Pharmacophore and Quantitative Structure−Activity Relationship Modeling: Complementary Approaches...

Pharmacophore and quantitative structure activityrelationship modelling of UDP-glucuronosyltransferase 1A1(UGT1A1) substratesMichael J. Soricha, Paul A. Smithb, Ross A. McKinnona and John O. Minersb

UDP-glucuronosyltransferase 1A1 (UGT1A1) is a

polymorphic enzyme responsible for the glucuronidation

of structurally diverse drugs, non-drug xenobiotics and

endogenous compounds (e.g. bilirubin). Thus, definition of

UGT1A1 substrate and inhibitor selectivities and binding

affinities assumes importance for the identification of

compounds whose elimination may be impaired in

subjects with variant genotypes, and for the prediction of

potentially inhibitory interactions involving xenobiotics and

endogenous compounds metabolized by UGT1A1. We

report the generation of two- and three-dimensional (2D

and 3D) quantitative structure activity relationships (QSAR)

and pharmacophore models for 23 known UGT1A1

substrates with diverse structure and binding affinity.

Initially, a simple procedure was developed to determine

apparent inhibition constants (K i,app) for these compounds.

Eighteen substrates were subsequently used to construct

models and the remaining five to validate the predictive

ability of the models. Three different models were

constructed: (i) three feature pharmacophore model able

to predict the K i,app on the basis of the degree to which a

substrate can fit to the arrangement of 3D features

(r2 0.87, K i,app for all five test substrates predicted within

log unit); (ii) 3D-QSAR using a ‘common features’

pharmacophore to align the substrates (r2 0.71, K i,app for

four out of five test substrates predicted within one log

unit); (iii) 2D-QSAR constructed with six chemical

descriptors (r2 0.92, K i,app of all five test substrates

predicted within one log unit). The common features

pharmacophore demonstrated the importance of two

hydrophobic domains separated from the glucuronidation

site by 4 A and 7 A, respectively. These models, which

represent the first generalized predictive models for a UGT

isoform, complement each other and are an important first

step towards computer based (in silico) models of

UGT1A1 for high throughput prediction of metabolism.

Pharmacogenetics 12:635–645 & 2002 Lippincott Williams

& Wilkins

Pharmacogenetics 2002, 12:635–645

Keywords: UGT1A1, QSAR, pharmacophore, UDP-glucuronosyltransferase

aSchool of Pharmaceutical, Molecular and Biomedical Sciences, University ofSouth Australia, South Australia and bDepartment of Clinical Pharmacology,Flinders University of South Australia, South Australia, Australia

Correspondence to Professor John Miners, Department of ClinicalPharmacology, Flinders Medical Centre, Bedford Park, SA 5042, AustraliaTel: +61 8 8204 4131; fax: +61 8 8204 5114;e-mail: [email protected]

Received 6 March 2002Accepted 31 July 2002

IntroductionConjugation with glucuronic acid, a reaction catalysed

by the microsomal enzyme UDP-glucuronosyltransfer-

ase (UGT), is an essential clearance mechanism for

drugs from almost all therapeutic classes [1]. Moreover,

glucuronidation serves as an elimination pathway for a

myriad of endogenous compounds, dietary chemicals

and environmental pollutants (including some chemical

carcinogens), and facilitates excretion of the products of

phase I metabolism. Endogenous compounds metabo-

lized by glucuronidation include bilirubin, bile acids,

fatty acids, hydroxysteroids and thyroid hormones. Con-

sistent with this substrate diversity, UGT exists as a

superfamily of enzymes. UGT gene products character-

ized to date have been classified into two gene families,

UGT1 and UGT2, based on sequence identity and

evolutionary divergence [2]. Fifteen functional UGT

proteins (isoforms) have been identified in humans.

These isoforms tend to exhibit distinct, but broadly

overlapping, substrate selectivities [3,4].

Extensive polymorphism has been described for UGTgenes [5]. Notable in this regard is UGT1A1. Because

UGT1A1 is the sole isoform involved in bilirubin

glucuronidation, and additionally has the capacity to

metabolize hydroxyoestrogens, thyroid hormones and

numerous drug and non-drug xenobiotics [6–8], genetic

polymorphism of UGT1A1 may be of physiological,

pharmacological and toxicological significance. More

than 50 lesions of UGT1A1 are associated with inherited

disorders of bilirubin conjugation, namely the Crigler–

Najjar syndromes (types 1 and 2) and Gilbert syndrome

[9]. Additionally, there is increasing evidence to suggest

that Gilbert syndrome, which arises from insertional

mutations in the TATAA element upstream of UGT1A1or from a limited number of structural mutations, may

be a risk factor in drug related toxicity. For example,

patients with variant UGT1A1 genotypes are over-

represented amongst those experiencing severe toxicity

to the anticancer drug irinotecan due to impaired

glucuronidation of the active metabolite SN-38 [10].

Original paper 635

0960–314X & 2002 Lippincott Williams & Wilkins

Indinavir, which appears to be a UGT1A1 substrate,

may precipitate jaundice in patients with Gilbert syn-

drome variant TATAA element alleles as a result of

competitive inhibition [11]. In general, reduced clear-

ance of drug substrates of UGT1A1 might be expected

in subjects with Gilbert syndrome [12].

It is apparent from such observations that the ability to

predict the interaction between UGT1A1 and any

newly developed drug or non-drug chemical may be of

therapeutic or toxicological importance, and could influ-

ence the further development of a new chemical entity.

Analogous arguments have been applied to poly-

morphic xenobiotic metabolizing cytochromes P450

(CYP), such as CYP2D6 [13]. More generally, elucida-

tion of CYP or UGT isoform substrate and inhibitor

specificity further assists the prediction of drug–drug

interactions and definition of those structural and

physicochemical features of compounds that confer

substrate and inhibitor selectivity.

Computational methods have found widespread use in

recent years for the investigation of CYP active sites

[14]. In particular, two-, three- and four-dimensional

(2D, 3D and 4D) quantitative structure activity rela-

tionships (QSAR), pharmacophores and homology mod-

els have been developed to infer CYP isoform active

site binding requirements [14,15]. QSAR and pharma-

cophore models are generated from the structures of

known substrates/inhibitors, whereas homology models

are constructed from experimentally determined 3D

coordinates of a crystallized protein. The 2D, 3D and

4D QSAR methodologies differ in respect to the

properties used as predictors in the model. In general,

two dimensional properties are independent of the 3D

conformation of the chemical, 3D properties are depen-

dent on conformation and are derived from a single

conformation, and 4D properties involve sampling 3D

properties of multiple conformations [16,17]. A pharma-

cophore is a defined 3D arrangement of chemical

features, whereas a homology model is the predicted

3D structure of the enzyme based on the structure of a

similar enzyme [18,19]. Models may be developed for

multiple purposes, such as predicting kinetic constants,

understanding the nature of the substrate–enzyme

interaction, searching a chemical database for new leads

and lead structure optimization [17,18,20].

Although the lack of an UGT crystal structure pre-

cludes homology modelling, it is apparent that compu-

tational methodologies are available which permit the

development of QSAR and pharmacophore models to

predict UGT isoform substrate selectivity and provide

a quantitative measure of substrate binding. We de-

scribe the validation of a simple experimental proce-

dure to determine the apparent inhibition constant

(Ki,app) of structurally diverse UGT1A1 substrates and

the use of these data to develop a number of models

capable of predicting the Ki,app of UGT1A1 substrates

based on their chemical and physicochemical proper-

ties. This represents the first report of the development

of generalized predictive models for substrate binding

to UGT1A1 and, together with models for UGT1A4

generated in this laboratory (Smith et al., unpublished

observations), the first generalized 2D and 3D QSAR

and pharmacophore models for UGT.

Materials and methodsChemicals and UGT1A1 expression

For inclusion in the model generation dataset, it was

necessary that chemicals were known substrates of

UGT1A1 and were significantly different to each other

in either structure or UGT1A1 activity. Thus, the 23

UGT1A1 substrates included in the dataset (Fig. 1)

exhibited a wide range of activities [8,21–24]. Except

for those chemicals referred to below, all of these

compounds and UDP-glucuronic acid (UDPGA; so-

dium salt), 4-methylumbelliferone �-D-glucuronide

(4MUG) and 1-naphthol �-D-glucuronide (1NPG) were

purchased from Sigma-Aldrich (St Louis, Missouri,

USA). The following compounds were provided as

gifts: SN-38 from Dr M. Kuboki (Yakult Honsha Co.,

Ltd, Tokyo, Japan), naltrexone from Dr G. Gourlay

(Flinders Medical Centre, Bedford Park, Australia), and

buprenorphine from Dr A. Somogyi (University of

Adelaide, Adelaide, Australia). All other chemicals and

reagents were of analytical reagent grade.

HK293 cells stably expressing UGT1A1 were grown in

Dulbecco’s modified Eagle’s medium (GibcoBRL) with

10% fetal calf serum and gentamicin (80 mg/l) in a

humidified incubator, with an atmosphere of 5% CO2,

at 37 8C. Microsomes were prepared by differential

centrifugation. Cells were probe sonicated and the

cellular homogenate was centrifuged at 9000 g for 10

min. The supernatant fraction was subsequently recen-

trifuged at 120 000 g for 2 h to obtain the microsomal

pellet, which was suspended in distilled water and

stored at –80 8C until use.

Assays for 4-methylumbelliferone (4MU) and 1-naphthol

(1NP) glucuronidation

4MU and 1NP glucuronidation by HK293 cell micro-

somes was measured according to a previously pub-

lished procedure [25]. Briefly, incubation mixtures

contained 4MU (20–750 �mol/l) or 1NP (50–1000

�mol/l), UDPGA (5 mmol/l), MgCl2 (5 mmol/l) and

HK293 microsomes (0.2 mg protein; 0.33 mg/ml) in

phosphate buffer (0.1 mol/l, pH 7.4) in a total volume

of 0.6 ml. Reactions were initiated by the addition of

UDPGA and performed in air at 37 8C for 2 h (shaking

water bath). Incubations were terminated by the addi-

tion of 0.6 mol/l glycine/0.4 mol/l trichloroacetic acid

(0.14 ml) and cooling on ice. Following the addition of

636 Pharmacogenetics 2002, Vol 12 No 8

Me

Me

Me

Me

CO2H

CO2 H

NH

NH

NH

NH

CH2

O

O

CH2

Z

Z

O

OOH

OH OOH

O

OH

HO

OH

OH

O

O

OH

HO

OH

S

Me COH

CH

HO

H

H H

S

S

R

R

S

MeO

OH

HO

H

H H

S

S

R

S

O

O

OH

HO

OOH

O

HO

Et

Et N

N

OH

O

OH

O

OS

(CH 2 ) 7 MeC O

O

OH

HO

HO CO2H

I

I

I

O

HO

NH2

S

MeOH

HO

H

H H

S

S

S

R

S

Bu-t

OMe

Me

O

HO

N

HO H

H

HRR

RS S

SR

Ph

OH

Ph

OH

O

O Me

O OHO

OHOMe

HO

CH2 CH CH2

Bu-t

HO

OMeC

O

HO

Et

HO

OH

N

O OHO H

S

S

R

R

Me

OHO

N

HO

H

H

H

SRS

R

R

Bilirubin(Ki,app � 0.5 µmol/l)

Phenolphthalein(Ki,app � 2 µmol/l)

Quercetin(Ki,app � 2 (3)* µmol/l)

Naringenin(Ki,app � 3 (3)* µmol/l)

Ethinylestradiol(Ki,app � 3 (4)* µmol/l)

4-Hydroxyestrone(Ki,app � 4 µmol/l)

Anthraflavic acid(Ki,app � 4 µmol/l)

Alizarin(Ki,app � 5 µmol/l)

SN-38(Ki,app � 5 µmol/l)

Octylgallate(Ki,app � 5 µmol/l)

Reverse triiodothyronine(Ki,app � 6 µmol/l)

Estradiol(Ki,app � 10 µmol/l)

Buprenorphine(Ki,app � 25 µmol/l)

4-Hydroxybiphenol(Ki,app � 60 µmol/l)

3-Hydroxyflavone(Ki,app � 90 (90)* µmol/l)

4-Methylumbelliferone(Km,app � 110 µmol/l)

1-Naphthol(C50 � 345 µmol/l)

Eugenol(Ki,app � 395 µmol/l)

4-t-Butylphenol(Ki,app � 870 µmol/l)

4-Hydroxybenzoic acidmethyl ester

(Ki,app �1000 (1200)* µmol/l)

4-Ethylphenol(Ki,app � 2200 µmol/l)

Naltrexone(Ki,app � 3900 µmol/l)

Morphine(Ki,app � 9000 µmol/l)

Fig. 1

Structures and experimentally determined K i,app, K m,app and C50 values of UGT1A1 substrates used to generate and test models. �Values inbrackets indicate K i,app calculated from a full kinetic analysis. All other K i,app values are determined from the procedure using alternate substrateconcentrations at a single 4MU concentration (110 �mol/l). K m,app values are given for 4-methylumbelliferone and 1-naphthol.

UDP-glucuronosyltransferase 1A1 substrate modelling Sorich et al. 637

0.1 ml of phosphate buffer (1 mol/l, pH 7.4), the mix-

ture was extracted with chloroform (7 ml) using a rotary

mixer and then centrifuged (1500 g for 10 min). A

0.6 ml aliquot of the aqueous phase was separated for

measurement of fluorescence (Perkin-Elmer model

3000 fluorescence spectrometer; Perkin-Elmer, Foster

City, California, USA). Excitation/emission wavelengths

were 315/365 nm and 290/330 nm for 4MUG and

1NPG, respectively. Aqueous 4MUG and 1NPG stan-

dards in the range 0.1–20 and 1–60 �mol/l were treated

in the same manner as incubation samples, and un-

known concentrations were determined by comparison

of fluorescence measurements with those of the appro-

priate standard curve.

Reaction rates for both 4MU and 1NP were linear for

incubation times to 2 h and for microsomal protein

concentrations to at least 0.5 mg/ml. The limit of

sensitivity of both assays was 0.1 �mol/l. Within-day

overall 4MUG assay imprecision, assessed by measuring

4MUG formation in five separate incubations of the

same batch of microsomes, was 2.9% and 1.6% for

substrate concentrations of 20 and 200 �mol/l, respec-

tively. Similar imprecision data have been reported

previously for the 1NP glucuronidation assay [25].

Inhibition experiments with alternate UGT1A1 substrates

The apparent inhibitor constant (Ki,app) of each of the

known UGT1A1 substrates (excluding 1NP) shown in

Fig. 1 was determined using 4MU as the probe

substrate. Assays performed for the calculation of Ki,app

contained 4MU at a concentration approximately equal

to its Km,app (110 �mol/l). Inhibition of 4MU glucuroni-

dation was measured for three alternate substrate con-

centrations. This approach was validated by calculation

of the Ki,app for five selected alternate substrates, using

three 4MU concentrations and four concentrations of

the alternate substrate at each 4MU concentration.

Analysis of kinetic data

All data points represent the mean of duplicate estima-

tions. Kinetic data were model-fitted using a nonlinear

regression method implemented by EnzFitter (Biosoft,

Cambridge, UK). The choice of model (Michaelis–

Menten or Hill function) for the calculation of Km,app

and Vmax values for 4MU and 1NP glucuronidation was

confirmed by F-test and coefficients of determination.

Similarly, Ki,app values for alternate UGT1A1 substrates

were calculated using EnzFitter, assuming a competi-

tive inhibition model.

Model generation

The molecular modelling studies were performed using

Silicon Graphics Octane2 (Silicon Graphics, Mountain

View, California, USA) and x86 Intel workstations.

Models were constructed using three different method-

ologies, as described below.

In order to test the predictive ability of the models, the

23 substrates were split into two groups: one to gener-

ate the models (18 chemicals) and one to test the

models (five chemicals). The test set of five chemicals

was selected to span a wide range of Ki,app values and

for diversity of chemical structure, so as to stringently

test the models generated. In general, prediction within

one log unit of the experimental Ki,app value is consid-

ered satisfactory [26]. Randomization tests were em-

ployed to assess statistical significance of the models.

Alternate training sets were generated by randomiza-

tion of the association between Ki,app to the substrate,

such that the Ki,app values were not associated with the

correct substrate. The model generation procedure was

repeated with these randomized datasets to calculate

the probability that the model was a result of a chance

correlation. To achieve a 95% confidence interval (95%

CI), 19 randomization tests were required, while 99

randomization tests were necessary to achieve a 99%

confidence level.

Discriminative features pharmacophore

The discriminative features pharmacophore was gener-

ated using modules of the Catalyst software suite

(Accelrys Inc., San Diego, California, USA). Chemical

structures of the UGT1A1 substrates were built using

the Visualizer module. The ConFirm module was used

to sample a maximum of 255 representative conforma-

tions for each substrate, all within a 20 kcal/mol energy

limit of the minimum energy conformation. Each sub-

strate and its associated conformers were subsequently

input to the Hypogen module to search for a 3D

arrangement of abstract chemical features (hydrogen

bond donor, hydrogen bond acceptor, hydrophobic

region, aromatic ring, glucuronidation feature) that

could be used to predict the Ki,app. The fit of the

substrates to this putative pharmacophore (i.e. how

closely the features of each chemical match the features

of the pharmacophore) was correlated with the ex-

perimentally determined –logKi,app (pKi,app) of the

substrates [27]. The glucuronidation feature was con-

structed specifically for use with UGT substrates to

recognize functional groups which could be glucuroni-

dated (i.e. –OH, –NH, –COOH) (Smith et al., unpub-

lished observations). Features in the pharmacophore

were assigned variable weighting. Finally, the Compare

module was used to align the test set substrates onto

the pharmacophore and to predict their Ki,app values.

Self organizing molecular field analysis (SOMFA) using a

common features pharmacophore to align substrates

The substrates (and associated set of sampled confor-

mations) were input to the HipHop module of Catalyst.

This module was used to search for 3D arrangements

of chemical features (hydrophobic region, aromatic ring,

nucleophile) common to the 23 substrates [28]. The

minimum spacing allowed between features in the


pharmacophore was set to 1.5 A to allow for the small

molecules in the training set. This pharmacophore was

then used with the Compare module to align the

substrates for the 3D QSAR.

SOMFA [29] is a 3D QSAR algorithm, related to both

CoMFA (Comparative Molecular Field Analysis) [30]

and similarity analysis [31]. SOMFA is able to deter-

mine the influence of shape and electrostatic field on

the activity of a dataset. By sampling the steric and

electrostatic fields of the aligned substrates at points on

a 3D grid surrounding the substrates, it was possible to

find the grid points where the steric and electrostatic

fields influenced the Ki,app of the substrates.

The aligned substrates were input to the program along

with their –logKi,app values. The shape and electrostatic

field for each chemical were sampled at 1 A intervals

on a 3D grid, and a set of points in 3D where the shape

and/or electrostatic field of the chemical influenced the

Ki,app was reported. Models generated were used subse-

quently to predict the Ki,app of the test set of substrates.

2D QSAR

A wide range of 2D descriptors were calculated from

the structure of each chemical using the Dragon

(Milano Chemometrics and QSAR Research Group,

Milan, Italy) and Cerius2 (Accelrys Inc.) programs.

These included topological, 2D autocorrelation, consti-

tutional, BCUT, thermodynamic and electronic de-

scriptors [16]. Prior to generating the model, the most

relevant and least collinear subsets of descriptors were

selected. Initially, all descriptors without a statistically

significant correlation to –logKi,app were removed. The

significantly correlated descriptors were input to a

program implementing the Unsupervised Forward Se-

lection (UFS) algorithm [32]. This method initially

selects the two descriptors which are least well corre-

lated, and then selects additional variables on the basis

of their multiple correlation with those already chosen,

thus selecting a subset of variables that are as close to

orthogonal as possible. Subsets of descriptors were

selected using this procedure with varying degrees of

collinearity allowed (r2max ¼ 0.1, 0.2, . . . , 0.9, 0.99).

A regression model was built for each descriptor subset

using partial least squares (PLS) regression [33]. The

variable subset giving the best leave-one-out (LOO)

cross-validated r2, a measure of model predictive ability

of the model, was chosen for further optimization. Any

descriptor found to have a significant correlation with

the residuals of the model was included if it improved

the LOO cross-validated r2. Descriptors were omitted

from the model if removal resulted in an increase in

the LOO cross-validated r2.

ResultsValidation of the experimental approach used to calculate

K i,app

4MU glucuronidation by UGT1A1 exhibited Michaelis–

Menten kinetics (Fig. 2a), whereas 1NP glucuroni-

dation by UGT1A1 exhibited sigmoidal kinetics (Fig.

2b). The mean apparent Km and Vmax values for 4MU

glucuronidation (n ¼ 7 determinations) were 113 �mol/l

(95% CI, 102–124 �mol/l) and 308 pmol/min per mg

(95% CI, 290–326 pmol/min per mg), respectively. The

300

250

200

150

100

50

0

V (p

mol

/min

per

mg)

(a)

0.0 0.5 1.0 1.5 2.0

V (pmol/min per mg)/S (µmol/l)

250

200

150

100

50

00.0 0.1 0.2 0.3 0.4 0.5

V (p

mol

/min

per

mg)

(b)

V (pmol/min per mg)/S (µmol/l)

Fig. 2

Eadie-Hofstee plots for (a) 4-methylumbelliferone and (b) 1-naphthol glucuronidation by UGT1A1. Points are experimentally determined values,while the solid lines show the computer-derived curves of best fit.


derived C50 and Vmax values for 1NP glucuronidation

were 345 �mol/l and 260 pmol/min per mg, respec-

tively, with a sigmoidocity factor (n) of 1.25.

Since 4MU glucuronidation exhibited Michaelis–

Menten kinetics, 4MU was used as the substrate for

the determination of Ki,app values of alternate UGT1A1

substrates. To validate the use of only three inhibitor

concentrations and a single concentration of 4MU to

determine the Ki,app, data generated using this approach

were compared to Ki,app determined using full kinetic

analysis for five compounds. Ki,app values determined

using the two methods are shown in Table 1, and

representative kinetic plots are shown in Fig. 3. Ki,app

values determined by the two approaches were close in

value, and hence the abbreviated kinetic method (i.e.

three inhibitor concentrations at a 4MU concentration

of 110 �mol/l) was subsequently used for the remaining

16 chemicals comprising the dataset. All experimentally

determined Ki,app values are shown in Fig. 1. These

data, along with the apparent Km values for 4MU and

1NP, were used for model generation. Nine other

alternate substrates were investigated, but not used for

model generation, due either to activation of 4MUG

formation (2-phenylphenol, paracetamol, carvacrol), in-

solubility in the incubation mixture (fisetin, all-trans

retinoic acid, retigabine) or interference with 4MUG

fluorescence (4-nitrophenol, 7-hydroxyflavone, 4-amino-

biphenyl).

Discriminative features pharmacophore

The best pharmacophore generated contained three

features; an aromatic ring, a hydrophobic region and a

hydrogen bond donor (Fig. 4a), and gave a fit of

r ¼ 0.93 (r2 ¼ 0.87). By way of example, Figure 4(b–d)

shows bilirubin (high affinity substrate), 3-hydroxy-

flavone (intermediate affinity substrate) and morphine

(low affinity substrate), respectively, aligned to the

pharmacophore. As shown in Table 2, all five of the

test set (i.e. those substrates not used for model

generation) were predicted to have a Ki,app within one

log unit of the experimentally determined value. In

order to demonstrate that the model was not likely to

be the result of a chance correlation, pharmacophore

generation was repeated with 19 randomized datasets.

All of the models generated by these training sets

exhibited an r2 , 0.87, indicating that it is highly

unlikely (P , 0.05) that the original model was the

result of a chance correlation.

SOMFA using a common features pharmacophore

alignment

The common features pharmacophore used to align the

substrates was based on a nucleophilic feature and two

hydrophobic regions (Fig. 5a). When aligned on this

pharmacophore, the sites of glucuronidation on each

substrate were overlaid. The best SOMFA model was

generated using the shape field alone (r ¼ 0.84,

r2 ¼ 0.71) and was capable of predicting the Ki,app for

four of the five test set chemicals within one log unit of

their experimental value (Table 2). Figure 5(b,c) shows

morphine (a low affinity substrate) and bilirubin (a high

affinity substrate), respectively, aligned on the SOMFA

model. Bilirubin predominantly occupies the cyan area,

where steric bulk enhances binding affinity. However,

a large section of morphine occupies the red area,

where steric bulk is associated with reduced binding

affinity.

2D QSAR

Of the 319 descriptors calculated for each chemical

using the Dragon and Cerius2 software, 161 were found

to be significantly correlated to the –logKi,app at the

95% CI. The UFS with an r2max of 0.9 gave a 12

descriptor model with the best LOO cross-validated r2

of 0.64. One other descriptor (GATS5m) was found to

be highly correlated with the residual of this model,

and to significantly increase the cross-validated r2.

Seven descriptors in the model were found to increase

the LOO cross-validated r2 when removed from the

model generation process. Thus, the optimal model

was a six descriptor, two-component model with an r2

of 0.92 and a LOO cross-validated r2 of 0.77. The

model is shown below in terms of the standardized

descriptors (� ¼ 0, � ¼ 1). The relative importance of

each descriptor is represented by the magnitude of its

coefficient:

–logKi,app ¼

4:5 þ 0:49�AlogP98–0:55 3 nRO8 þ 0:50 3 JGI7 þ 0:44

3 MATS1p–0:26 3 MATS7p þ 0:29 3 GATS5m

where: AlogP98 ¼ log of the octanol/water partition

coefficient calculated by an atom based method;

nR08 ¼ number of eight-membered rings; JGI7 ¼ mean

topological charge index of order 7; MATS1p ¼ Moran

autocorrelation of path length 1 weighted by atomic

polarizabilities; MATS7p ¼ Moran autocorrelation of

path length 7 weighted by atomic polarizabilities;

Table 1. Comparison of experimentally determined K i,app values forfive substrates using a single 4MU concentration and full kineticanalysis

Substrate

K i,app (�mol/l)Single 4MU

concentration(110 �mol/l)

K i,app (�mol/l)Full kinetic

analysis

Quercetin 2 3Naringenin 3 3Ethinylestradiol 3 43-Hydroxyflavone 90 904-Hydroxybenzoic acid methyl ester 1000 1200


GATS5m ¼ Geary autocorrelation of path length 5

weighted by atomic masses.

Predictive ability of the model was validated using the

test set. All five test substrates were predicted within

one log unit of their observed value (Table 2). The

Ki,app was permuted 100 times and models generated.

None of these models resulted in as good fit (based on

r2), showing that this model is very unlikely (P , 0.01)

to be a result of a chance correlation.

DiscussionUGT1A1 is a polymorphic enzyme responsible for the

glucuronidation of structurally diverse xenobiotics and

endogenous compounds. Thus, knowledge of UGT1A1

substrate and inhibitor selectivities and binding affi-

nities assumes importance for: (i) the identification of

those compounds whose elimination may be impaired

in subjects with variant UGT1A1 genotypes and (ii) the

prediction of inhibitory interactions with other xenobio-

tics and endogenous compounds (particularly bilirubin)

1/V

µm

ol/l

(pm

ol/m

in p

er m

g)

(d)

0.010

0.008

0.006

0.002

0.0000 50 100 150

0.004

[3-Hydroxyflavone] (µmol/l)

1/V

µm

ol/l

(pm

ol/m

in p

er m

g)

(b)

0.016

0.012

0.008

0.004

0.0000 1000 2000 3000 4000 5000

[4HBAME] (µmol/l)

55 µmol/l 4MU

110 µmol/l 4MU

225 µmol/l 4MU

0.015

0.010

0.005

0 100 150

[3-Hydroxyflavone] (µmol/l)

(c)

0 50

1/V

(pm

ol/m

in p

er m

g)

0.020

30 µmol/l 4MU

90 µmol/l 4MU

400 µmol/l 4MU0.030

0.020

0.010

0 1000 2000

[4HBAME] (µmol/l)

(a)

0.030

0.020

0.010

0 1000 2000

[4HBAME] (µmol/l)

1/V

(pm

ol/m

in p

er m

g)

Fig. 3

Dixon plots for the inhibition of UGT1A1 catalysed 4-methylumbelliferone glucuronidation by 4-hydroxybenzoic acid methyl ester (4HBAME) and 3-hydroxyflavone. (a,c) Showing data from the full kinetic analysis. (b,d) Showing corresponding data from the abbreviated kinetic method (i.e. threeinhibitor concentrations at a 4-methylumbelliforone of 110 �mol/l).


Fig. 4

(a) Discriminative Features Pharmacophore of UGT1A1, illustrating a hydrophobic area (cyan), a hydrogen bond acceptor (green) and an aromaticring feature (orange). Also indicated are the interbond angles and distances between pharmacophore features. (b,c,d) Showing bilirubin (high affinitysubstrate), 3-hydroxyflavone (intermediate affinity substrate) and morphine (low affinity substrate), respectively, aligned on the pharmacophore.

(a)

4.0Å

7.1Å

32º

2.7Å

Area (blue dots) where stericbulk enhances binding

Area (red dots) where stericbulk decreases binding

Area (blue dots) where stericbulk enhances binding

Area (red dots) where stericbulk decreases binding

(c)

(b)

Fig. 5

(a) Common Features Pharmacophore of UGT1A1 substrates, illustrating two hydrophobic areas (cyan) and a glucuronidation feature (purple). Alsoindicated are the interbond angles and distances between pharmacophore features. (b,c) Showing the SOMFA model with morphine and bilirubinaligned, respectively, illustrating areas where steric bulk enhances affinity (cyan dots) and areas where steric bulk decreases affinity (red dots).


metabolized by this enzyme. In the present study,

three different methodologies were used to develop

models which predict UGT1A1 substrate selectivity

and binding affinity. The predictive ability of models

was tested with compounds withheld from the training

set. Along with models generated concurrently for

UGT1A4 (Smith et al., unpublished observations),

these represent the first generalized 2D and 3D QSAR

and pharmacophore models developed for UGT.

A rapid and simple procedure was initially developed

here to calculate the Ki,app values of UGT1A1 sub-

strates. In the context of the range of values used to

construct the models (Ki,app 0.5–9000 �mol/l), the dif-

ferences in values between the abbreviated method

and full kinetic analysis are insignificant. The proce-

dure increases the viability of characterizing sufficient

substrates to construct a reliable predictive model

(i.e. . 20 compounds). Previous pharmacophores and

3D QSAR developed for CYP isoforms have generally

relied on kinetic constants generated in multiple

laboratories using different assay procedures, which

decreases data reliability and hence model predictive-

ness [34,35].

The common features pharmacophore (Fig. 5a) indi-

cates that hydrophobic regions on the substrate are

commonly found close to the glucuronidation site of

the chemical. This is very similar to the common

features pharmacophore generated in this laboratory for

substrates of UGT1A4 (Smith et al., unpublished ob-

servations). The discriminative features pharmacophore

is more difficult to analyse due to the absence of the

glucuronidation feature in the model, which is useful as

a common reference point. It indicates a geometrical

arrangement of an aromatic ring, a hydrogen bond

donor and a hydrophobic region, which may be impor-

tant in binding to the active site of the enzyme.

Bilirubin is a high affinity substrate for UGT1A1. Thus,

not unexpectedly, bilirubin fits the discriminative fea-

tures pharmacophore well (Fig. 4b). The 2D QSAR

model suggests that the logP of a substrate has an

important influence on the ability of that compound to

bind to the active site of UGT1A1. Previous analyses

have also indicated that this is an important property of

substrates [36–38]. This may be due to properties of

the active site of the enzyme and/or the membrane

environment of the enzyme. In this regard, it should be

noted that the UGT active site is believed to be

located on the luminal face of the microsomal mem-

brane, and hence substrates must traverse the endoplas-

mic reticulum to gain access to the active site. The

other descriptors from the 2D QSAR model are calcu-

lated from the atomic properties and connectivity of

the molecules. These descriptors are commonly found

to be useful in predicting ligand–protein interactions,

however, their physical interpretation is very difficult

[16].

As shown by statistical evaluation and test set predic-

tions, all three models have useful predictive ability,

with the 2D QSAR performing best. The ability of a

model to predict the Ki,app of a novel chemical is

dependent on the substrates used in the training set. If

the training set substrates are similar in chemical

structure, the models are easier to construct, but the

generalizability of the model is greatly reduced. Such a

training set is unlikely to be able to reliably predict the

Ki,app value of diverse chemical structures. Having been

trained on a diverse set of known UGT1A1 substrates,

both in terms of structure and Ki,app, the models

constructed here should be well placed to predict the

Ki,app of new substrates of UGT1A1, at least within one

order of magnitude (the ‘acceptable’ standard for QSAR

developed for CYP isoforms [26]). The accuracy of

predicted Ki,app values is likely to improve as additional

(novel) substrates are incorporated into the model.

Furthermore, the three models were constructed using

very different properties of the substrates and very

different methods. The discriminative features pharma-

cophore predicts the Ki,app on the basis of abstract

chemical features (e.g. hydrogen bond donors, hydro-

phobic regions) in 3D. The algorithm includes a search

of conformational space (i.e. set of possible conforma-

tions of the chemical in 3D) for each substrate. This

procedure can be classed as a pharmacophore (3D

arrangement of chemical features) based 4D QSAR.

Table 2. Test set predicted K i,app values and log residuals for the three models

Substrate Observed K i,app

Pharmacophorepredicted K i,app

SOMFApredicted K i,app

2D QSARpredicted K i,app

Naltrexone 3900 470 (0.9) 100 (1.6) 4900 (0.1)4-Hydroxy benzoic acidmethyl ester

1000 560 (0.3) 200 (0.7) 1600 (0.2)

Alizarin 5 10 (0.3) 40 (0.9) 4.4 (0.1)4-Hydroxyestrone 4 5 (0.1) 25 (0.8) 4.4 (0.0)Quercetin 2 7 (0.5) 10 (0.7) 1.9 (0.0)

K i,app in units of �mol/l. Values in parentheses represent the log residuals (i.e. log of observed value minuspredicted value).


The SOMFA methodology uses 3D field (steric and

electrostatic) properties to predict the Ki,app. The

SOMFA algorithm requires the input of the aligned

substrates and does not use the conformational space of

the substrates. This can be classed as a field-based 3D

QSAR. The 2D QSAR is generated from 2D properties

of the substrates, which require no conformational or

alignment searching. Each of these models will contain

unique information and they can be considered to be

complementary.

The SOMFA model reported here had useful predic-

tive ability, but was inferior to the two other models

(discriminative features pharmacophore and 2D QSAR).

It has been recognized for some time that the major

obstacle to generating a 3D QSAR for a diverse set of

compounds is the selection of the bioactive conforma-

tion and superposition method [20]. The great struc-

tural diversity of the substrates, which makes the

predictions of the models so useful, also makes the

search for the correct pharmacophore and alignment

very difficult. The alignment may be aided by incor-

poration of the reaction site on the substrate. When

bound to the active site of the enzyme, the substrates

should align the functional groups to be glucuronidated,

since this part of the molecule would need to be

located in the region adjacent to the bound UDPGA

(cosubstrate). An attempt to use this information in the

Catalyst software met with mixed success. A feature

that recognized potential glucuronidation sites (e.g.

–OH, –NH) was constructed, but software limitations

meant it was not possible to ensure that all chemicals

fitted this feature. Similarly, there was no way of

selecting the preferred site of glucuronidation when

more than one nucleophilic site was present on a

substrate. It may be possible to improve the predictiv-

ity by using Comparative Molecule Similarity Analysis

(CoMSIA) molecular fields. CoMSIA is a variation of

CoMFA, in which the molecular fields are based on

‘soft’ Gaussian functions that are less likely to be

influenced by small errors in the alignment. They also

explicitly take into account the hydrophobicity of the

chemicals, in addition to the electrostatics and shape

[39].

Few previous studies have applied molecular modelling

approaches to UGT. A CoMFA model has been

reported for triphenylalkyl carboxylic acid analogue

inhibitors of UGT1A1 [40]. These structures are all

very similar and this model is therefore only useful for

predicting the binding of closely related compounds. A

QSAR using 2D descriptors has been derived to predict

the glucuronidation of benzoic acid analogues in the

rat, but does not consider the involvement of individual

isoforms [41]. As noted in the Introduction, molecular

modelling has been used widely in recent years to infer

active site binding requirements of CYP. Many of the

models generated for CYP isoforms have incorporated

enzyme structure, but this is currently not an option for

UGT. However, in general, the UGT1A1 models gen-

erated here are of at least comparable statistical power

and predictive ability to those QSAR generated for

CYP isoforms, including the polymorphic CYP2C9 and

CYP2D6 which do not incorporate direct information

from the protein structure [13,34,35].

In summary, three different methodologies have been

used to construct predictive models from the Ki,app

values of 18 known structurally diverse substrates of

UGT1A1. The predictive capability of the models was

validated primarily using a test set of five substrates not

used in the model construction process. When used

together, the models are capable of predicting whether

a chemical is a likely substrate for UGT1A1 through

inspection of the 3D positioning of important func-

tional groups, and estimating an in-vitro Ki,app for

putative substrates of UGT1A1. The predictive ability

of these models should improve with increasing num-

bers of newly discovered UGT1A1 substrates, greater

understanding of the 3D structure of UGT and new

model generation algorithms.

AcknowledgementsThe authors gratefully acknowledge Daniel Robinson

and the Computational Chemistry Research Group

(Oxford University, UK) and Milano Chemometrics

and QSAR Research Group (University of Milano-

Bicocca, Italy) for making available the SOMFA and

Dragon software, respectively. This work was funded

by a grant from the National Health and Medical

Research Council of Australia. M.J.S. is the recipient of

an Australian Postgraduate Award.

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