Quantitative structure-pharmacokinetic relationship (QSPkP) analysis of the volume of distribution values of anti-infective agents from j group of the ATC classification in humans

In this study, a quantitative structure-pharmacokinetic relationship (QSPkR) model for the volume of distribution (Vd) values of 126 anti-infective drugs in humans was developed employing multiple linear regression (MLR), artificial neural network (ANN) and support vector regression (SVM) using theoretical molecular structural descriptors. A correlation-based feature selection (CFS) was employed to select the relevant descriptors for modeling. The model results show that the main factors governing Vd of anti-infective drugs are 3D molecular representations of atomic van der Waals volumes and Sanderson electronegativities, number of aliphatic and aromatic amino groups, number of beta-lactam rings and topological 2D shape of the molecule. Model predictivity was evaluated by external validation, using a variety of statistical tests and the SVM model demonstrated better performance compared to other models. The developed models can be used to predict the Vd values of anti-infective drugs. U radu je odreðen kvantitativni odnos strukture i farmakokineti~kih parametara (QSPkR) za volumen distribucije (Vd) 126 antiinfektivnih lijekova u ljudi koriste}i vi{estruku linearnu regresiju (MLR), umjetne neuronske mre`e (ANN), regresiju potpornim vektorima (SVM) i teorijske molekulske deskriptore. Selekcija na temelju korelacije (CFS) upotrjebljena je za izbor relevantnih deskriptora za modeliranje. Rezultati su pokazali da su glavni faktori koji utje~u na Vd antiinfektivnih lijekova 3D molekulski prikaz van der Waalsovih volumena atoma i Sandersonove elektronegativnosti, broj alifatskih i aromatskih skupina, broj beta-laktamskih prstena i topolo{ki 2D oblik molekule. Prediktivnost modela procijenjena je vanjskom validacijom, koriste}i razli~ite statisti~ke testove. SVM model pokazao se boljim od ostalih modela. Razvijeni model mo`e se upotrijebiti za predvi|anje vrijednosti Vd antiinfektivnih lijekova.

[1]  Michel Petitjean,et al.  Applications of the radius-diameter diagram to the classification of topological and geometrical shapes of chemical compounds , 1992, J. Chem. Inf. Comput. Sci..

[2]  Organización Mundial de la Salud.  Guidelines for ATC classification and DDD assignment , 1996 .

[3]  Johann Gasteiger,et al.  Chemical Information in 3D Space , 1996, J. Chem. Inf. Comput. Sci..

[4]  D J Rance,et al.  The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. , 1997, The Journal of pharmacology and experimental therapeutics.

[5]  T. Kennedy.  Managing the drug discovery/development interface , 1997 .

[6]  Johann Gasteiger,et al.  Neural networks in chemistry and drug design , 1999 .

[7]  S. Sathiya Keerthi,et al.  Improvements to the SMO algorithm for SVM regression , 2000, IEEE Trans. Neural Networks Learn. Syst..

[8]  Yoshitaka Yano,et al.  Prediction of human clearance from animal data and molecular structural parameters using multivariate regression analysis. , 2002, Journal of pharmaceutical sciences.

[9]  Paola Gramatica,et al.  The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models , 2003 .

[10]  Snezana Agatonovic-Kustrin,et al.  Multiple pharmacokinetic parameter prediction for a series of cephalosporins. , 2003, Journal of pharmaceutical sciences.

[11]  Geoff Holmes,et al.  Benchmarking Attribute Selection Techniques for Discrete Class Data Mining , 2003, IEEE Trans. Knowl. Data Eng..

[12]  Alexander Tropsha,et al.  Quantitative structure-pharmacokinetic parameters relationships (QSPKR) analysis of antimicrobial agents in humans using simulated annealing k-nearest-neighbor and partial least-square analysis methods. , 2004, Journal of pharmaceutical sciences.

[13]  Igor V. Tetko,et al.  Virtual Computational Chemistry Laboratory – Design and Description , 2005, J. Comput. Aided Mol. Des..

[14]  Franco Lombardo,et al.  Trend Analysis of a Database of Intravenous Pharmacokinetic Parameters in Humans for 670 Drug Compounds , 2008, Drug Metabolism and Disposition.

[15]  Qing-Song Xu,et al.  Support vector machines and its applications in chemistry , 2009 .

[16]  K. Roy,et al.  On Two Novel Parameters for Validation of Predictive QSAR Models , 2009, Molecules.

[17]  Roman Jerala,et al.  In silico discovery and biophysical evaluation of novel 5-(2-hydroxybenzylidene) rhodanine inhibitors of DNA gyrase B. , 2012, Bioorganic & medicinal chemistry.