This study reveals that artificial intelligence can revolutionize combination therapies by converting ineffective or harmful drug interactions (antagonism) into highly cooperative treatments (synergy). "Traditional methods struggle with the combinatorial complexity of drug pairs," explains co-author Peng Luo , "but our AI frameworks integrate multi-omics data to pinpoint optimal synergies, slashing experimental costs and accelerating discovery." By harmonizing genomic, proteomic, and transcriptomic data, models like AuDNNsynergy and PRODeepSyn achieve up to 0.90 AUC in predicting synergistic pairs—outperforming conventional screening by 7.2% in accuracy. We also identified DrugComboExplorer 's ability to map drug actions onto disease pathways, revealing why certain combinations overcome resistance in lung and breast cancers.
These tools address critical gaps in precision oncology. While current therapies often fail due to tumor heterogeneity or resistance, integrating dynamic multi-omics monitoring (e.g., single-cell sequencing during treatment) enables real-time regimen adjustments. However, Quan Cheng notes a hurdle : "Models require high-quality clinical pharmacokinetic data to translate in vitro predictions to patients, where gut microbiomes and tumor microenvironments alter drug efficacy." For instance, gut microbes metabolize drugs, and intratumoral bacteria can blunt gemcitabine's effects—factors omitted in earlier algorithms.
Future research in computational drug combination prediction must address several critical challenges to enhance clinical translation. Firstly, incorporating dynamic monitoring of drug resistance mechanisms through longitudinal multi-omics data (genomics, transcriptomics, proteomics) is essential for adapting therapies in real-time as tumors evolve. Secondly, integrating pharmacokinetic and physiological factors, such as drug absorption, distribution, metabolism, and tumor microenvironment penetration, will significantly improve the accuracy of in vivo efficacy predictions beyond current in vitro models. Thirdly, evaluating the impact of combinations on anti-tumor immunity, including potential immunotoxicity and modulation of the tumor immune microenvironment, is crucial for optimizing immunotherapy strategies and mitigating immune-related adverse events. Fourthly, integrating host-gut-intratumoral microbiome profiles will enable precision optimization by accounting for microbial influences on drug metabolism, efficacy, and toxicity. Finally, robust risk assessment and minimization strategies for adverse drug reactions (ADRs), potentially leveraging predictive models that balance efficacy and safety profiles, are vital for designing safer combination regimens.
