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Gonzalez2021 - QSAR Prediction Model for CYP2C9 Inhibitor and Substrate

ABSTRACT: Analysis of metabolic stability, determining the inhibition of CYP2C9 activity and whether the compounds are a substrate for the CYP2C9 enzyme. The data used to build these models has been publicly available at PubChem (AID1645842) by ADME@NCATS. Model Type: Predictive machine learning model. Model Relevance: The model predicts a chemical compound as an inihibitor and substrate of the CYP2C9 enzymes Model Encoded by: Zakia Yahya (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos5jz9

SUBMITTER: Zainab Ashimiyu-Abdusalam  

PROVIDER: MODEL2404160002 | BioModels | 2024-04-16

REPOSITORIES: BioModels

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Publications

Development of Robust Quantitative Structure-Activity Relationship Models for CYP2C9, CYP2D6, and CYP3A4 Catalysis and Inhibition.

Gonzalez Eric E   Jain Sankalp S   Shah Pranav P   Torimoto-Katori Nao N   Zakharov Alexey A   Nguyễn Ðắc-Trung ÐT   Sakamuru Srilatha S   Huang Ruili R   Xia Menghang M   Obach R Scott RS   Hop Cornelis E C A CECA   Simeonov Anton A   Xu Xin X  

Drug metabolism and disposition: the biological fate of chemicals 20210628 9

Cytochrome P450 enzymes are responsible for the metabolism of >75% of marketed drugs, making it essential to identify the contributions of individual cytochromes P450 to the total clearance of a new candidate drug. Overreliance on one cytochrome P450 for clearance levies a high risk of drug-drug interactions; and considering that several human cytochrome P450 enzymes are polymorphic, it can also lead to highly variable pharmacokinetics in the clinic. Thus, it would be advantageous to understand  ...[more]

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