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Updated: Oct 28, 2025

Analysis of Interactions between Endobiotics and Human Gut Microbiota Using In Vitro Bath Fermentation Systems

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Published on: August 23, 2019

Predicting drug-microbiome interactions with machine learning.

Laura E McCoubrey¹, Simon Gaisford¹, Mine Orlu¹

¹University College London, London, United Kingdom.

Biotechnology Advances

|July 14, 2021

Summary

This summary is machine-generated.

The human microbiome significantly impacts drug efficacy and response. Machine learning (ML) can predict personalized drug-microbiome interactions, advancing precision medicine.

Keywords:

Artificial intelligence Bacteria Big data Biopharmaceutics Drug discovery and development Information technology Metabolism of pharmaceuticals and medicines Microorganisms Pharmacokinetics Repurposing

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Area of Science:

Microbiology
Pharmacology
Computational Biology

Background:

The human microbiome plays a crucial role in health and drug responses.
Gastrointestinal microbiota can metabolically alter drug efficacy, impacting clinical outcomes.
Drug intake can modify gut microbiome composition, influencing health and future drug responses.

Purpose of the Study:

To review the current understanding of drug-microbiota interactions.
To present machine learning (ML) as a tool for predicting personalized drug-microbiome relationships.
To highlight the potential of ML in advancing precision medicine.

Main Methods:

Literature review of drug-microbiome interactions.
Exploration of machine learning techniques for predictive modeling.
Discussion of ML's application in characterizing drug-microbiota activities.

Main Results:

The drug-microbiota interface is complex and highly individualized.
Machine learning offers a powerful approach to model these unique interactions.
ML can potentially identify patient-specific risks associated with drug-microbiome effects.

Conclusions:

Personalized prediction of drug-microbiome interactions is essential for modern medicine.
Machine learning holds significant promise for understanding and forecasting these interactions.
Effective use of ML can transform pharmaceutical practices and patient care regarding the drug-microbiome axis.