Pharmacokinetics and drug-interactions determine optimum combination strategies in computational models of cancer evolution

The identification of optimal drug administration schedules to battle the emergence of resistance is a major challenge in cancer research. The existence of a multitude of resistance mechanisms necessitates administering drugs in combination, significantly complicating the endeavor of predicting the evolutionary dynamics of cancers and optimal intervention strategies. A thorough understanding of the important determinants of cancer evolution under combination therapies is therefore crucial for correctly predicting treatment outcomes. Here we developed the first computational strategy to explore pharmacokinetic and drug interaction effects in evolutionary models of cancer progression - a crucial step towards making clinically relevant predictions. We found that incorporating these phenomena into our multi-scale stochastic modeling framework significantly changes the optimum drug administration schedules identified, often predicting non-intuitive strategies for combination therapies. We applied our approach to an ongoing phase-Ib clinical trial (TATTON) administering AZD9291 and selumetinib to EGFR-mutant lung cancer patients. Our results suggest that the schedules used in the three trial arms have almost identical efficacies, but slight modifications in the dosing frequencies of the two drugs can significantly increase tumor cell eradication. Interestingly, we also predict that drug concentrations lower than the maximally tolerated dose are as efficacious, suggesting that lowering the total amount of drug administered could lower toxicities while not compromising on the effectiveness of the drugs. Our approach highlights the fact that quantitative knowledge of pharmacokinetic, drug interaction and evolutionary processes is essential for identifying best intervention strategies. Our method is applicable to diverse cancer and treatment types and allows for a rational design of clinical trials.

 

MAJOR FINDINGS

 

Drugs used in cancer treatment exhibit varied temporal dynamics of absorption and clearance, summarized by their pharmacokinetic (PK) parameters. Typically these dynamics are neglected in evolutionary models of cancer progression under combination therapy. By developing a computational framework to incorporate PK and drug interaction effects, we showed that the complex interplay of dynamics of drugs and various cancer clones significantly affect dosing strategies. Applied to an ongoing phase-Ib clinical trial, our framework predicted efficacies of different dosing schedules and suggested new strategies to significantly improve cancer cell eradication and reduce associated toxicities.

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