In Silico Prediction of Major Clearance Pathways of Drugs among 9 Routes with Two-Step Support Vector Machines

Pharmaceutical Research
Naomi WakayamaYuichi Sugiyama

Abstract

The clearance pathways of drugs are critical elements for understanding the pharmacokinetics of drugs. We previously developed in silico systems to predict the five clearance pathway using a rectangular method and a support vector machine (SVM). In this study, we improved our classification system by increasing the number of clearance pathways available for our prediction (CYP1A2, CYP2C8, CYP2C19, and UDP-glucuronosyl transferases (UGTs)) and by accepting multiple major pathways. Using the four default descriptors (charge, molecular weight, logD at pH 7.0, and unbound fraction in plasma), three kinds of SVM-based predictors based on traditional single-step approach or two-step focusing approaches with subset or partition clustering were developed. The two-step approach with subset clustering resulted in the highest prediction performance. The feature-selection of additional descriptors based on a greedy algorithm was employed to further improve the predictability. The prediction accuracy for each pathway was increased to more than 0.83 with the exception of CYP2C19 and UGTs pathways, whose accuracies were below 0.7. Prediction performance of CYP1A2, CYP3A4 and renal excretion pathways were found to be acceptable using external ...Continue Reading

References

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Jul 15, 2015·Pharmaceutical Research·Manthena V VarmaAyman F El-Kattan

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Citations

Aug 21, 2019·Drug Metabolism and Disposition : the Biological Fate of Chemicals·F Peter Guengerich
Sep 7, 2021·European Journal of Nuclear Medicine and Molecular Imaging·Sophie StotzAndreas Maurer

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Software Mentioned

CPathPred
SVM
Percepta
ACD
SciFinder
PreADMET
ADMET Predictor

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