Computational modeling of in vitro anti-cancer drug combinations to inform dose selection in oncology clinical trials
Andrew Goetz - Department of Biomedical Engineering, Yale University, New Haven, CT, US
Andrew is a biomedical engineering graduate student at Yale, studying cellular heterogeneity in signaling and metabolism under Purushottam Dixit. Andrew received his undergraduate degrees in applied mathematics and physics at Georgia Southern University. Andrew completed an industry internship at the biotech company Genentech under the supervision of Luca Gerosa, where he contributed to the development of systems pharmacology methods for targeted therapies of oncogenic signaling.
December Webinar Recording
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 work, we leverage preclinical in vitro cell line drug response data and computational modeling of signal transduction and pharmacokinetics to elucidate distinct dose requirements for the combination of a pan-RAF and a MEK inhibitor in melanoma patients. Our findings reveal a more synergistic, but narrower dosing landscape in NRAS vs BRAF mutant melanoma, which we linked to a mechanism of adaptive resistance through negative feedback. Based on these results, model analysis suggests drug dosing strategies that 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. In this webinar, we will present the general methodology as well as the aforementioned case study of a clinical trial in targeted therapies for melanoma.