Indian Journal of Biochemistry & BiophysicsVol. 43. December 2006, pp. 360-37 I

3D-QSAR of histone deacetylase inhibitors as anticancer agents by geneticfunction approximation

Nilesh K Wagh, Hemantkumar S Deokar, Dhanshri C Juvale, Shivajirao S Kadam and Vithal M Kulkarni'"Department of Pharmaceutical Chemistry, Poona College of Pharmacy. Bharati Vidyapeeth Deemed University.

Erandwane, Pune 411038. India

Received 08 June 2006: revised 31 Angust 2006

Histone deacetylases (HDACs) playa critical role in gene transcription and are implicated in cancer therapy and otherdiseases. Inhibitors of HDACs induce cell differentiation and suppress cell proliferation in the tumor cells. Although manysuch inhibitors have been designed and synthesized, but selective inhibitors for HDAC isoforrns are lacking. Varioushydroxamic acid analogues have been reported as HDAC inhibirors. Here. we report a three-dimensional quantitativestructure-activity relationship (3D-QSAR) study performed using genetic function approximation (GFA) for this class ofmolecules. QSAR models were generated using a training set of 39 molecules and the predictive ability of final model wasassessed using a test set of 17 molecules. The internal consistency of the final QSAR model was 0.712 and showed goodexternal predictivity of 0.585. The results of the present QSAR study indicated that molecular shape analysis (MSA),thermodynamic and structural descriptors are important for inhibition of HDACs.

Keywords: 3D-QSAR; Genetic function approximation, Anticancer agents. Histone deacetylases, Hydroxamic acidanalogues, Physico-chemical descriptors

Cancer IS a serious disease with a complexpathogenesis, which affects human life all over theworld. Thus, great efforts are being made to identifynovel anticancer targets and to discover newanticancer drugs. During the past 5 years, thesequencing human genome has provided us with anenormous number of potential targets associated withcancer therapy. As a result, the new drug discovery(NDD) is undergoing a transition "from gene todrug". Accordingly, targets for anticancer drugs arenow focused on some biological macromoleculartargets like farnesy I transferase (FTase) I, protei nkinases and histone deacetylases (HDACs)3, whichare associated with cancer and several interactivemechanisms, involved in the growth and metastasis ofcancer cells as well as tumor angiogenesis".

+To whom correspondence should be addressedTel: + 91-20-25437237Fax: + 91-20-25439383E-mail: vivivips5@gmail.comAbbreviations: CoMFA, comparative molecular field analysis;3D-QSAR. three-di rnensional quantitati ve structure-activityrelationship; Prase, farnesyl transferase; GFA, genetic functionapproximation; HATs, histone acetyltransferases; HDAC, histonedeacetylase; HDLP, histone deacetylase-like protein; LOF. Lack-of-Fit; MARS, multivariate adaptive regression splines; MSA,molecular shape analysis; NDD, new drug discovery; SAHA,suberoylanilide hydroxarnic acid; TSA, trichostatin A; VDW. vander Waals.

In eukaryotic cells, histone acetylation/deacetylation, which is co-regulated by enzymescalled histone acetyltransferases (HATs) and HDACsis essential for chromatin remodeling and thefunctional regulation of gene transcriptions. HDACsmodulate the deacetylation of s-amino groups ofIysines, located near the N-termini of core histoneproteins". Deregulation of HDAC activity IS

implicated in malignant diseases". HDACs constitutethree distinct structural classes, operated by zinc-dependent (class IIII) or NAD-dependent (class III)mechanisms. Class IIII HDACs are emerging astherapeutic targets for the 1reatment of cancer andother diseasest!". These enzymes, as part of multi-protein complexes catalyze the removal of acetylgroups from lysine residues on proteins, includinghistones.

HDAC inhibitors have been shown to bind directlyto the HDAC active site and thereby block substrateaccess, causing a resultant accumulation of acetylatedhistones8.,o.'3. These agents possess diverse biologicalactivities and can affect differentiation, growth arrest.and/or apoptosis in transformed cell cultures. In vivostudies have also demonstrated many of these agentsto be effective in the inhibition of tumor growth".Small molecules having a hydroxarnic acid functionalgroup, such as the natural product trichostatin A(TSA)'S or analogues'", suberoylanilide hydroxamic

acidhydHD.

con'chahydof'chatofur.theresactin]'ab:Wl

strSPsh.ofSi:dasuanthh)

at

ation/~ymesDACsi theDACsps ofistonety is.hitute

zinc-ss III)ng aser andmulti-acetyl

:luding

lirectlybstratetylated.logicalarrest,

In vivoagents

owth 14.ictional'atin Aoxarmc

W AGH et al.: 3D-QSAR OF HISTONE DEACETYLASE INHIBITORS 361

acid (SAHA) 17, eneyne oxamflatin" and the non-hydroxamate inhibitors 19 (Fig. 1) are highly. potentHDAC inhibitors. TSA and its analogues areconsidered to be mimics of histone acetyl-lysine sidechain and show common features as (i) .a largehydrophobic region that binds to the hydrophobic partof the enzyme near the active site, (ii) an aliphaticchain usually consisting of 5 or 6 carbons is attachedto the hydrophobic region, and (iii) an activefunctional group which is attached to the other end ofthe aliphatic chain, interacts with the Zn +2 ion and theresidues at the active site to disrupt the enzymaticactivity of HDAC20.21. Most of the reported HDACinhibitors are non-class-selective and little is knownabout the structure-activity relationship associatedwith class selectivity.

Recently, Wang et al.22 reported quantitativestructure-activity relationship (QSAR) on TSA andSAHA-like hydroxamic acid analogues and found thatshape, charge on carbonyl carbon and hydrophilicityof molecules are important for the biological activity.Similarly, Xie et al.23 in a QSAR study on diversedata set of 124 molecules, which includes differentstructural classes e. g., TSA analogs, SAHAanalogues, tetrapeptides and benzamide analogs foundthat van der Waals (VDW) surface area ofhydrophobic atoms is important for the activity.

In this paper, we report a 3D-QSAR study usinggenetic function approximation (GFA) method togenerate different models from the variousdescriptors. We followed the methodology usedpreviously for 3D-QSAR models on antifungal",antibacterial'", antitubercular'", antidiabetic." activitiesand cyclooxygenase-2 inhibitors28

.

o

~

oI ~"....

-N ~ N-OHI H

o 0

d~1;I~wOH~ H ,

-N . HI

TSA AnaloguesTrichostatin A

o 0~~~WOHU '=;r ~',

I HTSA Analogues

o)IN

~

H'0H4-

I~~

oHN,S'0''0

o9 II

o-~~~-OH

SAHA

~ 1. ,0y~.~~-:'tNH, ~

Oxamftatin SAHA ba8&d non-hydroxamate

Fig. l-Structure of HDAC inhibitors

Material and Methods

Chemical dataTo obtain a reliable and robust QSAR model, it is

desirable to build the model, based on a large data setthat covers reasonable chemical diversity andbiological activity spread. A prerequisite for QSARstudy is a congeneric series of compounds".Therefore, we have considered only hydroxamic acidanalogues of trichostatin A for the 3D-QSAR study.In the present study, a set of 56 molecules (Tables 1aand lb), belonging to hydroxamic acid analogues wastaken from the literatureIS.a,30,31.

The training and test sets were selected to cover allstructural features and biological activities. Selectionof the training and test set molecules was done byconsidering the fact that test set molecules represent arange of biological activity, similar to that of thetraining set. The mean biological activity of thetraining and test sets was 7.029 and 7.163,respectively, We have divided the molecules, in orderto have all possible structural features in both thetraining and test sets. A training set containing 39molecules (Table 1a) was used for generation ofQSAR models, whereas a test set of 17 molecules(Table lb) to test the predictive ability of generatedmodels.

Biological activityThe ICso values, normalized ICso values against

HDACl, and the negative logarithm of normalizedICso (prCso) of molecules were taken from theliterature and used in the present study",

Molecular modeling

SoftwareAll the molecular modeling studies were carried out

using Cerius2 (version 4. 1OL)32 running on Red HatLinux 3.0 WS operating system on Intel Pentium IV3.0 GHz processor. Structures were constructed andpartial charges were assigned using the chargeequilibrium method':' within Cerius2. Throughout thestudy, the universal force field 1.02 was used. Themolecules were subsequently minimized until a rootmean 'square deviation 0.001 kcallmol A wasachieved. Conformational analyses of all themolecules were performed using random samplingsearch with maximum number of conformers set to150. All molecules were aligned on lowest energyconformer of the most active molecule l7. Thealignment of training set molecules is shown in Fig. 2.

t

362 INDIAN J. BIOCHEM. BIOPHYS., VOL. 43, DECEMBER 2006

Table la-Structures and biological activities of the training set molecules

No. Structure

6

Observed pIC,.

7.30

8.00

7.54

7.28

7.36

6.95

6.99

7.15

6.30

~.3S

6.82

7.30

7,02

7.J5

S.:lO

R.W

No. Structure

I3 7.02

7.35

8,30

~.IO

R,W

8.35

7.60

7.-1·6

6.00

6.00

6.7{l

COllld-

8

9

10

II

12

14

--0 0

~~ '~-OH

o 0~~_OHBr)l) ,

? 0~_OH

15

16

1·1

15

If>

17

I~

I~

20

:'1

Z.l

.cd i"C~1)

.02

'.35

uo

~1.10

PO

8m

8.35

S.2~

7.60

7,.16

(lJ)(1

6.00

6.7l,I

COl/ld-

WAGH et al.: 3D-QSAR OF HISTONE DEACETYLASE INHIBITORS

Nu. Structure

Table la-Structures and biological activities of the training set molecules - Contd

26 ° ""}cr'"''s~)::)1(~,OH

~ H

Observed pIC""

5.70

4,'7

7.12

7.00

6.52

6.15

6.10

No. . Structure

33

~

oO,:---P -;7 ::::,..

0,'i 1 N,OH

F I' '-':: '~::::,.. H

F-\-O ~F

34o

O,'IP ~ OHj)'s,~AJ ~'

363

270",9 ~IT·/,yjl OHcrS'~N I ~'

36 ° ~::,....ONS'lO I N,OHxG '~~ H

37 ° ~::::,..O'0/..0 1 N,OH

1 ~S'N ::::,.. H

o H

Observed pIC .•,

6.22

6.52

7.00

(>,22

7.22

HIS

Observed pIC.\O

7.35

8.30

Contd-

28o

o 0 rrr""'" /IrYS"N-V j,~,OH

CI/V H

29 . 0O,O~::::,..II

JQI"S' 1 ~N-OH

1 '-':: '~::,.... H

CI r-?

CI

30. 0

o,O~:::""

j):'l 1 W

OH

1 '-':: 'N:::"" H

CI ~ HCI

31

~

oo 0 //"II" :::,... OH

(YS,~ ::::,..1 W02N~

32o

O"p ~ OH9. (YS,~AJ ~'

H/S~

2N \'o

38 <> ~ OH(YS~NA) ~'

O~ H/

39OCI~

Y'-'::'\:~0::::'" ~,OH

° ~ H/ '0

\

"

f/

,Table 1b - Structures and biological activities of the training set molecules

No. Structure

40 do °I: ~~/OHCI ~

do °I: ~~/OH

°2N ~

41

Observed plCso No. Strucrirre

7.13

6.52

49~,OH

50

\F

F F

° 0

~~'OH

,

364 INDIAN 1. BIOCHEM. BIOPHYS., VOL. 43, DECEMBER 2006

Table Ib-Structures and biological activities of test set molecules - Contd

No. Structure ObservedpIC.\{) No. Structure

OH0 0 51 N

42d7~/OH

7.05

~~_C><-N ~ ~ . ..,,;;. ~ .

I , OHdO 52 N43

I~ ~~/OH7.52 I

HO b . ~

~wC><

044 5.82 53

o 0d~_OH~/I IVS'~ ~ .45 7.19 0

54o O~~-OH':--II I

0 vS'~~46

~,OH6.87 Ib

~0

~ 55o O~~-OH':--// I

0 0 VS'N ~47

~~'OH7.46 I b 1I '-'::~

b"';; .56

o O~WOH

48o 0 £S:~'"I

~N'OH7.82 Ib? b H

F F F

'I'~' r "

\ I·~/

-.

, , /..;; .•. -c .,?

,~ ,~,

I ,wt'~/

r, "

\,~

::0

'" -. . 1 \1,",\ . ."

r.

';> ~ - , 'fit

~~~r i-"':~ " . /,/ - :.•... "~ ~r

I 'If ~).-r ,I\,} // " j 1

.-4

-, r ,, \ \'

t

"

Fig. 2-Alignment of training set molecules

Calculation of descriptors .,Different types ~f 'd~scriptors were calculated for

each molecule using default settings within Cerius2.Total 34 descriptors, belonging to different categories

ObservedplC50

g.07

8.40

6.05

7.00

6.22

7.00

like electronic, spatial, structural, thermodynamic andmolecular shape analysis (MSA) were calculatedusing Cerius2. A complete list of descriptors used inthe study is given in Table 2. MSA descriptors werecalculated using MSA34 module within Cerius2 Themost active molecule 17 was used as shape reference.

Generation of QSAR modelsQSAR analysis is an area of computational

research, which builds models for biological activityusing physico-chemical properties of a series ofmolecules. The underlying assumption is that thevariations in the biological activity within a series canbe correlated with changes in measured or computedmolecular features of the molecules. In the presentstudy, QSAR models were generated using GFAtechnique. GFA developed by Rogers, involved thecombination of Friedman's multivariate adaptive

regre,genetithat bfollovgenenfitnes.(LOF:wherebasisparamequahall te:mol eelpopula"cross

WAGH et ai.: 3D-QSAR OF HISTONE DEACETYLASE INHIBITORS 365

ved plC.lo

8.07

8.40

6.05

7.00

6.22

7.00

mic andlculatedused in

irs werels2. Theerence.

itationalactivity

eries ofthat theeriescanornputedpresent

19 GFAIved theadaptive

No. Descriptor Type.

Table 2-Descriptors used in the present study

Description

12345678910111213141516171819202122232425262728293031323334

VmAreaDensityRadOfGyrationPMI-magPMI-XPMI-YPMI-ZMWRotlbondsHbond acceptorHbond donorAlogPAlogP98LogPMolRefDipole-magDipole-XDipole-YDipole-ZChargeApolHOMOLUMOSrFoctFh20HfDIFFVCOSVfoNCOSVShape RMSSR Vol

SpatialSpatialSpatialSpatialSpatialSpatialSpatialSpatialStructuralStructuralStructuralStructuralThermodynamicThermodynamicThermodynamicThermodynamicElectronicElectronicElectronicElectronicElectronicElectronicElectronicElectronicElectronicThermodynamicThermodynamicThermodynamicMSAMSAMSAMSAMSAMSA

regression splines (MARS) algorithm with Holland'sgenetic algorithm, to evolve population of equationsthat best fit the training set data". This was done asfollows: (i) an initial population of equations wasgenerated by random choice of descriptors. Thefitness of each equation was scored by Lack-of-Fit(LOF) measure, LOp' = LSE/{l - [c + d*plm]}-2,where LSE is least square error, c is the number ofbasis functions in the model, d is the smoothingparameter, which controls the number of terms in theequations and p is the number of features contained inall terms of the models, and 111 is the number ofmolecules in the training set, (ii) pairs form thepopulation of equations were chosen at random and"crossovers" are performed and progeny equations are

ddefault descriptor

Molecular volumedMolecular surface area"Molecular density"Radius of gyration"Principle moment of inertiad

Principle moment of inertia X-componentPrinciple moment of inertia Y-componentPrinciple moment of inertia Z-componentMolecular weight"Number of rotatable bonds"Number of hydrogen bond acceptors"Number of hydrogen bond donors"Logarithm of partition coefficient"Logarithm of partition coefficient"Logarithm of partition coefficientMolar refractivity"Dipole moment"Dipole moment-X-componentDipole moment-Y-componentDipole moment-Z-componentSum of partial charges"Sum of atomic polarizabilities"Highest occupied molecular orbital energyLowest unoccupied molecular orbital energySuperdelocalizabilityDesolvation free energy for octanolDesolvation free energy for waterHeat of formationDifference volumeCommon overlap steric volumeCommon overlap volume ratioNon-common overlap steric volumeRMS to shape referenceVolume of shape reference molecule ,

generated, (iii) the fitness of each progeny equationwas assessed by LOF measure, and (iv) If the fitnessof new progeny equation was better, then it waspreserved. The model with proper balance of allstatistical terms was be used to explain variance in thebiological activity.

The application of the GFA algorithm allows the'construction of high-quality predictive models andmakes available additional information not providedby standard regression techniques, even for data setswith' many features". GFA was performed using100,000 crossovers, a smoothness value of 1.00 andother default settings. The number of terms in theequation was fixed to 5, inc1udi,ng a constant. Thegenerated equations were evaluated on' the basis of

:..

366 INDIAN J. BIOCHEM. BlOPHYS., VOL. 43, DECEMBER 2006

following: (a) LOF measure; (b) variable terms in theequation; and (c) internal and external predictiveability of the equation.

The predictive / was based only on molecules notincluded in the training set and is defined as: r2 pred =(SD - PRESS)/SD, where SD is the sum. of thesquared deviations between the biological activity ofmolecules in the test set and the mean biologicalactivity of the training set molecules and PRESS isthe sum of the squared deviations between predictedand actual activity values for every molecule in thetest set. Like cross-validated r2(r2CY)' the predictive /could assume a negative value, reflecting a complete'lack of predictive ability of the training set for themolecules included in the test see6

,37.

Results

Significance of molecular descriptorsFour models ~ere generated using combination of

different descriptors: model A: default descriptors;model B: default + thermodynamic descriptors; modelC: default + MSA descriptors; and model D:combination of all descriptors.

Model AQSAR equations using GFA were generated using

default descriptors. The resultant equations wereevaluated for their predictive power. Observation ofvariable usage graph indicated that Rotlbonds,AlogP98, Hbond donor, RadOf Gyration, PMI-mag,and Dipole-mag were frequently used in generation ofQSAR models. The best equation from the set ofequations was selected on the basis of predictivity,LOF and other statistical parameters such as F value.

pICso = 4.5874 + 0.1887 Rotlbonds+ 0.2375 RadOf Gyration + 0.0002 PMI-mag- 0.4605 Hbond donor + 0.1247 AlogP98 ... (1)

LOF = 0.473, r2= 0.600, LSE = 0.263, /CY = 0.456,F-value = 9.915, /pred = 0.286

pICso= 4.4522 + 0.2066 AlogP98 - 0.4889 Hbond donor+ 0.1233 Rotlbonds - 0,0369 Dipole-mag+ 0.4622 RadOf Gyration ... (2)

2 2LOF = 0.471, r = 0.602, LSE = 0.277, r cv = 0.441,F-value = 9.915, r2pred = 0.183

~Eqs 1 and 2 showed good internal predictivity and

also reasonable predictions for test set molecules. The

variable terms in the equations showed lowcorrelation among themselves.

ModelBThis model was built by combination of default and

thermodynamic descriptors. The resultant sets ofequations were evaluated on the basis of /cv. / andLOF. This resulted in the identification of two bestequations (Eqs 3,4), which were analyzed for theirpredictive power.

pICso = 6.5684 + 0.0378 Area - 0.0504 Ym+ 0.0004 PMI-mag + 0.20437 LogP- 0.0054Foct ... (3)

LOF = 0.454, r2= 0.630, LSE = 0.240, /Cy = 0.487,F-value = 1l.244, /pred = 0.213

pIC so = 5.0373 + 0.3566 Rotlbonds - 0.0886 Ym+ 0.0224 Area - 0.3183 Hbond donor+ 0.1948 MolRef ... (4)

LOF = 0.366, r2= 0.702, LSE = 0.199, /Cy = 0.575,F-value = 15.512, /pred = 0.273

Eq. 4 had good internal (/ = 0.702) and externalpredicti vity (/ pred = 0.273), as compared to Eq. 3(r2 = 0.630, r2pred = 0.213). Addition of six descriptorsto QSAR table improved the internal predictivity ofthe model moderately with low LOF and higher Fvalue than the model generated using defaultdescriptors.

Model CDeviation in the biological activity for a series of

molecules could be explained on the basis ofdifferences in the shape of the molecules. Hence, weconsidered the use of shape related descriptors forgeneration of QSAR models. Six MSA descriptorswere added to the QSAR table and model C wasgenerated (Eq. 5). The equation was analyzed on thebasis of important statistical parameters used in theearlier models. Descriptors like NCOSY, DIFFV,COSY, Hbond donor and Area were repeatedly usedto generate QSAR model. The external predictivity(/ pred = 0.395) was improved than that of models Aand B. Therefore, the equation clearly showed theimportance of shape related descriptors.

pICso= 5.9764 + 0.0004 PMI-mag- 0.1861 Hbond acceptor - 0.3315 Hbond donor- 0.0459 ShapeRMS + 0.2139 Rotlbonds ... (5)

,1F

lc

I low

ult andets ofr2 and

'0 bestr their

... (3)

~87,

n

... (4)

)75,

xternalEq. 3

rip torsvity ofgher Fdefault

ries ofsis ofce, werrs for:riptorsC wason thein the

>IFFV,y usedictivitydels Aed the

,donords ... (5)

W AGH et al.: 3D-QSAR OF HISTONE DEACETYLASE INHIBITORS 367

LOF = 0.483, r2= 0.606, LSE = 0.275,/cv = 0.488,2 '

F-value = 10.166, r pred = 0.395LOF = 0.413, r2 = 0,664, LSE = 0.226, /cv = 0.545,F-value = 13.020, / pred = 0.545 '

ModelDIt is well known that the variation in the observed

biological activity is influenced by combination ofdifferent types of descriptors. Therefore, QSARmodels were generated by combining together thedescriptors, belonging to different categories andallowing GF A to choose a proper combination ofdescriptors that have best internal and externalpredictivity, along with proper balance of otherstatistical parameters. Mode) D was generated using34 descriptors. The variable usage graph (Fig. 3)indicated dominant role of MolRef, LogP, Rotlbondsand DIFFV, as these were frequently used in thegeneration of QSAR models. Two best equations(Eqs 6 and 7) were selected.

pIC so = 4.8366 - 0.1117 ShapeRMS + 0.2038 AlogP98-0.1734 Hbondacceptor + 0.0002 PMI-mag+ 0.2233 Rotlbonds ... (7)

LOF = 0.501, r2= 0.590, LSE = 0.260, /cv = 0.452,F-value = 9.581, /pred = 0.634

Eq. 6 was chosen as representative of model D,which had proper balance of all statistical terms thanrest of the models. The incorporation of' all thedescriptors resulted in an increase in external'consistency of the model. Further biological activitiesof all molecules were predicted using Eq. 6. Molecule27 was over-predicted and chosen as an outlier. Thismight be due to its improper alignment on shapereference. QSAR equation generated withoutmolecule 27 (Eq. 8), showed improvement in internalpredictivity (r2 = 0.712) as well as externalpredictivity (r2 pred = 0.585).

pIC SO = - 15.158 + 0.1862 MolRef - 0.0004 NCOSV.+ 0.1165 LogP - 0.0627 DIFFV+ 0.4716 Rotlbonds ... (6)

5_Vn U$$ ? _lire;), use 9. 1I1oqP98 use, _ Hbonddonor use • _PHI -I\ag uae U -l(V use

1 _Rot.l.boM.$ use 3 _DIFl''i use2 _MoUld uee 4 -L0rJP use

Variable u,age vs. _ of ~ro":loyer:l100

~v--f~,--,.r,

.J'

80

'~...N\..../--"/v'.-J'~ •.•~_J\ __ /'.•..r'--'"J"2

60

I.

3-f

~~4J/¥""-""''''''-''~./ -J •..40

20~~~~~~·~-A-~A-J---~·-=~~,~~~_~~5~ f - - ........,'~ '-

~~~.~'---'" _.4_._ ~_ I9 - '"

o2 " 5

,dO 4

o 31

/I of crO"09cr:l

Fig. 3-Variable usage graph

368 INDIAN J. BIOCHEM. BIOPHYS., VOL. 43, DECEMBER 2006

pICso = - 11.842 + 0.1624 MolRef - 0.0017 NCOSV+ 0.1098 LogP - 0.0521 DIFFV+ 0.4041 Rotlbonds ... (8)

. LOF = 0.295, r2= 0.712, LSE = 0.160, /cv = 0.611,2 '

F-value = 15.811, r pred = 0.58.5

Hence, Eq. 8 of model D was selected to explainthe variation in the inhibitory activity of hydroxamicacid analogues. The observed and predicted biologicalactivities of training and test set molecules are givenin Tables 3 and 4, respectively.

Randomization testsTo determine the model's reliability and

significance, randomization procedure was performed

Table 3-0bserved and predicted biological activities of trainingset of molecules

Molecule Observed pIC50 Predicted pIC 50

1 7.3002 8.0003 7.5404 7.2805 7.3606 7.9507 6.9908 7.1509 6.30010 6.35011 6.82012 7.30013 7.42014 7.25015 8.30016 8.10017 8.70018 8.02019 8.35020 8.22021 7.60022 7.46023 6.00024 6.00025 6.70026 5.70028 7.12029 7.00030 6.52031 6.15032 6.10033 6.22034 6.52035 7.00036 7.00037 6.22038 7.22039 7.050

7.267.5777.8327.787.9377.3296.4826.9026.7867.0356.9276.8187.1747.3948.1087.988.5057.6248.1777.8197.8677.9166.2816.8376.5966.5156.7566.7526.8036.4676.0486.1086.5446.5556.6956.7586.6846.753

Residual

0.040.423-0.292

-0.5-0.577-0.3790.5080.248-0.486-0.685-0.1070.482-0.154-0.0440.1920.12

0.1950.3960.1730.401-0.267-0.456-0.281-0.8370.104-0.8150.3640.248-0.283-0.3170.0520.112-0.0240.4450.305-0.5380.5360.297

(19 trials) at 95% confidence level by repeatedlypermuting the dependent variable set. If the score ofthe original QSAR model proved better than thosefrom the permuted data sets, the model wasconsidered statistically significant, better than thoseobtained from the permuted data. The result of 19trials of randomization test is shown in Table 5. Thecorrelation coefficient / for the non-random QSARmodel was 0.712, significantly better than thoseobtained form randomized data. None of the permutedsets produced an / comparable with 0.712; hence, thevalue obtained for the original GFA model wassignificant.

DiscussionA distinctive feature of GFA is that it generates a

population of models (e.g. 100), instead of a singlemodel, as do most other statistical methods. The rangeof variations in this population provides addedinformation on the quality fit and importance of thedescriptors. For example, the frequency of use of aparticular descriptor in the population of equationsmay indicate how relevant the descriptor forprediction of activity".

The previously reported QSAR studies21,22 did not

provide 3D instructions for optimization of HDACinhibitors. We have considered spatial and MSAdescriptors, in addition to the structural descriptors.Using default set of descriptors, we obtainedreasonably well-predicted model (model A) with /cvof 0.456. Therefore, in order to optimize internal andexternal predictivity, the default descriptor set wasextended in two different ways by including(i) descriptors of model A + three electronic and

Table 4-0bserved and predicted biological activities of test setmolecules

Molecule4041424344454647484950515253545556

Observed pIC507.136.527.057.525.827.196.877.467.827.358.38.078.4

6.057

6.227

Residual-0.013-0.282-0.1330.344-0.7350.088-0.570.0880.6190.6190.7160.160.10.080.457-0.3391.024

th(ji(ngrprthde(n

10suthacwtere

Stre

MD

M

mpcmLIInacInIn

arInInh~m

Predicted pICso7.1436.8027.1837.1766.5557.1027.447.3727.2016.7317.5847.918.3

5.976.5436.5595.976

dly: ofosewasose19

TheARloseitedthe

was

es arglemgeldedthe

Df aIOnsfor

not)ACI1.SAtors.ined

2rev

andwasdingand

.t set

lual138233l435385788191916.6I)8571'39124

W AGH et al.: 3D-QSAR OF HISTONE DEACETYLASE INHIBITORS 369

Confidencelevel

Trials ,.2 non-random

Table 5 - Results of randomization test

SDlI

"Number of standard deviations of the mean value of ,Jof all random trials to the non-random ,J value"Standard deviation of the ,J values of all random trials from the mean value of ,J'Number of ,J values from random trials that are less than the ,Jvalue for the non-random trialdNumber of ,Jvalues from random trials that are greater than the ,J value for the non-random trial

95 % 19 0.712

three thermodynamic descriptors (model B), and(ii) descriptors of model A + six MSA descriptors(model C). With these additions, the models weregreatly improved in terms of internal and externalpredictivity, compared to those constructed only withthe default descriptors. This led us to combine all the e

descriptors of different classes to generate a model(model D).

Model D (Eq. 8), in all aspects had high r2, r2cv andlower LOF values, which clearly indicated that it wassuperior among all other models. Hence, we chosethis model to explain the observed biologicalactivities of HDAC inhibitors. Graphs of observedversus predicted biological activities of training andtest sets are shown in Fig. 4(A) and 4(B),respectively.

Structure-activity relationship of HDAC inhibitors withrepresentative QSAR equation

Eq. 8 includes thermodynamic descriptors-MolRef and LogP, shape descriptors - NCOSV andDIFFV and structural descriptor - Rotlbonds.

MoIRe!It represents molecular refractivity index of a

molecule, which is a combined measure of size andpolarizability. It also represents the real volume of themolecule and related to both the volume and to theLondon dispersive force that acts in the drug-receptorinteractions. The positive correlation of MolRef toactivity indicates that in addition to hydrophobicinteractions, the electronic interactions also play animportant role in HDAC inhibitory activity.

LogPIt is related to hydrophobic character of a molecule

and is positively correlated with biological activity. Itindicates incorporation of hydrophobic atoms wouldincrease biological activity. Most active molecule 17has higher LogP value as compared to the less activemolecule 32.

,J random(Mean)

? <cr: ') »dr:SD"

0.358 6.593 0.073 19 o

9

8.5 J A •8

7.5

7

6.5

Z' 6

:~ 5.5ti<1l 5.5 6 6.5 7 7.5 8 8.5 9"0Q)

.~8:1 B"0

~o, >.87.5

7

6.5

61 • •5.5

5.5 6 6~ 7 7.5 8 8.5 9

Observed activity

Fig. 4-0bserved and GFA-predicted activities of (A): trainingset (Model D, Eg. 8); and (B) of test set (Model D, Eg. 8)

NCOSVA MSA descriptor, is the difference. between the

individual molecule's steric volume and the commonoverlap steric volume. It is negatively correlated withthe activity, lower NCOSV values are important for.activity. Its presence also indicates the importance ofconformational similarity with the most activemolecule.

DIFFVA shape descriptor, differential volume represents,

the difference between the volumes of individualmolecule and shape reference molecule. It wascorrelated negatively with the biological activity iinthe present QSAR analysis. The naphthalene ring .containing hydroxamic acid analogues (20-22,

.....

370 INDIAN I. BIOCHEM. BIOPHYS., VOL. 43, DECEMBER 2006

Table la) having approximately the same volume,compared to the shape reference molecule, showedgood HDAC inhibition. The moderately activemolecules with spacer carbon chain n < 3 (23-26,Table la) occupied less volume in the HDAC pocket,as compared to the shape reference molecule.

RotlbondsA structural descriptor, correlated positively with

biological activity. It indicates that conformationallyless rigid molecules show good biological activity.This is evident from better inhibitory activities ofmolecules having single bonds in spacer chain(molecules 2, l7, 21), compared to molecules withdouble bonds (molecules 26, 31, 32). From this, wecan conclude that the hydrophobic binding site inHDAC is highly flexible.

Previously reported QSAR studies21.22 suggested

the importance of shape of the molecule, charge oncarbonyl carbon and VDW surface area of thehydrophobic atoms for the biological activity. Thepresent QSAR analysis indicated the importance ofconformation for binding with the HDAC bindingsite. The study emphasizes the importance of aliphaticcarbon chain length (n - 5-7) for the effective bindingof inhibitors with HDAC. As reported previouslyr',increase in number of aromatic atoms will decreasethe activity.

Earlier, a 3D-QSAR study was carried out by ourresearch group using comparative molecular fieldanalysis (CoMFA)38 for hydroxamic acid-type ofHDAC inhibitors. Both the CoMFA and GFA studiesindicated that electronic interactions also play animportant role in HDAC inhibitory activity, inaddition to hydrophobic interactions.

Crystal structures of HDAC-like protein (HDLP)with TSA and SAHA revealed that hydroxamic acid-based HDAC inhibitors bind to deacetylase core byinserting their aliphatic chains into HDLP pocket andtheir hydroxamic acid group coordinates the Zn2

+ ionat the polar bottom of the pocker". The hydrophobicend of inhibitor binds to hydrophobic region at theentrance of HDLP-active site. HDLP has a tube-like,11 A deep channel which leads to the active site. Thepresent QSAR study emphasizes that the size andconformation of spacer chain is important forinteraction of hydroxamic acid with the metal-bindingregion of the enzyme. As the spacer binding channelis narrow; the bulky substituents reduce the activity.On the other hand, large lipophilic groups could beaccommodated in the hydrophobic pocket.

Conclusion3D-QSAR analysis of a series of hydroxamic acid

analogues with inhibitory activity against HDACIwas performed using statistical technique GFA.Various combinations of different classes ofdescriptors were tried to obtain meaningful QSARequation. The selected model had good internal andexternal predictivity. The variables in the correlationrevealed that MSA, thermodynamic and structuraldescriptors contribute significantly to the biologicalactivity of HDAC inhibitors. The study emphasizesthe importance of aliphatic carbon chain length(n - 5-7) for the effective binding of inhibitors withHDAC and underlined that the hydrophobic bindingsite of HDAC is very flexible. On the basis of thedeveloped QSAR models, novel molecules could bedesigned as potential HDAC inhibitors.

AcknowledgementsThe authors gratefully acknowledge Dr. K R

Mahadik, Vice-Principal, BVDU, Poona College ofPharmacy, Pune.

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