Methods of predicting clinically significant DRUG-DRUG interactions beyond validated interaction checkers

Drug-drug interactions (DDIs) are an important determinant of the safety of pharmacotherapy, yet only a fraction carry clinically significant consequences. Validated, database-driven interaction checkers detect potential interactions quickly and with high sensitivity, but their low specificity generates numerous alerts of doubtful clinical relevance. There is a pressing need for methods that predict clinically significant interactions in a specific patient, ideally before harm occurs.

This narrative review, based on a search of PubMed and Google Scholar for papers on the detection and prediction of clinically significant DDIs (from inception to 31 May 2026), summarizes the limitations of database-based interaction checkers and surveys the spectrum of complementary approaches.

No single method currently provides reliable, real-time prediction of clinically significant DDIs for the individual patient. Checkers and computational or artificial intelligence models excel at exhaustively detecting potential interactions but lack clinical context, whereas contextualized systems improve positive predictive value at the cost of considerable complexity. The way forward is an integrative, contextualized 'umbrella' system that couples comprehensive detection with patient-specific data, real-time literature surveillance and disproportionality analysis, and a human-in-the-loop feedback design. Realizing it will require standardization and a nation-wide rather than an individual research effort.