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Metabolism site prediction based on xenobiotic structural formulas and PASS prediction algorithm.

Anastasia V Rudik1, Alexander V Dmitriev, Alexey A Lagunin

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A novel computational method accurately predicts sites of metabolism (SOMs) for drug compounds using labeled multilevel neighborhoods of atoms (LMNA) and prediction of activity spectra for substances (PASS). This approach enhances drug development by improving the prediction of xenobiotic metabolism by cytochrome P450 enzymes.

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Area of Science:

  • Computational chemistry
  • Pharmacology
  • Drug metabolism

Background:

  • Cytochrome P450 enzymes are crucial for drug metabolism.
  • Accurate prediction of sites of metabolism (SOMs) is vital for drug design and safety.
  • Existing prediction methods have limitations in accuracy and scope.

Purpose of the Study:

  • To develop a new ligand-based method for predicting SOMs of xenobiotics.
  • To apply the method to major cytochrome P450 isoforms (1A2, 2C9, 2C19, 2D6, 3A4).
  • To validate the method's accuracy against existing tools and experimental data.

Main Methods:

  • Development of a ligand-based method using labeled multilevel neighborhoods of atoms (LMNA) descriptors.
  • Integration of the prediction of activity spectra for substances (PASS) algorithm.
  • Application to predict SOMs for five key cytochrome P450 isoforms.
  • Validation using leave-one-out cross-validation and external evaluation sets of cardiovascular drugs.

Main Results:

  • The developed method achieved an average invariant accuracy of prediction (IAP) of 0.89 for SOMs.
  • External validation demonstrated an average IAP of 0.83 for regioselectivity.
  • The method outperformed RS-Predictor across all tested CYP isoforms.
  • Performance was comparable to or better than SMARTCyp for CYP 2C9 and 2D6.

Conclusions:

  • The novel LMNA-PASS method provides a highly accurate approach for predicting sites of metabolism.
  • This method offers improved accuracy over existing tools like RS-Predictor and SMARTCyp for specific isoforms.
  • The developed tool can significantly aid in the early stages of drug discovery and development by predicting metabolic pathways.