本研究探讨了临床前体外细胞系反应数据和计算模型在确定黑色素瘤治疗中泛 RAF (Belvarafenib) 和 MEK (Cobimetinib) 抑制剂的最佳剂量需求方面的潜力。我们的研究动机是药物组合在增强抗癌反应方面的关键作用，以及需要缩小围绕选择有效剂量策略以最大限度地发挥其潜力的知识差距。在 43 种黑色素瘤细胞系的药物组合筛选中，我们确定了特定剂量NRAS 与 BRAF 突变黑色素瘤的 panRAF 和 MEK 抑制剂的情况。两者都获得了益处，但对于 NRAS 突变型黑色素瘤具有明显更强的协同作用和更窄的剂量范围（NRAS 的平均 Bliss 得分为 0.27，而 BRAF 突变体的平均 Bliss 得分为 0.1）。计算模型和后续分子实验将这种差异归因于负反馈的适应性抵抗机制。我们通过高精度地捕获细胞抑制和细胞毒性反应来预测异种移植物中的肿瘤生长，从而验证了体外剂量反应图的体内可翻译性。我们分析了 Belvarafenib 与 Cobimetinib 的 1 期临床试验的药代动力学和肿瘤生长数据，表明协同作用要求对 NRAS 突变黑色素瘤患者施加了更严格的精确剂量限制。利用临床前数据和计算模型，我们的方法提出了剂量策略，可以优化药物组合的协同作用，同时也带来了保持在精确剂量范围内的现实挑战。总的来说，这项工作提出了一个帮助药物组合剂量选择的框架。

This study explores the potential of pre-clinical in vitro cell line response data and computational modeling in identifying the optimal dosage requirements of pan-RAF (Belvarafenib) and MEK (Cobimetinib) inhibitors in melanoma treatment. Our research is motivated by the critical role of drug combinations in enhancing anti-cancer responses and the need to close the knowledge gap around selecting effective dosing strategies to maximize their potential.In a drug combination screen of 43 melanoma cell lines, we identified specific dosage landscapes of panRAF and MEK inhibitors for NRAS vs. BRAF mutant melanomas. Both experienced benefits, but with a notably more synergistic and narrow dosage range for NRAS mutant melanoma (mean Bliss score of 0.27 in NRAS vs. 0.1 in BRAF mutants). Computational modeling and follow-up molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated the in vivo translatability of in vitro dose-response maps by predicting tumor growth in xenografts with high accuracy in capturing cytostatic and cytotoxic responses. We analyzed the pharmacokinetic and tumor growth data from Phase 1 clinical trials of Belvarafenib with Cobimetinib to show that the synergy requirement imposes stricter precision dose constraints in NRAS mutant melanoma patients.Leveraging pre-clinical data and computational modeling, our approach proposes dosage strategies that can optimize synergy in drug combinations, while also bringing forth the real-world challenges of staying within a precise dose range. Overall, this work presents a framework to aid dose selection in drug combinations.