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Predicting drug metabolic stability is crucial for assessing therapeutic value and toxicity. New computational models and the MetaStab-Analyzer tool offer accurate predictions for drug compounds in mice, rats, and humans.

Keywords:
clearancecomputer‐aided predictiondrug‐like compoundshalf‐lifemetabolic stability

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Area of Science:

  • Pharmacokinetics and Drug Metabolism
  • Computational Chemistry and Cheminformatics
  • Toxicology and Drug Safety

Background:

  • Drug metabolic stability, a key determinant of pharmacokinetic profiles, influences therapeutic efficacy and toxicological risk.
  • In vitro assays using hepatocytes and liver microsomes are standard for determining metabolic stability via parameters like half-life (t1/2) and clearance (CL).
  • Existing tools often lack a combined qualitative and quantitative approach for metabolic stability prediction across multiple species.

Purpose of the Study:

  • To develop and validate computational models for predicting drug metabolic stability.
  • To create a user-friendly web application integrating these predictive models.
  • To provide both qualitative and quantitative metabolic stability assessments for drug candidates.

Main Methods:

  • Collected over 8000 compounds with experimental metabolic stability data from ChEMBL and PubChem.
  • Employed Naive Bayesian and Self-Consistent Extreme Classifier (SCEC) algorithms with MNA and QNA descriptors for classification models.
  • Utilized Self-Consistent Regression (SCR) for developing quantitative prediction models.
  • Integrated models into the freely available MetaStab-Analyzer web application.

Main Results:

  • Classification models achieved high accuracy, with AUC values exceeding 0.85 for most.
  • Regression models showed varying predictive power, with R-squared values ranging from 0.35 to 0.7.
  • MetaStab-Analyzer provides qualitative (stable/unstable/moderate) and quantitative predictions with numerical confidence metrics.
  • The tool supports predictions for metabolic stability in mice, rats, and humans.

Conclusions:

  • Developed accurate computational models for predicting drug metabolic stability using machine learning.
  • MetaStab-Analyzer offers a novel, integrated platform for dual qualitative-quantitative metabolic stability assessment.
  • The tool enhances drug discovery by providing interpretable and reliable predictions, aiding in the selection of safer and more effective drug candidates.