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Pharmacogenetics of Phase I Enzymes: Cytochrome P450 Isozymes01:28

Pharmacogenetics of Phase I Enzymes: Cytochrome P450 Isozymes

Cytochrome P450 (CYP450) enzymes are a superfamily of heme-containing monooxygenases that play a pivotal role in Phase I drug metabolism by catalyzing oxidation and reduction reactions.These enzymes transform lipophilic xenobiotics into more hydrophilic metabolites, facilitating subsequent Phase II conjugation and eventual excretion. The CYP450 family is classified into families (e.g., CYP1–CYP3) and subfamilies (e.g., CYP2A, CYP2C), based on amino acid sequence homology.CYP450 isoenzymes,...
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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Related Experiment Video

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Transfer learning for cytochrome P450 isozyme selectivity prediction.

Reiji Teramoto1, Tsuyoshi Kato

  • 1Forerunner Pharma Research Co., Ltd, 1-6, Suehiro-cho, Turumi-ku, Yokohama, Kanagawa 230-0045, Japan. rteramotjp@gmail.com

Journal of Bioinformatics and Computational Biology
|July 22, 2011
PubMed
Summary
This summary is machine-generated.

Transfer learning improves prediction of drug metabolism by cytochrome P450 (CYP) isozymes. This approach leverages data from multiple CYP enzymes to enhance selectivity predictions for new chemical entities, aiding drug discovery.

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A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

  • Computational chemistry and cheminformatics
  • Drug discovery and development
  • Pharmacology and toxicology

Background:

  • Understanding drug metabolic fate is critical in drug discovery to prevent drug-drug interactions.
  • Cytochrome P450 (CYP) isozyme selectivity prediction is vital for screening drugs with suitable metabolism profiles.
  • Conventional supervised learning methods independently model each CYP isozyme, failing to utilize cross-isozyme activity data.

Purpose of the Study:

  • To develop a transfer learning approach for predicting P450 isozyme selectivity by leveraging activity data from multiple isozymes.
  • To improve the predictive performance of P450 isozyme selectivity models by exploiting inter-isozyme relationships.

Main Methods:

  • Utilized a large-scale dataset of P450 isozyme selectivity data obtained via quantitative high-throughput screening with a bioluminescence assay.
  • Applied a transfer learning algorithm to learn prediction models from activity data across five P450 isozymes (CYP1A2, CYP2C9, CYP3A4, CYP2D6, and CYP2C19).
  • Compared the performance of the transfer learning model against conventional supervised learning algorithms, including SVM, Weighted k-nearest neighbor, Bagging, Adaboost, and LSI.

Main Results:

  • The transfer learning algorithm demonstrated superior performance compared to conventional supervised learning methods across the evaluated P450 isozymes.
  • Exploiting multiple P450 isozyme activity data in the learning process significantly improved predictive performance.
  • The developed model effectively predicts P450 selectivity for new chemical entities.

Conclusions:

  • Transfer learning offers a more effective approach for P450 isozyme selectivity prediction than traditional methods.
  • Leveraging data from multiple P450 isozymes enhances the accuracy and utility of predictive models in drug discovery.
  • The proposed algorithm serves as a valuable tool for screening drug candidates with favorable metabolic profiles.