HGMMX: Host Gut Microbiota Metabolism Xenobiotics Database

Anton S Kolodnitsky1, Nikita S Ionov1, Anastasia V Rudik1

  • 1Institute of Biomedical Chemistry (IBMC), 10 Bldg. 8, Pogodinskaya Str., Moscow, 119121, Russia.

Insights

The gut microbiota significantly impacts drug metabolism, potentially altering drug efficacy and safety. This study introduces the HGMMX database, a valuable resource for understanding drug biotransformation by gut bacteria.

Area of Science:

  • Microbiology
  • Pharmacology
  • Computational Chemistry

Background:

  • The human gut microbiome possesses extensive genetic capacity for xenobiotic metabolism, influencing drug efficacy and safety.
  • Gut bacteria-mediated drug biotransformation can lead to reduced therapeutic effects or severe adverse events.
  • Current in vitro methods for studying drug-microbiota interactions are time-consuming and costly, with limited standardization.

Purpose of the Study:

  • To create a comprehensive database detailing the metabolism of drug-like compounds by the human gut microbiota.
  • To facilitate the development of computational tools for predicting microbial drug metabolism.
  • To aid in pharmaceutical development and drug repurposing by providing insights into microbiota-drug interactions.

Main Methods:

  • Compilation of a dataset on drug-like compounds metabolized and not metabolized by gut microbiota.
  • Curation of information on specific chemical structures and their metabolic fate.
  • Development of the Human Gut Microbiota Metabolism (HGMMX) database.

Main Results:

  • The HGMMX database contains information on 368 metabolized and 310 non-metabolized structures.
  • The database provides a foundational resource for computational modeling of microbial drug metabolism.
  • The freely accessible database is available at https://www.way2drug.com/hgmmx.

Conclusions:

  • Understanding gut microbiota's role in drug metabolism is crucial for drug development and personalized medicine.
  • The HGMMX database addresses the need for curated data to advance in silico prediction models.
  • This resource will support the development of computational approaches to analyze microbiota-mediated drug metabolism.

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