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

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.