine

crystal habit: A molecular modeling study, J. Cryst. Growth, 310 (2008) 3034-3041.

[3] S.R. Modi, A.K. Dantuluri, V. Puri, Y.B. Pawar, P. Nandekar, A.T. Sangamwar, S.R. Perumalla,

C.C. Sun, A.K. Bansal, Impact of crystal habit on biopharmaceutical performance of celecoxib,

Cryst. Growth Des., 13 (2013) 2824-2832.

[4] A. Tiwary, Modification of crystal habit and its role in dosage form performance, Drug Dev.

Ind. Pharm., 27 (2001) 699-709.

[5] A.K. Tiwary, Modification of crystal habit and its role in dosage form performance, Drug Dev.

Ind. Pharm., 27 (2001) 699-709.

[6] A. Nokhodchi, N. Bolourtchian, R. Dinarvand, Crystal modification of phenytoin using different

solvents and crystallization conditions, Int. J. Pharm., 250 (2003) 85-97.

[7] N. Blagden, M. De Matas, P. Gavan, P. York, Crystal engineering of active pharmaceutical

ingredients to improve solubility and dissolution rates, Adv. Drug Deliv. Rev., 59 (2007) 617-630.

[8] P. Erk, H. Hengelsberg, M.F. Haddow, R. van Gelder, The innovative momentum of crystal

engineering, Cryst.Eng.Comm, 6 (2004) 475-484.

[9] C. Schmidt, C. Yurudu, A. Wachsmuth, J. Ulrich, Modeling the morphology of benzoic acid

crystals grown from aqueous solution, CrystEngComm, 13 (2011) 1159-1169.

[10] P. Hartman, P. Bennema, The attachment energy as a habit controlling factor: I. Theoretical

considerations, J. Cryst. Growth, 49 (1980) 145-156.

[11] S. Gnanasambandam, S. Enemark, R. Rajagopalan, First-principle prediction of crystal habits

in mixed solvents: α-glycine in methanol/water mixtures, CrystEngComm, 13 (2011) 2208-2212.

[12] M. Lahav, L. Leiserowitz, The effect of solvent on crystal growth and morphology, Chem. Eng.

Sci., 56 (2001) 2245-2253.

[13] X. Duan, C. Wei, Y. Liu, C. Pei, A molecular dynamics simulation of solvent effects on the

crystal morphology of HMX, J. Hazard. Mater., 174 (2010) 175-180.

[14] R.B. Hammond, V. Ramachandran, K.J. Roberts, Molecular modelling of the incorporation of

habit modifying additives: α-glycine in the presence of l-alanine, CrystEngComm, 13 (2011) 4935-

4944.

[15] C. Schmidt, J. Ulrich, Morphology prediction of crystals grown in the presence of impurities

and solvents—an evaluation of the state of the art, J. Cryst. Growth, 353 (2012) 168-173.

19

[16] E. Tedesco, D. Giron, S. Pfeffer, Crystal structure elucidation and morphology study of

pharmaceuticals in development, Cryst.Eng.Comm, 4 (2002) 393-400.

[17] I. Hod, Y. Mastai, D.D. Medina, Effect of solvents on the growth morphology of DL-alanine

crystals, CrystEngComm, 13 (2011) 502-509.

[18] J. Koehler, W. Saenger, W. Van Gunsteren, A molecular dynamics simulation of crystalline α-

cyclodextrin hexahydrate, Eur. Biophys. J., 15 (1987) 197-210.

[19] G. Chen, M. Xia, W. Lei, F. Wang, X. Gong, A study of the solvent effect on the morphology of

RDX crystal by molecular modeling method, J. Mol. Model., 19 (2013) 5397-5406.

[20] H. Gu, R. Li, Y. Sun, S. Li, W. Dong, J. Gong, Molecular modeling of crystal morphology of

ginsenoside compound K solvates and its crystal habit modification by solvent molecules, J. Cryst.

Growth, (2012).

[21] J. Chen, J. Wang, Y. Zhang, H. Wu, W. Chen, Z. Guo, Crystal growth, structure and morphology

of hydrocortisone methanol solvate, J. Cryst. Growth, 265 (2004) 266-273.

[22] L. Alnaim, N. Abou Alsoud, I. Zaghloul, M. Al-Jaser, Effects of fluconazole on the

pharmacokinetics and pharmacodynamics of antimony in cutaneous leishmaniasis-infected

hamsters, Int. J. Antimicrob. Ag., 29 (2007) 728-732.

[23] G.Z. Papageorgiou, D. Bikiaris, F.I. Kanaze, E. Karavas, A. Stergiou, E. Georgarakis, Tailoring

the release rates of fluconazole using solid dispersions in polymer blends, Drug Dev. Ind. Pharm.,

34 (2008) 336-346.

[24] P. Rivera, M. Martinez-Oharriz, M. Rubio, J. Irache, S. Espuelas, Fluconazole encapsulation in

PLGA microspheres by spray-drying, J. Microencapsul., 21 (2004) 203-211.

[25] A. Glomme, J. Marz, J.B. Dressman, Comparison of a miniaturized shake flask solubility

method with automated potentiometric acid/base titrations and calculated solubilities, J. Pharm.

Sci., 94 (2005) 1-16.

[26] M.R. Caira, K.A. Alkhamis, R.M. Obaidat, Preparation and crystal characterization of a

polymorph, a monohydrate, and an ethyl acetate solvate of the antifungal fluconazole,

J.Pharm.Sci., 93 (2004) 601-611.

[27] C. Schmidt, J. Ulrich, Predicting crystal morphology grown from solution, Chem. Eng.

Technol., 35 (2012) 1009-1012.

[28] J. Lu, J. Ulrich, An improved prediction model of morphological modifications of organic

crystals induced by additives, Cryst. Res. Technol, 38 (2003) 63-73.

[29] Z. Berkovitch-Yellin, J. Van Mil, L. Addadi, M. Idelson, M. Lahav, L. Leiserowitz, Crystal

morphology engineering by" tailor-made" inhibitors; a new probe to fine intermolecular

interactions, J. Am. Chem. Soc., 107 (1985) 3111-3122.

[30] D. Kumar, N.R. Shastri, Designed Isomorphism of Nifedipine: A Joint Experimental and

Molecular Simulation Study with Screened Solvents and Antisolvents, Cryst. Growth Des., 14

(2013) 326-338.

[31] A. Fiebig, M.J. Jones, J. Ulrich, Predicting the effect of impurity adsorption on crystal

morphology, Cryst. Growth Des., 7 (2007) 1623-1627.

[32] H.C. Andersen, Molecular dynamics simulations at constant pressure and/or temperature, J.

Chem. Phys., 72 (1980) 2384.

[33] P. Ewald, Evaluation of optical and electrostatic lattice potentials, Ann. Phys, 64 (1921) ll.

[34] Y. Yani, P.S. Chow, R.B. Tan, Molecular Simulation Study of the Effect of Various Additives on

Salbutamol Sulfate Crystal Habit, Mol. Pharm., 8 (2011) 1910-1918.

[35] Y.-H. Kiang, C.-Y. Yang, R.J. Staples, J. Jona, Crystal structure, crystal morphology, and surface

properties of an investigational drug, Int. J. Pharm., 368 (2009) 76-82.

[36] K. Thalberg, D. Lindholm, A. Axelsson, Comparison of different flowability tests for powders

for inhalation, Powder Technol., 146 (2004) 206-213.

20

[37] J.C.R. Corrêa, C. Duarte Vianna-Soares, H.R.N. Salgado, Development and Validation of

Dissolution Test for Fluconazole Capsules by HPLC and Derivative UV Spectrophotometry, Chrom.

Res.J, 2012 (2012).

[38] J.J. Lu, J. Ulrich, An improved prediction model of morphological modifications of organic

crystals induced by additives, Cryst. Res. Technol., 38 (2003) 63-73.

[39] Q. Yi, J. Chen, Y. Le, J. Wang, C. Xue, H. Zhao, Crystal structure and habit of dirithromycin

acetone solvate: A combined experimental and simulative study, J. Cryst. Growth, (2013).

[40] K.C. Garala, J.M. Patel, A.P. Dhingani, A.T. Dharamsi, Preparation and evaluation of

agglomerated crystals by crystallo-co-agglomeration: An integrated approach of principal

component analysis and Box Behnken experimental design, Int. J. Pharm., 452 (2013) 135-156.

21

Table 1. Attachment energy and surface free energy of generated by GM and EM models

Table 2. Densities and flowability of fluconazole crystals

Key: FLU – plain drug; FLUM – fluconazole recrystallized from methanol

hkl dhkl BFDH model GM model EM model

%Total facet area

Attachment energy (kcal/mol)

%Total facet area

Surface energy (kcal/mol/A2)

%Total facet area

{0 0 1} 11.90 42.96 -13.57 46.76 0.119 25.86

{0 1 0} 7.53 21.68 -20.55 26.73 0.115 20.26

{1 0 0} 7.25 20.04 -31.61 12.95 0.172 8.10

{0 1 1} 6.57 2.81 -29.07 - 0.152 2.44

{1 0 1} 6.40 3.01 -34.02 0.91 0.165 3.77

{01 -1} 6.18 - -28.29 - 0.137 4.95

{1 1 0} 5.98 7.03 -35.06 0.26 0.158 2.30

{10 -1} 5.99 - -42.02 - 0.192 0.93

Code Bulk density

(gm/mL)

Tapped density

(gm/mL)

Angle of

repose (θθθθ))))

Hausner’s

ratio

Carr’s

index

FLU 0.325 0.414 33.66 1.27 21

FLUM 0.425 0.466 14.06 0.91 8

22

HIGHLIGHTS

• Attempts were made to correlate predicted and experimental habits of fluconazole

• Simulation was carried out using layer docking approach

• Modification of fluconazole crystal habits provided enhanced dissolution rate

• Modified crystal habit of fluconazole exhibited better flow and compressibility

• This study demonstrates a methodical approach for crystal habit modification

23

Figure captions

Fig 1. Chemical Structure (A) and crystal structure of fluconazole (B)

Fig 2. Predicted vacuum morphology of fluconazole by A) BFDH model B) Growth morphology model C) Equilibrium morphology model

Fig 3. Modified attachment energies of various faces of crystal and their surface area

Fig 4. Modified crystal habit of fluconazole in methanol A) Experimental habit B) Predicted habit

Fig 5. Comparison of dissolution profiles in 0.1N HCl of fluconazole (A) and fluconazole recrystallised in methanol (B)

Fig 6. Fluconazole crystal slices showing polar and non-polar groups

Fig. 1

24

Fig. 2

25

Fig. 3

fig. 4

26

27

Fig. 5

28

Fig 6