Synergism or mirage: Current progress and an empirical approach for elucidating combination drug effects

• FDC drugs have garnered significant attention in treating complex diseases such as cancers and viral infections. By synergistically targeting multiple sites, FDCs offer an advantage over traditional single-drug approaches in terms of efficacy. • The wide availability of high-throughput drug combination data has made computer-aided design of drug combinations a viable approach for optimizing treatment effectiveness at a lower cost. • Despite the growing interest in combination therapies, there is no consensus on a standardized model for evaluating drug synergism. Various software tools and web servers facilitate the analysis of drug combination data using different reference models. However, inconsistencies arise because of the different assumptions and definitions of drug–drug interaction effects used in these models. The lack of standardization complicates the use of these models in training machine learning algorithms for predicting synergy scores. • The proposed empirical heatmap-based method provides a robust, data-driven approach to quantifying drug synergism without relying on assumptions about drug–drug interactions. Although it might not predict combination effects, it offers consistent evaluations, particularly for synergistic pairs. This method could serve as a foundation for developing predictive AI-driven models to design optimal combination therapies. Its current limitations, such as inadequate handling of antagonistic effects, indicate the need for further refinement to enhance its applicability in drug discovery. In the context of the multitarget paradigm, fixed-dose combination (FDC) drugs, that is, compounds that synergistically target multiple sites when combined, have gained attention for treating complex diseases like cancers and viral infections, as traditional single-drug approaches are often inadequate. This review examines current methods for evaluating and predicting drug synergism in advancing combination drug discovery, highlighting their limitations and providing a unified mathematical framework. Additionally, we present a novel solution to resolve these limitations and improve synergism evaluation, demonstrated through a case study with 20 pairs of FDA-approved chemotherapy drugs for colorectal cancer.

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