This review explores the concept of synergy in pharmacology, emphasizing its importance in optimizing treatment outcomes through the combination of drugs with different mechanisms of action. Synergy, defined as an effect greater than the expected additive effect elicited by individual agents according to specific predictive models, offers a promising approach to enhance therapeutic efficacy while minimizing adverse events. The historical evolution of synergy research, from ancient civilizations to modern pharmacology, highlights the ongoing quest to understand and harness synergistic interactions. Key concepts, such as concentration-response curves, additive effects, and predictive models, are discussed in detail, emphasizing the need for accurate assessment methods throughout translational drug development. Although various mathematical models exist for synergy analysis, selecting the appropriate model and software tools remains a challenge, necessitating careful consideration of experimental design and data interpretation. Furthermore, this review addresses practical considerations in synergy assessment, including preclinical and clinical approaches, mechanism of action, and statistical analysis. Optimizing synergy requires attention to concentration/dose ratios, target site localization, and timing of drug administration, ensuring that the benefits of combination therapy detected bench-side are translatable into clinical practice. Overall, the review advocates for a systematic approach to synergy assessment, incorporating robust statistical analysis, effective and simplified predictive models, and collaborative efforts across pivotal sectors, such as academic institutions, pharmaceutical companies, and regulatory agencies. By overcoming critical challenges and maximizing therapeutic potential, effective synergy assessment in drug development holds promise for advancing patient care.

中文翻译：

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药物相互作用和协同作用：从药理学模型到临床应用


本综述探讨了药理学中协同作用的概念，强调了其通过联合使用具有不同作用机制的药物来优化治疗结果的重要性。协同作用被定义为根据特定预测模型，单个药物引起的大于预期附加效应的效果，提供了一种有前途的方法来提高治疗效果，同时最大限度地减少不良事件。协同研究的历史演变，从古代文明到现代药理学，凸显了对理解和利用协同相互作用的持续追求。详细讨论了关键概念，例如浓度-反应曲线、加性效应和预测模型，强调了在整个转化药物开发过程中需要准确的评估方法。尽管存在各种用于协同分析的数学模型，但选择合适的模型和软件工具仍然是一个挑战，需要仔细考虑实验设计和数据解释。此外，本综述讨论了协同评估中的实际考虑，包括临床前和临床方法、作用机制和统计分析。优化协同作用需要注意浓度/剂量比、靶位点定位和给药时间，确保在实验室中检测到的联合治疗的益处可转化为临床实践。总体而言，该综述倡导采用系统的方法进行协同评估，结合稳健的统计分析、有效和简化的预测模型，以及跨关键部门（如学术机构、制药公司和监管机构）的协作努力。 通过克服关键挑战并最大限度地发挥治疗潜力，药物开发中的有效协同评估有望推进患者护理。