文章：

药物反应计算模型揭示黑色素瘤中泛RAF与MEK抑制剂剂量方案的突变特异性限制因素

Computational Modeling of Drug Response Identifies Mutant-Specific Constraints for Dosing panRAF and MEK Inhibitors in Melanoma

原文发布日期：22 August 2024

DOI: 10.3390/cancers16162914

类型: Article

开放获取: 是

英文摘要：

Purpose: 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. Results: 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. Conclusion: 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.

摘要翻译： 

目的：本研究旨在探讨临床前体外细胞系反应数据与计算模型在确定黑色素瘤治疗中泛RAF抑制剂（Belvarafenib）与MEK抑制剂（Cobimetinib）最佳剂量方案方面的潜力。本研究的动机在于药物联合治疗对增强抗癌反应的关键作用，以及当前亟需填补关于选择有效给药策略以最大化治疗潜力的知识空白。 结果：通过对43株黑色素瘤细胞系进行药物联合筛选，我们明确了泛RAF抑制剂与MEK抑制剂在NRAS突变型与BRAF突变型黑色素瘤中的特异性剂量效应图谱。两类突变型均从联合治疗中获益，但NRAS突变型黑色素瘤表现出更显著的协同效应且有效剂量窗更窄（NRAS突变型平均Bliss评分为0.27，而BRAF突变型为0.1）。计算模型与后续分子实验将这种差异归因于负反馈调节引发的适应性耐药机制。我们通过高精度预测异种移植瘤的细胞抑制与细胞毒性反应，验证了体外剂量-反应图谱在体内的可转化性。通过分析Belvarafenib联合Cobimetinib的Ⅰ期临床试验药代动力学与肿瘤生长数据，我们发现NRAS突变型黑色素瘤患者因协同效应要求而需要更精确的剂量控制。 结论：本研究通过整合临床前数据与计算模型，提出了可优化药物联合协同效应的剂量策略，同时揭示了临床实践中维持精确剂量范围所面临的实际挑战。总体而言，本工作构建了一个有助于指导联合用药剂量选择的系统性框架。

原文链接：

Computational Modeling of Drug Response Identifies Mutant-Specific Constraints for Dosing panRAF and MEK Inhibitors in Melanoma