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Research ArticleArticle

In Silico Prediction of Major Drug Clearance Pathways by Support Vector Machines with Feature-Selected Descriptors

Kouta Toshimoto, Naomi Wakayama, Makiko Kusama, Kazuya Maeda, Yuichi Sugiyama and Yutaka Akiyama
Drug Metabolism and Disposition November 2014, 42 (11) 1811-1819; DOI: https://doi.org/10.1124/dmd.114.057893
Kouta Toshimoto
Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Tokyo, Japan (K.T., Y.A.); Drug Metabolism and Pharmacokinetics Japan, Biopharmaceutical Assessment Core Function Unit, Eisai Product Creation Systems, Eisai Co., Ltd., Ibaraki, Japan (N.W.); Laboratory of Pharmaceutical Regulatory Science (M.K.) and Laboratory of Molecular Pharmacokinetics (K.M.), Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan; and Sugiyama Laboratory, RIKEN Innovation Center, RIKEN Research Cluster for Innovation, RIKEN, Yokohama, Japan (Y.S.)
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Naomi Wakayama
Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Tokyo, Japan (K.T., Y.A.); Drug Metabolism and Pharmacokinetics Japan, Biopharmaceutical Assessment Core Function Unit, Eisai Product Creation Systems, Eisai Co., Ltd., Ibaraki, Japan (N.W.); Laboratory of Pharmaceutical Regulatory Science (M.K.) and Laboratory of Molecular Pharmacokinetics (K.M.), Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan; and Sugiyama Laboratory, RIKEN Innovation Center, RIKEN Research Cluster for Innovation, RIKEN, Yokohama, Japan (Y.S.)
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Makiko Kusama
Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Tokyo, Japan (K.T., Y.A.); Drug Metabolism and Pharmacokinetics Japan, Biopharmaceutical Assessment Core Function Unit, Eisai Product Creation Systems, Eisai Co., Ltd., Ibaraki, Japan (N.W.); Laboratory of Pharmaceutical Regulatory Science (M.K.) and Laboratory of Molecular Pharmacokinetics (K.M.), Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan; and Sugiyama Laboratory, RIKEN Innovation Center, RIKEN Research Cluster for Innovation, RIKEN, Yokohama, Japan (Y.S.)
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Kazuya Maeda
Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Tokyo, Japan (K.T., Y.A.); Drug Metabolism and Pharmacokinetics Japan, Biopharmaceutical Assessment Core Function Unit, Eisai Product Creation Systems, Eisai Co., Ltd., Ibaraki, Japan (N.W.); Laboratory of Pharmaceutical Regulatory Science (M.K.) and Laboratory of Molecular Pharmacokinetics (K.M.), Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan; and Sugiyama Laboratory, RIKEN Innovation Center, RIKEN Research Cluster for Innovation, RIKEN, Yokohama, Japan (Y.S.)
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Yuichi Sugiyama
Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Tokyo, Japan (K.T., Y.A.); Drug Metabolism and Pharmacokinetics Japan, Biopharmaceutical Assessment Core Function Unit, Eisai Product Creation Systems, Eisai Co., Ltd., Ibaraki, Japan (N.W.); Laboratory of Pharmaceutical Regulatory Science (M.K.) and Laboratory of Molecular Pharmacokinetics (K.M.), Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan; and Sugiyama Laboratory, RIKEN Innovation Center, RIKEN Research Cluster for Innovation, RIKEN, Yokohama, Japan (Y.S.)
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Yutaka Akiyama
Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Tokyo, Japan (K.T., Y.A.); Drug Metabolism and Pharmacokinetics Japan, Biopharmaceutical Assessment Core Function Unit, Eisai Product Creation Systems, Eisai Co., Ltd., Ibaraki, Japan (N.W.); Laboratory of Pharmaceutical Regulatory Science (M.K.) and Laboratory of Molecular Pharmacokinetics (K.M.), Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan; and Sugiyama Laboratory, RIKEN Innovation Center, RIKEN Research Cluster for Innovation, RIKEN, Yokohama, Japan (Y.S.)
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Abstract

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.

Footnotes

    • Received February 28, 2014.
    • Accepted August 14, 2014.
  • K.T. and N.W. contributed equally to this work.

  • This research was partly supported by the Okawa Foundation (to K.T. and Y.A.) and by the Grant-in-aid for Young Scientists (A) [Grant 24689009] (to K.M.).

  • dx.doi.org/10.1124/dmd.114.057893.

  • Copyright © 2014 by The American Society for Pharmacology and Experimental Therapeutics
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Drug Metabolism and Disposition: 42 (11)
Drug Metabolism and Disposition
Vol. 42, Issue 11
1 Nov 2014
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Research ArticleArticle

Clearance Pathway Prediction by Support Vector Machines

Kouta Toshimoto, Naomi Wakayama, Makiko Kusama, Kazuya Maeda, Yuichi Sugiyama and Yutaka Akiyama
Drug Metabolism and Disposition November 1, 2014, 42 (11) 1811-1819; DOI: https://doi.org/10.1124/dmd.114.057893

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Research ArticleArticle

Clearance Pathway Prediction by Support Vector Machines

Kouta Toshimoto, Naomi Wakayama, Makiko Kusama, Kazuya Maeda, Yuichi Sugiyama and Yutaka Akiyama
Drug Metabolism and Disposition November 1, 2014, 42 (11) 1811-1819; DOI: https://doi.org/10.1124/dmd.114.057893
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