Immunological Drug-Drug Interactions in Immune Checkpoint Inhibitor Therapy: Mechanisms, Clinical Evidence, and Artificial Intelligence
Abstract
Purpose of review: Immune checkpoint inhibitors (ICIs) have transformed cancer therapy, producing durable responses across multiple malignancies. However, treatment outcomes may be influenced by immunological drug-drug interactions (DDIs) arising from commonly prescribed concomitant medications. Unlike classical pharmacokinetic or pharmacodynamic DDIs, these interactions operate through systemic mechanisms that modulate anti-tumour immunity, including alterations to the gut microbiome, immune signalling pathways, and the tumour microenvironment. This review proposes a conceptual framework for "immunological DDIs" (iDDIs), extending beyond metabolic interactions toward a system-level understanding of immune regulation.
Recent findings: We synthesise current evidence on commonly used medication classes-organised by their primary immunological mechanisms: (1) gut microbiome-mediated effects, (2) systemic immunosuppression, and (3) tumour microenvironment modulation-and their impact on ICI efficacy and safety. Meta-analyses suggest that certain medications, particularly antibiotics and proton pump inhibitors, are associated with poorer clinical outcomes, although confounding by indication and disease severity remain important limitations. Artificial intelligence (AI) is an emerging approach to detect and characterise complex DDIs using large-scale clinical and real-world data. Natural language processing, machine learning models, and large language models show potential for…
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