Modeling calcium channel antagonistic activity of dihydropyridine derivatives using QTMS indices analyzed by GA-PLS and PC-GA-PLS.

The usefulness of a novel type of electronic descriptors called quantum topological molecular similarity (QTMS) indices for describing the quantitative effects of molecular electronic environments on the antagonistic activity of some dihydropyridine (DHP) derivatives has been evaluated. QTMS theory produces a matrix of descriptors, including bond (or structure) information in one dimension and electronic effects in another dimension, for each molecule. Some different modeling tools such as multiple linear regression (MLR), principal component analysis (PCA), partial least squares (PLS) and genetic algorithms (GA) were employed to find some appropriate models for noted biological activity. GA was used in order to select the proper variables and also PCA was used for data compression. Then modeling was performed by MLR and PLS. The model performances were accessed by both cross-validation and external prediction set. The results showed that the proposed models could explain above 90% of variances in the biological activity. The significant effects of chemical bonds on the antagonistic activity were identified by calculating variable important in projection (VIP). It was obtained that those belonging to the substituted 4-phenyl ring represent high influence on the biological activity which, confirms their importance in mechanism of action of DHP derivatives.

[1]  Paul L. A. Popelier,et al.  Quantum molecular similarity. 1. BCP space , 1999 .

[2]  Y. Takahata,et al.  Quantitative Structure-Activity Relationships for 1,4-Dihydropyridine Calcium Channel Antagonists (Nifedipine Analogues): A Quantum ChemicalKlassical Approach , 1994 .

[3]  R. M. Muir,et al.  Correlation of Biological Activity of Phenoxyacetic Acids with Hammett Substituent Constants and Partition Coefficients , 1962, Nature.

[4]  Tudor I. Oprea,et al.  An automated PLS search for biologically relevant QSAR descriptors , 2004, J. Comput. Aided Mol. Des..

[5]  Bahram Hemmateenejad,et al.  Application of ab initio theory to QSAR study of 1,4‐dihydropyridine‐based calcium channel blockers using GA‐MLR and PC‐GA‐ANN procedures , 2004, J. Comput. Chem..

[6]  Paul L. A. Popelier,et al.  Quantum topological molecular similarity. Part 4. A QSAR study of cell growth inhibitory properties of substituted (E)-1-phenylbut-1-en-3-ones , 2002 .

[7]  A. Shafiee,et al.  Synthesis and calcium channel antagonist activities of 3-nitrooxyalkyl, 5-alkyl 1,4-dihydro-2,6-dimethyl-4-(1-methyl-5-nitro-2-imidazolyl)-3, 5-pyridinedicarboxylates. , 2002, Farmaco.

[8]  M. Shamsipur,et al.  Quantitative Structure‐Activity Relationship Study of Recently Synthesized 1, 4‐Dihydropyridine Calcium Channel Antagonists. Application of the Hansch Analysis Method , 2002, Archiv der Pharmazie.

[9]  W G Richards,et al.  QSAR of binding of dihydropyridine‐type calcium antagonists to their receptor on ileal smooth muscle preparations , 1986, The Journal of pharmacy and pharmacology.

[10]  R. Bader.  Atoms in molecules : a quantum theory , 1990 .

[11]  Paul L. A. Popelier,et al.  Quantum Molecular Similarity. 3. QTMS Descriptors , 2001, J. Chem. Inf. Comput. Sci..

[12]  Bahram Hemmateenejad,et al.  Genetic Algorithm Applied to the Selection of Factors in Principal Component-Artificial Neural Networks: Application to QSAR Study of Calcium Channel Antagonist Activity of 1, 4-Dihydropyridines (Nifedipine Analogous) , 2003, J. Chem. Inf. Comput. Sci..

[13]  J P Doucet,et al.  QSAR and classification study of 1,4-dihydropyridine calcium channel antagonists based on least squares support vector machines. , 2005, Molecular pharmaceutics.

[14]  A M Triggle,et al.  1,4-Dihydropyridine antagonist activities at the calcium channel: a quantitative structure-activity relationship approach. , 1988, Journal of medicinal chemistry.

[15]  D. Triggle,et al.  Lipophilic 4-isoxazolyl-1,4-dihydropyridines: synthesis and structure-activity relationships. , 1999, Journal of medicinal chemistry.

[16]  Ying Liu,et al.  A Comparative Study on Feature Selection Methods for Drug Discovery , 2004, J. Chem. Inf. Model..

[17]  Edmund R. Malinowski,et al.  Factor Analysis in Chemistry , 1980 .

[18]  B. Hemmateenejad,et al.  Synthesis, study of 3D structures, and pharmacological activities of lipophilic nitroimidazolyl-1,4-dihydropyridines as calcium channel antagonist. , 2006, Bioorganic & medicinal chemistry.

[19]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[20]  L. Eriksson.  Multi- and megavariate data analysis , 2006 .

[21]  M. Karelson.  Molecular descriptors in QSAR/QSPR , 2000 .

[22]  Tao Wang,et al.  QSAR study of 1,4-dihydropyridine calcium channel antagonists based on gene expression programming. , 2006, Bioorganic & medicinal chemistry.

[23]  Ş. Niculescu.  Artificial neural networks and genetic algorithms in QSAR , 2003 .

[24]  Bahram Hemmateenejad,et al.  Correlation ranking procedure for factor selection in PC-ANN modeling and application to ADMETox evaluation , 2005 .

[25]  Paul L A Popelier,et al.  Computation of relative bond dissociation enthalpies (DeltaBDE) of phenolic antioxidants from quantum topological molecular similarity (QTMS). , 2006, The journal of physical chemistry. A.

[26]  David J. Triggle,et al.  Molecular level model for the agonist/antagonist selectivity of the 1,4-dihydropyridine calcium channel receptor , 1991, J. Comput. Aided Mol. Des..

[27]  R. Miri,et al.  Synthesis and calcium channel modulating effects of modified Hantzsch nitrooxyalkyl 1,4‐dihydro‐2,6‐dimethyl‐3‐nitro‐4‐(pyridinyl or 2‐trifluoromethylphenyl)‐5‐pyridinecarboxylates , 2000 .

[28]  Paul L. A. Popelier,et al.  Quantum topological molecular similarity. Part 5. Further development with an application to the toxicity of polychlorinated dibenzo-p-dioxinsThe IUPAC name for dibenzo-p-dioxin is dibenzo[b,e][1,4]dioxin.(PCDDs) , 2002 .

[29]  P. Popelier,et al.  The cytotoxicity of ortho alkyl substituted 4-X-phenols: a QSAR based on theoretical bond lengths and electron densities. , 2006, Bioorganic & medicinal chemistry letters.

[30]  Roberto Todeschini,et al.  Handbook of Molecular Descriptors , 2002 .

[31]  P. Popelier,et al.  QSAR models based on quantum topological molecular similarity. , 2006, European journal of medicinal chemistry.

[32]  D. Triggle.  Calcium‐Channel Drugs: Structure‐Function Relationships and Selectivity of Action , 1991, Journal of cardiovascular pharmacology.

[33]  Douglas M. Hawkins,et al.  The Problem of Overfitting , 2004, J. Chem. Inf. Model..

[34]  Corwin Hansch,et al.  Comparative QSAR: Toward a Deeper Understanding of Chemicobiological Interactions. , 1996, Chemical reviews.

[35]  Bahram Hemmateenejad,et al.  Optimal QSAR analysis of the carcinogenic activity of drugs by correlation ranking and genetic algorithm‐based PCR , 2004 .

[36]  John N. Weinstein,et al.  Comparison of a Neural Net-Based QSAR Algorithm (PCANN) with Hologram- and Multiple Linear Regression-Based QSAR Approaches: Application to 1, 4-Dihydropyridine-Based Calcium Channel Antagonists , 2001, J. Chem. Inf. Comput. Sci..

[37]  B. Hemmateenejad,et al.  Quantum Chemical‐QSAR Study of Some Newly Synthesized 1,4‐Dihydropyridine Calcium Channel Blockers , 2003 .

[38]  David W. Salt,et al.  Variable Selection—Spoilt for Choice? , 2005 .