Retrospective assessment of rat liver microsomal stability at NCATS: data and QSAR models

Vishal B. Siramshetty, Pranav Shah, Edward Kerns, Kimloan Nguyen, Kyeong Ri Yu, Md Kabir, Jordan Williams, Jorge Neyra, Noel Southall, Ðắc Trung Nguyễn, Xin Xu

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Hepatic metabolic stability is a key pharmacokinetic parameter in drug discovery. Metabolic stability is usually assessed in microsomal fractions and only the best compounds progress in the drug discovery process. A high-throughput single time point substrate depletion assay in rat liver microsomes (RLM) is employed at the National Center for Advancing Translational Sciences. Between 2012 and 2020, RLM stability data was generated for ~ 24,000 compounds from more than 250 projects that cover a wide range of pharmacological targets and cellular pathways. Although a crucial endpoint, little or no data exists in the public domain. In this study, computational models were developed for predicting RLM stability using different machine learning methods. In addition, a retrospective time-split validation was performed, and local models were built for projects that performed poorly with global models. Further analysis revealed inherent medicinal chemistry knowledge potentially useful to chemists in the pursuit of synthesizing metabolically stable compounds. In addition, we deposited experimental data for ~ 2500 compounds in the PubChem bioassay database (AID: 1508591). The global prediction models are made publicly accessible (https://opendata.ncats.nih.gov/adme). This is to the best of our knowledge, the first publicly available RLM prediction model built using high-quality data generated at a single laboratory.

Original languageEnglish
Article number20713
JournalScientific Reports
Volume10
Issue number1
DOIs
StatePublished - Dec 2020

Fingerprint

Dive into the research topics of 'Retrospective assessment of rat liver microsomal stability at NCATS: data and QSAR models'. Together they form a unique fingerprint.

Cite this