Computational modeling of drug response identifies mutant-specific constraints for dosing panRAF and MEK inhibitors in melanoma.

PURPOSE

This study explores the potential of preclinical cell line response data and computational modeling in identifying 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 unique 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. Computational modeling and molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated translatability of dose-response maps by accurately predicting tumor growth in xenografts. Then, we analyzed 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.