Abstracts of QSAR-related Publications: ADME, Toxicology

21 8C under illumination at 4 000 lux on a 12-hlight cycle].

Computational methods:Molecularmodeling

(geometry optimization of the molecules wasperformed employing molecular mechanicsMM2 method implemented in the Chem 3Dv6.0 software);

MRA (Multivariate Regression Analysis performedusing SPSS software).

Data calculated:Descriptors [calculated chemical descriptors were m, dipole

moment (Debye); ELUMO, EHOMO, energy (eV)of the lowest unoccupied and highest occupiedmolecular orbitals, respectively; Hardness,ELUMO�EHOMO (eV); MR, molar refractivity].

Chemical descriptors:log P (logarithm of the partition coefficient in 1-octa-

nol/water).Results: TBG isolated from Z. pellucidum was shown to

be very efficient in preventing recruitment of larval settle-ment onto underwater structures in the sea. In order to im-prove the compatibility of TBG and its analogues with otheringredients in antifouling paints, structural modification ofTBG was performed mainly on halogen substitution and N-substitution of the molecule. Two halogen-substitute graminesand four derivatives which contain ester functional groups atN-position of gramines were synthesized. Algal inhibitory po-tencies of the synthesized compounds against N. closteriumwere evaluated and EC50 values were observed between 1.06and 6.74mg/mL. Compounds that had a long chain ester groupexhibited extremely high antifouling activity. QSAR studiesemploying MRA were applied to find correlation betweencalculated molecular descriptors and biological activity of thesynthesized compounds. The study yielded Eq. 1.

pEC50¼0.351(�0.059) log Pþ3.634 (1)n¼6 r¼0.948 s¼0.105 F¼35.32

The results show that the toxicity (pEC50) is correlatedwell with the partition coefficient log P. It was suggested thatthese substances have potential function as antifoulingagents.(B. B.)

ADME, ToxicologyDOI: 10.1002/qsar.200970073

317/2009

Title: QSAR application for the prediction of compoundpermeability with in silico descriptors in practical use.

Authors: Nakao, K.; Fujikawa, M.; Shimizu, R.; Akamat-su*, M.

Division of Environmental Science and Technology, Grad-uate School of Agriculture, Kyoto University

Kyoto 606-8502, Japan.E-mail: [email protected]: J. Comput. Aided Mol. Des. 2009, 23(5), 309 – 319.Compounds: 71 Compounds.Biological material: Caco-2 cell.

Data taken from the literature:Data set [71 compounds including peptide related com-

pounds (drugs, and other chemicals)];logPapp-pampa [permeability coefficient (dimension not giv-

en)];pKa (negative logarithm of the acidic dissociation

constant was taken from the CQSAR data-base);

log Poct (experimental logarithm of the partition coeffi-cient in 1-octanol/water).

Data determined:PAMPA (Parallel Artificial Membrane Permeation As-

say a model developed for the prediction oftranscellular permeation in the process of drugabsorption);

Papp-pampa [permeability coefficient determined usingPAMPA (dimension not given)].

Computational methods:MLR (Multivariate Linear Regression analysis);L5O (Leave-Five-Out cross-validation).

Data calculated:log P [logarithm of the partition coefficient in 1-octa-

nol/water was estimated by the following pro-grams: CLOGP, KOWWIN, ACD/LogP, mi-LogP, AB/LogP, AlogP98, XLOGP, ALOGPs,IA_LogP];

pKa (negative logarithm of the acidic dissociationconstant was estimated by the following pro-grams: ACD/pKa, AB/pKa, Marbin_pKa);

PSA [Polar Surface Area (�2) was estimated by thefollowing programs: TPSA, PSA/MNDO, PSA/AM1];

RMSEpred (predicted root-mean-squares error);RMSEcv (L5O cross-validated root-mean-squares er-

ror).Results: A practical QSAR application for the prediction

of compound permeability employing in silico descriptors hasbeen reported. In a previous report, the authors reported thatPAMPA permeability is governed by log P, pKa, and the hy-drogen-bonding ability of compounds. In order to construct anew filtering method for selecting informative compoundsfrom a combinatorial library, an attempt has been made topredict PAMPA permeability by employing in silico descrip-tors. For this purpose, log P, pKa, and polar surface areas(PSA) as a hydrogen-bonding descriptor have been calculat-ed by commercially available or free web programs. Five-foldcross-validations and conventional regression analyses wereemployed for the training set compounds including peptiderelated compounds, drugs, and other chemicals. By compari-son of statistical indices, four equations (Eqs. 1 – 4) have beenselected and then the model with the best combination of insilico descriptors was determined based on external valida-tion.

log Papp-pampa¼0.430(�0.099) log Poct�0.292(�0.078) jpKa-pH j�1.059(�0.290) cPSA�4.862(�0.311) (1)n¼60 r¼0.863 s¼0.359 F3,56¼54.6

r2cv¼0.712 RMSEcv¼0.368

log Papp-pampa¼0.325(�0.092) CLOGP�0.275(�0.085) jACD/pKa-pH j�1.202(�0.362) TPSA�4.759(�0.333) (2)n¼60 r¼0.836 s¼0.389 F3,56¼43.4

r2cv¼0.653 RMSEcv¼0.404

1578 � 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.qcs.wiley-vch.de QSAR Comb. Sci. 28, 2009, No. 11-12, 1563 – 1624

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www.qcs.wiley-vch.de

log Papp-pampa¼0.348(�0.107) KOWWIN�0.278(�0.090) jACD/pKa-pH j�1.174(�0.378) TPSA�4.818(�0.359) (3)n¼60 r¼0.823 s¼0.404 F3,56¼39.2

r2cv¼0.620 RMSEcv¼0.423

log Papp-pampa¼0.350(�0.107) ACD/Log P�0.314(�0.095) jACD/pKa-pH j�1.311(�0.269) TPSA�4.714(�0.342) (4)n¼60 r¼0.823 s¼0.403 F3,56¼39.3

r2cv¼0.613 RMSEcv¼0.427

It was suggested that Eq. 1, developed in this report couldbe applied for the prediction of both Caco-2 cell permeabilityand human intestinal absorption of mainly passively-trans-ported drugs.(B. B.)

318/2009

Title: Determination and prediction of xenoestrogens byrecombinant yeast-based assay and QSAR.

Authors: Li, F.; Chen*, JingWen; Wang, Z.; Li, J.; Qiao, X.Key Laboratory of Industrial Ecology and Environmental

Engineering (MOE), Department of Environmental Scienceand Technology, Dalian University of Technology

Linggong Road 2, Dalian 116024, China.E-mail: [email protected]; Tel./Fx: 86-411-8470-6269.Source: Chemosphere 2009, 74(9), 1152 – 1157.Compounds: 25 Structurally diverse xenoestrogens: steroi-

dal estrogens, synthetic estrogens, fugal resorcyclic acid lac-tones, phytoestrogens, alkylphenols, organochlorines andphthalates.

Biological material:ER (intracellular Estrogen Receptor).

Data taken from the literature:Compounds [25 Xenoestrogens, with measured estrogenic

activities (EC50)];EC50 [effective concentration of the test substance

(mM) required for exerting 50% of the mea-sured effect using a recombinant yeast-basedassay].

Computational methods:MLR (forward stepwise Multivariate Linear Regres-

sion analysis);PLS (Partial Least Squares projections to latent

structures analysis was performed using SIM-CA-S v6.0 software);

LOO (Leave-One-Out cross-validation).Data calculated:

RP [Relative Potency calculated as RP¼EC50(E2)/EC50(tested compound)�100];

Descriptors (3D molecular structural descriptors have beencalculated using DRAGON software, 705 de-scriptors were computed, and then those withconstant or near constant values were discard-ed, forward stepwise regression was adopted toselect significant molecular descriptors);

Descriptorsselected

[Rþ7u, R maximal autocorrelation of lag 7/un-weighted; Rþ1m, R maximal autocorrelation oflag 1/weighted by atomic masses; Rþ7e, R maxi-mal autocorrelation of lag 7/weighted by atomicSanderson electronegativities; E3p, 3rd compo-nent accessibility directional WHIM index/weighted by atomic polarizabilities; G1v, 1stcomponent symmetry directional WHIM index/

weighted by atomic van der Waals volumes;G1p, 1st component symmetry directionalWHIM index/weighted by atomic polarizabili-ties; Mor28p, 3D-MoRSE-signal 28/weighted byatomic polarizabilities; RDF085p, Radical Distri-bution Function 8.5/weighted by atomic polariz-abilities; RDF070p, Radical Distribution Func-tion 7.0/weighted by atomic polarizabilities;RDF080p, Radical Distribution Function 8.0/weighted by atomic polarizabilities];

RMSE (root-mean-squares error);q2

EXT (squared correlation coefficient for the valida-tion set);

q2LOO (squared cross-validated correlation coeffi-

cient).Results: The measured logRP values were employed of an

independent external data set were employed to validate theQSAR model developed for estrogenic activity. The QSARmodel was calculated using PLS regression and molecular de-scriptors derived by DRAGON software. A statistically sig-nificant linear regression equation (Eq. 1) has been calculatedfor the 25 xenoestrogens in the training set.

log RP¼36.92 Rþ7u�6.856 E3p�28.54 G1V�11.18 G1pþ1.99Mor28p�0.270 0RDF085pþ3.772 (1)n¼25 r¼0.943 s not given F not given RMSE¼0.481

q2LOO¼0.897 Popt¼3

Fig. 1 shows the plot of the predicted versus observedlogRP values of the compounds calculated using Eq. 1, whereclosed and open circles denote the training set and validationset compounds, respectively.

Fig. 1

For the external validation set, the predicted log RP val-ues were consistent with the observed values, with a RMSEvalue of 0.736 log units and the Q2

EXT value was 0.775. Thedescriptors in the QSAR model indicated that the log RP val-ue was related to molecular size, shape profiles, symmetryand polarizability. The developed model had good robustnessand predictivity.(B. B.)

319/2009

Title: Molecular modelling evaluation of the cytotoxic ac-tivity of podophyllotoxin analogues.

Authors: Alam, M. A.; Naik*, P. K.

QSAR Comb. Sci. 28, 2009, No. 11-12, 1563 – 1624 www.qcs.wiley-vch.de � 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 1579

Abstr. 318 – 319


Department of Bioinformatics and Biotechnology, JaypeeUniversity of Information Technology

Waknaghat, Solan 173215, Himachal Pradesh, India.E-mail: [email protected]: J. Comput. Aided Mol. Des. 2009, 23(4), 209 – 225.Compounds: 154 Structurally diverse podophyllotoxin an-

alogues, e.g., 22 compounds of type I, where R1 and R2¼di-verse substituents.

Biological material: P-388 leukemia cell line.Data taken from the literature:

Crystalstructure

[crystal coordinates of the tubuline-podophyl-lotoxin complex have been taken from theBrookhaven Protein Data Bank (pdb code:1SA1)];

IC50 [concentration of the test substance (mM) re-quired for causing 50% cytotoxicity against P-388 cells].


[the Schrçdinger Glide v4.0 program has beenused for docking, the best 10 poses and corre-sponding scores have been evaluated usingGlide in single precision mode (Glide SP) foreach ligand from the virtual library of podo-phyllotoxin, for each screened ligand, the posewith the lowest Glide SP score has been takenas the input for the Glide calculation in extraprecision mode (Glide XP), for each ligand,the pose with the lowest Glide score was re-scored using Prime/MM-GBSA approach, thisapproach was used to predict the free energyof binding for set of ligands to receptor, thedocked poses were minimized using the localoptimization feature in Prime and the energiesof complex were calculated using the OPLS-AA force field and generalized-Born/surfacearea (GB/SA) continuum solvent model, thebinding free energy (DGbind) was estimated us-ing Eq. 1];

GLIDE (program for docking ligands by approximatinga complete systematic search of the conforma-tional, orientational, and positional space ofthe docked ligand, followed by torsionally flex-ible energy optimization on an OPLS-AA non-bonded potential grid for a few hundred surviv-ing candidate poses, the best candidates arefurther refined via a Monte Carlo sampling ofpose conformation);


DGbind [binding free energy (kcal/mol) estimated usingEq. 1,

DGbind¼ER:L� (ERþEL)þDGsolvþDGSA (1)

where ER : L is energy of the complex, ERþEL is sum of the energies of the ligand and un-liganded receptor, using the OPLS-AA forcefield, DGsolv (DGSA) is the difference betweenGBSA solvation energy (surface area energy)of complex and sum of the corresponding ener-gies for the ligand and unliganded protein];

RMSE (root-mean-squares error);r2

cv (cross-validated correlation coefficient).Results: Podophyllotoxin and its structural analogs, a class

of tubulin polymerization inhibitors, have been the target ofnumerous studies to develop better and safer anti-cancerdrugs. A library of podophyllotoxin analogues has been de-signed consisting of 154 analogues. Their molecular interac-tions and binding affinities with tubulin protein (pdb code:1SA1) have been studied using a docking-molecular mechan-ics based procedure employing the generalized Born/surfacearea (MM-GBSA) solvation model. QSAR models havebeen developed between the cytotoxic activity (pIC50) ofthese compounds and molecular descriptors like dockingscore and binding free energy. For both cases the r2 was in therange of 0.642 – 0.728 indicating good data fit and r2

cv was inthe range of 0.631 – 0.719 indicating that the predictive capa-bilities of the models were reasonable.

pIC50¼�0.938(�0.592) G-score�8.725(�0.644) (2)n¼120 r¼0.801 s¼0.692 F¼211.9 r2

cv¼0.631

PRESS¼58.4

pIC50¼�0.143(�0.148) DGbind�2.604(�0.008) (3)n¼120 r¼0.853 s¼0.603 F¼316.6 r2

cv¼0.719

PRESS¼44.415

In addition, a linear correlation was observed between thepredicted and experimented pIC50 for the validation data setwith correlation coefficient r2 of 0.806 and 0.887, suggestingthat the docked structure orientation and the interaction en-ergies were acceptable. Fig. 1 shows the plot of the experi-mental pIC50 versus Prime/MM-GBSA energy values of thecompounds.

Fig. 1

Fig. 2 shows the plot of the predicted versus experimentalpIC50 values for the 16 validation set compounds calculatedusing the Prime/MM-GBSA energy.


Abstr. 319


Fig. 2

Low levels of RMSE values for the majority of inhibitorsyielded by the docking and Prime/MM-GBSA based predic-tion model renders the procedure an efficient tool for gener-ating more potent and specific inhibitors of tubulin proteinby testing rationally designed lead compounds based on po-dophyllotoxin derivatization.(B. B.)

320/2009

Title: QSARs for congener-specific toxicity of polyhalo-genated dibenzo-p-dioxins with DFT and WHIM theory.

Authors: Gu*, C.; Jiang, X.; Ju, X.; Gong, X.; Wang, F.;Bian, Y.; Sun, C.

State Key Laboratory of Soil and Sustainable Agriculture,Institute of Soil Science, Chinese Academy of Sciences

Nanjing 210008, PR China.E-mail: [email protected]; Fax: 86-25-83593239.Source: Ecotoxicol. Environ. Safety 2009, 72(1), 60 – 70.Compounds:

a) 75 Polychlorinated dibenzo-p-dioxins of general type I;b) 11 Polybrominated (and chlorinated) dibenzo-p-dioxins of

general type I.

Biological material:a) Aryl hydrocarbon hydroxylase (AHH);b) 7-Ethoxyresorufin O-deethylase (EROD);c) Rat hepatocytes.

Data taken from the literature:EC50 [effective concentration of the test substance

(mM) required for 50% inhibition (BA) or 50%induction potencies of AHH or EROD in ivrat hepatocytes assays].


[quantum chemical calculations were per-formed employing the ab initio in Gaussian03suite of programs, Becke �s three parametershybrid DFT with 6-311G (d,p)basis set hasbeen chosen for modeling, the electronic struc-

tures of both chlorinated and brominated com-pounds, plus the unsubstituted dibenzo-p-diox-in (DD) were all optimized globally withoutany a priori symmetry restriction, after optimi-zation, vibration analysis was performed, whichwas considered as a guarantee of energeticallyminima point on the potential surface];

DFT (Density Functional Theory);WHIM (Weighted Holistic Invariant Molecular indices

to rotations and translations contain informa-tion about the 3D structure in terms of size,shape, symmetry, and atom distribution derivedfrom Cartesian coordinates);

MLR (stepwise Multivariate Linear Regression anal-ysis);


Descriptors [major electronic parameters,such as dipole mo-ments (m), quadrupole moments (Q, Qxx, Qxy,Qyy, Qyz, Qzz), polarizabilities (a, axx, axy, ayy, ayz,azz), and first hyperpolarizbilities (b, bxx, bxy, byy,byz, bzz), were computed using ab initio quantumchemical methodologies];

L1 m (first component size WHIM index by atomicmasses);

sPRESS (standard deviation of cross-validated predic-tions);

q2 (cross-validated correlation coefficient).Results: Polyhalogenated dibenzo-p-dioxins (PHDDs) are

among the most hazardous pollutants in the environment.The mechanism of the congener-specific toxicity of PHDDs isnot well understood. In order to explaining the structure-tox-icity relationships of the binding affinities of PHDDs as wellas their potencies of AHH and EROD induction, a QSARstudy has been performed by the combined application ofDFT and WHIM theory. The study yielded yielding Eqs. 1 – 6.

pEC50(BA)¼0.020 axxþ0.082 Qyyþ0.594 bzzzþ7.959 ayz

�0.402 (1)n¼25 r¼0.876 s¼0.762 F¼16.5 q2

LOO¼0.635 sPRESS¼0.855

pEC50(BA)¼0.403 L1 mþ0.495 bzzz�0.04 bxyyþ1.223 (2)n¼25 r¼0.957 s¼0.451 F¼75.03 q2


pEC50(AHH)¼0.156 Qzzþ0.941 bzzzþ87.435 Qyzþ6.583 (3)n¼13 r¼0.914 s¼0.788 F¼15.2 q2


pEC50(EROD)¼0.212 Qzzþ0.703 bzzz�0.007 bxxyþ6.785 (4)n¼13 r¼0.909 s¼0.882 F¼14.4 q2


pEC50(AHH)¼9.442 E1s�0.706 bxyzþ0.487 (5)n¼13 r¼873 s¼0.895 F¼16.1 q2


pEC50(EROD)¼10.842 E1s�0.534 bxyz�0.398 (6)n¼13 r¼878 s¼0.965 F¼16.8 q2


Fig. 1 shows the plot of the calculated versus observedbinding affinities of the compounds calculated using Eq. 2.

The results suggested that dispersion interaction along thelateral sites of PHDDs should explain the overwhelming ma-jority of variance in binding affinities as well as the conse-quent toxicity. The electrostatic interaction is comparativelyless influential, however, it should not be neglected. Long-range dispersion interaction was also represente in theQSARs with minute influence. The quadrupole moment ten-

QSAR Comb. Sci. 28, 2009, No. 11-12, 1563 – 1624 www.qcs.wiley-vch.de � 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 1581

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sor perpendicular to the ring plane, i.e., Qzz and its putativeelectrostatic interaction also plays an important role in thecontribution to induction potencies. The study indicated thatDFT methodology and WHIM descriptors used to developQSARs gave satisfactory performance and partly supportedeach other in the interpretation of toxicity variance. It is forthe first time that long-range dispersion interaction is quanti-fied through QSARs, although it is considered to be the leasteffective components governing binding affinities and poten-cies of AHH or EROD induction. However, the toxicologyof dioxins is yet well understood, especially for the brominat-ed congeners.

Fig. 1

(B. B.)

Data Base, Virtual ScreeningDOI: 10.1002/qsar.200970074

321/2009

Title: A comparison of ligand based virtual screeningmethods and application to corticotropin releasing factor 1receptor.

Authors: Tresadern*, G.; Bemporad, D.; Howe, T.Johnson & Johnson, Pharmaceutical Research & Develop-

ment, Janssen-Cilag S.A.Calle Jarama, 75, Poligono Industrial, 45007 Toledo,

Spain.E-mail: [email protected]; Tel.: 34-925-24-5782.Source: J. Mol. Graphics Modell. 2009, 27(8), 860 – 870.Compounds: 176,457 Structurally diverse compounds.Biological material: Corticotropin releasing factor 1

(CRF1) receptor, an attractive target in neuroscience, the en-dogenous 41-amino acid peptide ligand, CRF regulates thebody�s response to stress through the release of ACTH.

Data taken from the literature:Data set (the dataset used for the retrospective analysis

consisted of 1261 active and 175,196 inactiveHTS molecules, the active set was a combina-tion of 899 reported reference compounds ex-tracted from CRF1 patents and 362 in-houseCRF1 compounds with antagonistic pIC50

activity>6, the structures for the 899 referenceactives are provided in the supplementary in-formation, the confirmed inactive compoundswere taken from the output of a previous in-house CRF1 antagonist H, for the prospectivepart of this work the available compoundshave been retrieved from the Johnson & John-son corporate collection);

IC50 [concentration of the test substance (mM) re-quired for 50% inhibition of CRF1].

Computational methods:VS (ligand based Virtual Screening approaches ap-

plied to the CRF1 receptor: ECFP6 finger-prints, FTrees, Topomers, Cresset FieldScreen,ROCS OpenEye shape Tanimoto, OpenEyecomboscore and OpenEye electrostatics; thescitegic 2D fingerprint method, ECFP6, andsimple descriptors such as MW, ALogP and el-ement counts are used for comparison).

Data calculated:Tanimoto [Tanimoto similarity index between used in the

2D VS process is calculated as Tanimoto¼FC/(FAþFBþFC), where FC is the number offeatures present in both query and target mole-cule divided by the sum of itself (FC), the num-ber of features present only in the query (FA)and the target (FB); the index ranges between0, for molecules with no features in common,to 1, in the case of identical molecules];

OEST [OpenEye Combo Score shape Tanimoto simi-larity is calculated as Shape Tanimoto¼VC/(VAþVB�VC), where VC is the volume incommon between the 2 molecules, VA is thetotal volume of the query molecule, VB is thetotal volume of the target molecule; the coeffi-cient ranges from 0, no shape overlap, to 1,identical shape];

OEET [OpenEye Electrostatic Tanimoto similarity iscalculated as Electrostatic Tanimoto¼A · B/(A · AþB · B�A· B), where A is the electro-static potential around the query molecule andB is the electrostatic potential around the tar-get molecule; the coefficient ranges between�1/3 when the two molecules have equal butopposite potentials, and 1, when the two mole-cules have identical potentials];

AlogP (logarithm of the partition coefficient in 1-octa-nol/water);

MW [molecular weight (g/mol)];PSA [Polar Surface Area (�2)];Vol (molecular volume (�3)].

Results: Several ligand based VS methods have been com-pared and applied to the CRF1 receptor. The 3D methodsOpenEye Shape Tanimoto, combo-score and Topomers per-formed the best at separating actives from inactives in retro-spective VS experiments. Due to their higher enrichment per-formance these methods identified more active scaffolds thanthe rest. However, amongst a given number of active com-pounds the Cresset and OpenEye electrostatic methods con-tained more scaffolds and returned ranked compounds withgreater diversity. These methods were employed to recom-mend compounds for screening in a prospective experiment.New CRF1 actives antagonists were identified. Fig. 1 showsthe four query molecules Query 01 – Query 04.


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Documents

Abstracts of QSAR-related Publications: ADME, Toxicology