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bioRxiv · 10.1101/2022.10.08.510500

A data-driven approach for predicting the impact of drugs on the human microbiome

Algavi, Y.·Borenstein, E.
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Abstract

Many medications can negatively impact the bacteria residing in our gut, depleting beneficial species and causing adverse effects. To determine individualized response to pharmaceutical treatment, a comprehensive understanding of the impact of various drugs on the gut microbiome is needed, yet, to date, experimentally challenging to obtain. Towards this end, we developed a data-driven approach, integrating information about the chemical properties of each drug and the genomic content of each microbe, to systematically predicts drug-microbiome interactions. We show that this framework successfully predicts outcomes of in-vitro pairwise drug-microbe experiments, as well as drug-induced microbiome dysbiosis in both animal models and clinical trials. Applying this methodology, we systematically map all interactions between pharmaceuticals and bacteria and demonstrate that medications anti-microbial properties are tightly linked to their adverse effects. This computational framework has the potential to unlock the development of personalized medicine and microbiome-based therapeutic approaches, improving outcomes and minimizing side effects.

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Algavi, Y., Borenstein, E.. 2022-10-08. A data-driven approach for predicting the impact of drugs on the human microbiome. https://doi.org/10.1101/2022.10.08.510500

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