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

Simple Summary: Combining drugs is crucial for enhancing anti-cancer responses. However, the potential of pre-clinical data in identifying suitable combinations and dosage is often underutilized. In this study, we leverage pre-clinical in vitro cell line drug response data and computational modeling of signal transduction and of pharmacokinetics to elucidate distinct dose requirements for the combination of pan-RAF and MEK inhibitors in melanoma. Our findings reveal a more synergistic but narrower dosing landscape in NRAS vs. BRAF mutant melanoma, which we link to a mechanism of adaptive resistance through negative feedback. Further, our analysis suggests the importance of drug dosing strategies to optimize synergy based on mutational context yet highlights the real-world challenges of maintaining a narrow dose range. This approach establishes a framework for translational investigation of drug responses in the refinement of combination therapy, balancing the potential for synergy and practical feasibility in cancer treatment planning. 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. [ABSTRACT FROM AUTHOR]