research article 3d-qsar study on a series of vegfr-2

Hindawi Publishing CorporationJournal of ChemistryVolume 2013 Article ID 374804 8 pageshttpdxdoiorg1011552013374804

Research Article3D-QSAR Study on a Series of VEGFR-2 Kinase Inhibitors3-Pyrrole Substituted Indolin-2-Ones Compounds

Shunlai Li Rutao Zhang and Hongguang Du

College of Science Beijing University of Chemical Technology 15 Beisanhuan East Road Chaoyang District Beijing 100029 China

Correspondence should be addressed to Hongguang Du dhgmailbucteducn

Received 4 May 2013 Revised 2 September 2013 Accepted 29 September 2013

Academic Editor Mire Zloh

Copyright copy 2013 Shunlai Li et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

The vascular endothelial growth factor receptor-2 kinases (VEGFR-2) are attractive targets for the development of anticancer agentsSelf-organizing molecular field analysis (SOMFA) (a simple three-dimensional quantitative structure-activity relationship (3D-QSAR) method) is used to study the structure-activity correlation of 3-pyrrole substituted indolin-2-ones VEGFR-2 inhibitorsThe statistical results cross-validated 1199032CV (05267) and non-cross-validated 1199032 (05623) show a reliable predictive ability Thecontributions of shape and electrostatic fields are 427 and 573 respectively Analysis of SOMFA models through shape andelectrostatic grids provide useful information for the design and optimization of new 3-pyrrole substituted indolin-2-one basedVEGFR-2 inhibitors

1 Introduction

Cancer is the second leading cause of death in the worldResearchers have found that receptor tyrosine kinases (RTKs)play an important role in oncogenic transformation of cells[1] VEGFR-2 (vascular endothelial growth factor receptor-2) as a member of the family of RTKs is widely investigatedin the pathogenesis of several disorders [2ndash7] It is not onlywidely distributed in the organization of vascular endothelialcells but also distributed in some tumor cells it plays animportant role in the cell signalling of VEGF and tumorproliferation [8] Recent research has shown that the blockadeof VEGFR-2 signalling by small molecular inhibitors to thekinases domain can inhibit the growth of solid tumors [9ndash12] Therefore inhibition of the VEGFR-2 has become animportant research direction in the treatment of cancers [13]

In recent years a number of VEGFR-2 kinase inhibitorshave been developed as anticancer agents including ana-logues of quinazoline indolin ketones pyridazine andquinoline structures [14ndash17] Recent research found thata series of 3-pyrrole substituted indolin-2-one compoundsshow good inhibitory activity against VEGFR-2 shown inTable 1 including sunitinib (compound 4) which has beenrecently approved by US FDA for the treatment of gastroin-terstinal stromal tumor (GIST) and renal cell carcinoma

(RCC) However there are fewer quantitative structure-activity relationship (2D-QSAR and 3D-QSAR) studies andother molecular modeling works on VEGFR-2 targets [18ndash23]

The self-organizing molecular field analysis (SOMFA)[24] is a simple 3D-QSAR technique which has been devel-oped by Robinson et al The method has similarities toboth comparative molecular field analysis (CoMFA) [25]and molecular similarity studies Like CoMFA a grid-basedapproach is used however SOMFA only uses steric andelectrostatic maps which are related to interaction energymaps no probe interaction energies need to be evaluatedTheweighting procedure of the grid points bymean-centeredactivity is an important ingredient of the SOMFA procedureLike the similarity methods it is the intrinsic molecularproperties such as the molecular shape and electrostaticpotential which are used to develop the QSAR models

A SOMFA model could suggest a method of tacklingthe all-important alignment which all 3D-QSAR meth-ods have faced The inherent simplicity of this methodallows the possibility of aligning the training compoundsas an integral part of the model derivation process and ofaligning prediction compounds to optimize their predictedactivities

2 Journal of Chemistry

Table 1 Structures of 34 compounds and their VEGFR-2 inhibitory activities

(a)

R1

R2

R3

NH

NH

O

Compd R1 R2 R3 IC50 (120583M) pIC50

1 H H H 123 59102 H H (CH2)2N(CH2CH2)NCH3 03 65233 H H CONH(CH2)2N(C2H5)2 005 73014 F H CONH(CH2)2N(C2H5)2 008 70975 Cl H CONH(CH2)2N(C2H5)2 0027 75696 Br H CONH(CH2)2N(C2H5)2 0032 74957 F H CONH(CH2)2N(CH3)2 008 70978 F H CONH(CH2)2-pyrrolidin-1-yl 006 72229 F H CONHCH2CH(CH2CH2)2N-CH3 0025 760210 F H CONH(CH2)2-morpholin-4-yl 009 704611 F H CONHCH2-pyridin-4-yl 016 679612 F H CONH(CH2)2-triazol-1-yl 0085 707113 H H (CH2)2COOH 243 561414 Br H (CH2)2COOH 173 576215 COOH H (CH2)2COOH 007 715516 SO2NH2 H (CH2)2COOH 126 590017 H OCH3 (CH2)2COOH 829 508118 H Phenyl (CH2)2COOH 014 685419 H 3-OCH3phenyl (CH2)2COOH 03 652320 H 2-OCH3phenyl (CH2)2COOH 437 536021 H 4-OCH3phenyl (CH2)2COOH 052 6284

(b)

HOOC

R1

R2

NH

NH

O

Compd R1 R2 IC50 (120583M) pIC50

22 H H 002 769923 CH3 H 02 669924 Br H 035 645625 H 3-OCH3phenyl 283 454826lowast H 3-OCH2CH3phenyl 1 6000

Journal of Chemistry 3

(c)

COOH

R1

R2

NH

NH

O


27lowast H H 214 567028lowast COOH H 024 662029lowast SO2NH2 H 092 603630lowast H OCH3 135 587031lowast H phenyl 03 652332lowast H 3-OCH3phenyl 009 704633lowast H 2-OCH3phenyl 147 583334lowast H 4-OCH3phenyl 045 6347lowastTest set

The purpose of this paper is to describe the applicationof self-organizing molecular field analysis (SOMFA) on aseries of 3-pyrrole substituted indolin-2-one compounds anovel class of VEGFR-2 kinases inhibitors Therefore ourmain objective is to provide some useful structure-activityinformation by SOMFA analysis and design novel inhibitorsof VEGFR-2 kinases in the hope that these molecules will bedeveloped into powerful anticancer agents

2 Materials and Method

21 Data Set The structures and bioactivities of 3-pyrrolesubstituted indolin-2-one compounds 1ndash34were chosen fromthe papers by Sun et al [26 27]Theywere classified into threegroups according to the common parent structure shown inTable 1

Thirty-four compounds were divided into two sets Com-pounds 1ndash25 were used as a training set while the remaining 9compounds were used as a test set The training set was usedto build SOMFAmodels and the test set was used to evaluatethe models Their activities shown in Table 1 were describedby IC50(120583M) which were converted into pIC

50 Higher pIC

50

value indicated greater inhibitory activity

22 Molecular Modeling and Alignment The 3D structures ofall analogues were constructed with ArgusLab 401 software[28] running on an AMDAthlon 64 X2Dual Core Processor5000+ CPUMicrosoft Win XP platform

Unless otherwise indicated parameters are default Fullgeometry optimizations are performed by MM2 method inthe ArgusLab software The conformations are then per-formed by RMS overlapping and fitted with compound 4as a reference Two different alignments are selected todefine overlap The first superposition of molecules which

NH

O

(a)

(b)

Figure 1 Superposition of compounds using alignment A basedon common structure (a) Common fragment for superposition(b) superposition of compounds in training and test sets

are optimized by energy minimization and overlaid basedon common structure using alignment A has been shownin Figure 1 The second superposition of molecules whichare optimized by energy minimization and overlaid basedon common structure using alignment B has been shown inFigure 2

For all analogues the final active conformations searchhas also been performed by dock into active site methodin eHiTS software [29] The crystal structure of VEGFR-2 in the complex with its inhibitor 00J (PDB entry code2XIR resolution 15 A) is downloaded from RCSB ProteinData Bank (httpwwwrcsborgpdbhomehomedo) After


NH

NH

O

(a)

(b)

Figure 2 Superposition of compounds of using alignment B basedon common structure (a) Common fragment for superposition(b) superposition of compounds in training and test sets

removing inhibitor 00J and water molecules and adding allthe hydrogen atoms the site where 00J bindswith proteinwasdefined as the active binding site

The third superposition of molecules which are obtainedbased on the binding conformation and their alignment inactive site of VEGFR-2 has been shown in Figure 3

According to the three alignments of these analoguesthey are then performed using SOMFAanalysis UsingVEGAsoftware [30 31] the electrostatic parameters of overlaidanalogues are assigned charges by three different Hamilto-nian semiempirical methods (AM1 PM3 andMNDO) Aftercalculation they are converted into CSSR file format the onlyfile format which the SOMFA2 program can accept to processa SOMFA analysis

23 SOMFA 3D-QSAR Models In the SOMFA study similarto our previous works [32 33] a 40 times 40 times 40 A grid originat-ing at (minus20 minus20 minus20) with a resolution of 05 10 and 15 Arespectively is generated around the aligned compoundsTwenty-seven models using different alignments chargesand resolutions of grid are shown in Table 2

For all of the studied compounds shape and electrostaticpotential are generated To sum up the predictive powerof these two properties into one final model we combinetheir individual predictions using a weighted average of theshape and electrostatic potential based QSAR using amixingcoefficient (119888

1) as illustrated in the following [24]

Activity = 1198881Activityshape + (1 minus 1198881)ActivityESP (1)

Clearly multiproperty predictions could have beenobtained through multiple linear regression

Using (1) instead gives greater insight into the resultantmodel by allowing the study of the variation in predictivepower with different values of 119888

1

With the highest value of 1199032 the SOMFA models thenare derived by the partial least squares (PLS) implementedin SPSS software [34] with cross-validation

The predictive ability of the model is quantitated in termsof 1199032CV which is defined in the following

1199032

CV =(SD minus PRESS)

SD (2)

where PRESS = 120590(119884pred minus 119884actual) and SD = 120590(119884actual minus 119884mean)SD is the sum of squares of the difference between the

observed values and their meaning and PRESS is the predic-tion error sumof squaresThe finalmodels are constructed bya conventional regression analysis with the optimum value ofmixing coefficient (119888

1) being equal to that yielding the highest

1199032 and 1199032CV value according to (2)

3 Results and Discussion

SOMFA a novel 3D-QSAR methodology is employed forthe analysis with the training set composed of 25 variouscompounds whose biological activities have been knownStatistical results of 27 SOMFA models are listed in Table 2

Among the 27 models the 20th model is the bestpredictive model which showed cross-validated 1199032CV valueof 05267 non-cross-validated 1199032 value of 05623 standarderror of 05165 and 119865 value of 4111 proved a good statisticalcorrelation and predictive ability It was also indicated thatthe SOMFA model was reliable and able to predict activitiesof new analogues aswell According to the value of 119888

1 the con-

tributions of steric and electrostatic field are 427 and 573respectively Consequently electrostatic field had a slightlybigger effect than that of steric field on the bioactivities of theanalogues

Taking alignments into account we found that the align-ment had a profound influence on the result The modelwhich was built by docking-based alignment showed thehighest value of 1199032 and 1199032CV of the three alignments becauseit reflected a true pharmacophoric conformation docked inthe cavity of receptor indicating that it was more reliable andaccurate

Taking charges into account according to alignment Band C we found that the results were less sensitive to thequantum chemistry charge That is to say it made a slightdifference that we use various semiempirical methods forexample AM1 PM3 and MNDO

Taking resolution of grid into account we found thatthe values of 1199032 and 1199032CV presented in model 10ndash18 indicatedthe correlation and reliability of models 05 A gt 10 A gt15 A while in model 1ndash10 18ndash27 10 A gt 05 A gt 15 Abecause it is known that a finer grid resolution produced abetter correlation and reliability However if the grid is fartoo fine the amount of noise in the data is increased and thusthe model is inaccurate In model 20 10 A grid resolutionproduced the best result

Taking the values of 1198881into account we found that the

values of models which were built by alignment A showedhigher values That is to say the contribution of steric field


(a) (b)

Figure 3 Superposition of compouds of using alignment C based on docking (a) Thirty-four analogues in the active site of VEGFR-2(b) superposition of compouds in training and test sets

Table 2 Statistical results of the various SOMFA models

Model no Alignment Charge Resolution of grid ( A) 1198881

119904 119865 1199032

1199032

CV 1199032

pred

1 (A) AM1 05 0475 05685 2836 04698 04541 050622 (A) AM1 10 0590 05650 2910 04763 04603 051253 (A) AM1 15 0465 05739 2723 04597 04434 049724 (A) PM3 05 0710 05721 2760 04631 04491 050395 (A) PM3 10 0758 05672 2863 04722 04577 051136 (A) PM3 15 0661 05779 2639 04520 04374 049527 (A) MNDO 05 0838 05731 2739 04612 04472 050668 (A) MNDO 10 0869 05678 2849 04710 04567 051419 (A) MNDO 15 0805 05798 2601 04484 04336 0498410 (B) AM1 05 0513 05267 3831 05449 05281 0632211 (B) AM1 10 0510 05272 3817 05440 05263 0632912 (B) AM1 15 0432 05294 3761 05403 05208 0631613 (B) PM3 05 0613 05323 3684 05352 05223 0630614 (B) PM3 10 0630 05346 3626 05312 05177 0629715 (B) PM3 15 0553 05395 3502 05225 05073 0632616 (B) MNDO 05 0584 05343 3633 05317 05169 0625417 (B) MNDO 10 0595 05356 3599 05294 05137 0624618 (B) MNDO 15 0511 05416 3450 05188 05006 0621319 (C) AM1 05 0409 05210 3986 05547 05171 0612520 (C) AM1 10 0427 05165 4111 05623 05267 0623621 (C) AM1 15 0345 05215 3971 05537 05112 0612422 (C) PM3 05 0467 05226 3943 05520 05159 0604323 (C) PM3 10 0480 05176 4082 05605 05263 0615924 (C) PM3 15 0420 05244 3892 05488 0508 0602025 (C) MNDO 05 0407 05216 3970 05537 05164 0609926 (C) MNDO 10 0426 05172 4092 05612 05255 0620827 (C) MNDO 15 0346 05225 3944 05521 05093 060891199032 Non-cross-validated correlation coefficient 1199032CV cross validated correlation coefficient 119865 119865-test value 119904 standard error of estimate 1198881 mixing coefficientof SOMFA model 1199032pred predictive 119903

2

is four times more than that of electrostatic field Due tothe structure-based alignment they were given such a highcontribution of steric field While according to alignmentB and alignment C we found that steric field had almostthe same influence to the electrostatic field indicating thatthe steric and electrostatic interactions of molecules couldbe two equally important factors for the bioactivities of theanalogues

The experimental and predicted activities of trainingset and test set are reported in Table 3 Figure 4 shows thecorrelation between experimental and predicted activities ofSOMFA model for the training set and test set

It is known that the best way to validate the 3D-QSARmodel is to predict bioactivities of compounds in test setTheSOMFA analysis of the test set composed of 9 compounds isreported in Figure 4 indicating a satisfying linear correlation


Table 3 The comparison of experimental and predicted activities of 34 compounds in training set and test set

Compd Experimental pIC50 Predicted pIC50 Residuala Compd Experimental pIC50 Predicted pIC50 Residuala

1 5910 6338 minus0428 18 6854 6171 06832 6523 6503 0020 19 6523 5998 05253 7301 7312 minus0011 20 5360 5654 minus02944 7097 7317 minus0220 21 6284 5955 03295 7569 7337 0232 22 7699 7963 minus02646 7495 7341 0154 23 6699 6903 minus02047 7097 7318 minus0221 24 6456 6644 minus01888 7222 7392 minus0170 25 4548 5512 minus09649 7602 6551 1051 26lowast 6000 5683 031710 7046 7230 minus0184 27lowast 5670 6230 minus056011 6796 7079 minus0283 28lowast 6620 6127 049312 7071 7046 0025 29lowast 6036 6123 minus008713 5614 6043 minus0429 30lowast 5870 6061 minus019114 5762 6344 minus0582 31lowast 6523 5955 056815 7155 6047 1108 32lowast 7046 6037 100916 5900 5817 0083 33lowast 5833 5687 014617 5081 5850 minus0769 34lowast 6347 5909 0438lowastTest set Residuala = Experimental ndash Predicted

4 5 6 7 84

5

6

7

8

Training setTest set

Pred

icte

d pl

C 50

Experimental plC50

Figure 4 Correlation between experimental and predicted activi-ties of SOMFA model for the training set and test set

andmoderate difference between experimental and predictedactivities From Figure 4 we found that compound 32 had alarge residual and thus was classified as outlier There may bea complicated relationship between structure and activity inthis compound On the whole the model performed well inthe activity prediction of most of the test compounds

SOMFA calculation for both shape and electrostaticpotentials is performed then combined to get an optimalcoefficient 119888

1= 0427 according to (1) indicating that the

electrostatic field contribution is of a little high importanceThe master grid maps derived from the best model are

used to display the contribution of shape and electrostaticpotentials The master grid maps give a direct visualizationof which parts of the compounds differentiate the activitiesof compounds in the training set under study The mastergrid also offers an interpretation as to how to design andthen synthesize some novel compounds with much higheractivities The visualization of the shape master grid andelectrostatic master grid of the best SOMFA model is shownin Figures 5 and 6 respectively with compound 4 (sunitinib)as the reference

Each master grid map is colored in two different colorsfor favorable and unfavorable effects In other words theelectrostatic features are red (more positive charge increasesactivity or more negative charge decreases activity) and blue(more negative charge increases activity or more positivecharge decreases activity) and the shape features are red(more steric bulk increases activity) and blue (more stericbulk decreases activity) respectively

As shown in Figure 5 the shape master grid shows red-colored regions where steric bulk enhances activity and blue-colored regions where steric bulk detracts from activity Wefound that there was a high density of blue lattice pointssurrounding 6-position of indolinone ring and 4-position ofpyrrole ring which suggested that more bulky substituentsin these areas would decrease the bioactivitiesWe also founda big region of red lattice points appearing near 2-positionand diethylamine group which suggested that more bulkysubstituents in these areas would remarkably increase thebioactivities

As shown in Figure 6 the electrostatic potential mastergrid shows red-colored regions where increased positivecharge is favorable for bioactivities and blue-colored regionswhere increased negative charge is favorable for bioactivityWe found that there was a high density of red lattice points


Figure 5 The shape master grid with compound 4 Red steric bulkenhances the activity in this region Blue steric bulk detracts fromactivity in this region

Figure 6 The electrostatic potential master grid with compound 4Red positive charge is favoured in this region or negative charge isdisfavoured in this region Blue negative charge is favoured in thisregion or positive charge is disfavoured in this region

surrounding indolinone ring and pyrrole ring suggestingthat reducing electronic density would increase bioactivitiesWhile diethylamine group surrounded by blue lattice pointssuggested that negatively charged substituents would increasethe bioactivities

4 Conclusion

In this study according to different alignments 3D-QSARanalysis was carried out to construct a highly accurate andpredictive SOMFA model (1199032CV = 05267 119903

2= 05626) The

contributions of steric and electrostatic fields are 427 and573 respectively indicating that the electrostatic interac-tion of molecules could be a little more important factor forthe bioactivities of the analogues The final grid maps helpto better interpret the structure-activity relationship of theseanalogues Generally the small-sized electropositive poten-tial substituents (eg methyl ethyl and aliphatic amines) atthe indolinone ring and pyrrole ring increase the activityand the big-sized electronegative potential substituents (egbenzene ring with electron-withdrawing groups and pyridinering) at the diethylamine group increase the activity All anal-yses of SOMFAmodelsmay provide some useful informationin the design of new analogues of sunitinib

Conflict of Interests

Theauthors declare that they have no conflict of interests witheach other and they also have no conflict of interests withSOMFA ArgusLab eHiTS VEGA or SPSS software

Acknowledgments

This work was supported by the National Natural Scienceof Foundation of China (no 21272022) The authors grate-fully acknowledge Daniel Robinson and the ComputationalChemistry Research Group (Oxford University UK) for theuse of the SOMFA software

References

[1] D R Robinson Y-M Wu and S-F Lin ldquoThe protein tyrosinekinase family of the human genomerdquo Oncogene vol 19 no 49pp 5548ndash5557 2000

[2] G A Fava ldquoAffective disorders and endocrine disease newinsights from psychosomatic studiesrdquo Psychosomatics vol 35no 4 pp 341ndash353 1994

[3] D A Walsh and L Haywood ldquoAngiogenesis a therapeutictarget in arthritisrdquoCurrent Opinion in Investigational Drugs vol2 no 8 pp 1054ndash1063 2001

[4] L P Aiello R L Avery P G Arrigg et al ldquoVascular endothe-lial growth factor in ocular fluid of patients with diabeticretinopathy and other retinal disordersrdquo New England Journalof Medicine vol 331 no 22 pp 1480ndash1487 1994

[5] M Detmar ldquoThe role of VEGF and thrombospondins in skinangiogenesisrdquo Journal of Dermatological Science vol 24 no 1pp S78ndashS84 2000

[6] J Folkman ldquoAnti-angiogenesis new concept for therapy of solidtumorsrdquo Annals of Surgery vol 175 no 3 pp 409ndash416 1972

[7] L A Liotta P S Steeg and W G Stetler-Stevenson ldquoCancermetastasis and angiogenesis an imbalance of positive andnegative regulationrdquo Cell vol 64 no 2 pp 327ndash336 1991

[8] S Shinkaruk M Bayle G Laın and G Deleris ldquoVascularEndothelial Cell Growth Factor (VEGF) an emerging target forcancer chemotherapyrdquo Current Medicinal Chemistry vol 3 no2 pp 95ndash117 2003

[9] S Baka A R Clamp and G C Jayson ldquoA review of thelatest clinical compounds to inhibit VEGF in pathologicalangiogenesisrdquoExpertOpinion onTherapeutic Targets vol 10 no6 pp 867ndash876 2006

[10] B M Klebl and G Muller ldquoSecond-generation kinase inhib-itorsrdquo Expert Opinion on Therapeutic Targets vol 9 no 5 pp975ndash993 2005

[11] K Holmes O L Roberts A MThomas andM J Cross ldquoVas-cular endothelial growth factor receptor-2 structure functionintracellular signalling and therapeutic inhibitionrdquo CellularSignalling vol 19 no 10 pp 2003ndash2012 2007

[12] H Zhong and J P Bowen ldquoMolecular design and clinicaldevelopment of VEGFR kinase inhibitorsrdquo Current Topics inMedicinal Chemistry vol 7 no 14 pp 1379ndash1393 2007

[13] N Ferrara K J Hillan H-P Gerber andW Novotny ldquoDiscov-ery and development of bevacizumab an anti-VEGF antibodyfor treating cancerrdquo Nature Reviews Drug Discovery vol 3 no5 pp 391ndash400 2004

[14] T InaiMMancusoHHashizume et al ldquoInhibition of vascularendothelial growth factor (VEGF) signaling in cancer causesloss of endothelial fenestrations regression of tumor vesselsand appearance of basement membrane ghostsrdquo AmericanJournal of Pathology vol 165 no 1 pp 35ndash52 2004

[15] M A J Duncton E L Piatnitski Chekler R Katoch-Rouse et al ldquoArylphthalazines as potent and orally bioavailable


inhibitors of VEGFR-2rdquo Bioorganic and Medicinal Chemistryvol 17 no 2 pp 731ndash740 2009

[16] A LThomas BMorganMAHorsfield et al ldquoPhase I study ofthe safety tolerability pharmacokinetics andpharmacodynam-ics of PTK787ZK 222584 administered twice daily in patientswith advanced cancerrdquo Journal of Clinical Oncology vol 23 no18 pp 4162ndash4171 2005

[17] J S Beebe J P Jani E Knauth et al ldquoPharmacologicalcharacterization of CP-547632 a novel vascular endothelialgrowth factor receptor-2 tyrosine kinase inhibitor for cancertherapyrdquo Cancer Research vol 63 no 21 pp 7301ndash7309 2003

[18] Z Ye X Mengzhu Z Xue et al ldquoSubstituted indolin-2-ones asp90 ribosomal S6 protein kinase 2 (RSK2) inhibitors moleculardocking simulation and structure-activity relationship analy-sisrdquo Bioorganic amp Medicinal Chemistry vol 21 pp 1724ndash17342013

[19] J K Sanmati J Rahul S Lokesh et al ldquoQSAR analysis on 34-disubstituted-1H-pyrazol-5(4H)-ones asKDRVEGFR-2 kinaseinhibitorsrdquo Pharmacia Lettre vol 4 pp 1060ndash1070 2012

[20] X Jiang G Ou D Yan M Zhang and X Yuan ldquo3D-QSARstudies of VEGFR-2 kinase inhibitors based on dockingrdquo Lettersin Drug Design and Discovery vol 8 no 10 pp 926ndash942 2011

[21] H Zeng and H Zhang ldquoCombined 3D-QSAR modeling andmolecular docking study on 14-dihydroindeno[12-c]pyrazolesas VEGFR-2 kinase inhibitorsrdquo Journal of Molecular Graphicsand Modelling vol 29 no 1 pp 54ndash71 2010

[22] X Lu Y Chen and Q You ldquoPharmacophore guided 3D-QSARCoMFA analysis of amino substituted nitrogen heterocycleureas as KDR inhibitorsrdquoQSAR and Combinatorial Science vol28 no 11-12 pp 1524ndash1536 2009

[23] B K Sharma S K Sharma P Singh and S SharmaldquoA quantitative structure-activity relationship study of novelpotent orally active selective VEGFR-2 and PDGFR120572 tyrosinekinase inhibitors derivatives of N-Phenyl-N1015840-4-(4-quinolyloxyphenylurea as antitumor agentsrdquo Journal of Enzyme Inhibitionand Medicinal Chemistry vol 23 no 2 pp 168ndash173 2008

[24] D D Robinson P JWinn P D Lyne andWG Richards ldquoSelf-organizing molecular field analysis a tool for structure-activitystudiesrdquo Journal of Medicinal Chemistry vol 42 no 4 pp 573ndash583 1999

[25] R D Cramer III D E Patterson and J D Bunce ldquoCompar-ative molecular field analysis (CoMFA)mdash1 Effect of shape onbinding of steroids to carrier proteinsrdquo Journal of the AmericanChemical Society vol 110 no 18 pp 5959ndash5967 1988

[26] L Sun C Liang S Shirazian et al ldquoDiscovery of 5-[5-fluoro-2-oxo-12-dihydroindol-(3Z)-ylidenemethyl]-2 4-dimethyl-1H-pyrrole-3-carboxylic acid (2-diethylaminoethyl)amide a noveltyrosine kinase inhibitor targeting vascular endothelial andplatelet-derived growth factor receptor tyrosine kinaserdquo Journalof Medicinal Chemistry vol 46 no 7 pp 1116ndash1119 2003

[27] L Sun N Tran C Liang et al ldquoDesign synthesis and eval-uations of substituted 3-[(3- or 4- carboxyethylpyrrol-2-yl)methylidenyl]indolin-2-ones as inhibitors of VEGF FGFand PDGF receptor tyrosine kinasesrdquo Journal of MedicinalChemistry vol 42 no 25 pp 5120ndash5130 1999

[28] M A Thompson ldquoArgusLab 401rdquo Planaria Software LLCSeattle Wash USA 2001 httpwwwarguslabcom

[29] Z Zsoldos D Reid A Simon S B Sadjad and A P JohnsonldquoeHiTS a new fast exhaustive flexible ligand docking systemrdquoJournal of Molecular Graphics and Modelling vol 26 no 1 pp198ndash212 2007

[30] A Pedretti L Villa and G Vistoli ldquoVEGA a versatile pro-gram to convert handle and visualize molecular structureon Windows-based PCsrdquo Journal of Molecular Graphics andModelling vol 21 no 1 pp 47ndash49 2002

[31] VEGA ZZ Release 20552 a versatile program to converthandle and visualize molecular structure on Windows-basedPCs httpwwwddlunimiitvegaindex2htm

[32] S Li Y Zheng and W Yin ldquoThree dimensional quantitativestructure-activity relationship studies on a series of typicaltricycle compoundsrdquo Journal of Beijing University of ChemicalTechnology vol 34 no 3 pp 249ndash253 2007 (Chinese)

[33] S-L Li M-Y He and H-G Du ldquo3D-QSAR studies on aseries of dihydroorotate dehydrogenase inhibitors analogues ofthe active metabolite of leflunomiderdquo International Journal ofMolecular Sciences vol 12 no 5 pp 2982ndash2993 2011

[34] SPSS software version 16 IBM Corporation Somers NY USA2007 httpwwwspsscom

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Table 1 Structures of 34 compounds and their VEGFR-2 inhibitory activities

(a)

R1

R2

R3

NH

NH

O

Compd R1 R2 R3 IC50 (120583M) pIC50

1 H H H 123 59102 H H (CH2)2N(CH2CH2)NCH3 03 65233 H H CONH(CH2)2N(C2H5)2 005 73014 F H CONH(CH2)2N(C2H5)2 008 70975 Cl H CONH(CH2)2N(C2H5)2 0027 75696 Br H CONH(CH2)2N(C2H5)2 0032 74957 F H CONH(CH2)2N(CH3)2 008 70978 F H CONH(CH2)2-pyrrolidin-1-yl 006 72229 F H CONHCH2CH(CH2CH2)2N-CH3 0025 760210 F H CONH(CH2)2-morpholin-4-yl 009 704611 F H CONHCH2-pyridin-4-yl 016 679612 F H CONH(CH2)2-triazol-1-yl 0085 707113 H H (CH2)2COOH 243 561414 Br H (CH2)2COOH 173 576215 COOH H (CH2)2COOH 007 715516 SO2NH2 H (CH2)2COOH 126 590017 H OCH3 (CH2)2COOH 829 508118 H Phenyl (CH2)2COOH 014 685419 H 3-OCH3phenyl (CH2)2COOH 03 652320 H 2-OCH3phenyl (CH2)2COOH 437 536021 H 4-OCH3phenyl (CH2)2COOH 052 6284

(b)

HOOC

R1

R2

NH

NH

O


22 H H 002 769923 CH3 H 02 669924 Br H 035 645625 H 3-OCH3phenyl 283 454826lowast H 3-OCH2CH3phenyl 1 6000


(c)

COOH

R1

R2

NH

NH

O







50 Higher pIC

50




NH

O

(a)

(b)





NH

NH

O

(a)

(b)











1



1199032


SD (2)








1 the con-








(a) (b)




119904 119865 1199032

1199032

CV 1199032

pred


2








4 5 6 7 84

5

6

7

8


Pred

icte

d pl

C 50

Experimental plC50














4 Conclusion


2= 05626) The




Acknowledgments


References














































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


(c)

COOH

R1

R2

NH

NH

O







50 Higher pIC

50




NH

O

(a)

(b)





NH

NH

O

(a)

(b)











1



1199032


SD (2)








1 the con-








(a) (b)




119904 119865 1199032

1199032

CV 1199032

pred


2








4 5 6 7 84

5

6

7

8


Pred

icte

d pl

C 50

Experimental plC50














4 Conclusion


2= 05626) The




Acknowledgments


References














































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


NH

NH

O

(a)

(b)











1



1199032


SD (2)








1 the con-








(a) (b)




119904 119865 1199032

1199032

CV 1199032

pred


2








4 5 6 7 84

5

6

7

8


Pred

icte

d pl

C 50

Experimental plC50














4 Conclusion


2= 05626) The




Acknowledgments


References














































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


(a) (b)




119904 119865 1199032

1199032

CV 1199032

pred


2








4 5 6 7 84

5

6

7

8


Pred

icte

d pl

C 50

Experimental plC50














4 Conclusion


2= 05626) The




Acknowledgments


References














































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of





4 5 6 7 84

5

6

7

8


Pred

icte

d pl

C 50

Experimental plC50














4 Conclusion


2= 05626) The




Acknowledgments


References














































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of





4 Conclusion


2= 05626) The




Acknowledgments


References














































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of

research article 3d-qsar study on a series of vegfr-2

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