QSAR of cytochrome inhibitors

Review

10.1517/17425250903158940 © 2009 Informa UK Ltd ISSN 1742-5255 1245All rights reserved: reproduction in whole or in part not permitted

QSARofcytochromeinhibitorsKunal Roy† & Partha Pratim RoyJadavpur University, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Drug Theoretics and Cheminformatics Lab, Kolkata 700 032, India

Cytochrome P450 (CYP450) enzymes are predominantly involved in the Phase I metabolism of xenobiotics. Metabolic inhibition and induction can give rise to clinically important drug–drug interactions. Metabolic stability is a prerequisite for sustaining the therapeutically relevant concentrations, and very often drug candidates are sacrificed due to poor metabolic profiles. Computational tools such as quantitative structure–activity relationships are widely used to study different metabolic end points successfully to accelerate the drug discovery process. There are a lot of computational studies on clini-cally important CYPs already reported in recent years. But other clinically significant families are to yet be explored computationally. Powerfulness of quantitative structure–activity relationship will drive computational chemists to develop new potent and selective inhibitors of different classes of CYPs for the treatment of different diseases with least drug–drug interac-tions. Furthermore, there is a need to enhance the accuracy, interpretability and confidence in the computational models in accelerating the drug discovery pathways.

Keywords: ADMET, cytochrome, metabolism, QSAR

Expert Opin. Drug Metab. Toxicol. (2009) 5(10):1245-1266

1. Introduction

In late 1990s, pharmaceutical companies faced the problem of late stage failure of important drug candidates. Unfavorable absorption, distribution, metabolism, elimination and toxicity (ADMET) properties have been identified as major causes of failure for candidate molecules in late stage of drug development [1,2]. Drug discovery and development is a lengthy and costly process, taking an average of 15 years and around $880 million to generate a successful medicine [3]. The process is characterized by high attrition rate: up to 76% between target and investiga-tional new drugs [4] and ∼ 90% by the end of clinical trials [5]. This is, at least, partly due to inadequate ADMET properties [5,6]. Reduction of attrition is one in which in silico modeling can have major impact. Over the past 10 years, in vitro experimental tools to characterize pharmacokinetic profiles of compounds have been applied in early stages of the drug discovery process, which has now been recog-nized by the FDA [7,8]. Among the pharmacokinetic properties (ADME), metabo-lism is possibly the most complicated one because of involvement of numerous enzymes in Phase I and II metabolism [9]. Lead compound with high therapeutic efficacy has often been sacrificed in favor of optimal metabolic profile indicating an optimal balance between efficacy and overall pharmacokinetics. A core knowl-edge and understanding of the cytochrome P450 (CYP450) system in addition to rule of ‘5’ accelerates the drug discovery process as the metabolic profile of a lead compound is extremely important to its probable success in clinical trials [10,11]. Critical metabolic transformations of pharmaceuticals profoundly impact their bioavailability, efficacy, chronic toxicity, excretion rate and route. Both the parent molecule and its metabolites may also interfere with the endogenous metabolism of co-administered drugs. Induction of drug metabolizing enzymes such as CYP450 is known to cause drug–drug interactions due to increased elimination

1. Introduction

2. Types of CYPs

3. QSAR

4. QSAR of CYP450 enzyme

inhibitors

5. Expert opinion

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1246 ExpertOpin.DrugMetab.Toxicol.(2009) 5(10)

of co-administered drugs [12,13]. The elimination may lead to significant reduction or complete loss of efficacy of co-administered drugs [14]. Due to significance of such drug interactions, many pharmaceutical companies use screening and characterization models which predict CYP induction to avoid or attenuate potential drug interactions with new drug candidates [15]. The metabolic behavior of drugs not only depends on the physicochemical properties of compounds but also on the characteristics of the involved metabolizing system, whose expression depends on a number of genetic and envi-ronmental factors [16]. The key issues in drug metabolism include: i) identification of the enzymes involved; ii) site of metabolism in the molecules (regioselectivity); iii) resulting metabolites; and iv) stability and inhibition or induction of drug metabolism. Drug metabolism is now an integral part of drug discovery process and the CYP450s (EC 1.14.14.1) are the most important family of enzymes involved in drug metabolism. Among the Phase I metabolic enzymes, CYPs are responsible for variable drug metabolism and various com-plicated issues in different areas such as pharmacology and toxicology in drug development, preclinical toxicity studies, clinical trials, drug therapy, environmental exposures and risk assessment. Metabolism is the main route of clearance for ∼ 70% of currently used drugs. Ten individual CYP iso-forms in the adult human liver carry out virtually the whole CYP mediated metabolism [17,18]. The hemeprotein nature of the unusual carbon monoxide binding pigment of liver microsomes termed cytochrome P450, was first reported by Omura and Sato in 1964 [19]. There are > 50 mammalian CYP450 genes in at least 17 families. Microsomal CYP enzymes catalyze specific steps in the biosynthesis of steroid hormones, cholesterol, prostanoids and bile acids, participate in the catabolism of endogenous compounds, including fatty acids and steroids, and are involved in the degradation of exogenous compounds, including a wide variety of structur-ally diverse drugs and carcinogens [20-24]. CYP super family consists of > 7000 named sequence in animals, plants, bacteria and fungi [25]. CYP1, CYP2 and CYP3 families are more closely associated with the Phase I metabolism of xenobiotics whereas CYP4 enzymes are involved in the oxidation of fatty acids, postaglandins and leukotrienes [26-28].

The human drug metabolizing P450s include primarily CYP1A2, CYP1B1, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1 and CYP3A4 [27,29]. Abundance of different CYP isoforms in liver is in the following order: CYP3A4/5/7 > CYP2C9 > CYP2E1 > CYP1A2 > CYP2A6 > CYP2C8 > CYP2C19 > CYP2B6/D6. Metabolizing enzymes that exhibit genetic polymorphism (CYP2D6, CYP2C19) are involved in undesirable drug–drug interactions and this may cause rejection of a compound as a useful target [30,31]. Only CYP3A4 is involved in > 30% of all drug oxidations medi-ated by P450s and associated with different drug interac-tions [27]. Lots of in silico structure based approaches and quantitative structure–activity relationship (QSAR) models have been applied to predict drug metabolism [32]. Earlier,

computational modeling efforts have mainly focused on ligand based approaches as there was no crystal structure of any human CYP enzymes. First mammalian membrane bound CYP450, a rabbit CYP2C5, was introduced in 2000 [33]. This was followed by rapid success in crystallizing human CYP450s, 2C9, 2C8, 3A4 and 2D6 and this promoted the use of structure based approaches [34-38]. Different pharma-cophore modeling, 2D and 3D QSAR analyses have resulted in a number of predictive models [39-42]. Some forms of CYP show polymorphic behavior leading to potential drug inter-action or loss of activity resulting in poor or higher metabolic status. But, crystal structures of CYPs with or without substrates or inhibitors will provide the ultimate answer. Thus, increasing knowledge of CYP structure–activity and regulation along with generation of new data are giving momentum to the development of computational models to predict the metabolic end points of new compounds. In this regard, computational tools such as QSAR can be used to predict interactions between drugs and metabolic enzymes and the models can be used either for avoiding potential metabolic issues or providing alerts in the drug development program.

2. TypesofCYPs

The characteristics of different isoforms of CYPs [43] are summarized in Table 1.

2.1 CYP1A2CYP1A2 is the only hepatic member of CYP1 family. CYP1A1 and CYP1B1 are the other enzymes in this family of which CYP1A1 is the major human extra hepatic CYP form [44]. These enzymes are generally distinguished from other families of P450s by their capacity to oxidize a variety of polynuclear aromatic hydrocarbons (PAHs) [45]. The CYP1 family is of great importance in the Phase I metabolism of many xeno-biotic compounds [46]. CYP1A2, which is a member of the CYP1A family of CYP450s, accounts for about 13% of the total CYP content of human liver microsomes and is involved in about 8% of Phase I drug oxidations [27]. Initially, the expression of CYP1A2 was thought to be limited only to liver but recent studies have shown its expression in lung along with CYP1A1. Sansen et al. [47] first reported the structure of microsomal P450 of family 1 which is highly adapted for the oxidation of relatively large, planar molecules such as (heterocyclic) aryl amines and PAHs. CYP1A2 exhibits < 40% amino-acid sequence identity when compared to other struc-turally characterized mammalian microsomal P450s [47]. The volume of its cavity was estimated to be 375 Å3, which is larger than that of P450 2A6 (260 Å3) [47]. The P450 1A2 is capable of oxidizing a vast range of chemicals depending on the size, shape, properties of the chemical complementary to the active site cavity. CYP1A2 is involved in the metabolism of theophylline, caffeine, imipramine, acetaminophen and pro-pranolol as well as the metabolism of endogenous substances such as 17β-estradiol and uroporphyrinogen III [44,48].

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ExpertOpin.DrugMetab.Toxicol.(2009) 5(10) 1247

Tab

le1

.Dif

fere

nt

CY

Pis

ofo

rms

and

th

eir

char

acte

rist

ics

[42]

.

CY

Pis

ofo

rms

Site

so

fex

pre

ssio

na

nd

ab

un

dan

ceR

eact

ion

sC

linic

alis

sues

CY

P1A

2Ex

pres

sed

in t

he li

ver

and

cont

ains

10

– 1

5% o

f th

e to

tal P

450

cont

ent

The

P450

1A

2 is

cap

able

of

oxid

izin

g a

vast

ran

ge o

f ch

emic

als.

The

act

ivity

of

both

enz

ymes

can

be

indu

ced

by e

xpos

ure

to P

AH

s. N

-hyd

roxy

latio

n of

pro

carc

inog

enic

he

tero

cycl

ic a

min

es t

o ca

rcin

ogen

ic c

ompo

unds

is c

atal

yzed

by

CY

P1A

2. It

has

rel

ativ

ely

larg

e ac

tive

site

with

sev

eral

, ov

erla

ppin

g bi

ndin

g si

tes

Hig

h le

vels

of

P450

1A

2 ac

tivity

hav

e al

so b

een

asso

ciat

ed w

ith in

effe

ctiv

enes

s of

the

ophy

lline

the

rapy

(f

or a

sthm

a). T

he in

hibi

tion

of C

YP1

A1

and

CY

P1A

2 en

zym

e is

rec

ogni

zed

as a

can

cer

chem

opre

vent

ion

stra

tegy

CY

P2A

6C

YP2

A6

cons

titut

es 5

– 1

0% o

f th

e

tota

l CY

P in

hum

an li

ver

as w

ell a

s ot

her

tissu

es, n

asal

muc

osa,

tra

chea

, lun

g an

d es

opha

geal

muc

osa

The

mos

t ch

arac

teris

tic a

nd s

peci

fic r

eact

ion

of P

450

2A

6 is

cou

mar

in 7

-hyd

roxy

latio

n. P

450

2A6

is a

lso

in

volv

ed in

the

met

abol

ism

of

nico

tine

Maj

or is

sue

rega

rdin

g P4

50 2

A6

poly

mor

phis

ms

is t

he

effe

cts

on lu

ng a

nd e

soph

agea

l can

cers

and

sm

okin

g ha

bits

CY

P2B6

P450

2B6

is e

xpre

ssed

prim

arily

in li

ver

an

d lu

ng. R

elat

ive

abun

danc

e 1%

of

tota

l P4

50, w

ith v

alue

s ra

rely

exc

eedi

ng 5

%

Hum

an C

YP2

B6 m

etab

oliz

es a

bout

3%

of

drug

s in

cl

inic

al u

se. K

etam

ine

and

(S)-

mep

heny

toin

und

ergo

es

N-d

emet

hyla

tion,

pro

pofo

l und

ergo

es h

ydro

xyla

tion

by

thi

s en

zym

e

As

a pr

ogno

stic

fac

tor

for

pros

tate

can

cer

and

may

, th

eref

ore,

be

clin

ical

ly r

elev

ant

CY

P2C

8C

YP2

C8

acco

unts

for

abo

ut 6

% o

f th

e

tota

l liv

er C

YP

cont

ent.

The

enz

yme

is

expr

esse

d in

live

r an

d ki

dney

. It

is a

lso

ex

pres

sed

in a

dren

al g

land

, bra

in, u

teru

s,

mam

mar

y gl

and,

ova

ry a

nd d

uode

num

as

wel

l

Dru

gs m

etab

oliz

ed b

y C

YP2

C8

do n

ot s

hare

any

com

mon

ch

emic

al p

atte

rn. I

n ge

nera

l, P4

50 2

C8

has

rela

tivel

y

low

cat

alyt

ic a

ctiv

ity t

owar

d th

e kn

own

subs

trat

es o

f

P450

s 2C

9 an

d 2C

19. I

t ha

s a

rela

tivel

y la

rge

activ

e si

te

cavi

ty (1

438

Å3 )

. Tw

o bi

ndin

g m

odes

wer

e ob

serv

ed,

one

of w

hich

cor

resp

onds

to

the

obse

rved

oxi

datio

n.

It is

stim

ulat

ed b

y cy

toch

rom

e b 5

Indu

ctio

n an

d in

hibi

tion

of P

450

2C8

are

not

part

icul

ar

issu

es a

t th

is p

oint

. Alth

ough

P45

0 2C

8 m

ay p

lay

a pr

omin

ent

role

in t

he h

epat

ic a

nd r

enal

oxi

datio

n of

ar

achi

doni

c ac

id a

nd r

etin

oic

acid

, no

dise

ase

etio

logy

ha

s be

en im

plic

ated

at

this

poi

nt. T

he m

ost

serio

us

issu

e is

pro

babl

y an

y im

pact

on

the

disp

ositi

on o

f th

e ca

ncer

che

mot

hera

peut

ic a

gent

pac

litax

el

CY

P2C

9P4

50 2

C9

is p

rimar

ily a

hep

atic

P45

0. A

ll P4

50 2

C e

nzym

es a

re a

bsen

t in

fet

al li

ver,

in

clud

ing

P450

2C

9. L

evel

of

hepa

tic P

450

2C9

does

not

cha

nge

with

age

, at

leas

t to

68

yea

rs. P

450

2C9

is a

lso

expr

esse

d in

the

sm

all i

ntes

tine.

The

leve

l of

expr

essi

on is

pr

obab

ly t

he h

ighe

st, o

n th

e av

erag

e,

exce

pt f

or P

450

3A4

Cyt

ochr

ome

2C9

is r

espo

nsib

le f

or t

he h

epat

ic c

lear

ance

of

15%

of

clin

ical

ly r

elev

ant

drug

s. P

450

2C9

is t

he

maj

or c

atal

yst

of o

xida

tion,

and

pol

ymor

phis

ms

affe

ct t

he

inv

ivo

phar

mac

okin

etic

par

amet

ers.

P45

0 2C

9 lig

ands

sh

owed

sev

eral

sub

stra

te b

indi

ng m

odes

. The

bas

ic

phar

mac

opho

re c

onta

ins

a hy

drog

en b

ond

dono

r si

te

and/

or a

nion

ic m

oiet

y pl

aced

7 –

8 Å

fro

m t

he s

ite o

f m

etab

olis

m. I

t is

stim

ulat

ed b

y cy

toch

rom

e b 5

The

maj

or is

sue

rega

rdin

g P4

50 2

C9

is it

s ro

le in

dru

g de

velo

pmen

t be

caus

e of

the

siz

eabl

e fr

actio

n of

dru

gs

oxid

ized

by

this

enz

yme

Furt

herm

ore,

CY

P2C

9 pa

rtic

ipat

es in

the

met

abol

ism

of

the

end

ogen

ous

subs

tanc

es, s

uch

as e

icos

anoi

ds

and

arac

hido

nic

acid

, and

the

reby

see

ms

to a

ffec

t

the

regu

latio

n of

vas

cula

r ho

meo

stas

is. A

noth

er is

sue

abou

t P4

50 2

C9

is p

ossi

ble

rele

vanc

e to

can

cer

risk

CY

P2C

19A

ppar

ently

sig

nific

ant

expr

essi

on o

nly

oc

curs

in t

he li

ver,

no

gend

er d

iffer

ence

. P4

50 2

C19

is a

rel

ativ

ely

min

or P

450

in

its

abun

danc

e, p

roba

bly

acco

untin

g

for

< 5

% o

f to

tal P

450

(S)-

mep

heny

toin

4′-h

ydro

xyla

tion

is t

he c

lass

ic r

eact

ion

attr

ibut

ed t

o P4

50 2

C19

. As

with

oth

er P

450

2C

subf

amily

enz

ymes

, P45

0 2C

19 a

ctiv

ities

are

usu

ally

st

imul

ated

by

cyto

chro

me

b 5. A

mid

es o

r w

eak

base

s

with

tw

o hy

drog

en b

ond

acce

ptor

s co

mpo

unds

are

m

etab

oliz

ed b

y th

is e

nzym

e

The

issu

e is

the

pol

ymor

phis

m, p

artic

ular

ly f

or d

rugs

m

arke

ted

in A

sian

pop

ulat

ions

. Hep

atoc

ellu

lar

canc

er

in P

Ms

and

lack

of

asso

ciat

ion

of le

ukem

ia w

ith

poly

mor

phis

m

PAH

: Pol

ynuc

lear

aro

mat

ic h

ydro

carb

on.

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CY

Pis

ofo

rms

Site

so

fex

pre

ssio

na

nd

ab

un

dan

ceR

eact

ion

sC

linic

alis

sues

CY

P2D

6It

was

the

firs

t ‘x

enob

iotic

-met

abol

izin

g’

P450

rec

ogni

zed

to b

e un

der

mon

ogen

ic

regu

latio

n. P

450

2D6

is e

xpre

ssed

mai

nly

in

liver

and

lung

(bro

nchi

al m

ucos

a an

d lu

ng

pare

nchy

ma)

, bra

in, e

spec

ially

in t

he

mid

brai

n in

low

leve

l as

wel

l. P4

50 2

D6

acco

unts

for

∼ 5

% o

f to

tal P

450

(w

ith w

ide

varia

tion)

This

enz

yme

is in

volv

ed in

the

oxi

datio

n of

∼ 2

5% o

f al

l dr

ugs

oxid

ized

by

P450

s. P

450

2D6

cata

lyze

s m

any

of t

he

basi

c ki

nds

of o

xida

tive

reac

tions

of

P450

s, f

or e

xam

ple,

al

ipha

tic a

nd a

rom

atic

hyd

roxy

latio

ns, h

eter

oato

m

deal

kyla

tions

and

so

on. T

he b

asic

pha

rmac

opho

re o

f C

YP2

D6

subs

trat

es c

onsi

sts

of a

bas

ic n

itrog

en t

hat

is

prot

onat

ed a

t ph

ysio

logi

cal p

H a

nd s

ite o

f m

etab

olis

m

is a

t 5

– 7

Å d

ista

nce

from

the

nitr

ogen

The

clin

ical

issu

es r

egar

ding

P45

0 2D

6 ar

e co

nsid

erab

le d

ue t

o th

e la

rge

varia

tion

in t

he g

enet

ics

in t

he p

opul

atio

n. A

noth

er is

sue

with

P45

0 2D

6 is

the

re

leva

nce

of t

he p

olym

orph

ism

to

canc

er r

isks

(lu

ng c

ance

r). A

fina

l iss

ue is

tha

t of

aut

oant

igen

s

CY

P2E1

The

grea

test

con

cent

ratio

n is

in t

he li

ver

as

wel

l as

in m

any

extr

ahep

atic

site

s in

clud

ing

lung

, eso

phag

us, s

mal

l int

estin

e, b

rain

, na

sal m

ucos

a an

d pa

ncre

as. I

t ac

coun

ts

for

7% o

f to

tal C

YP

cont

ent

in t

he li

ver

P450

2E1

was

orig

inal

ly c

hara

cter

ized

as

an e

than

ol-

oxid

izin

g en

zym

e. P

450

2E1

can

oxid

ize

som

e co

mpo

unds

th

at a

re p

rese

nt in

the

bod

y, in

clud

ing

acet

one

and

po

ssib

ly o

ther

ket

ones

invo

lved

in c

erta

in p

hysi

olog

ical

sy

ndro

mes

. P45

0 2E

1 ha

s be

en s

how

n to

be

a

maj

or P

450

invo

lved

in t

he o

xida

tion

of a

num

ber

of

low

mol

ecul

ar m

ass

canc

er s

uspe

cts

incl

udin

g no

t

only

nitr

osam

ines

but

als

o be

nzen

e, s

tyre

ne. O

ne o

f

the

issu

es in

P45

0 2E

1 re

actio

ns is

the

nee

d fo

r

cyto

chro

me

b 5. T

he s

ubst

rate

s of

CY

P2E1

usu

ally

con

sist

of

hyd

roph

obic

and

low

mol

ecul

ar m

ass

com

poun

ds

The

maj

or c

linic

al is

sues

invo

lve

the

role

of

P450

2E1

in

the

oxi

datio

n of

cer

tain

dru

gs, a

lcoh

olis

m, o

xida

tive

stre

ss a

nd r

isk

of c

ance

r

CY

P3A

4P4

50 3

A4

is t

he m

ost

abun

dant

P45

0 in

hu

man

live

r an

d in

the

sm

all i

ntes

tine.

P4

50 3

A4

is a

lso

expr

esse

d in

som

e ex

trah

epat

ictis

sues

, inc

ludi

ng lu

ng, s

tom

ach,

colo

n an

d ad

rena

l (w

eak)

. CY

P3A

4

has

the

high

est

abun

danc

e in

the

hu

man

live

r (∼

40%

)

It m

etab

oliz

es >

50%

of

the

clin

ical

ly u

sed

drug

s. In

the

co

urse

of

thes

e re

actio

ns, P

450

3A4

cata

lyze

s ex

ampl

es o

f so

me

atyp

ical

rea

ctio

ns in

clud

ing

desa

tura

tion,

oxi

dativ

e ca

rbox

ylic

aci

d es

ter

clea

vage

and

oxi

datio

n of

a n

itrile

to

an

am

ide.

One

of

the

clas

sic

(and

fas

test

) rea

ctio

ns

cata

lyze

d by

P45

0 3A

4 is

tes

tost

eron

e 6β

-hyd

roxy

latio

n.

P450

3A

4 is

stim

ulat

ed b

y cy

toch

rom

e b 5

. Sev

eral

bin

ding

su

b-po

cket

s in

the

act

ive

site

. CY

P3A

4 su

bstr

ates

hav

e

been

sug

gest

ed t

o in

clud

e a

hydr

ogen

bon

d ac

cept

or

atom

5.5

– 7

.8 Å

fro

m t

he s

ite o

f m

etab

olis

m a

nd 3

Å

from

the

oxy

gen

mol

ecul

e

The

maj

or is

sues

invo

lvin

g P4

50 3

A4

in d

rug

deve

lopm

ent

and

clin

ical

use

are

rel

ated

to

the

ro

le o

f th

e en

zym

e in

dru

g di

spos

ition

, par

ticul

arly

bi

oava

ilabi

lity

and

drug

–dru

g in

tera

ctio

ns d

ue t

o in

duct

ion

or in

hibi

tion.

P45

0 3A

4 is

als

o of

som

e

inte

rest

reg

ardi

ng c

ance

r, r

egar

ding

exo

geno

us

carc

inog

ens,

dru

gs u

sed

to t

reat

can

cer,

and

m

etab

olis

m o

f st

eroi

ds o

r ot

her

com

poun

ds t

hat

may

af

fect

can

cer

risk

or r

espo

nse

to c

hem

othe

rapy

CY

P11B

2P4

50 1

1B2

is e

xpre

ssed

in t

he a

dren

al

cort

ex (z

ona

glom

erul

osa)

and

to

a le

sser

ex

tent

in t

he h

eart

, bra

in a

nd v

ascu

lar

sm

ooth

mus

cle

cells

P450

11B

2 ca

taly

zes

the

thre

e-st

ep c

onve

rsio

n of

11

-deo

xyco

rtic

oste

rone

to

aldo

ster

one

with

11

(3-h

ydro

xyla

tion,

18-

hydr

oxyl

atio

n an

d 2-

elec

tron

ox

idat

ion

of t

he 1

8-ca

rbin

ol

The

issu

es o

f co

ngen

ital a

dren

al h

yper

plas

ia a

nd

type

s I a

nd II

cor

ticos

tero

ne m

ethy

loxi

dase

defi

cien

cy

in in

divi

dual

s an

d es

sent

ial h

yper

tens

ion

as w

ell a

s in

crea

sed

left

ven

tric

ular

siz

e

PAH

: Pol

ynuc

lear

aro

mat

ic h

ydro

carb

on.

Tab

le1

.Dif

fere

nt

CY

Pis

ofo

rms

and

th

eir

char

acte

rist

ics

[42]

(co

nti

nu

ed).

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CYP1A2 preferentially oxidizes heterocyclic and aromatic amines, aromatic hydrocarbons [27]. The metabolic activity of P450 1A2 is inhibited by several 8-methyl-xanthines [49] as well as by some quinolones [50]. Substrates of CYP1A2 are moderately basic and planar molecules with medium volume and low total interaction energies (ΔE value), but there is no clear correlation between compound lipophilicity and inhib-itory effect [50-56]. It was reported that N-hydroxylation of procarcinogenic heterocyclic amines to carcinogenic com-pounds is catalyzed by CYP1A2 plays important roles in the carcinogenesis of such chemicals [57-60]. The activity of both enzymes can be induced by exposure to PAHs. The induction of CYP1A activities by PAHs is applied in aquatic toxicology to assess the water contamination with PAHs, because it is shown that the levels of CYP1A1 and CYP1A2 activity cor-relate with the range of water pollution [61]. The inhibition of CYP1A1 and CYP1A2 enzymes is recognized as a cancer chemoprevention strategy [62].

2.2 CYP2familyA major family involved in human drug metabolism is CYP2 with > 54% of all P450 mediated Phase I oxidations being carried out by this family [27]. The human CYP2 family is very diverse and comprises a number of important CYPs without sharing any common regulation pattern and substrates specificities.

2.2.1 CYP2A6At the quantitative level, CYP2A6 is a minor component among hepatic CYPs. CYP2A6 constitutes 5 – 10% of the total CYPs in human liver [63] and it can metabolize some marketed drugs [64,65]. CYP2A6 was first identified as the human coumarin 7-hydroxylase [66-69]. Crystal structures of CYP2A6 in complexes with coumarin (substrate) and methoxsalen (inhibitor) have recently been published [45,70]. The characteristics of its substrates somewhat resemble CYP1A2 substrates, small planar molecules. The structures of the complexes indicate that Asn297, the only polar residue in the active site, is the hydrogen-bonding residue in the active site interacting with carbonyl oxygen of the ligands. Coumarin is specifically hydroxylated to 7-hydroxycoumarin in mice by CYP2A5 and in humans by CYP2A6. These enzymes share an 82% similarity in their amino-acid sequences. Most of the known CYP2A5 ligands include a lactone moiety [71]. The active site of P450 2A6 is six times smaller than that of human P450 2C8 [38,45], even smaller than that of bacterial P450 101A1 [72]. Furthermore, CYP2A6 is the primary enzyme responsible for metabolizing nicotine to its inactive metabolite cotinine [73] making CYP2A6 a putative smoking cessation treatment target by inhibiting nicotine metabolism [74,75]. As it has little role in drug metabolism, modulating the activity of this enzyme usually does not lead to adverse events. Recent evidences also suggest the involvement of CYP2A6 in developing various types of cancer. Therefore, it is of critical importance to develop in silico models to predict

the interactions with CYP2A6 in the process of drug discovery in the hope of reducing the attrition rates due to adverse side effects as well as identifying inhibitors for smoke cessation and chemoprevention of CYP2A6-associated cancers [76,77].

2.2.2 CYP2D6This isoform accounts for 2% of total CYP expression. CYP2D6 is a polymorphic P450 isoform where the active enzyme is absent in 5 – 10% of Caucasians and 1% of Asians. CYP2D6 is expressed in the human brain, especially the midbrain as well as in the liver [78]. CYP2D6 locus exhibits a high degree of genetic polymorphism that clearly has been linked to the variable pharmacological response to a variety of analgesic, cardiovascular and antidepressant drugs [79-81]. Approximately 20 – 25% of CYP450 mediated oxidation of drugs (including β-blockers, neuroleptics, antidepressants and antiarythmics) are performed by CYP2D6 [82-85]. Therefore, much emphasis is placed on CYP2D6 and its potential for clinically relevant drug interactions, early in drug discovery. The basic pharma-cophore of CYP2D6 substrates consists of a basic nitrogen that is protonated at physiological pH and the site of metabolism is at 5 – 7 Å distance from the nitrogen [86,87]. Metabolism of compounds not containing a basic amine by CYP2D6 has also been reported [88]. CYP2D6 is not inducible by pharmacological agents.

2.2.3 CYP2C9CYP2C subfamily is one of the most important families and consists primarily of two enzymes, CYP2C9 and CYP2C19. CYP2C9 is one of the major isoforms of the CYP450 2C subfamily and plays a significant role in metabolizing approxi-mately one-fifth of all drugs. CYP2C9 is responsible for the hepatic clearance of 15% of clinically relevant drugs as the first step in drug clearance and limiting their oral bioavail-ability [27,89]. CYP2C9 makes significant contributions to drug metabolism, and is capable of binding a number of compounds with high affinity. Disruption of CYP2C9 activity by metabolic inhibition or pharmacogenetic variability under-lies many of the adverse drug reactions that are associated with the enzyme. CYP2C9 is selective for substrates that are small or moderately sized, lipophilic, and either weakly acidic or neutral [90,91]. The basic pharmacophore contains a hydrogen bond donor site and/or anionic moiety placed 7 – 8 Å from the site of metabolism. Among CYP2C9 substrates are anti-inflammatory agents, such as diclofenac, ibuprofen, naproxen and piroxicam, anticoagulant compounds such as (S)-warfarin as well as progesterone and hypoglycemics [92-99]. Furthermore, CYP2C9 participates in the metabolism of endogenous sub-stances, such as eicosanoids and arachidonic acid, and thereby seems to affect the regulation of vascular homeostasis [97,98]. CYP2C9 is also the first human P450 crystallized.

2.2.4 CYP2C19CYP2C19 is one of the polymorphic members of cytochrome family. About 20% of Asians and 3 – 5% of Caucasians are

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poor metabolizers, whereas in 20% Japanese this isoform is absent [100]. Drugs metabolized through CYP2C19 are usually amides or weak bases with two hydrogen bond acceptors [101]. CYP2C19 genes represent a smaller proportion polymorphism than CYP2D6 gene and perhaps have less clinical signifi-cance [102,103]. CYP2C19 is involved in the metabolism of many commonly used pharmaceuticals, for example, diazepam, imipramine, (S)-mephenytoin and most of the proton pump inhibitors. Unlike CYP2C9, it has little preference for anions despite sharing high sequence homology [104].

2.2.5 CYP2E1CYP2E1 is a major CYP isoform expressed in the human liver and accounts for 7% of total CYP content in the liver [105]. Human CYP2E1 is responsible for activation of carcinogenic nitrosamines such as dimethyl and diethylnitrosamine [106-108]. Human CYP2E1 has characteristics in its substrate recogni-tion as follows: CYP2E1 metabolizes hydrophilic substrates such as ethyl alcohol and acetone [109] and small molecules such as benzene and chloroform [110,111]. CYP2E1 is induced by alcohol intake, fasting and diabetes, and is found to be important for the investigation of drug–drug interactions [112-

114]. Chemical agents which are both an inducer of CYP2E and activated by the enzyme to a more reactive species may represent a significant toxic hazard to the organism as detoxifying metabolic pathways and DNA toxicity through enhancement of CYP2E activity mechanisms could become overwhelmed [115]. Although CYP2E1 is one of the most abundant hepatic CYPs, only a few pharmaceuticals are metabolized through this enzyme. It exhibits polymorphism and is a toxicologically important enzyme. The substrates of CYP2E1 usually consist of hydrophobic and low molecular mass compounds [101,116,117].

2.2.6 CYP2B6In the human P450 enzyme family, one of the less-charac-terized forms is CYP450 2B6. Initial studies reported that 2B6 levels were only 0.2% of the total P450 content in human liver microsomes [118,119]. Interindividual variability in 2B6 protein levels due to genetic polymorphisms and/or exposure to environmental inducers and inhibitors are reported [120,121]. Human CYP2B6 metabolizes about 3% of drugs in clinical use [27]. Substrates tend to have distinct chemical structures typified by both high lipophilicity, non-planar geometry and are often comprised of two aromatic rings with a central tetrahedral carbon atom. P450 2B6 has involvement in the metabolism of a number of clinically important drugs such as cyclophosphamide, bupropion efavirenz, ifosamide, pethidine, artemisinin, propofol, ketamine and selegiline [122,123]. In addition to pharmaceuticals, CYP2B6 seems to both detoxify and bioactivate a number of procarcinogens. Drug–drug inter-actions resulting from inhibition or induction of CYP2B6 can have serious consequences in the case of substrate drugs with a narrow therapeutic index, such as cyclophosphamide [124]. CYP2B6 has been virtually ignored by pharmaceutical

researchers for many years. CYP2B6 is expressed in hepatic and extra hepatic tissues and has recently been suggested as a prognostic factor for prostate cancer and may, therefore, be clinically relevant [125].

2.2.7 CYP2C8Like in case of CYP2B6, the importance of CYP2C8 for drug metabolism has been explained quite recently partly due to lack of diagnostic inhibitors [126]. CYP2C8 is an important member of the CYP2C family and it shows genetic variations and these lead to marked difference in activity. CYP2C8 accounts for about 6% of the total liver CYP content [127]. In addition to hepatocytes, CYP2C8 protein has been detected in salivary ducts, intestine, kidney and adrenal cortical cells [128]. However, the protein expression of CYP2C8 in the intestine is low [129]. It has a relatively large active site cavity (1438 Å3), similar in size to that of CYP450 3A4 (1386 Å3), but the shapes of the cavities differ considerably [38]. Although CYP2C8 and CYP2C9 share 78% sequence identity, they exhibit rela-tively minor overlap of their substrate and inhibitor profile. While substrates of CYP2C9 are medium sized acidic mole-cules with one or two H-bond acceptors, substrates of CYP2C8 can be characterized as large, elongated, acidic or neutral molecules [10]. The active site of CYP2C8 is consider-ably more spacious than that of CYP2C9 [38]. Drugs metabo-lized by CYP2C8 do not share any common chemical pattern [38]. CYP2C8 plays a major role in the metabolism of several drugs, including amodiaquine, cerivastatin, paclitaxel, pioglitazone, repaglinide and rosiglitazone. In addition, certain endogenous agents such as arachidonic acid and retinoic acid can be metabolized by CYP2C8 [38,130-138].

2.3 CYP3A4CYP3A4 is of the particular clinical significance because it is the major P450 in human liver and intestine [118,139]. CYP3A4, like all mammalian P450 enzymes, is a membrane bound protein [140]. CYP3A4, previously known as nifedipine oxidase, is a member of the CYP450 mixed function oxidase system [141]. CYP3A4 is one of the most important enzymes involved in the metabolism of xenobiotics in our body (∼ 30% of all known xenobiotic oxidations). CYP3A4 has the highest abundance in the human liver (∼ 40%) and metabolizes > 50% of the clinically used drugs [27,118]. CYP3A4 catalyzes the reac-tions such as alkyl carbon hydroxylation, O- and N-dealkylation, epoxidation and less frequently, aromatic ring hydroxyla-tion [142]. Domanski et al. [143] proposed that there are several binding sub-pockets in the active site and a number of studies have provided evidences that CYP3A4 accommodates two or more substrates in the active site simultaneously. The molecular structures of human CYP3A4 revealed an active site of sufficient size and topography to accommodate large ligands as suggested by the heterotropic and homotropic cooperative of the enzyme. CYP3A4 is known to metabolize a large variety of compounds varying in molecular mass from lidocaine (MM = 234) to cyclosporine (MM = 1203) [144,145]. The

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active pocket in CYP3A4 is ∼ 520 Å [36]. CYP3A4 catalyzes the initial step in the clearance of many pharmaceuticals and foreign chemicals. In fact, most of the drug–drug interactions that resulted in withdrawal of drugs already on the market can be traced back to the CYP3A4 inhibition [146-148]. As CYP3A4 is considered the major oxidative enzyme involved in drug metabolism, numerous drug–drug interactions have been reported, in which the inhibition of this enzyme by a drug results in the decreased clearance of other drugs. CYP3A4 substrates bind with Asn74 residue of CYP3A4 using hydro-gen bonding. Structural requirements of CYP3A4 substrates have been suggested to include a hydrogen bond acceptor atom 5.5 – 7.8 Å from the site of metabolism and 3 Å from the oxygen molecule [149].

2.4 OtherCYPsAromatase CYP19 is the only enzyme in vertebrates known to catalyze the biosynthesis of all estrogens from andro-gens [150-152]. Aromatase (CYP19) is produced in high levels in breast tissue, and particularly in those areas in and around tumor sites [153]. Unlike the active sites of many microsomal P450s that metabolize drugs and xenobiotics, aromatase has an androgen-specific cleft that binds the androstenedione molecule snugly [154]. Hydrophobic and polar residues exqui-sitely complement the steroid backbone. This enzyme is an important pharmacological target in the anticancer therapy, because intratumoral aromatase is the source of local estrogen production in breast cancer tissues [153]. Aldosterone synthase (CYP11B2), a mitochondrial CYP450 enzyme plays a pivotal role in congestive heart failure and hyperaldosteronism and myocardial fibrosis. It catalyzes hydroxylation of 11-deoxy-corticosterone to corticosterone and in the next steps, it cata-lyzes the hydroxylation and oxidation in 18-position of the steroid leading to aldosterone [155] in the adrenal cortex. A new member of the CYP450 super family, CYP2S1 [156], has recently been identified in human and mouse. CYP2S1 is most strongly expressed in the epithelial cells of tissues that are exposed to the environment, for example, the respira-tory, gastrointestinal and urinary tracts and the skin. CYP2S1 metabolizes toxic and carcinogenic compounds similar to other dioxin-inducible CYPs.

2.5 InhibitorsofCYPsEach CYP has the ability to metabolize several substrates which are responsible for the large number of drug–drug interactions associated with CYP inhibition. CYP inhibition occurs either as reversible inhibition, quasi-irreversible inhibition or irrevers-ible inhibition. A representative list of inhibitors of different isoforms of CYP is given in Table 2.

3. QSAR

Similar molecules with just a slight variation in their structures can exhibit either different magnitudes of a particular biological activity or quite different types of biological activities. This

kind of relationship between molecular structure and changes in biological activity developed on a quantitative basis is the center of focus for the field of QSARs. QSARs represent predictive models derived from application of statistical tools correlating biological activity (including therapeutic and toxic) of chemicals (drugs/toxicants/environmental pollutants) with descriptors representative of molecular structure and/or prop-erty [157]. Among different techniques available for screening of new chemical entity, QSAR is the one practiced very often. The success of drug discovery efforts in the pharmaceutical industry depends heavily on utilization of structure–activity relationship techniques for these and related purposes. The use of statistical models to predict biological and physico-chemical properties started with linear regression models developed by Hansch in the 1960s [158]. QSAR models are essentially pattern recognition models which identify trends in structural features that correlate with the observed activity. QSAR models are useful in a number of cases, such as sug-gesting structural modifications to enhance activity and explanation for outliers. QSARs are being applied in many disciplines such as risk assessment, toxicity prediction and regulatory decisions [159] apart from drug discovery and lead optimization [160]. In the 1980s, several 3D QSAR approaches such as molecular shape analysis, distance geometry, compara-tive molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSiA), hypothetical active site lattice, receptor surface analysis, molecular similarity matrices and comparative binding energy emerged [161]. The details of differ-ent QSAR models have been reviewed elsewhere [162]. QSAR is basically a ligand based approach while molecular docking represents one of the growing applications in computational biology wherein molecular modeling techniques are used to pre-dict how a protein (receptor) interacts with small drug-like molecules. Given the biological significance of molecular dock-ing, considerable efforts have been directed in understanding the process of molecular docking in recent years [163].

Obtaining a good quality QSAR model depends on many factors, such as the quality of biological data, the choice of descriptors and statistical methods. Any QSAR modeling should ultimately lead to statistically robust models capable of making accurate and reliable predictions of biological activities of new compounds. The application of QSAR models for virtual screening places a special emphasis on statistical significance and predictive ability of these models as their most crucial characteristics. The process of QSAR model development can be generally divided into three stages: data preparation, data analysis and model validation. The validation strategies check the reliability of the developed models for their possible application on a new set of data, and confidence of prediction can thus be judged. Validation is a crucial aspect of any QSAR modeling. It is the process by which the reliability and relevance of a procedure are estab-lished for a specific purpose [164]. For many in the QSAR community, the validation of a model is little more than an assessment of statistical fit and, occasionally, predictivity

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using cross-validation techniques. However, it is now being accepted that validation is a more holistic process that includes assessment of issues such as data quality, applicability of the model and mechanistic interpretability in addition to statistical assessment [165-172]. For validation of QSAR models, usually four strategies are adopted [173]: i) internal validation or cross-validation (leave-one-out or leave-group-out technique); ii) vali-dation by dividing the data set into training and test compounds; iii) true external validation by application of model on external data; and iv) data randomization or Y-scrambling. QSAR methods are now urgently needed for predicting ADMET properties to select lead compounds for optimization at early stages of drug discovery. In recent years, lots of in silico models have been applied to predict drug metabolism.

4. QSARofCYP450enzymeinhibitors

QSAR modeling for CYP catalyzed biotransformations has progressed in parallel with increasing availability of CYP isoform substrates and inhibitors. Computational modeling efforts mainly focused on ligand based approaches before invention of crystal structures of different CYP isoforms.

4.1 CYP1A2Sanz et al. studied inhibition of caffeine N-demethylation by xanthine derivatives using molecular electrostatic potential

distributions [51]. The same group also studied the inhibitory effect of 44 quinolone antibacterials and derivatives (common structure: 4-oxoquinoline-3-carboxylic acid) on CYP1A2 activity. Using the developed QSAR models, it is possible to explain the potency of the quinolones to inhibit CYP1A2 at a molecular level [174]. Multiple stepwise regression was used to correlate the inhibitory potency of caffeine N-dealkylation. The keto group, the carboxylate group and the core nitrogen at position 1 are likely to be the most important groups for binding to the active site of CYP1A2, because the molecular electrostatic potential of all inhibitors is very similar to that of caffeine in these regions. The determination coefficient between measured and calculated activity values was R2 = 0.56.

Moon et al. [56] used QSAR analysis of a set of 19 fla-vonoids (1) as CYP1A2 inhibitors. Multiple linear regression (MLR) and back propagation neural network were used as chemometric tools for the analysis. Models were developed from training set compounds (n = 14) and the developed models were validated (externally) using the test set com-pounds (n = 5). Statistical quality of the back propagation neural network model was superior to the MLR model. The analysis indicates the importance of Hammett constant, HOMO (highest occupied molecular orbital energy signifying nucleo-philicity), the non overlap steric volume and the partial charge of C3 atom and HOMO π coefficients of C3, C

/3 and C/

4 carbon atoms of flavonoids in CYP1A2 inhibition.

Table2.InhibitorsofdifferentCYPisoforms.

Isoenzymes Inhibitors

CYP1A2 Ciprofloxacin (fluoroquinolone bactericidal), enoxacin, erythromycin, ofloxacin (antibiotic agents), fluvoxamine (selective serotonin reuptake inhibitor antidepressant), verapamil (calcium channel blocker), ticlopidine (antiplatelet agent) and cimetidine (H2 receptor blocker)

CYP2A6 Methoxsalen, pilocarpine (muscarinic receptor agonist), tranylcypromine (antidepressant), ketoconazole (antifungal) and cannabidiol

CYP2D6 Citalopram, fluoxetine, paroxetine, sertraline (antidepressant), amiodarone, quinidine (antiarrhythmic), bupropion, duloxetine (antidepressant), terbinafine (antifungal), chlorpheniramine, hydroxyzine, promethazine (H1 receptor blockers), cimetidine, ranitidine (H2 receptor blockers), celecoxib (nonsteriodal anti-inflammatory drug) and cocaine (recreational drug)

CYP2C9 Benzbromarone, isoniazid, metronidazole, sulfamethoxazole, trimethoprim (antimicrobial agents), fluvoxamine, paroxetine, sertraline (antidepressant agents), amiodarone (antidysrhythmic agent), fluconazole, miconazole, voriconazole (azole antifungals), fluvastatin (statin) and zafirlukast (leukotriene antagonist)

CYP2C19 Moclobemide, fluoxetine, fluvoxamine, paroxetine (antidepressant), ketoconazole (antifungal agent) ticlopidine (antiplatelet agent), lansoprazole, omeprazole (proton pump inhibitor) and felbamate (anticonvulsant agent)

CYP2E1 Disulfiram (alcoholism rehabilitation drug), diethyldithiocarbamate and cimetidine (H2 receptor blocker)

CYP2B6 Orphenadrine (analgesic), paroxetine, fluoxetine, sertraline (antidepressant), estradiol (sex hormone), ritonavir (antiretroviral), thiotepa (alkylating antineoplastic agent) and ticlopidine (antiplatelet)

CYP2C8 Gemfibrozil (hypolipidemic), rosiglitazone, pioglitazone (antidiabetic), montelukast (leukotriene receptor antagonist), quercetin (anti-inflammatory) and trimethoprim (antimicrobial)

CYP3A4 Ciprofloxacin, clarithromycin, erythromycin, norfloxacin (antibiotic agents), fluvoxamine, nefazodone (antidepressant agents), amiodarone (antidysrhythmic agent), fluconazole, itraconazole, ketoconazole (antifungal agents), diltiazem, verapamil (calcium-channel blockers), grapefruit juice (food product), cimetidine (H2 receptor blocker), indinavir, nelfinivir, ritonavir and saquinavir (HIV protease inhibitors)

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QSAR study of 21 naturally occurring flavonoids (1) by Lee et al. [52] showed that biological activity is correlated with volume:surface area ratio, Hammet coefficient of substituents on B ring and electron density at C4 atom. Small volume:surface area ratio values of molecules show high inhibitory activ-ity indicating the importance of planar molecules for higher inhibition.

QSAR studies on inhibitory effects of flavonoids (1) on CYP1A1 and CYP1A2 have been reported using theoretical descriptors obtained from quantum mechanical calcula-tions [175-177]. More recently, a kinetic reactivity model pro-posed by Jung et al. has shown that CYP1A2 is responsible for the metabolism of planar-conjugated compounds such as caffeine [178].

Korhonen et al. developed 3D QSAR models using CoMFA and GRID interaction energy for CYP1A2 inhibitors. Fifty-two compounds were used in this study which included naphthalene, lactone and quinoline derivatives. Both CoMFA (R2 = 0.87, Q2 = 0.69) and GRID (R2 = 0.90, Q2 = 0.79) models were statistically significant. The CoMFA model proved to be predictive for six external compounds with the residual error varying from 0 to 0.67 log units. The CoMFA map indicated the importance of negative charge around the nitro-gen atom in the quinoline ring and electronegative substitutions at position 1 of the naphthalene ring [179].

A global CYP1A2 inhibition model was reported by Chohan et al. [180]. The training set was composed of 109 compounds and the validation set composed of 249 oral drugs. Four statistical tools, PLS (partial least squares), MLR, classification and regression tree and Bayesian neural networks, were used together with consensus modeling to build the CYP1A2 model. All the modeling techniques indicate the importance of lipophilicity, aromaticity, charge, HOMO and LUMO (lowest unoccupied molecular orbital) energies for potent inhibition. The fitted models have good R2 (R2 ranging from 0.72 to 0.84) and root mean square error (RMSE) values.

Recently, a classification technique for CYP1A2 inhibitors and non-inhibitors has been reported for a large data set (training and test sets consisted of ∼ 400 and 7000 compounds, respectively) [181]. Binary QSAR, support vector machine (SVM), random forest, κ nearest neighbor, and decision tree methods were used to develop in silico models based on Volsurf and molecular operating environment (MOE) descriptors. The

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combination of BestFirst variable selection method with SVM, random forest and κ nearest neighbor methods yielded the best models with 73 – 76% of accuracy on the test set pre-diction (Matthews correlation coefficients of 0.51 and 0.52). This model predicts 67% of the compounds correctly and gives a simple and interesting insight into the issue of clas-sification, and can be used as simple filters in the drug discovery process.

Roy and Roy [172] have recently reported a QSAR study for 21 naturally occurring flavonoids (1) using 2D (topological, structural and thermodynamic) and 3D (spatial) descriptors. The chemometric tools used for the analyses are stepwise MLR, PLS, genetic function algorithm (GFA) and genetic PLS (G/PLS). The generated QSAR models were of statistical significance both internally as well as externally. The derived QSAR equations suggest the importance of the double bond present at 2, 3-positions and requirement of absence of hydroxyl substituent or glycosidic linkage at the 3-position of the 1,4-benzopyrone nucleus. Furthermore, the phenyl ring pres-ent at 2-position of the 1,4-benzopyrone ring should not be substituted with hydroxyl group. Moreover, hydroxyl groups present at 5 and 7-positions of the benzopyran nucleus should not be glycosylated for good CYP1A2 enzyme inhibitory activity.

Recently, a CoMSiA analysis on a set of 36 flavonoids has been carried out by Li et al. [182]. The model with the com-bined electrostatic and hydrophobic fields was found to be statistically acceptable (the determination coefficient R2 of 0.948 and the cross-validation determination coefficient Q2 of 0.630). α-Naphthoflavone molecules showed significantly improved CYP1A2 inhibitory activity in comparison to flavones. It was noted that electropositive substituents or hydrophobic groups at the 6, 3′ and 4′ ring positions or electronegative counterparts at the 5 ring position can enhance the inhibi-tory potency against CYP1A2 according to the CoMSiA contour maps.

4.2 CYP3A4The first QSAR model of CYP3A4 ligands was on CYP3A4 substrates and it was built using CATALYST (Accelrys) based on the data derived from the literature [183]. CATALYST was used to build 3D QSAR pharmacophore models for CYP3A4 substrates. A combined 3D pharmacophore and 3D/4D PLS strategy was then applied to the CYP3A4 inhibi-tion data [184]. Three models were trained with competitive inhibitors of midazolam 1-hydroxylation (14 compounds) and cyclosporin A hydroxylation (32 compounds). Good observed versus predicted correlation was obtained from the three phar-macophore models (r = 0.91, 0.77, 0.92). The corresponding 4D-QSARs generated by PLS MS-WHIM for these data sets were of comparable quality as judged by cross-validation.

Wanchana et al. [185] reported a 2D QSAR model of 44 structurally diverse drugs using MOLCONN Z descriptors (EduSoft, Ashland, VA, USA), topological and electrotopological indices with genetic algorithm and PLS

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analysis as chemometric tools. The probe substrate was 7-benzyloxy-4-trifluoromethylcoumarin. The data set was divided into training (n = 35) and test set (n = 9) and leave-one-out cross validation was used to optimize the number of principal components and remove overfitting. The model gave reasonable predictions with an Rpred (leave-one-out) of 0.75 and Rpred (test) of 0.74 for the training and test sets, respec-tively. A comparison with 3D QSAR models was done but this exercise suggested that 2D QSAR models were comparable with 3D QSAR models in terms of predictability.

Riley et al. developed a generalized QSAR model for 30 diverse chemicals as inhibitors of CYP3A4. This model reflected the importance of lipophilicity and the applicability to selected set of compounds [186].

Several PLS models for inhibition of CYP3A4-mediated erythromycin N-demethylation were built using various molec-ular descriptors and compared for their predictivity [187,188]. Kriegl et al. used SVM parameter space in combination with extensive cross-validation to classify CYP3A4 inhibitors. A total of 1345 molecules were split into training set (n = 807) and a complementary test set (n = 538). Four groups of molecular descriptors were used: ‘in-house 2D descriptor’ encoding chemical properties based on the 2D structure; ‘MOE 2D descriptors’ including physical properties, con-nectivity and topological descriptors (Chemical Computing Group, Montreal, Canada); ‘VolSurf descriptors’ based on the molecular interaction field of the 3D structure; and ‘in-house QM descriptors’ encoding electrostatic properties derived from quantum mechanical calculations. All SVM models performed significantly better than PLS discriminant analysis models, which were generated from the corresponding descriptor sets. The SVM models correctly classified > 70% of the test com-pounds as compared to the PLS discriminant analysis best model which correctly classified 65% of all three classes for the test set. Multiple pharmacophore hypothesis is a conceptual extension of the traditional QSAR approach used to a large variety of drugs as CYP3A4 inhibitors [189]. Application of the approach to the in silico filtering of test compounds for potential inhibitors of CYP3A4 was also presented.

Pharmacophore models for 8-geranyloxypsoralen ana-logues (2) and their evaluation as inhibitors of CYP3A4 were reported by Row et al. [190]. The pharmacophore models were derived from two CATALYST hypotheses based on the inhibition of midazolam 1-hydroxylation and quinine metab-olism. The pharmacophore models provided good fit to fura-nocoumarin analogues (8-geranyloxypsoralen and bergamottin). One common structural feature in the pharmacophore models seems to be the presence of a hydrophobic bind-ing region along the alkyl chain and importance of furan moiety for interaction with the enzyme. 8-Alkyloxyfurano-coumarin analogues were found to inhibit CYP3A4 activity in a dose-dependent manner and with moderate potency [190].

Recently, a QSAR study of binding affinity of azole compounds with CYP3A and CYP2B was reported by Itokawa et al. [191]. The binding affinity data (measured as

pKd and pIC50) for 18 azole compounds (which include some commercial fungicides) were used as the model data set to build bilinear models. Good correlation with the bilinear model was observed between the binding affinities and partition coefficient (log P) (r = 0.988 for pIC50 and 0.981 for pKd). The model suggested an optimum log P value of the azole compounds and indicated the importance of imidazole moiety for the binding affinity. Using the same data set, Roy and Roy [192] developed QSAR models for the binding affinity as well as selectivity models using GFA and G/PLS techniques as chemometric tools. The analyses were performed using elec-tronic (Apol, Dipole, HOMO, LUMO and Sr), spatial (radius of gyration, Jurs descriptors, shadow indices, area, PMI-mag, density, Vm), topological (E-state index, κ shape index, molec-ular connectivity index, subgraph cont, information content indices) and thermodynamic (AlogP, AlogP98, Molref) descrip-tors. The derived binding affinity models are of high statistical quality (leave-one-out Q2 ranging from 0.946 to 0.977). The selectivity models are also statistically sound (Q2 ranging from 0.680 to 0.761). The models indicate that the binding affinity of these compounds was related to topological, steric, electronic and spatial properties of the molecules. Comparative QSAR study using different statistical techniques was also reported recently for CYP3A4 inhibitors [193]. Twenty-eight structurally diverse CYP3A4 inhibitors were classified into training (n = 22) and test (n = 6) sets, and the QSAR models were developed using electronic, spatial, topological and thermodynamic descriptors. The statistical tools for the analysis were MLR with factor analysis as preprocessing step, stepwise MLR, PLS, GFA, G/PLS and artificial neural network (ANN). All the models were statistically significant based on the internal and external validation criteria. All the five linear modeling methods indicate the importance of n-octanol/water log P along with different topological and electronic parameters. The authors used r2

m(overall) criteria for choosing the best model from among the comparable models.

Molnar and Keseru presented a neural network model differentiating inhibitors and substrates of CYP3A4 based on the literature data for 290 compounds [194]. The ANN model based on the 2D unity fingerprint descriptors allowed the dis-crimination between inhibitors and non-inhibitors of CYP3A4. The approach allowed the authors to classify correctly 97% of inhibitors and 95% of non-inhibitors and externally predict correctly eight out of nine CYP3A4 inhibitors.

Another virtual screening filter for identifying CYP3A4 library was developed using PLS regression [195]. The PLS model with different molecular descriptors was able to predict cor-rectly attribute of 95% of the molecules of the training set (n = 311) and 90% of a semi-independent validation set of 50 compounds. This approach gave better results than the ANN approach applied on the same data set [195].

4.3 CYP2D6QSAR analysis of CYP2D6 inhibition has typically utilized recursive partitioning (RP) techniques. Ekins et al. [13] used

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a commercially available data set of 1750 compounds to build RP trees with augmented atom descriptors. A statistically sig-nificant rank ordering of the test set was achieved (Spearman’s ρ = 0.61; training set r2 = 0.88). This represented an increased rate of identifying the best compounds when compared with the random rate. A similar analysis for CYP3A4 gave rise to an equally valuable model (Spearman’s ρ = 0.48; training set r2 = 0.82). This type of analysis compares favorably with that obtained from homology modeling and molecular dock-ing, in which good prediction statistics are obtained for 2D6 inhibition (r2 = 0.61, q2 = 0.59) [196].

RP was also applied to CYP2D6 inhibition by Susnow and Dixon [197]. The model utilizes 2D structural descriptors to classify a compound as either inhibitor or non-inhibitor and assigns a confidence level to that prediction. RP approach generated a large number of trees from a diverse training set of 100 compounds and each tree gives a binary prediction on a given compound. Internal and external validation tests indicate that correct classifications may be expected 75 – 80% of the time.

RP was also applied to CYP450 2D6 and 1A2 inhibition by Burton et al. [198]. Two global data sets of 498 and 306 compounds were available for CYP2D6 and CYP1A2, respectively. The building of models was preceded by the evaluation of the chemical space covered by the data sets. The descriptors used are those available in the MOE software suite. The models reached at least 80% of accuracy. CYP2D6 data sets provided 11 models with accuracy > 80%, while CYP1A2 data sets counted five high accuracy models.

Alternatively, neural networks have been applied to model 2D6 inhibition. Two neural networks and one Bayesian model were constructed using data from fluorescence inhibition assays of ∼ 2400 compounds [199]. Recently, Bazeley et al. used docking scores and compound properties as attributes for building a neural network model for CYP2D6 inhibition. Low energy conformations of CYP2D6 were generated using sim-ulated annealing and a collection of 82 compounds with known CYP2D6 affinities are docked. A prediction accuracy of 85 ± 6% was achieved. Compound’s formal charge, number of aromatic rings and AlogP were found to be important for binding to the enzyme. In another study, metabolic stability of CYP2D6 was assessed by three-point pharmacophore-fingerprinting package (triplets of pharmacophoric points) [200].

Jensen et al. [201] used Gaussian kernel weighted k-nearest neighbor models based on extended connectivity fingerprints that classify CYP2D6 and CYP3A4 inhibition. Data used for modeling consisted of diverse sets of 1153 and 1382 drug

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candidates tested for CYP2D6 and CYP3A4 inhibition in human liver microsomes. For CYP2D6, 82% of the classified tests set compounds were predicted to the correct class while 10 – 14% of the compounds were not classified. It was found in the study that in silico models based on Tanimoto similarity searches on extended connectivity fingerprints could be used to select CYP2D6 and CYP3A4 non-inhibitors in an early stage of discovery projects.

Recently, a stereo isometric effect on the bioactivities of the chiral isomers quinidine and quinine interacting with CYP2D6 was reported [202]. CoMFA CoMSiA, molecular electrostatic potential analysis and docking method were used for the study. CoMFA model with steric and electrostatic fields exhibiting 0.67, 0.99 and 0.88 of Q2, R2 and R2

pred, respectively, and a CoMSiA model with steric, electrostatic and H-bond acceptor fields displaying values of 0.72, 0.97 and 0.84 for Q2, R2 and R2

pred, respectively, were obtained.Vaz et al. [203] performed a CoMSiA for CYP450 2D6

inhibition on a series of aryloxypropanolamines (3). The study suggests U shape conformation for the optimal activity and requirement of bulky substituents on the nitrogen of aryloxy-propanolamines moiety, which was supported by large hydro-phobic pocket formed by the residues such as Phe120, Val370, Met374, Phe483 and Leu484 [204]. The results were in agree-ment with a recent study on the same data set by Roy and Roy [205]. These authors performed molecular shape analysis and molecular field analysis. Impact of 2D descriptors was also explored in the study. The whole data set (n = 36) was split into a training set (n = 26) and a test set (n = 10) by K-means clustering technique. The models were developed from the training set and predictive performance of the models was measured on the test set compounds. All the models were statistically significant both internally and externally. Contrary to the earlier study, these models were able to explain impor-tance of appropriate substituents in both the phenyl and indole rings for potential inhibition towards CYP2D6 enzyme. Validation of the developed models was also reported which was absent in the previous study by Vaz et al. The molecular shape analysis models indicate the importance of distribu-tion of positive and negative charges on the surface of the molecules. The QSAR models with 2D descriptors reveal the importance of bulk, branching and presence of different fragments. In another study [206], O’Brien and de Groot used neural network and Bayesian approaches to model CYP2D6 inhibition data of 600 compounds (106 positives, 494 nega-tives). They demonstrated the advantage of the consensus learning with CYP2D6 inhibition models [206].

4.4 CYP2A6Poso et al. studied CYP2A6 enzyme and reported several 3D QSAR models [207-209]. A data set of 23 CYP2A5/6 inhibitors were analyzed using CoMFA and GOLPE/GRID and validated their models using an external test set of five compounds. All models have high internal (Q2 > 0.7) and external predictive power (R2

pred > 0.75) and resulting 3D QSAR models support

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each other. CoMFA and GOLPE studies were able to high-light that the properties near the lactone moiety are important for inhibition. The size of the substituents at the 7-position of coumarin was also deemed significant. In addition, the maps reveal that 2A6 disfavors negative charge near the lactone moiety of coumarin. They also studied the importance of CoMFA models in predicting the inhibition potential of naphthalene towards mouse CYP2A5 and human CYP2A6 enzymes. The results indicate that the CoMFA model was a convenient and useful tool to evaluate the quantitative inter-action potency of naphthalene with CYP2A5 and CYP2A6. In another study on a series of 26 naphthalene and 16 non-naphthalene derivatives, several CoMFA models were developed to find out what types of steric and electrostatic properties are required for potent inhibitors. CoMFA maps including LUMO (energy signifying electrophilicity) fields revealed novel properties required for the inhibitors. The potent inhibitors found in their study were 2-bromo and 2-fluoro naphtha-lenes but these were not as potent as methoxsalam due to lack of hydrogen bond acceptor atoms capable of interacting with Asn297.

Classical Hansch-type, CoMFA and GRID/GOLPE methods were applied to 28 coumarin 7-hydroxylation inhibitors for CYP450 mouse CYP2A5 and human CYP2A6 enzymes and 30 11β-, 16α- and 17α-substituted estradiol derivatives (4) for predicting the biological activity of estradiol and CYP450 ligands [210]. External predictability of the models was tested with several randomized training and test sets to ensure the validity and reliability of the models. Hansch-type QSAR gave the best external performance in the 500 randomized test sets with CYP2A5 data, although electronic Eigenvalue with MgVol parameters and one PLS component performed almost equally well. In general, the results indicate that the spectroscopic EVA and electronic Eigenvalue methods provide a promising alternative to conventional QSAR methods for predicting the biological activity of estradiol and CYP450 ligands.

Poso et al. developed 3D QSAR models to find out which structural characteristics are important for inhibition potency towards CYP2A6 enzyme [211]. Hydrophobic and hydrogen donor features were found to affect the inhibition potency. A total of 22 candidate molecules were selected and tested for the inhibition potency. They suggest N1-(4-fluoro-phenyl) cyclopropane-1-carboxamide compound to be a lead in the design of CYP2A6 inhibitor drugs to combat nicotine addiction.

Van Damme and Bultinck [212] used discrete Fourier transform properties such as electron density, HOMO,

LUMO and Fukui f function in the steric and electrostatic field as 3D fields. Structure–activity relationships of 46 P450 2A6 inhibitors were analyzed using the 3D QSAR method-ology and external prediction was carried against a test set of five compounds. Electron density and Fukui f function appeared in the best model with high statistical quality (R2 = 0.82 and Q2 = 0.72) and this indicates the impor-tance of discrete Fourier transform properties in 3D QSAR molecular interactions.

Recently, Roy and Roy [213] have explored QSAR and QAAR studies on a series of naphthalene and non-naphthalene derivatives (n = 42) having CYP450 2A6 and 2A5 inhibi-tory activities. The analyses were performed using electronic, spatial, shape and thermodynamic descriptors to develop quan-titative models for prediction of the inhibitory activities and to explore the importance of different descriptors for the responses. The data set was divided into training and test sets (with test set size ∼ 25% of the full data set size) based on K-means clustering applied on the standardized descriptor matrix. GFA and G/PLS were used as chemometric tools for modeling although different equations varied in quality in a wide range: R2: 0.561 – 0.898, Q2: 0.495 – 0.814 and R2

pred: 0.615 – 0.914. The CYP2A5 and CYP2A6 inhibition of these compounds is related to charge distribution, surface area and electronic, hydrophobic and spatial properties of the molecules. These observations are in agreement with the CoMFA results of the previous study [209] which showed the importance of charge distribution for the binding affinity.

Very recently, Leong et al. have developed a new predictive model to study interactions with human CYP450 enzyme using pharmacophore ensemble/SVM (PhE/SVM) approach [214]. The total data set was split into a training set (n = 24) and a test set (n = 9) with the idea that training set molecules spans the whole range of activities. Conformational ensembles of each molecule were generated by the Macro Model package (Schrödinger, Portland, OR, USA) using mixed Monte Carlo multiple minimum and pharmacophore generation was carried out in CATALYST. The prediction of PhE/SVM models were satisfactory for the training set (R2 = 0.94, Q2 = 0.85, RMSE = 0.30) as well for the test set (R2 = 0.96, RMSE = 0.29). The developed in silico models were also subjected to prediction of one set of benzene and naphthalene derivatives (n = 45, R2 = 0.81, RMSE = 0.46) and one set of amine neurotransmitters (n = 4, R2 = 0.98, RMSE = 0.32). The results obtained were good agreement with the experi-mental ones. In this work, an in silico model, based on the combination of PhE, which takes into account protein plas-ticity while interacting with structurally distinct small mol-ecules, and SVM, which provides robust and fast regression, has been built to accurately predict the interactions between CYP2A6 and its inhibitors with excellent predictability and statistical significance. PhE/SVM model can be used as a tool for predictions and a device for high-throughput screening and data mining to facilitate drug discovery by reducing the attrition rates [214].

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4.5 CYP2C9The first QSAR analysis for CYP2C9 inhibition was carried out for 19 coumarin derivatives, 5 carboxylate containing drugs, 2 sulfonamides and phenytion by Jones et al. [215]. The generated CoMFA model had a Q2 of 0.7 and standard error of fitting was 0.17 log units. They refined their study by a combined CoMFA-homology model for the enzyme. F110 and F114 were identified as potential hydrophobic, aromatic active-site residues [216]. A refined CoMFA model was also reported and this CoMFA model was used to predict the affinity of 14 structurally diverse compounds not in the training set. Leave-one-out cross-validated partial least-squares give a Q2 value between 0.6 and 0.8 for the various models indicating internal consistency. Random assignment of bio-logical data to structure leads to negative Q2 values [217]. Another 3D QSAR analysis was applied to two classes of reversible inhibitors, the benzbromarones and the N-3 sub-stituted phenobarbitals, to study the active site characteristics of CYP2C9 and 2C19, respectively [218]. As benzbromarones or the phenobarbital ligands bind very tightly, it can be assumed that these structures complement the binding pocket(s) for these enzymes. The models could be useful to predict drug–drug interactions.

Afzelius et al. reported the use of alignment-independent descriptors for obtaining qualitative and quantitative predic-tions of the competitive inhibition of CYP2C9 on a series of highly structurally diverse compounds [219]. Alignment independent descriptors were calculated in ALMOND. These Grid Independent Descriptors (GRIND) represent the most important GRID-interactions as a function of the distance instead of the actual position of each grid-point. The inhibitor data set consisted of 35 structurally diverse competitive stereo-specific inhibitors of the CYP450 2C9 and non-inhibitor data set of 46 compounds. PLS discriminant analysis with a two component model was obtained with an R2 = 0.74 and Q2 = 0.64. The model was externally validated with 39 compounds (14 inhibitors/25 non-inhibitors). The model was able to correctly predict 74% of compounds that made up a test set and 13% of compounds were predicted as borderline cases. The authors also generated a quantitative model for 21 competitive inhibitors using PLS and GRIND descrip-tors. The model with R2 = 0.77 and Q2 = 0.60 was able to predict 11 out of 12 external test set compounds in 0.5 log units of the measured data. Other alignment independent

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models were reported in the literature for diverse competitive CYP2C9 inhibitors. The same group of authors also developed a 3D QSAR model for 29 structurally diverse CYP2C9 inhibitors [220]. The authors combined 3D QSAR and homology modeling approach. The data set was divided into a training (n = 21) and validation set (n = 8). The GRID/GOPLE model was able to predict the activities of external data set in 0.5 log units of the observed values. The same group also studied alignment and conformer independent 3D QSAR models for CYP2C9 inhibitors with GRIND descriptors [221]. But the GRIND descriptors were calculated from flexible molecular interaction fields calculated in GRID and the subsequent description of these fields was made by the use of alignment independent descriptors derived in ALMOND.

Recently Peng et al. [222] reported CoMFA analyses for a series of 4H-chromen-4-one analogues (5) which were struc-turally similar to benzbromarone. A CoMFA model for the 11 compounds showed a Q2 of 0.836 with ln(standard error) of 0.243. This model reflected that steric factor was more weighted than the electrostatic factor. With 83 compounds and 6 components, the derived CoMFA model was weighted with steric and electrostatic factors equally and it gave a Q2 of 0.721 with ln(standard error) of 0.822. It was also revealed that electrostatic factor was slightly more important than hydro-phobic interaction. Favorable electrostatic and hydrophobic interactions of all the binding modes were responsible for tight binding of these compounds.

In a recent study [223], in silico models were generated to predict the extent of inhibition of CYP450 isoenzymes (CYP1A2, CYP2C9, CYP19, CYP2D6 and CYP3A4) using a set of relatively interpretable descriptors in conjunction with PLS and regression trees. The compounds selected for screening consisted of 384 oral drugs and 1152 compounds selected from AstraZeneca compound collection. These 1536 compounds represent a diverse set of compounds spanning many chemotypes. The models range from low to moderate predictivity, but all performed considerably better than random and thus could prove useful in assessing the P450 liability of molecules for a particular isoform. The 3A4 in silico model was considerably more predictive than models for other iso-forms, possibly due to its more open active site, giving rise to less stringent molecular recognition requirements. In contrast, only poorer models could be produced for 2D6 and 2C19 using bulk molecular descriptors alone suggesting that molec-ular recognition may play a more important role for these. The authors also demonstrate that the hybrid models using bulk descriptors and fragmental descriptors did significantly better in modeling CYP450 inhibition than bulk property QSAR descriptors alone [223].

Recently, Byvatov et al. [224] have constructed a QSAR model for virtual screening of CYP2C9 inhibitors. A total of 1100 structurally diverse molecules were tested for CYP2C9 inhibition under identical conditions. Chemical structures were encoded using various 2D descriptors including three-point pharmacophoric fingerprints and the statistical models used

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were SVM and PLS. The predictive ability of the models was judged on test sets of 238 diverse and 344 GPCR-targeted compounds. These models collectively are able to identify true CYP2C9 inhibitors with relatively low rates of false positives and discriminate the inhibitors of CYP2C9 from the inactive molecules. The three-point pharmacophoric-based filter also allows visualizing important substructures and func-tional groups, which are linked to protein–ligand interactions for CYP2C9, as illustrated for selected structure–activity series. The authors also demonstrated the models to consistently provide guidelines for reducing CYP2C9 inhibition in novel candidate molecules [224].

4.6 OtherCYPsQSARs for a series of CYP2C9 and CYP2C19 inhibitors were reported by Lewis et al. [225]. Lipophilicity (log P) was an important factor in explaining the variation in inhibitory potency. Locuson et al. reported a CoMFA model of benz-bromarone analogues (6) along with phenobarbital analogues as CYP2C19 inhibitors [226]. The developed models were statistically significant internally (Q2 varies from 0.5 to 0.7). The important features for binding were low acidity and hydrophobic substituents adjacent to phenol moiety. Korhonen et al. [227] reported a CoMFA model for CYP2B6 inhibitors to identify novel potent and selective inhibitors of CYP2B6 for in vitro research purposes. Two CoMFA models

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were created which revealed the key molecular characteristics of inhibitors of the CYP2B6 enzyme. The created CoMFA model was of high quality with the following statistical values with two components: Q2 = 0.71, SPRESS = 0.64, R2 = 0.85 and predictive R2 = 0.80. The created CoMFA model was of high quality and predicted accurately the inhibition potency of a test set (n = 7) of structurally diverse compounds. CBP [4-(4-chlorobenzyl) pyridine], BP [4-benzylpyridine] and NBP [4-(4-nitrobenzyl) pyridine] were identified as novel, potent and selective inhibitors of CYP2B6 and among these compounds, CBP especially is a suitable inhibitor for in vitro screening studies. Recently, a QSAR study for CYP2B6 inhib-itors was reported by Roy and Roy [192]. CoMFA for a series of new aromatase inhibitors consisting of imidazole, triazole ring linked to fluorine and indenodiazine or coumarin scaf-fold was reported by Leonetti et al. [228]. Steric and electro-static fields were the main attributes for aromatase inhibitory potency. Classical QSAR, CoMFA and CoMSiA analysis were reported for flavones derivatives (1) as aromatase inhib-itors by Nagar et al. The developed models were statistically significant and they indicate the importance of steric and hydrophobic properties and contribution of hydrogen bond acceptor and donor properties of the molecules. In a recent study, Castellano et al. tried to explore the optimum molec-ular scaffold flexibility for aromatase inhibitors (7) lacking hydrogen bond accepting substituents using CoMFA methodology [229,230].

5. Expertopinion

Over the past few years, there has been increasing pressure on the pharmaceutical industry to produce a continuous pipeline of beneficial drugs. Predictive ADMET is the hip area in drug discovery now-a-days. The field of computa-tional (in silico) ADMET is receiving increased attention due to better appreciation that these molecular properties should be considered early in drug discovery process. In the field of metabolism, the particular interest has been the resolution of crystal structures of different metabolic enzymes (especially CYP450 enzymes). Although we are facing unprecedented blossoming of new computational models for predicting metabolic end points, still there is much to do to increase the confidence in in silico models. Most of the reported models lack sufficient interpretability and offer poor predictivity for novel drugs. These may be due to inaccuracy or inconsistency of data sets. Curation of data according to quality and rele-vance will enhance the probability of obtaining good predictive models. Often the success of models lies in their use and the expectations of the user. In this respect, tools showing even a small gain compared to random choice may suffice to enrich libraries with druggable compounds. There is a lot of work already done in the field of CYP450 enzymes computation-ally. Further work has to be done with different clinically significant CYP families. CYPs have been largely overlooked in cancer drug development for their important role in

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Phase I metabolism of chemotherapeutics. Potent inhibitors of aromatase (CYP19) have been recently used as first-line drugs for the treatment of breast cancer, while inhibitors of CYP17 for the treatment of prostate cancer. CYP1A2, CYP2A6, CYP2E1, CYP2S1 and CYP2B6 enzymes are responsible for various cancers. Work has been already done with CYP2A6, CYP1A2 enzymes and a little bit on aromatase enzymes but a lot of work has to be done on the remaining families. Another important enzyme of this class is aldosterone synthase (CYP11B2) which plays a major role in congestive heart failure and hyperaldosteronism and myocardial fibrosis. Recent studies indicate that inhibitors of CYP11B2 could be a prom-ising alternative to mineralocorticoid receptor antagonist and ACE inhibitors. But QSAR models for this enzyme are still rare. Powerfulness of computational tools can enhance the drug

discovery process of new chemical entity. The docking approach, however, might be of a limited utility because of the large binding sites revealed in the crystal structures of many CYPs. QSAR techniques will be more important for isoforms not having crystal structures. Crystal structures of limited number of human CYPs (CYP2C9, CYP2C8, CYP3A4, CYP2A6, CYP2B4 and CYP2D6) are available [231]. The ligand-based approaches will continue to play the key roles but adoption of mixed (ligand- and structure-based) or consensus approaches in this field may improve the situation.

Declarationofinterest

PP Roy thanks the University Grants Commission (UGC), New Delhi for a fellowship.

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AffiliationKunal Roy† & Partha Pratim Roy†Author for correspondenceJadavpur University, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Drug Theoretics and Cheminformatics Lab, Kolkata 700 032, India Tel: +91 98315 94140; Fax: +91 33 2837 1078; E-mail: [email protected]

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QSAR of cytochrome inhibitors

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