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The Cancer Drug Fraction of Metabolism Database

Liyan Hua1, Chien-Wei Chiang2, Wang Cong1

  • 1College of Automation, Harbin Engineering University, Harbin, China.

CPT: Pharmacometrics & Systems Pharmacology
|June 18, 2019
PubMed

Insights

This study created a database of cancer drug metabolism by cytochrome P450 enzymes. It predicts drug-drug interactions, identifying 31 potential pairs with clinical evidence for 20.

Area of Science:

  • Pharmacology
  • Drug Metabolism
  • Computational Chemistry

Background:

  • Cytochrome P450 (CYP) enzymes are crucial for drug metabolism.
  • Understanding CYP-mediated metabolism is vital for predicting drug-drug interactions (DDIs).
  • Existing databases lack comprehensive data on CYP isozyme-specific metabolism for cancer drugs.

Purpose of the Study:

  • To develop a database quantifying the fraction of metabolism (f_m) for FDA-approved cancer drugs by major cytochrome P450 isozymes.
  • To establish a reproducible protocol for data extraction from in vitro CYP inhibition studies.
  • To demonstrate the database's utility in predicting in vitro DDIs for cancer drugs.

Main Methods:

  • A systematic data collection protocol was designed to extract substrate-depletion and metabolite-formation data from public in vitro CYP inhibition studies.
  • The fraction of metabolism (f_m) for each cancer drug was estimated using the curated data.
  • In vitro drug-drug interaction predictions were performed using the developed database for 42 cancer drugs.

Main Results:

  • A comprehensive database quantifying CYP-mediated metabolism of cancer drugs was successfully created.
  • In vitro drug-drug interaction predictions identified 31 potential DDIs involving at least one cancer drug, with predicted area under concentration ratios > 2.
  • Literature review confirmed clinical evidence for 20 out of the 31 predicted DDIs.

Conclusions:

  • The developed database provides a valuable resource for understanding cancer drug metabolism by CYP enzymes.
  • The database effectively predicts potential in vitro drug-drug interactions for cancer therapeutics.
  • This work facilitates improved drug development and clinical management of cancer patients by anticipating DDIs.

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