PreMetabo: An in silico phase I and II drug metabolism prediction platform

Sungbo Hwang, Hyun Kil Shin, Seong Eun Shin, Myungwon Seo, Hyeon Nae Jeon, Da Eun Yim, Dong Hyun Kim, Kyoung Tai No

Research output: Contribution to journalArticle

Abstract

This study aimed to develop a drug metabolism prediction platform using knowledge-based prediction models. Site of Metabolism (SOM) prediction models for four cytochrome P450 (CYP) subtypes were developed along with uridine 5′-diphosphoglucuronosyltransferase (UGT) and sulfotransferase (SULT) substrate classification models. The SOM substrate for a certain CYP was determined using the sum of the activation energy required for the reaction at the reaction site of the substrate and the binding energy of the substrate to the CYP enzyme. Activation energy was calculated using the EaMEAD model and binding energy was calculated by docking simulation. Phase II prediction models were developed to predict whether a molecule is the substrate of a certain phase II conjugate protein, i.e., UGT or SULT. Using SOM prediction models, the predictability of the major metabolite in the top-3 was obtained as 72.5–84.5% for four CYPs, respectively. For internal validation, the accuracy of the UGT and SULT substrate classification model was obtained as 93.94% and 80.68%, respectively. Additionally, for external validation, the accuracy of the UGT substrate classification model was obtained as 81% in the case of 11 FDA-approved drugs. PreMetabo is implemented in a web environment and is available at https://premetabo.bmdrc.kr/.

Original languageEnglish
Pages (from-to)361-367
Number of pages7
JournalDrug Metabolism and Pharmacokinetics
Volume35
Issue number4
DOIs
Publication statusPublished - 2020 Aug

All Science Journal Classification (ASJC) codes

  • Pharmacology
  • Pharmaceutical Science
  • Pharmacology (medical)

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    Hwang, S., Shin, H. K., Shin, S. E., Seo, M., Jeon, H. N., Yim, D. E., Kim, D. H., & No, K. T. (2020). PreMetabo: An in silico phase I and II drug metabolism prediction platform. Drug Metabolism and Pharmacokinetics, 35(4), 361-367. https://doi.org/10.1016/j.dmpk.2020.05.007