Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA; Center for Bioinformatics and Computational Medicine, Ann Arbor, MI 48109, USA; Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
Drug Discov Today. 2022 Jun;27(6):1639-1651. doi: 10.1016/j.drudis.2022.04.006. Epub 2022 Apr 7.
Combination therapies can overcome antimicrobial resistance (AMR) and repurpose existing drugs. However, the large combinatorial space to explore presents a daunting challenge. In response, machine learning (ML) algorithms are being applied to identify novel synergistic drug interactions from millions of potential combinations. Here, we compare ML-based approaches for combination therapy design based on the type of input information used, specifically: drug properties, microbial response and infection microenvironment. We also provide a compilation of publicly available drug interaction datasets relevant to AMR. Finally, we discuss limitations of current ML-based methods and propose new strategies for designing efficacious combination therapies. These include consideration of in vivo conditions, design of sequential combinations, enhancement of model interpretability and application of deep learning algorithms.
联合疗法可以克服抗菌药物耐药性 (AMR) 并重新利用现有药物。然而，需要探索的组合空间非常庞大，这是一个艰巨的挑战。为此，机器学习 (ML) 算法正被应用于从数百万种潜在组合中识别新的协同药物相互作用。在这里，我们根据所使用的输入信息类型比较了基于 ML 的组合疗法设计方法，具体包括：药物特性、微生物反应和感染微环境。我们还提供了与 AMR 相关的公开可用药物相互作用数据集的汇编。最后，我们讨论了当前基于 ML 的方法的局限性，并提出了设计有效联合疗法的新策略。这些策略包括考虑体内条件、设计序贯组合、增强模型可解释性和应用深度学习算法。
