title: QSPR models for the prediction of apparent volume of distribution.
abstract: An estimate of volume of distribution (V(d)) is of paramount importance both in drug choice as well as maintenance and loading dose calculations in therapeutics. It can also be used in the prediction of drug biological half life. This study employs quantitative structure-pharmacokinetic relationship (QSPR) techniques for the prediction of volume of distribution. Values of V(d) for 129 drugs were collated from the literature. Structural descriptors consisted of partitioning, quantum mechanical, molecular mechanical, and connectivity parameters calculated by specialized software and pK(a) values obtained from ACD labs/log D database. Genetic algorithm and stepwise regression analyses were used for variable selection and model development. Models were validated using a leave-many-out procedure. QSPR analyses resulted in a number of significant models for acidic and basic drugs separately, and for all the drugs. Validation studies showed that mean fold error of predictions for the selected models were between 1.79 and 2.17. Although separate QSPR models for acids and bases resulted in lower prediction errors than models for all the drugs, the external validation study showed a limited applicability for the equation obtained for acids. Therefore, the universal model that requires only calculated structural descriptors was recommended. The QSPR model is able to predict the volume of distribution of drugs belonging to different chemical classes with a prediction error similar to that of the other more complicated prediction methods including the commonly practiced interspecies scaling. The structural descriptors in the model can be interpreted based on the known mechanisms of distribution and the molecular structures of the drugs.
authors.affiliation: Drug Design and Chemometrics Laboratory, Drug Applied Research Centre and School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran. t.ghafourian@kent.ac.uk
authors.forename: Taravat
authors.initials: T
authors.lastname: Ghafourian
authors.forename: Mohammad
authors.initials: M
authors.lastname: Barzegar-Jalali
authors.forename: Siavoush
authors.initials: S
authors.lastname: Dastmalchi
authors.forename: Tina
authors.initials: T
authors.lastname: Khavari-Khorasani
authors.forename: Nasim
authors.initials: N
authors.lastname: Hakimiha
authors.forename: Ali
authors.initials: A
authors.lastname: Nokhodchi
mesh_terms: D000465:Algorithms; D008954:Models, Biological; D015394:Molecular Structure; D004364:Pharmaceutical Preparations / Q000737:chemistry / Q000378:metabolism; D010599:Pharmacokinetics; D011485:Protein Binding; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D015203:Reproducibility of Results
chemical_list: D004364:Pharmaceutical Preparations