RT Journal Article
SR Electronic
T1 In Silico Prediction of Major Drug Clearance Pathways by Support Vector Machines with Feature-Selected Descriptors
JF Drug Metabolism and Disposition
JO Drug Metab Dispos
FD American Society for Pharmacology and Experimental Therapeutics
SP 1811
OP 1819
DO 10.1124/dmd.114.057893
VO 42
IS 11
A1 Kouta Toshimoto
A1 Naomi Wakayama
A1 Makiko Kusama
A1 Kazuya Maeda
A1 Yuichi Sugiyama
A1 Yutaka Akiyama
YR 2014
UL http://dmd.aspetjournals.org/content/42/11/1811.abstract
AB We have previously established an in silico classification method (“CPathPred”) to predict the major clearance pathways of drugs based on an empirical decision with only four physicochemical descriptors—charge, molecular weight, octanol-water distribution coefficient, and protein unbound fraction in plasma—using a rectangular method. In this study, we attempted to improve the prediction performance of the method by introducing a support vector machine (SVM) and increasing the number of descriptors. The data set consisted of 141 approved drugs whose major clearance pathways were classified into metabolism by CYP3A4, CYP2C9, or CYP2D6; organic anion transporting polypeptide–mediated hepatic uptake; or renal excretion. With the same four default descriptors as used in CPathPred, the SVM-based predictor (named “default descriptor SVM”) resulted in higher prediction performance compared with a rectangular-based predictor judged by 10-fold cross-validation. Two SVM-based predictors were also established by adding some descriptors as follows: 1) 881 descriptors predicted in silico from the chemical structures of drugs in addition to 4 default descriptors (“885 descriptor SVM”); and 2) selected descriptors extracted by a feature selection based on a greedy algorithm with default descriptors (“feature selection SVM”). The prediction accuracies of the rectangular-based predictor, default descriptor SVM, 885 descriptor SVM, and feature selection SVM were 0.49, 0.60, 0.72, and 0.91, respectively, and the overall precision values for these four methods were 0.72, 0.77, 0.86, and 0.98, respectively. In conclusion, we successfully constructed SVM-based predictors with limited numbers of descriptors to classify the major clearance pathways of drugs in humans with high prediction performance.