QSAR, docking and ADMET studies of camptothecin derivatives as inhibitors of DNA topoisomerase-I

QSAR, docking and ADMET studies ofcamptothecin derivatives as inhibitors of DNAtopoisomerase-IDharmendra K. Yadav and Feroz Khan*

In the present work, quantitative structure–activity relationship (QSAR) models of camptothecin derivatives againstDNA Topoisomerase-I (DNA Topo-I) were developed by multiple linear regression method using leave-one-outvalidation approach. The r2 and rCV2 of the model were 0.89 and 0.86, respectively. The QSAR study indicates thatchemical descriptors, namely Connectivity Index (order 1, standard), Electron Affinity (eV), Molecular Weight, GroupCount (ether) are correlated well with activity. Further, screening for drug likeness, ADME and toxicity showed thatcompound CPT9, CPT14, CPT20, CPT21 and CPT22 exhibits marked anticancer activity and possesses two times morepotent than standard drug camptothecin. The docking study showed a high binding affinity of predicted activederivatives and showed H-bond formation with GLY363, ARG364, LYS374, GLN421, ARG488, and ASP-533 residues,therefore considered as more stable and potent anticancer compounds. The obtained results can be used for thedesign of novel potent and selective inhibitors of DNA Topo-I. Copyright © 2013 John Wiley & Sons, Ltd.Supporting information may be found in the online version of this paper

Keywords: camptothecin; anticancer; DNA Topo-I; QSAR; docking; drug likeness; ADMET

1. INTRODUCTION

Since decades ago, cancer has been one of the leading diseasesand causes of death of the human population in a crucial manner,and it is predicted to continue to be the same in the coming years[1]. The mainstay or an important way to treat the malignanttumors is chemotherapy, that is, the use of chemical agents todestroy cancer cells. Surgery and radiation therapies are limitedto treating cancers that are confined to specific areas. Chemother-apy is advantageous over such forms of treatment due to its abilityto treat widespread or metastatic cancers. The chemotherapy hasusedmany researchers’ interests, and a great deal of current effortshas been focusing on the design and development of varied anti-cancer drugs. Camptothecin (CPT) is a cytotoxic quinoline alkaloidand a class of promising antitumor agents isolated from the woodand bark of the Chinese tree Camptotheca acuminate by Wall Meand co-workers in 1966. It inhibits DNA enzyme topoisomerase-I(DNA Topo-I) and has shown remarkable anticancer activity againstthe mouse leukemia [2] apart from low solubility and (high)adverse drug reaction. Irinotecan and Topotecan are A-ring, B-ringmodified analogues of CPT that are now being marketed in manycountries, and a number of other analogues are the subject ofongoing clinical or preclinical evaluation [3]. Although CPT showedimpressive activity in a number of human tumors [4,5], its clinicaldevelopment came to a halt in the early 1970s because of verylow water solubility and other toxicities, for example, myelosup-pression, diarrhea and hemorrhagic cystitis [6–10]. The interest inCPT was renewed mainly because of the recognition of its noveland unique mechanism of action. It was not until 1985 that thenuclear enzyme DNA Topo-I was identified as its molecular targetreported by Liu and co-workers [4]. Various DNA transactions suchas replication, transcription and recombination implicate theenzyme in their mechanisms. CPT and its analogs bind to a

complex formed by DNA with the DNA Topo-I enzyme, whichinhibits tumor growth. Because of the unique mechanism ofaction, the research interests in CPT have becomemanifold, hence,the production of many synthetic and semi-synthetic analogs/derivatives have taken place till now.

In 1989, it was found that extraordinary anticancer activityagainst human cancer xenografts in nude mice were presentin CPT and some of its semisynthetic derivatives [6]. Chemicalproperties wise, two groups of CPTs have been developedleading to clinical trials. The first group, which can be calledwater-soluble CPT, consists of Topotecan (1) 20 and Irinotecan(2) [11]. Both of them are now commercially available asaqueous solutions, which are injected intravenously for humantreatment. The second group of water-insoluble comprises theparent compound, CPT and its semisynthetic derivatives,namely 9-nitrocamptothecin and 9-aminocamptothecin. Manyattempts have beenmade to modify the CPT structure to reduceits toxicity and maintain or increase activity. Topotecan ispresently indicated as a second-line therapy for advancedovarian cancer and small-cell lung cancer. Irinotecan isapproved for use in the treatment of advanced colorectalcancer, both as first-line therapy in combination with 5-FU andas salvage treatment in 5-FU refractory disease. There are more

* Correspondence to: Feroz Khan, Metabolic and Structural Biology Department,CSIR-Central Institute of Medicinal and Aromatic Plants, P.O.-CIMAP, KukrailPicnic Spot Road, Lucknow-226015 (U.P.) India.E-mail: [email protected]

D. K. Yadav, F. KhanMetabolic and Structural Biology Department, CSIR-Central Institute of Medic-inal and Aromatic Plants, P.O.-CIMAP, Kukrail Picnic Spot Road, Lucknow226015 U.P., India

Research Article

Received: 29 September 2012, Revised: 22 October 2012, Accepted: 5 January 2013, Published online in Wiley Online Library: 10 February 2013

(wileyonlinelibrary.com) DOI: 10.1002/cem.2488

J. Chemometrics 2013; 27: 21–33 Copyright © 2013 John Wiley & Sons, Ltd.

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than 10 other CPT analogs in various stages of clinical evaluation[12] including 9-AC, 9-NC, GI-147211, exatecan mesylate, andkarenitecin. So far, a lot of CPT analogs/derivatives have beensynthesized and evaluated; several 3D quantitative structure–activity relationship (QSAR) studies of the CPT were reportedfrom laboratories [13–15]. Prior studies by Yoon et al. [14],suggested the development of QSAR model with CPT derivativeCPT11 and other prodrugs using hydrolysis percentage of serumesterase activity, and designed an easily activated SN-38prodrug. In two other reports, the authors built the QSAR mod-els with limited CPT datasets and few substitutes. Furthermore,because of lack of crystal structure, they all aligned the com-pounds with the multiple linear regression (MLR) fit [16–19]. Inthe present work, in order to understand the inhibition mecha-nism of CPT and to guide structural modification for rationaldrug design of CPT derivatives, a 2D QSAR study was performed.Besides the 2D, contour maps were also compared with theavailable X-ray crystal structure of target protein (PDB entry:1T8I) [20], retrieved from the Protein Databank (PDB; http://www.rcsb.org). We successfully developed the QSAR modelwith r2 and rCV2 of 0.89 and 0.86, respectively. The QSAR studyindicates that chemical descriptors, namely Connectivity Index(order 1, standard), Electron Affinity (eV), Molecular Weight,Group Count (ether) are correlated well with anticancer activity.The molecular docking study showed high binding affinity ofthe predicted active derivatives. Moreover, screening studiesfor drug likeness, ADME and toxicity risk assessment alsoshowed that compounds, namely CPT9, CPT14, CPT20, CPT21and CPT22 exhibit marked anticancer activity and possess twotimes more potency than standard drug CPT.

2. MATERIALS AND METHODS

2.1. Molecular modeling parameters and energyminimization

The biological activity data, inhibitory concentration for 50%samples (IC50) in nM, were converted to negative logarithmicconcentration in moles for QSAR studies. ChemBioDraw Ultrav12.0 software (Cambridge Soft Corporation, Software PublishersAssociation, NW, WA, USA) was used for the sketching of theseries molecules. The sketched structures were stored as .cdx file.Molecular docking, construction, geometry optimization andenergy minimization of CPT derivatives was carried out usingthe Sybyl X 1.3 molecular modeling and drug discoverysoftware (SYBYL-X 1.3, Tripos International, 1699 South HanleyRd., St. Louis, MO, 63144, USA). All the molecules were initiallydesigned in Sybyl, whereas the molecular construction, geometryoptimization, and the energy minimization process was performedby using HP XW4600 workstation having Intel Core 2 Duo E8400(3.2GHz) processor and 4GB of RAM, running the Red HatW

Enterprise Linux 4.0 (32-bit compatible) operating system (SiliconGraphics Inc., Mountain View, CA, USA). The Tripos force field witha distance-dependent dielectric and Powell gradient algorithmwith a convergence criterion of 0.001 kcalmol�1 was used foroptimization. Partial atomic charges were assessed by using theGasteiger–Hückel method. 2D structures were converted to 3Dstructures using the program Concord v4.0 and the maximumnumber of iterations performed in the minimization was set to2000. Further geometry optimization was performed throughMOPAC-6 package using the semi-empirical PM3 Hamiltonianmethod [21,22].

2.2. Parameters for QSAR model development

Initially, a total of 24 compounds/drugs (Supl. Table 1) were used inthe training set for the development of QSAR model for activityprediction against DNA Topo-I taken from the earlier reportedwork [23] using 52 chemical descriptors (physico-chemical proper-ties), calculated for each compound (Supl. Table 2). Variousdescriptors such as electronic, steric, and thermodynamic werecalculated by the Scigress Explorer software. On the basis of smallmolecules structural similarity, compound selection was per-formed to ensure diverse training data set. While selecting the bestsubset of chemical descriptors, highly correlated descriptors wereexcluded by using correlation matrix (Supl. Table 3) approach orco-variance analysis. Finally, out of 52, only 33 descriptors wereused for model development through forward stepwise MLRmethod. The resulting QSAR model showed a high regressioncoefficient. Themodel was validated successfully by using test data-set compounds. The models were evaluated for the robustness ofits predictions through cross-validation coefficient. Leave-one-out(LOO) method was used for validating QSAR models. The bestmodel was selected on the basis of various statistical parameterssuch as correlation coefficient (r) and square of the correlationcoefficient (r2); the quality of the each model was estimated fromthe cross-validated squared correlation coefficient (rCV2), whichconfirms the robustness and applicability of QSAR equation.

2.3. Molecular docking studies

To find the possible bioactive conformations of CPT9, CPT14, CPT15,CPT20, CPT21 CPT22, CPT23 and CPT24, the Sybyl X 1.3 interfacedwith Surflex-Dock program was operated to dock the compoundsinto the active site of the known anticancer target protein DNATopo-I (PDB ID: 1T8I). The program automatically docks ligand intothe binding pocket of a target protein by using Protomol-basedalgorithm and empirically produced scoring function. The protomolis a very important and necessary factor for docking algorithm andworks as a computational representation of proposed ligand thatinteracts into the binding site. Surflex-Dock’s scoring function hasseveral factors that play an important role in the ligand-receptorinteraction, in terms of hydrophobic, polar, repulsive, entropic andsolvation, and it is a worldwide well-established and recognizedmethod. The most standard docking protocols have ligand flexibil-ity in the docking process, while counting the protein as a rigidstructure. The present molecular docking study involves the severalsteps, namely import of protein structure into Surflex, the additionof hydrogen atoms and generation of protomol using a ligand-based strategy. During the second step, two parameters first calledprotomol_bloat, which determines how far the site should extendfrom a potential ligand; and another called protomol_threshold,which determines deepness of the atomic probes, used to definethe protomol penetration into the protein, were specified to formthe appropriate binding pocket. Therefore, protomol_bloat andprotomol_threshold was set to 0 and 0.50, respectively. All thecompounds were docked into the binding pocket and 20 possibleactive docking conformations with different scores were obtainedfor each compound. During the docking process, all of the otherparameters were assigned their default values.

2.4. Selection of structural chemical descriptors for QSARmodeling

The biological activity of CPT derivatives can be expressed quan-titatively as in the concentration of a substance required to give

D. K. Yadav and F. Khan

wileyonlinelibrary.com/journal/cem Copyright © 2013 John Wiley & Sons, Ltd. J. Chemometrics 2013; 27: 21–33

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http://www.rcsb.org

http://www.rcsb.org

a certain biological response. Additionally, when physicochemi-cal properties or structures are expressed by numbers, one canform a mathematical relationship between the two. The mathe-matical expression can then be used to predict the biologicalresponse of other chemical structures. Before the novelcompounds could be used as potential drugs, the predictionof toxicity/activity ensures the calculation of risk factor associ-ated with the administration of that particular compound/drug.A QSAR model ultimately helps in predicting these importantparameters, for example, IC50 or LD50 values. The importantchemical descriptors used in MLR analysis are reported in theReferences [24–26].

2.5. Statistical calculations used in QSAR modeling

2.5.1. Selecting a statistical method: stepwise multiple linearregression

The stepwise MLR method calculates QSAR equations by addingone variable at a time and testing each addition for significance.Only variables found to be significant are used in the QSARequation. This regression method is especially useful when thenumber of variables is large and when the key descriptors arenot known. In the forward mode, the calculation begins withno variables and builds a model by entering one variable at atime into the equation. In the backward mode, the calculationbegins with all variables included and drops variables one ata time until the calculation is complete. Backward regressioncalculations can lead to over fitting.

2.5.2. Multiple correlation coefficient (r)

Variation in the data is quantified by the correlation coefficient(r), which measures how closely the observed data tracks thefitted regression line. This is a measure of how well the equationfits the data, that is, it measures how good the correlation is.A perfect relation has r =+1 (positively correlated) or �1 (nega-tively correlated); no correlation has r = 0. Regression coefficient(r2) is sometimes quoted, and this gives the fraction of thevariance (in %) that is explained by the regression line. The morescattered the data points, the lower will be r. A satisfactoryexplanation of the data is usually indicated by an r2 of at least0.9. Compare r= 0.9 (r2 = 0.81; 81% of the variance is explained)with r= 0.7 (r2 = 0.49; 49% of the variance is explained; 51% areunexplained). Errors in either the model or in the data will leadto a bad fit. This indicator of fit to the regression line is calcu-lated as follows:

r2 ¼ sum of squares of the deviations from the regression line

=sum of squares of the deviations from the mean

r2 ¼ Regression variance=Original variance

Where the “Regression variance” is defined as the “Originalvariance” minus the Variance around the regression line. The“Original Variance” is the sum of the squares distances of theoriginal data from the mean.

2.5.2.1. QSAR analysis: forward stepwise regression.

Predicted log IC50 nMð Þ ¼ �0:591058

� Connectivity Index order 1; standardð Þþ3:0669 � Electron Affinity eVð Þþ0:0312332 � Molecular Weight

�1:00655 � Ether Group Count

�5:42856

�Regression coefficient r2

� �

¼ 0:89;Cross validation coefficient rCV2� � ¼ 0:86

�

The developed QSAR model equation showed a relationshipbetween in vitro experimental activity (IC50 in nM) as dependentvariable and three chemical descriptors as independent variables.The r2 = 0.89, refers to regression coefficient, which indicates 89%of correlation between activity (dependent variable) and chemicaldescriptors (independent variables) of the training data setcompounds whereas rCV2= 0.86, refers to cross validation regres-sion coefficient, which indicates 86% prediction accuracy of QSARmodel (Figure 1). It is evident from the mentioned equation thatthe molecular descriptors, electron affinity (eV) and molecularweight (MW) are showing positive correlation, that is, if theyincrease, the biological activity will also be increased againstDNA Topo-I. On the other hand, connectivity index (order 1,standard) and group count (ether) showing negative correlations,means that if the values of these descriptors increase, then biolog-ical activity will be decreased.

2.6. Validation of QSAR model

Quantitative structure–activity relationship model was validatedto test the internal stability and predictive ability by the internaland external validation, and randomization test procedure.

2.6.1. Internal validation

Internal validation was carried out using LOOmethod. For calculat-ing cross validation regression coefficient (rCV2), each molecule inthe training set was eliminated once, and the activity of the

Figure 1. Graph of multiple linear regression analysis indicating linearrelationship between experimental and predicted activities of the trainingdata set.

QSAR studies of CPT derivatives against DNA Topo-I

J. Chemometrics 2013; 27: 21–33 Copyright © 2013 John Wiley & Sons, Ltd. wileyonlinelibrary.com/journal/cem

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eliminated molecule was predicted by using the model developedby the remaining molecules. The cross validation regressioncoefficient (rCV2) was calculated using the equation that describesthe internal stability of a model.

r2 ¼ 1�X

Ypred� Yð Þ2X

Y � 0Y

� �2

Where, rCV2 refers to cross validation regression coefficient,Yexperimental and Ypred activity of the molecule in the trainingset, respectively, and Ÿ is the average activity of all moleculesin the training set.

2.6.2. External validation

For external validation, the activity of each molecule in the testset was predicted using the model developed by the trainingset. The regression coefficient (r2) value is calculated as follows.

r2cv ¼ 1�X

Ypred testð Þ � Y testð Þ� �2

XY testð Þ �

0Y trainingð Þ

� �2

Where, r2 refers regression coefficient, Yexperimental and Ypredare activity of the molecule in the training set, respectively, andŸtraining is the average activity of all molecules in the trainingset. Both summations are over all molecules in the test set. Thus,the regression coefficient (r2) (Figure 2) value is indicative of thepredictive power of the current model for the external test set.For this, we have used only eight compounds for the test. Gener-ally, a QSAR model was considered to have a high predictivepower only if the rCV2 was >0.6 for the test set [27].

2.6.3. Randomization test

To evaluate the statistical significance of the QSAR model for anactual dataset, one tail hypothesis testing was used. The robust-ness of the models for training sets was examined by comparingthese models with those derived from random data sets. Randomsets were generated by rearranging the activities of the moleculesin the training set. The statistical model was derived using variousrandomly rearranged activities (random sets) with the selected

descriptors, and the corresponding CV2 were calculated. The signif-icance of the models hence obtained was derived on the basis of acalculated Z score [27].A Z score value is calculated by the following formula:

Zscore ¼ h� mð Þs

where h is the rCV2 value calculated for the experimental dataset,m the average rCV2, and s is its standard deviation calculated forvarious iterations using models built by different random data-sets. The probability (a) of the significance of randomization testis derived by comparing Z score value with Z score critical valueas reported in References [27–29], if Z score value is less than 4.0;otherwise, it is calculated by the formula as given in the litera-ture. For example, a Z score value greater than 3.10 indicates thatthere is a probability (a) of less than 0.001 that the QSAR modelconstructed for the real dataset is random. The randomizationtest suggests that all the developed models have a probabilityof less than 1% that the model is generated by chance (Figure 3).Forward stepwise regression by randomization test:

Predicted log IC50 nMð Þ ¼ þ0:56345258

� Connectivity Index order 1; standardð Þþ 3:0591 � Electron Affinity eVð Þþ0:00856429 � Molecular Weight

�1:00001 � Ether Group Count

�0:898707

�Regression coefficient r2

� � ¼ 0:89;Cross validation coefficient rCV2� �

¼ 0:86�

2.7. Screening through pharmacokinetic properties

The ideal oral drug is one that is rapidly and completely absorbedfrom the gastrointestinal track, distributed specifically to its site ofaction in the body, metabolized in a way that does not instantlyremove its activity, and eliminated in a suitable manner, withoutcausing any harm. It is reported that around half of all drugs indevelopment fail to make it to the market because of poor

Figure 2. Graph of experimental and predicted activities for trainingand test set compounds. (A) Training set (black color dots) and (B) Testset (red color dots).

Figure 3. Graph of experimental and predicted activities of the trainingand test set compounds showing model validation results as part of therandomization test.



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pharmacokinetics (PK) [30–32]. The PK properties depend on thechemical properties of the molecule. PK properties such as absorp-tion, distribution, metabolism, excretion and toxicity (ADMET) areimportant in order to determine the success of the compoundfor human therapeutic use. Some important chemical descriptorscorrelate well with PK properties such as polar surface area (PSA)as a primary determinant of fraction absorption, low MW for oralabsorption. The distribution of the compound in the human bodydepends on factors such as blood–brain barrier (Log BB), perme-ability such as apparent Caco-2 permeability, apparent MDCKpermeability, Log Kp for skin permeability, volume of distributionand plasma protein binding (Log Khsa for Serum protein binding).It has been reported that excretion process which eliminates the

compound from human body depends on the MW and octanol–water partition coefficient. Similarly, rapid renal clearance is associ-ated with small and hydrophilic compounds. The metabolism ofmost drugs that takes place in the liver is associated with largeand hydrophobic compounds. Higher lipophilicity of compoundsleads to increased metabolism and poor absorption, along withan increased probability of binding to undesired hydrophobicmacromolecules, thereby increasing the potential for toxicity. Inspite of some observed exceptions to Lipinski’s rule, the propertyvalues of the vast majority (90%) of the orally active compoundsare within their cut-off limits [24–26,33]. Molecules violating morethan one of these rulesmay have problems with bioavailability. Forstudying PK properties, Lipinski’s “Rule of Five” screening was used

Table I. Predicted chemical descriptors and in vitro activity IC50 (nM) correlated through derived QSAR model equation

Chemical sample Connectivity Index(order 1, standard)

Electronaffinity (eV)

Molecularweight

Group count(ether)

Pred. log IC50 (nM)

CPT * 12.525 1.534 348.357 0 2.753Topotecan* 13.884 1.177 395.414 0 2.324CPT1 13.464 1.393 375.38 1 2.119#

CPT2 17.308 1.332 489.527 2 2.231#

CPT3 17.808 1.396 503.554 2 2.223#

CPT4 16.611 1.382 474.512 0 4.285CPT5 18.095 1.292 514.577 0 4.324CPT6 18.095 1.377 518.522 2 2.589#

CPT7 18.595 1.277 528.604 0 4.603CPT8 18.595 1.315 532.549 2 2.781#

CPT9 15.752 1.267 445.471 2 1.527#

CPT10 16.79 1.467 475.454 2 2.449#

CPT11 16.79 1.431 474.469 2 2.466#

CPT12 16.252 1.522 460.442 2 2.384#

CPT13 18.595 1.466 532.506 2 3.127CPT14 13.086 1.449 362.384 1 1.812#

CPT15 15.603 1.523 424.455 1 2.284#

CPT16 13.942 1.466 390.395 0 3.902CPT17 13.942 1.436 391.382 0 3.459CPT18 14.48 1.513 405.409 0 3.949CPT19 14.086 1.408 391.426 1 1.883#

CPT20 13.586 1.554 377.399 1 1.927#

CPT21 14.086 1.514 390.438 1 1.84#

CPT22 14.586 1.507 404.465 1 1.985#

CPT23 14.586 1.492 405.452 1 2.155#

CPT24 14.942 1.692 419.436 1 2.692#

CPT24A 12.525 1.457 348.357 0 2.719#

CPT25 12.525 1.457 348.357 0 4.989CPT26 12.525 1.459 348.357 0 5.343CPT27 14.025 1.6 390.395 1 2.719#

CPT28 12.525 1.458 348.357 0 5.8CPT29 14.957 1.552 420.421 2 1.607#

CPT30 14.957 1.549 420.421 2 1.728#

CPT31 13.957 1.481 392.41 1 2.214#

CPT32 14.508 1.49 405.409 1 1.969#

CPT33 14.508 1.492 405.409 1 2.155#

CPT34 14.508 1.474 404.421 1 1.873#

CPT35 13.957 1.483 392.41 1 2.051#

Note:**indicates standard anticancer compound used as control.##marked compounds indicate predicted active comptothecin derivatives.



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to assess the drug likeness properties of CPT derivatives. Briefly,this rule is based on the observation that most orally administereddrugs have a MW of 500 or less, a LogP no higher than five, five orfewer hydrogen bond donor sites and 10 or fewer hydrogen bondacceptor sites (N and O atoms). In addition, the bioavailability ofderivatives was assessed through topological PSA analysis. Wecalculated the PSA by using a method based on the summationof tabulated surface contributions of polar fragments termed astopological PSA (TPSA) (ChemAxon-Marvinview 5.2.6: PSA plugin.PSA is formed by polar atoms of a molecule. This descriptor wasshown to correlate well with passive molecular transport throughmembranes and therefore, allows prediction of transport proper-ties of drugs and has been linked to drug bioavailability. Thepercentage of the dose reaching the circulation is called thebioavailability. Generally, it has been seen that passively absorbedmolecules with a PSA> 140Å2 are thought to have low oralbioavailability. Besides, the number of rotatable bonds are also asimple topological parameter used by researchers, under

extended Lipinski’s rule, as a measure of molecular flexibility. Ithas been shown to be a very good descriptor of oral bioavailabilityof drugs [34,35]. Rotatable bond is defined as any single non-ringbond, bounded to non-terminal heavy (i.e., non-hydrogen) atom.Amide C–N bonds are not considered because of their high rota-tional energy barrier. Moreover, some researchers also included asum of H-bond donors and H-bond acceptors as a secondarydeterminant of fraction absorption. The primary determinant offraction absorption is PSA. According to the extended rule, thesum of H-bond donors and acceptors should be less than 12 orequal to 12 or PSA should be less than 140Å2 or equal to 140Å2,and the number of rotatable bonds should be less than 10 or equalto 10. Calculations of other important ADME properties of CPTderivatives were performed through QikProp, version 3.2,Schrödinger, LLC, USA (2009). We also screened CPT derivativesthrough Jorgensen’s rule of three, which states that LogS shouldbe more than �5.7, PCaco should be more than 22nm/s andnumber of primarymetabolites should be less than 7 (Schrödinger,

Figure 4. Chemical structures of virtually designed 35 camptothecin derivatives.



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USA, 2009). It is assumed that CPT derivatives with preferably noviolation of Jorgensen’s rule are more likely to be orally available.

3. RESULTS AND DISCUSSION

3.1. Quantitative chemical structure-activity relationship

In the present work, derivatives of CPT were evaluated for theiranticancer activity through QSAR and docking studies. Structureactivity relationship has been denoted by QSAR model showingsignificant activity–descriptors relationship accuracy of 89%(r2 = 0.89) and activity prediction accuracy of 86% (rCV2= 0.86). Atotal of 24 drugs were used for QSAR modeling against 50 chemi-cal descriptors. Only four descriptors were found to be significantand seem to be responsible for anticancer activity (Table I). Aforward feedMLRQSARmodel was developed using LOO approachfor the prediction of biological activity of cleomiscosin molecules.Results of QSAR indicate that compounds CPT9, CPT14, CPT15,CPT20, CPT21 CPT22, CPT23 and CPT24 showed significant activitysimilar or higher to CPT. On the basis of structure activity relation-ship, we designed a prototype in which CPT was used as one ofthe lactone ring. The designed compounds virtually optimized toa number of CPT derivatives based on conformation restrictedlactone ring, as a basic unit along with different linear side chainsmodification at different positions. In the present work, we reportanticancer activity of newly designed camptothecin derivativescomparable with potent anticancer compounds (Figure 4).

3.2. Docking based exploration of binding affinity on DNATopoisomerase-I

The aim of the molecular docking study was to elucidate whetherCPT derivativesmodulate the anticancer target, and also to identifythe binding site pocket against well-known human anticancermolecular target DNA Topo-I. The results of the molecular dockingsuggest that derived compounds inhibit the activity of DNA Topo-I(UniProtKB/Swiss-Prot: P11387, Sequence length: 765 AA). In thework presented here, we explored the orientations and bindingaffinities (in terms of the total score) of CPT derivatives towardsanticancer target DNA Topo-I (PDB ID: 1T8I). Topoisomerases (EC5.99.1.2) are enzymes that regulate the overwinding or underwind-ing of DNA. Topoisomerases are isomerase enzymes that act onthe topology of DNA Topo-I, which cuts one strand of a DNAdouble helix; relaxation occurs, and then the cut strand is rean-nealed. Contrary to this, Topoisomerase II cuts both strands ofone DNA double helix and then reanneals the cut strand. Topi-somerases II utilizes ATP energy. Prior studies suggest that DNAtopoisomerases are molecular targets of anticancer and antibacte-rial drugs. CPTs and novel non-CPTs in clinical development, forexample, Indenoisoquinolines and ARC-111 mainly target eukary-otic type IB Topo I, whereas human type IIA topoisomerases(Top2 alpha and Top2 beta) are the molecular targets of the widelyused anticancer agents etoposide, anthracyclines (doxorubicin,daunorubicin), and mitoxantrone. Bacterial type II topoisomerases(Gyrase and Topo IV) are the targets of quinolones and aminocou-marin antibiotics [19,34]. This induces breaks in the DNA that

Figure 4. Continued



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ultimately leads to programmed cell death (apoptosis). HumanDNA Topo-I is a multidomain enzyme that contains two highlyconserved globular domains (the core and the COOH-terminaldomain) that are crucial for catalytic activity, and two other regions(NH2-terminal and linker) that are not strictly required for its cata-lytic and relaxation functions. The phosphate of the tyrosi-neeDNAphosphodiester bond makes close interactions with the guanidi-nium groups of ARG-364 and ARG-488. Both arginines are impli-cated in the reaction mechanism of the enzyme [36,37]. ARG-488makes non-bonded interaction with thymine nucleotide in theminor groove (T16); LYS-532 forms a hydrogen bond withthymine nucleotide (T10), another hydrogen bond formedbetween THR-718 and Guanine (G12). ASP-533 forms non-bondedinteraction with backbone sugar of Adenine (A114) whereasTYR-426 forms interaction with backbone sugar of Thymine (T10).

The docking reliability was validated by using the known crystal-lize X-ray structure of anticancer target Topo-I complexed with EHD:4-ethyl-4-hydroxy-1,12-dihydro-4 h-2-oxa-6,12a-diaza-dibenzo[b,h]fluorene-3,13-dione. The co-crystallized EHD inhibitor was re-docked into the binding site, and the docked conformation withthe highest total score of 11.9934 was selected as the mostprobable binding conformation. The low root mean-squaredeviation (RMSD) of 0.5098 Å between the docked and the crys-tal conformations indicates the high reliability of Surflex-docksoftware in reproducing the experimentally observed binding

mode for this inhibitor. As shown in (Figure 5(A)), redockedmolecules were almost in the same position with co-crystallizedat the active site of EHD inhibitor.Similarly, the docking results for CPT14 against target protein

Topo I showed high binding affinity docking score indicated bytotal score of 12.2767 and the formation of a hydrogen bondof length 1.9 Å to the basic residue ARG-364. In docking pose,the conserved binding site pocket amino acid residues within aselection radius of 4 Å from bound ligand were GLU-356 (acidic),ARG-364 (basic), LYS-532 (basic), ASP-533 (amide) THR-498(nucleophilic) and ASN-722 (amide). On the other hand, thedocking results for reference compound CPT with DNA Topo-Ishowed a total score of 11.3898 with hydrophobic interactionand formed no H-bond with any binding site residues (Figure 5(B)). Likewise, active compound CPT20 against target Topo I, theconserved binding site pocket amino acid residues within aselection radius of 4 Å from bound ligand, were ARG-364 (basic),LYS-532 (basic), ASP-533 (amide) THR-498 (nucleophilic) andASN-722 (amide); the formation of a hydrogen bond of length1.9 Å to the basic residue ARG-364 showed the total score of11.7416 (Figure 5(C)), and the same amino acid showed withCPT22 the total score of 13.0123 and the formation of a hydro-gen bond of length 1.9 Å (Figure 5(D)).Lastly, the docking results for CPT derivatives CPT9, CPT15,

CPT21, CPT23 and CPT24 against anti-cancer target DNA Topo-I

Figure 5. Docking poses of the predicted active camptothecin (CPT) derivatives and co-crystallized inhibitor of DNA Topo-I (PDB: 1T8I). Theco-crystallize structure of EHD (4-ethyl-4-hydroxy-1,12-dihydro-4h-2-oxa-6,12a- diaza-dibenzo[b,h]fluorene-3,13-dione) inhibitor re-docked into the bindingsite of DNA Topo-I with 0.5098Å of RMSD between docked and crystallized conformation and Sybyl docking total score of 11.9934. (A) Docking pose ofCPT14 on DNA Topo-I with high binding affinity, as indicated by total scores of 12.2767; (B) docking pose of CPT20 on DNA Topo-I with high binding affinity,as indicated by total scores of 11.7416; (C) docking pose of CPT22 on DNA Topo-I with high binding affinity, as indicated by total scores of 13.0123; and (D)DNA Topo-I adopt the same active conformation and all the proposed active inhibitors bind in a similar manner, within an enlarged hydrophobic pocket,which is a characteristic feature of the DNA Topo-I active conformation.



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also showed high binding affinity with a total score indicating10.8512, 11.255, 10.7961, 12.5595 and 12.5274, the formationof one hydrogen bond with CPT9, CPT15, CPT21, CPT24 andtwo hydrogen bonds with CPT23 of length 2.2, 1.9, 1.7, 1.9and 19, 2.1 Å to the basic residue ARG-364 with CPT15,

CPT21 and CPT24 and ASP-533 (amide) with CPT9 and CPT24(Supl. Table 4). Therefore, the bound compound that showedstrong hydrophobic interaction with DNA Topo-I, thus lead tomore stability and activity in this compound. QSAR results indi-cate that these active compounds CPT9, CPT14, CPT20, CPT21

Table II. Comparison of binding affinities of standard anticancer drugs and predicted active campthotecin derivatives againstDNA Topo-I

Compoundname

Docking totalscore (Sybyl)

Amino acid involved inactive pocket in 4Ă

Involvedresidue

of amino acid

Length ofH-bond Ă

No. ofhydrogenbond

CPT 9.8421 PHE-361, GLY-363, ARG-364, HIS-367, LYS-493,THR-498, ALA-499, LYS-532, ASP-533

— — —

Topatecan 11.2265 GLU-356, PHE-361, GLY-363, ARG-364, HIS-367,LYS-374, LYS-425, THR-498, ALA-499, LYS-532,ASP-533

— — —

CPT9 10.8512 THR-718, ASN-722, LYS-532, ASP-533, ARG-364,ASN-352, GLU-356, LYS-425, TYR-426

ASP533 2.2 2ARG-364

CPT14 12.2767 GLU-356, ARG-364, LYS-532, ASP-533, THR-718,ASN-722

ARG-364 1.9 1

CPT15 11.255 ARG-364, LYS-532, ASP-533, ILE-535, THR-718,ASN-722

ARG-364 1.998 1

CPT20 11.7416 ARG-364, LYS-532, ASP-533, THR-718, ASN-722 ARG-364 2.033 1CPT21 10.7961 GLU-356, ARG-364, LYS-532, ASP-533, ILE-535,

THR-718ARG-364 1.745 1

CPT22 13.0123 ARG-364, LYS-532, ASP-533, THR-718, ASN-722 ARG-364 2.075 1CPT23 12.5595 GLU-356, ARG-364, ARG-488, LYS-532, ASP-533,

ILE-535, HIS632, THR-718ASP-533 1.976 2ARG-364 21.93

CPT24 12.5274 GLU-356, ARG-364, ARG-488, LYS-532, ASP-533,ILE-535, HIS632, THR-718, ASN-722

ARG-364 2.110 1

Table III. Compliance of campthothecin derivatives with computational parameters of drug likeness and ADME properties

Compound Pharmacokinetic property (ADME) dependent on chemical descriptors Rule of 5violation

ADM AE ADME AD

oralbioavailability:TPSA (Å2)

MW Log P H-bond donor H-bond acceptor

Aminegroup count

Sec-aminegroup count

Hydroxylgroup count

Nitrogenatom count

Oxygenatom count

Camptothecin 79.73 348.35 0.558 0 0 1 2 4 0Topotecan 125.45 395.41 �2.88 1 0 2 3 5 0CPT9 98.19 460.48 1.028 0 0 1 2 6 0CPT14 68.73 362.38 0.836 0 0 0 2 4 0CPT15 68.73 424.45 2.617 0 0 0 2 4 0CPT20 94.75 377.39 0.413 1 0 0 3 4 0CPT21 68.73 390.43 1.647 0 0 0 2 4 0CPT22 68.73 404.46 2.043 0 0 0 2 4 0CPT23 94.75 405.45 0.098 0 0 0 3 4 0CPT24 111.82 419.43 �0.306 0 3 5 0

Note: A, absorption; D, distribution; M, metabolism; E, excretion; TPSA, topological polar surface area; MW, molecular weight; Log P,octanol/water partion coefficient.



29

and CPT22 showed two times more potency than CPT, there-fore, showing high binding affinity against DNA Topo-I duringdocking and also indicating conserved nature of binding sitepocket residues within a radius of 4 Å. All these compoundsshowed the conserved nature of binding site residues withina radius of 4 Å. Comparison indicates that the highly con-served hydrophobic amino acid residues within a radius of4 Å of binding site pocket were GLU-356 (acidic), ARG-364(basic), LYS-532 (basic), ASP-533 (amide) THR-498 (nucleophilic)and ASN-722 (amide) (Table II).

3.3. Compliance with pharmacokinetic parameters

We have considered several physiochemical properties relatedto PK while screening the active CPT derivatives. The virtualscreening results indicate that all the active CPT derivativesfollowed Lipinski’s “Rule of Five” for drug likeness propertiescompliance (Table III). The compound’s hydrophilicity wasmeasured through LogP value. Low hydrophilicity andtherefore high LogP values may cause poor absorption or

permeation. It has been shown for compounds to have a rea-sonable probability of being well absorbed when their LogPvalue must not be >5. The virtual screening study for druglikeness suggests that except compound CPT2, CPT3, CPT6,and CPT8, all compounds were within acceptable limits. Typi-cally, a low solubility goes along with a bad absorption,and therefore the general aim is to avoid poorly solublecompounds. This property was measured through aqueoussolubility (LogS). The LogS of a compound significantly affectits absorption and distribution characteristics. The calculatedLogS values of studied active compounds were withinacceptable limits.The rest of the active derivatives follow the Lipinski’s rule

and have reliable polarity for better permeation and absorp-tion as revealed by H-bond donors and H-bond acceptors.Similarly, ADME parameters were also calculated for the activeCPT derivatives CPT9, CPT14, CPT20, CPT21 and CPT22, whichalso showed compliance with CPT and fall within the standardrange of 95% of known drugs for drug likeness. Calculationsrelated to aqueous solubility, serum protein binding, blood-

Table IV. Compliance of predicted active campthothecin derivatives with standard computational parameters of pharmacokinetics(ADME)

Compoundname

log S foraqueoussolubility

log Khsa forserum protein

binding

log BB forbrain/blood

No. of metabolicreactions

PredictedCNS activity

log HERG forK +Channelblockage

CPT �4.483 �0.157 �0.797 3 �1 �5.144Topotecan �3.264 �0.256 �1.551 9 �2 �5.557CPT01 �3.331 �0.322 �0.745 6 �1 �4.511CPT02 �2.131 �0.375 �0.355 6 1 �6.832CPT03 �2.697 �0.221 �0.396 6 1 �6.965CPT06 �5.704 0.14 �1.67 5 �2 �5.564CPT08 �6.133 0.278 �1.699 5 �2 �5.621CPT09 �5.491 0.272 �0.977 5 �1 �5.183CPT10 �5.243 �0.094 �1.91 3 �2 �5.732CPT11 �5.218 �0.107 �1.898 3 �2 �5.686CPT12 �4.656 �0.233 �2.368 4 �2 �5.426CPT14 �3.064 �0.433 �0.474 2 �1 �5.07CPT15 �4.421 0.01 �0.472 2 �1 �6.24CPT19 �2.734 �0.206 �0.297 3 1 �6.149CPT20 �2.59 �0.181 �0.679 4 1 �5.976CPT21 �3.919 �0.178 �0.629 2 �1 �5.492CPT22 �4.67 0.006 �0.765 2 �1 �5.861CPT23 �3.284 0.023 �0.939 4 0 �6.265CPT24 �3.148 �0.518 �1.885 4 �2 �4.37CPT24A �3.734 �0.218 �1.385 4 �2 �5.032CPT29 �2.521 �0.23 �1.16 6 �2 �5.344CPT30 �3.725 �0.024 �1.011 5 �2 �6.152CPT31 �4.457 0.004 �0.82 5 �1 �4.761CPT32 �4.275 �0.133 �0.971 4 �1 -5.055CPT33 �4.69 0.04 -0.912 5 �1 �4.995CPT34 �4.458 0.025 �0.826 4 �1 �4.944CPT35 �4.79 0.132 �0.845 4 �1 �4.961Stand. range* (�6.5 / 0.5) (�1.5/1.5) (�3.0/1.2) (1.0 / 8.0) –2 (inactive)

+2 active)(concern below �5)

Note:**For 95% of known drugs based on Schrödinger, USA—Qikprop v3.2 (2009) software results.



30

brain barrier (log BB and apparent MDCK cell permeability),gut-blood barrier (Caco-2 cell permeability), predicted centralnervous system activity, number of likely metabolic reactions,log IC50 for HERG K + channel blockage, transdermal transportrate (Jm), skin permeability (Kp) and human oral absorption ingastro-intestinal tract showed that these values for the activederivatives fell within the standard ranges generally observedfor drugs (Table IV).

3.4. Toxicity risks assessment

It is now possible to predict the toxicity risks of compoundsthrough reliable bioinformatics tools. In the present study,we have calculated toxicity risks parameters such as mutage-nicity, tumorogenicity, irritation and reproductive or develop-mental toxicity of all the camptothecin derivatives CPT1–CPT35(Table V). The toxicity risk predictor locates fragments withina molecule, which indicate a potential toxicity risk. Toxicityscreening results showed that compounds CPT14, CPT15,

CPT19, CPT20, CPT21, CPT22, CPT23 and CPT24 pose no riskof tumorogenicity and reproductive toxicity; however, theyindicate partial mutagenicity. On the other hand, compoundsCPT2, CPT6, CPT8, CPT9, CPT10, CPT11, CPT12, CPT24A,CPT29, CPT30, CPT31, CPT32, CPT33, CPT34 and CPT35 indi-cate high risk of mutagenicity, tumorogenicity and mediumrisk reproduction thus rejected, whereas compounds CPT1–CPT35 indicate no risk of irritation. Compounds CPT24A,CPT29, CPT30, CPT31, CPT32, CPT33, CPT34 and CPT35indicate medium risk of reproduction. To judge the activecompounds’ overall potential to qualify for a drug, wecalculated overall drug score, which combines drug-likeness,hydrophilicity (CLogP), aqueous solubility (LogS), MW, and tox-icity risk parameters. Predicted active compounds showed theircalculated parameters within acceptable limits. The results oftoxicity risk assessment screening showed an overall drug scoreof predicted active compounds, namely CPT9, CPT14, CPT20,CPT21 and CPT22, moderate to good, as compared with stan-dard anticancer compounds CPT and topatecan.

Apparent Caco-2permeability

(nm/s)

Apparent MDCKpermeability

(nm/s)

log Kp forskin

permeability

Jm,maxTransdermaltransport rate

Jorgensen Ruleof 3 Violations

% HumanOral Absorptionin GI (+-20%)

Qual. Model forHuman OralAbsorption

527.805 247.957 �2.931 0.070 0 85.866 HIGH15.783 6.176 �6.899 0.000 2 50.034 Medium

530.401 249.276 �3.183 0.119 0 84.428 HIGH28.777 13.078 �7.437 0.000 0 44.589 Medium28.8 13.089 �7.467 0.000 0 46.785 Medium

192.791 83.486 �3.909 0.000 1 58.234 Medium192.329 83.27 �3.936 0.000 1 60.105 Medium531.038 249.599 �3.267 0.001 0 93.329 Medium128.965 54.06 �4.216 0.000 0 76.897 HIGH126.964 53.154 �4.22 0.000 0 75.428 HIGH39.888 15.206 �5.206 0.000 0 61.292 HIGH

1107.99 552.698 �2.309 1.538 0 92.997 HIGH1450.86 739.687 �1.425 0.606 0 100 HIGH246.207 120.309 �4.28 0.038 0 80.026 HIGH94.712 42.84 �5.078 0.008 0 69.709 HIGH

1169.414 585.89 �2.069 0.401 0 100 HIGH1123.037 560.816 �2.014 0.084 0 100 HIGH

82.259 36.785 �5.051 0.002 0 72.654 HIGH49.165 39.316 �4.089 0.024 0 62.544 HIGH

160.089 68.29 �3.991 0.007 0 73.736 HIGH33.817 14.074 �6.257 0.001 0 58.251 HIGH97.307 44.109 �5.179 0.001 0 74.258 HIGH

528.45 248.285 �3.307 0.007 0 88.979 HIGH397.97 182.741 �3.431 0.008 0 83.257 HIGH536.941 252.599 �3.176 0.006 0 89.142 HIGH534.865 251.544 �3.208 0.009 0 89.213 HIGH531.921 250.048 �3.263 0.004 0 90.589 HIGH

(<25 poor,>500 great)

(<25 poor,>500 great)

(�8.0 to �1.0,Kp in cm/h)

(micrograms/cm^2-h)

(maximum is 3) (<25% is poor) (>80% is high)



31

4. CONCLUSION

The work presented in this study shows how chemical features of aset of compounds along with their activities ranging over severalorders of magnitudes can be used to generate pharmacophorehypothesis that can successfully predict activity. The QSAR modelswere not only predictive within the same series of compounds butalso valid for other chemical classes and also effectively mappedonto most of the features important for anticancer activity. Themolecular docking results of seven CPT derivatives showed thatthe main residues in the active pocket of DNA Topo-I are hydro-phobic. There was a significant correlation between bindingaffinity (total scores) and the experimental pIC50 with a correlationcoefficient (r2) of 0.89 (i.e., 89%) and cross validation coefficient(rCV2) of 0.86 (i.e., 86%). Main influencing factors ofmolecular inter-actions (docking pose) between DNA Topo-I and CPT are H-bond,hydrophobic and electrostatic. Virtual screening studies throughQSAR model, in silico ADME and toxicity risk assessment suggestthat the compounds CPT9, CPT14, CPT20, CPT21 and CPT22marked anticancer activity then CPT and topatecan.

CONFLICT OF INTEREST

Authors have no personal financial or non-financial conflictsof interest.

ACKNOWLEDGEMENT

We acknowledge the Indian Council of Medical Research, NewDelhi (www.icmr.nic.in), for financial support as senior researchfellowship (SRF) (No.:45/9/2011/BMS/CMB) at the CSIR-CentralInstitute of Medicinal and Aromatic Plants, Lucknow (www.cimap.res.in), India.

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Note: MUT, mutagenicity; TUMO, tumorogenicity; IRRI, irritation; REP, reproduction; MW, molecular weight; CLP, ClogP; S, solubility;DL, drug-likeness; DS, drug-score.##indicate QSAR based predicted active campthothecin derivatives.



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QSAR, docking and ADMET studies of camptothecin derivatives as inhibitors of DNA topoisomerase-I

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Transcript of QSAR, docking and ADMET studies of camptothecin derivatives as inhibitors of DNA topoisomerase-I