Role of the gut microbiome in shaping drug response in immunocompromised hosts

BACKGROUND: There is an increasing amount of evidence on microbiome-drug interactions in several clinical settings, including in immunocompromised patients. The gut microbiome has been shown to directly and indirectly influence drug efficacy and toxicity, offering high potential for clinical translation. OBJECTIVES: This narrative review aims to provide an up-to-date overview of the relationship between gut microbes and drugs, with a focus on immunochemotherapy in immunocompromised hosts, including oncological and transplant patients. SOURCES: We searched PubMed to identify relevant literature in English up to February 2026, as well as included articles known to the authors (prioritizing clinical studies wherever possible). CONTENT: For commonly used anticancer drugs in untargeted conventional chemotherapy, gut microbes may directly activate prodrugs, inactivate biologically active drugs, and/or interfere with their toxicity. Furthermore, indirect mechanisms of immune system modulation have been shown to enhance or worsen therapeutic outcomes, including in targeted immunotherapy. For immunosuppressants in transplant recipients, there is less available evidence overall. Nevertheless, existing studies support the role of the gut microbiome in influencing pharmacokinetics, including enterohepatic recirculation, also through modulation of host drug-metabolizing enzymes. Notably, some studies have demonstrated the potential of targeted microbiome manipulation to improve therapeutic outcomes. However, most of this information derives from small, heterogeneous studies, including animal models and in vitro studies. IMPLICATIONS: The translational implications of microbiome research in pharmacology are of paramount importance. Well-designed clinical studies and the integration of in vivo and ex vivo models will be essential for advancing knowledge and providing mechanistic insights into microbiome-drug interactions. In parallel, advanced computational approaches such as artificial intelligence and machine learning tools will facilitate the analysis of complex microbiome data. These approaches will help identify clinically relevant microbial signatures, including high-risk microbiome-drug interactions. This will enable the development of personalized precision strategies to improve clinical outcomes and prolong disease-free survival.