Pharmacophore modeling, 3D-QSAR, docking and ADME prediction of quinazoline based EGFR ...

25 Pages·2016·5.83 MB·English

by  Garima Verma

ArabianJournalofChemistry(2016)xxx,xxx–xxx
King Saud University
Arabian Journal of Chemistry
www.ksu.edu.sa
www.sciencedirect.com
ORIGINAL ARTICLE
Pharmacophore modeling, 3D-QSAR, docking and
ADME prediction of quinazoline based EGFR
inhibitors
Garima Vermaa, Mohemmed Faraz Khana, Wasim Akhtara,
Mohammad Mumtaz Alama, Mymoona Akhtera, Ozair Alama,
Syed Misbahul Hasanb, Mohammad Shaquiquzzamana,*
aDrug Design andMedicinal Chemistry Lab,Departmentof Pharmaceutical Chemistry, Facultyof Pharmacy,JamiaHamdard,
NewDelhi 110062,India
bCollegeof Pharmacy,AlJouf University, POBox-2994,Sakaka,Al Jouf,SaudiArabia
Received 20June 2016; accepted 17September 2016
KEYWORDS Abstract Pharmacophoremodeling,moleculardockingandinsilicoADMEpredictionhavebeen
performed for quinazoline based EGFR inhibitors. This study has been carried out to determine
3D-QSAR;
Moleculardocking; thebindingmodeanddruglikelinessnatureofcompounds.Afivepointmodel(AAARR.7)wasgen-
Quinazoline; eratedusing64compounds.Thegeneratedmodelwasfoundtobestatisticallysignificantasithada
Epidermalgrowthfactor highcorrelationcoefficient(R2=0.9433),crossvalidationcoefficient(Q2=0.8493)andFvalueof
receptor 97.10at6componentPLSfactor.Theresultsofexternalvalidationwerealsoindicativeofhighpre-
dictivepower(R2=0.86).ThegeneratedmodelalsopassedTropsha’stestforpredictiveabilityand
Y-randomizationtest.TheDomainofApplicability(APD)ofthemodelwasalsosuccessfullydefined
toascertainthatagivenpredictioncanbeconsideredreliable.Inordertoevaluatetheeffectivenessof
thedockingprotocol,co-crystallizedligandwasextractedfromtheligandbindingdomainofthepro-
teinandwasre-dockedintothesameposition.Theconformerobtainedonre-dockingandtheco-
crystallizedligandweresuperimposedandtherootmeansquaredeviationbetweenthetwowasfound
tobe1.005A˚ .OutcomesofthisstudyprovideaninsightfordesigningnovelEGFRinhibitors.
(cid:1)2016TheAuthors.ProductionandhostingbyElsevierB.V.onbehalfofKingSaudUniversity.Thisis
anopenaccessarticleundertheCCBY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Abbreviations:R2,regressioncoefficient;Q2,crossvalidationcorrelationcoefficient;Gscore,Glidescore;RMSD,rootmeansquaredeviation;A,
hydrogenbondacceptor;R,aromaticring;H,hydrophobic;PLS,partialleastsquare;SD,standarddeviation;EGFR,epidermalgrowthfactor
receptor;APD,domainofapplicability
* Correspondingauthor.
E-mailaddress:[email protected](M.Shaquiquzzaman).
PeerreviewunderresponsibilityofKingSaudUniversity.
Production and hosting by Elsevier
http://dx.doi.org/10.1016/j.arabjc.2016.09.019
1878-5352(cid:1)2016TheAuthors.ProductionandhostingbyElsevierB.V.onbehalfofKingSaudUniversity.
ThisisanopenaccessarticleundertheCCBY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Pleasecitethisarticleinpressas:Verma,G.etal.,Pharmacophoremodeling,3D-QSAR,dockingandADMEpredictionofquinazolinebasedEGFRinhibitors.
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2 G. Verma etal.
1.Introduction designprocess(Cicheroetal.,2014,2016,2011a,2011b;Cicheroand
Fossa, 2012). With these facts, we herein report the pharmacophore
Canceris oneofthe leadingcauses ofdeathin bothdevelopingand modeling, 3D-QSAR, molecular docking and ADME prediction of
developedcountries(Hanetal.,2016).Thetotalnumberofnewcancer quinazolineanalogstoprovideaninsightintothekeystructuralfea-
casesisprojectedtoincreasefrom14millionin2012to24millionin turesrequiredfordesigningEGFRtargetingagents.
2035(Liuetal.,2016).Despitetheavailabilityofabroadrangecom-
pounds, cancer therapy still has many limitations. Side effects and 2. Materials andmethods
resistance to the current agents are the major ones (Pape et al.,
2016). These factors necessitate the need for development of novel 2.1. Dataset
andsaferanticanceragents.
The Epidermal Growth Factor Receptor (EGFR), a member of
ErbBfamilywhichsignalsthroughatyrosinekinase(TK)isinvolved Asetcomprisingof64compoundsbearingquinazolinemoiety
inregulationofgrowth,migrationandapoptosisoftumorcells(Yan were selected from the available literatures (Qin et al., 2016a,
etal., 2015). It has beendescribedin the nucleusof primarytumors 2016b;Qinetal.,2015).Theselectedcompoundsforthedata
(Rodriguesetal.,2016).Hence,itisconsideredasoneoftheimportant setsharedthesameassayprocedurewithsignificantvariations
targetsforthedevelopmentofnovelanticanceragents. intheirstructuresandpotencyprofiles.Inhibitorypotenciesof
EGFRinhibitors,suchasgefitinibanderlotinibhavemadesignif- the compounds included in data set, reported as IC values
50
icantprogressinthetreatmentofpatientswithcancer(Johnsonetal.,
varied from 2.0 to 16,500nM and were converted into molar
2005;CiardielloandTortora,2008).Gefitinibanderlotinibinhibitthe
values. These were then converted into pIC values using
tyrosine kinase activity by competitively binding to the adenosine 50
theformula given below:
triphosphate (ATP) binding pocket of the intracellular EGFR TK
domain, thereby blocking downstream signal transduction pathways pIC ¼(cid:2)log ½IC (cid:3)
50 10 50
in cancer cells. However, the success of such EGFR tyrosine kinase
inhibitorsislimitedbytheup-regulationofbypasssignalingpathways The3Dstructuresofligandsweregeneratedusingthebuilder
andacquiredpointmutations(Oxnardetal.,2011).Aftertreatmentof panel in Maestro and subsequently optimized using LigPrep
10–16monthswiththeseinhibitors,mostofthecancerpatientsacquire module (v3.1, Schro¨dinger 2016-1). Partial atomic charges
anadditionalgatekeepermutation(T790M)(Kobayashietal.,2005). were ascribed and possible ionization states were generated
The secondary T790M mutation decreases sensitivity to gefitinib or atapHof 7.0.The OPLS_2005forcefieldwas usedforopti-
erlotinib and increases the binding affinity for ATP (Ou, 2012). In mizationforproductionoflowenergyconformeroftheligand
ordertoovercometheT790Mmutationrelatedresistance,severalirre-
(Shivakumar et al., 2010). The energy minimization was per-
versible EGFR second-generation covalent inhibitors, which form a
formedforeachligandtillitreachedarootmeansquaredevi-
covalentbondwithCys797withintheEGFRactivesite,haveshown
ation(RMSD) cutoff of0.01A˚ .The resultingstructures were
preclinical activity against EGFR with T790M mutation (Xia et al.,
then taken for performing modeling studies. In the current
2014). Afatinib was the first approved irreversible EGFR inhibitor
by the US FDA for the treatment of late-stage cancer patients with study, we used phase (v4.0)shape screening for flexible align-
actively mutated EGFR. Other examples include canertinib and mentofthe selected EGFR inhibitors.
dacomitinib(DungoandKeating,2013).
Structuralinformationprovidedbytheco-crystalstructuresofgefi- 2.2. Pharmacophore3D-QSAR modeling
tiniborerlotinib(Yunetal.,2007;Stamosetal.,2002)withEGFRhas
played a major role in rational design of potent EGFR inhibitors.
Phase (v4.6, Schro¨dinger 2016-1) was used to generate phar-
Analysisofbindingmodeofgefitinibsuggestsvariouskeyinteractions
macophore and 3D-QSAR models for EGFR inhibitors
withtheATPbindingpocketofEGFR,includingthecriticalhydrogen
bondoftheN1ofthequinazolinemoietywiththehingeresidueMet (Dixon et al., 2006). The prepared ligands were imported for
793 of active site, the deep hydrophobic pocket filled up by the 3- developing pharmacophore model panel of Phase with their
chloro-4-fluoroanilinesubstituentandthesolventregionoccupation respectivebiologicalactivityvalues.Theligandswereassigned
of6-morpholinopropoxygroup.Avarietyofpolarfunctionalgroups asactiveswithathresholdofpIC >7.7andinactiveswitha
50
aregenerallyaccommodatedwithalittleaffectionontheefficacyby thresholdofpIC <6.0.Remainingcompoundswereconsid-
50
thesolventregion(Yinetal.,2014).Agoodexampleofthisstrategy eredasmoderatelyactive.Phase(v4.0)hasbeenproventobe
is the success of icotinib, which mimics erlotinib with a crown ether
an important tool for flexible ligand superposition (Sastry
fusedquinazoline(Liuetal.,2015;Guanetal.,2014).
etal.,2011; Milleret al.,1999).Inthecurrentstudy, weused
TheEGFRkinasedomainadoptsabilobate-foldandcharacteristic
NH terminallobe (N-lobe)is composed of mainlyb-strandsand a- phase (v4.0) shape screening for flexible alignment of the
helix2.COOHlobe(C-lobe)ismostlya-helical.Thesetwolobesaresep- selected EGFR inhibitors. The most active compound 3 was
aratedbyacleft,similartotheonepossessedbyATP.ATPanalogs takenasthetemplateandpharmacophoretypevolumescoring
andATPcompetitiveinhibitorsareknowntobindinthiscleft.Cat- was kept as default. The default settings were used, with a
alyticmachineryofN-lobeincludesglycine-richnucleotidephosphate maximum of 10 conformations per rotatable bonds and with
binding loop (Gly 695 – Gly 700). C-lobe comprises of DFG motif thisamaximumof100conformersweregenerated.Conforma-
(Asp 831 – Gly 833), presumptive catalytic loop (Asp 813), the cat- tions of these compounds were varied and at most one align-
alyticloop(Arg812–Asn818)andtheA-loop(Asp831–Val852). mentforeveryligand wasretained.
However,Erlotinib,anapprovedEGFRinhibitorisfoundinthecleft
Theselectionoftrainingandtestsetsisthemostimportant
betweentheN-andC-terminallobes(Stamosetal.,2002).
and also the most difficult step, which often consumes much
Emergence of resistance and side effects of the currently used
time in building QSAR models. Random division is a widely
agentsfurtherpushthescientistsacrosstheglobetoengagethemselves
used approach in establishing the QSAR model robustness
inthedevelopmentofnovelEGFRtargetingagents(Tangetal.,2013;
Costaetal.,2008;Zhengetal.,2014).Nowadays,computationalstud- (Wang et al.,2015). Therefore,for assigning training andtest
ies based on ligand- and structure-based approaches, are considered set for 64 compounds, random selection was made using the
effective tools in medicinal chemistry, useful to accelerate the drug Automated Random Selection option present in Phase (v4.0)
Pleasecitethisarticleinpressas:Verma,G.etal.,Pharmacophoremodeling,3D-QSAR,dockingandADMEpredictionofquinazolinebasedEGFRinhibitors.
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Quinazoline basedEGFR inhibitors 3
software. In this method, ten trials (70, 75, 80, 85 and 90%) is the result of a substantial extrapolation of the model and
were run. The trials were based on structural diversity and a maynotbereliable.
wide rangeof activity– the rangeof biological activityof the APD can also be defined using similarity measurements
test-setmoleculeswassimilartothatofthetrainingset.Thus, based on the Euclidean distances among all training and test
thetestsetchosenwasatruerepresentativeofthetrainingset, compounds. The distance of a test compound to its nearest
as per recommendations of Golbraikh and Tropsha, 2003. neighborinthetrainingsetiscomparedtoapredefinedthresh-
Resultantly, 20 compounds were included in the test set and old (APD), and the prediction is considered unreliable when
remaining 44 compounds were included in training set. Phar- the distance is higher than that. APD was calculated based
macophore sites for these compounds included default set of onthe following formula:
chemical features of Phase: three hydrogen bond acceptors
APD¼hdiþZr
(A)andtwoaromaticrings(R).Pharmacophorematchingtol-
erancewassetas1A˚ .Atotalof19variantsweregeneratedby Calculation of hdi and r was performed as follows: first, the
keeping 5 and 4 as the maximum and minimum number of
average of Euclidean distances between all pairs of training
sitesrespectively. Itwasspecified thatatleast9 ofthe actives
compoundswascalculated.Next,thesetofdistancesthatwere
mustmatch.Finally,itresultedin12possiblecombinationsof lowerthantheaveragewasformulated.hdiandrwerefinally
features which couldgive rise tocommon pharmacophores.
calculated as the average and standard deviation of all dis-
tances are included in this set. Z is an empirical cutoff value
2.3.Modelvalidation and for this work, it was chosen as 0.5 (Zhang et al., 2006).
Enalos Domain – similarity node that executes the aforemen-
The QSAR model AAARR.7 with 6 component PLS factor tioned procedure was included in our workflow and used to
was characterized as the best model. Pharmacophoric model assess domain of applicability of the proposed model. For
was validated by its accuracy in prediction of the activity of the calculation of the domain of applicability, we have used
training set ligands. The predicted EGFR inhibitory activity EnalosDomain KNIME nodes.
of training set ligands elicited a correlation (R2) of 0.85 with
observed EGFR inhibitory activity. The efficacy of model 2.5. Y-randomization test
AAHR.11wasalsoexaminedwithexternalvalidation(Table1:
SupplementaryMaterialandFig.4).Graphofactualvaluevs.
Y-Randomization technique ensures the robustness of a
predicted value and residual value vs. predicted value was
QSAR model (Tropsha et al., 2003; Shen et al., 2004). The
plotted.
dependent variable vector is randomly shuffled and a new
The predictive power of the produced 3D-QSAR model
QSAR model is developed, using the given modeling algo-
wasalsoassessedbyusingEnalosModelAcceptabilityCriteria
rithm. The procedure is repeated several times and the new
KNIMEnode(MelagrakiandAfantitis,2013),whichincludes QSAR models are expected to have low R2 and Q2 values. If
allproposedtestsbyTropsha(Zhangetal.,2006).Criteriafol-
theoppositehappensthenanacceptableQSARmodelcannot
lowedindevelopingactivity/propertypredictors,especiallyfor
beobtained forthe specific modeling methodand data.
continuousQSAR,areasfollows:(i)correlationcoefficientR
between the predicted and observed activities; (ii) coefficients
of determination (predicted versus observed activities R2 and 2.6. Moleculardocking
0
observedversuspredictedactivitiesR02forregressionsthrough
0
theorigin);and(iii)slopeskandk0 ofregressionlinesthrough ThecatalyticdomainofEGFRenzymeincomplexwitherlo-
the origin. The criterions for the acceptability of the QSAR tinib (PDB Code: 1M17 resolution 2.6A˚ ) was obtained from
modelare represented in leftcolumn of Fig.5. protein data bank (Stamos et al., 2002) and prepared using
the protein preparation wizard (Sastry et al., 2013), available
inSchro¨dingersuite2016-1.Crystallographicwatermolecules
2.4.Domain ofapplicability
i.e.without3Hbondsweredeletedandhydrogenbondscorre-
spondingtopH7wereaddedconsideringtheappropriateion-
In order to screen a QSAR model for new compounds, its
ization states for both acidic and basic amino acid residues.
domain of application (APD) (Tropsha et al., 2003; Shen
OPLS_2005 force field was used for energy minimization of
et al., 2004) must be defined and predictions for only those
the crystal structure (Shivakumar et al., 2010). Active site
compounds that fall into this domain may be considered reli- was defined with a radius of 14A˚ around the ligand present
able.ExtentofExtrapolation(Tropshaetal.,2003)andEucli-
in crystal structure. Grid box was generated at a centroid of
deanDistances are twosimple approachesto definethe APD.
active site. For docking, low energy conformations of all the
Extent of Extrapolation is based on the calculation of the
compounds were docked into the catalytic domain of EGFR
leverage(h)foreachchemical,wheretheQSARmodelisused
i protein(PDBCode:1M17)usingGridbasedLigandDocking
to predictitsactivity.
with Energetics (Glide v7.0, Schro¨dinger 2016-1) (Lipinski
h ¼xðXTXÞ(cid:2)1xT etal.,2001)inextraprecisionmodewithoutapplyinganycon-
i i i straints. The best docked structure was identified using Glide
In the above equation, x is the row vector containing the k score function, Glide energy and Glide Emodel energy
i
model parameters of the query compound and X is the n(cid:4)k (Table2:SupplementaryMaterial).Thelowestenergydocked
matrix containing the k model parameters for each one of compound 3 was selected for further studies. A map of
the n training compounds. A leverage value greater than hydrophobic and hydrophilic fields for inhibitor 3 was also
3k/n is considered large. It means that the predicted response generated(Fig. 8).
Pleasecitethisarticleinpressas:Verma,G.etal.,Pharmacophoremodeling,3D-QSAR,dockingandADMEpredictionofquinazolinebasedEGFRinhibitors.
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4 G. Verma etal.
2.7. Lipinski’s rule fordruglikeliness andinsilico ADME andsurvivalactives(Dixonetal.,2006),asshowninTable1.
prediction Based on the sites, a maximum of five features were allowed
for developing the hypotheses. 3D-QSAR model was gener-
ated using Partial Least Square (PLS) regression statistics
Wefurtherpredictedthedrug-likebehaviorofthecompounds
andkeepingthegridspacingof1A˚ .ThenumberofPLSfac-
through the analysis of pharmacokinetic profile of the com-
torsincludedinmodeldevelopmentis6becauseanincremen-
poundsby usingQikProp module (v4.3, Schro¨dinger 2016-1).
tal increase in the statistical significance and predictivity was
ThecompoundspreparedbyLigPrepmodule(v3.1,Schro¨din-
observed till6 PLSfactor (Table 2).
ger 2016-1) were utilized for the calculation of pharmacoki-
ThebestfittedmodelAAARR.7(R2=0.9433,Q2=0.8493
netic parameters by QikProp v4.3. The program QikProp
andF=97.10)consistsofthreehydrogenbondacceptorsand
v4.3, utilizes the method of Jorgensen19 to compute pharma-
twoaromaticringfeatureswithhighestsurvivalscore(6.202).
cokinetic properties and descriptors. Physically significant
The hypothesis AAARR.7 is shown in Fig. 1. The distance
descriptors and pharmaceutically relevant properties of all
andanglesbetweendifferentsitesofthemodelAAARR.7are
the test compounds such as molecular weight, log p, H-bond
presentedinTables3and4respectively.Thisisalsoapparent
donors,andH-bondacceptorsaccordingtotheLipinski’srule
from the comparison from the deduction of survival inactive
offivewereanalyzed.Lipinski’sruleoffiveisaruleofthumb
from survival active. It deducts inactive features from the
to evaluate drug likeness, or determine whether a chemical
hypothesis and was decisively highest for the hypothesis
compoundwithacertainpharmacologicalorbiologicalactiv-
AAARR.7. Among the pharmacophore features, intersite
ityhaspropertiesthatwouldmakeitalikelyorallyactivedrug
distancesandanglesbetweensitepointswere foundtobethe
inhumans.Theruledescribesmolecularpropertiesimportant
principal attribute and the point of difference between active
for drug pharmacokinetics in the human body, including its
and inactive is due to the interstitial site distances as evident
absorption, distribution, metabolism and excretion (ADME)
by the pharmacophore hypothesis AAARR.7alignment over
(Cheng etal., 2014).
active(pIC >7.7)andinactivecompounds(pIC <6.0)in
50 50
Fig.2aandbrespectively.Usingthemodel,AAARR.7,fitness
3. Results anddiscussion
scoreofalltheligandswasalsoevaluated(Table5).
3.1. Pharmacophore and3D-QSAR models
3.2. Modelvalidation, domain ofapplicability andY-
randomizationtest
Phase(v4.0)(Dixonetal.,2006)moduleofSchro¨dinger2016-1
wasusedforthedevelopmentofpharmacophoremodel.Atom
based3D-QSARunveiledtheeffectofsubstituentsonactivity. Thepredictabilityandvalidityofthecommonpharmacophore
The generated hypotheses were scored and ranked in accor- model,AAARR.7(testset),basedonactivecompoundswere
dance with their vector, volume, site scores, survival scores judged by cross validation coefficient (Q2=0.84) (Table 2).
Table1 Scoreofdifferentparametersofthehypotheses.
S.No. Hypothesis Survivalscore Survivalinactive Site Vector Matches Activity Inactive
1 AAARR.7 6.202 4.215 1.00 1.000 9 8.036 1.988
2 AAARR.25 5.816 4.310 0.68 0.859 9 8.036 1.507
3 AAARR.94 5.755 4.249 0.66 0.838 9 8.036 1.507
4 AAARR.23 5.444 3.929 0.50 0.797 9 7.963 1.514
5 AAARR.24 5.398 3.656 0.41 0.826 9 8.699 1.743
6 AAARR.19 5.388 3.664 0.47 0.797 9 7.963 1.723
7 AAARR.93 5.388 3.664 0.47 0.797 9 7.963 1.723
8 AAARR.95 5.354 3.356 0.44 0.779 9 8.174 1.998
9 AAARR.96 5.354 3.356 0.44 0.779 9 8.174 1.998
A:Acceptor;R:Aromaticring.
Table2 PLSstatisticalparametersofthemodelAAARR.7.
PLS SD R2 F P Stability RMSE Q2 Pearson-R
1 0.6002 0.5030 40.50 1.459e(cid:2)007 0.9624 0.6358 0.5582 0.7619
2 0.5052 0.6568 37.30 8.758e(cid:2)010 0.9717 0.5333 0.6891 0.8370
3 0.3623 0.8280 61.00 1.373e(cid:2)014 0.8017 0.3744 0.8468 0.9320
4 0.3046 0.8816 68.90 1.237e(cid:2)016 0.7706 0.3791 0.8429 0.9260
5 0.2531 0.9205 83.30 9.158e(cid:2)019 0.7523 0.3804 0.8418 0.9222
6 0.2167 0.9433 97.10 2.468e(cid:2)020 0.7013 0.3713 0.8493 0.9244
SD: Standard deviation of regression;R2: Regression coefficient; F: Ratio of the model variance to the observedactivity variance (variance
ratio);P:Significancelevelofvarianceratio;Q2:Crossvalidatedcorrelationcoefficientforthetestset;RMSE:theRMSerrorinthetestset
predictions.
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Quinazoline basedEGFR inhibitors 5
Figure1 Hypothesis,AAARR.7.Pinksphereswitharrow,hydrogenbondacceptor(A);orangeopencircle,aromaticring(R).
Table 3 Angles between the pharmacophoric sites of
AAARR.7. Table4 Intersitedistancesbetweenthepharmacophoricsites
ofAAARR.7.
Entry Site1 Site2 Distance
Entry Site1 Site2 Site3 Angle
AAARR.7 A3 A4 2.821
AAARR.7 A3 A5 4.800 AAARR.7 A4 A3 A5 28.5
AAARR.7 A3 R12 3.747 AAARR.7 A4 A3 R12 99.7
AAARR.7 A3 R13 2.798 AAARR.7 A4 A3 R13 59.5
AAARR.7 A4 A5 2.682 AAARR.7 A5 A3 R12 71.3
AAARR.7 A4 R12 5.056 AAARR.7 A5 A3 R13 31.0
AAARR.7 A4 R13 2.788 AAARR.7 R12 A3 R13 40.3
AAARR.7 A5 R12 5.055 AAARR.7 A3 A4 A5 121.4
AAARR.7 A5 R13 2.803 AAARR.7 A3 A4 R12 46.9
AAARR.7 R12 R13 2.422 AAARR.7 A3 A4 R13 59.9
AAARR.7 A5 A4 R12 74.6
AAARR.7 A5 A4 R13 61.6
AAARR.7 R12 A4 R13 13.0
Regression coefficient of the training set was 0.94, which AAARR.7 A3 A5 A4 30.1
exhibited relevance of the model. Stability of the generated AAARR.7 A3 A5 R12 44.6
model ranges from 0.7013 to 0.9624 on the maximum scale AAARR.7 A3 A5 R13 31.0
AAARR.7 A4 A5 R12 74.7
of 1. F value was found to be 97.10. Additionally, P value of
2.468e(cid:2)020 and Pearson r of 0.9244 indicated greater degree AAARR.7 A4 A5 R13 61.1
AAARR.7 R12 A5 R13 13.6
of confidence on the model. Standard deviation (SD) value
AAARR.7 A3 R12 A4 33.4
of0.2167androotmeansquareerror(RMSE)of0.3713indi-
AAARR.7 A3 R12 A5 64.1
cate the stability of the generated model for prediction of AAARR.7 A3 R12 R13 48.3
unknowncompoundsinthetest.Scatterplotsforexperimental AAARR.7 A4 R12 A5 30.8
andpredictedactivitiesofligandselicitedsignificantlinearcor- AAARR.7 A4 R12 R13 15.0
relationandmoderatedifferencebetweentheexperimentaland AAARR.7 A5 R12 R13 15.8
predictedvaluesasshowninFig.3aandb.Inordertoevalu- AAARR.7 A3 R13 A4 60.7
atetheefficacyofthegeneratedmodel,itwasfurthervalidated AAARR.7 A3 R13 A5 118.0
AAARR.7 A3 R13 R12 91.4
usinganexternaltestset(GolbraikhandTropsha,2002a).The
AAARR.7 A4 R13 A5 57.3
calculated pIC values of the compounds included in pre-
50 AAARR.7 A4 R13 R12 152.1
dicted and external test set are given in Table 5 and Table 1
AAARR.7 A5 R13 R12 150.6
(SupplementaryMaterial)respectively.Aplotofexperimental
vs.predictedpIC ofexternaltestsetisshowninFig.4aanda
50
Pleasecitethisarticleinpressas:Verma,G.etal.,Pharmacophoremodeling,3D-QSAR,dockingandADMEpredictionofquinazolinebasedEGFRinhibitors.
ArabianJournalofChemistry(2016),http://dx.doi.org/10.1016/j.arabjc.2016.09.019
6 G. Verma etal.
Figure2 (a)Mappingoftheactivecompoundsontothepharmacophore.(b)Mappingofinactivecompoundsontothepharmacphore.
plot of residual vs. predicted value is shown in Fig.4b. These ableandcouldbeusedinthedesignofnewEGFRinhibitors
twoplotsare vitalfor thepredictive abilityofQSAR.Scatter withinthisstructural motifof molecules.
residualplotswereusedtoidentifytheoutliersfromtheQSAR It is important that the limitations of the model are also
model (Golbraikh and Tropsha, 2002b). Fig.4b elicits that described via the APD. This gives an important indication as
there is no outlier in the study. Thereby, the model is consid- the user can freely and creatively design novel molecules but
ered as stable and as expected. It was able to sort the experi- will be warned for the reliability of the prediction when the
mental pIC values for compounds in the external test set. structural characteristics cannot be tolerated by the model.
50
PredictivecorrelationcoefficientR2valueof0.86wasobserved After model validation, the APD of our model was also
fortheexternaltestsetforthedevelopedQSARmodel.AnR2 definedto ascertain that a given prediction can be considered
valueofmorethan0.5betweenthepredictedandexperimental reliable. Enalos Domains – Similarity (Euclidean Distances)
values renders the model to be good and able to predict the and Leverage (Extent of Extrapolation) nodes that execute
inhibitory activity of compounds not included in the model the aforementioned procedure are included in our workflow
developmentprocess(GolbraikhandTropsha,2002a,2002b). andwereusedtoassessAPDoftheproposedmodel.InEucli-
Furthermore, the Enalos Model Acceptability Criteria deanDistancesmethod,theAPDlimitvaluewasdefinedequal
KNIME node has also been applied to the data. The model to0.633,whereasinExtentofExtrapolationmethod,theAPD
passed Tropsha’s recommended tests for predictive ability limitwasdefinedas0.15.Theselimitsweredefinedonthebasis
andtheresultsaredepictedinFig.5.Theseresultssuggestthat of equations provided in Methods section. In both types of
this alignment can effectively take into consideration the APD prediction, all compounds in the test set had values in
ligand-receptorinteractions,andtheQSARmodelisthusreli- therangeoftheAPD.Thepredictionsforallcompoundsthat
Pleasecitethisarticleinpressas:Verma,G.etal.,Pharmacophoremodeling,3D-QSAR,dockingandADMEpredictionofquinazolinebasedEGFRinhibitors.
ArabianJournalofChemistry(2016),http://dx.doi.org/10.1016/j.arabjc.2016.09.019
Quinazoline basedEGFR inhibitors 7
Table5 CalculatedpIC forcompoundsinpredictedset.
50
Ligandname Chemicalstructure Experimentalactivity Predictedactivity Residualactivity Fitness
F
Cl
N NH
1t 7.64 7.82 (cid:2)0.18 2.56
N O
O
O
N NH
2 7.96 8.02 (cid:2)0.06 2.56
N O
O
O
F
Cl
N NH
3t 8.69 8.26 0.40 3.00
N O
O
O
N NH
4t N O 8.03 7.81 0.22 2.58
O
O
O
N NH
5 N O 8.35 8.22 0.13 2.81
O
O
(continuedonnextpage)
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8 G. Verma etal.
Table5 (continued)
Ligandname Chemicalstructure Experimentalactivity Predictedactivity Residualactivity Fitness
Cl
N NH
6 N O 8.17 8.32 (cid:2)0.15 2.59
O
O
O-
N+
O
N NH
7 8.01 8.10 (cid:2)0.09 2.98
N O
O
O
F
Cl
N NH
8t N O 7.88 7.12 0.76 2.56
O
O
O
N NH
9 N O 7.67 7.79 (cid:2)0.12 2.94
O
O
Pleasecitethisarticleinpressas:Verma,G.etal.,Pharmacophoremodeling,3D-QSAR,dockingandADMEpredictionofquinazolinebasedEGFRinhibitors.
ArabianJournalofChemistry(2016),http://dx.doi.org/10.1016/j.arabjc.2016.09.019
Quinazoline basedEGFR inhibitors 9
Table5 (continued)
Ligandname Chemicalstructure Experimentalactivity Predictedactivity Residualactivity Fitness
F
N NH
10 N O 6.69 6.90 (cid:2)0.21 2.57
O
O
O
O
N NH
N O
11 6.79 6.70 0.09 2.94
O
O
O
F O-
N+
O
N NH
12 N O 7.35 6.86 0.49 2.52
O
O
O
O-
N+
O
N NH
13t N O 7.12 6.84 0.28 2.74
O
O
O
(continuedonnextpage)
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10 G. Verma etal.
Table5 (continued)
Ligandname Chemicalstructure Experimentalactivity Predictedactivity Residualactivity Fitness
Cl O-
N+
O
N NH
14t N O 7.44 6.82 0.62 2.53
O
O
O
F
N
N NH
15 N O 6.41 6.52 (cid:2)0.11 2.77
O
O
O
F
Cl
N NH
N O
16 7.61 7.46 0.15 2.83
O
O
O
N NH
N O
17 7.56 7.45 0.11 2.90
O
O
O
Pleasecitethisarticleinpressas:Verma,G.etal.,Pharmacophoremodeling,3D-QSAR,dockingandADMEpredictionofquinazolinebasedEGFRinhibitors.
ArabianJournalofChemistry(2016),http://dx.doi.org/10.1016/j.arabjc.2016.09.019