Pharmacokinetics and Drug Interactions Determine Optimum Combination Strategies in Computational Models of Cancer
Shaon Chakrabarti1,2, Franziska Michor3,2
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts.
Abstract:
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 multiscale stochastic modeling framework significantly changes the optimum drug administration schedules identified, often predicting nonintuitive 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 MTD 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. Cancer Res; 77(14); 3908-21. ©2017 AACR.
Insights
Optimizing cancer drug schedules is complex due to resistance. This study introduces a computational model predicting that modified dosing frequencies and lower drug concentrations can improve tumor cell eradication and reduce toxicity in combination therapies.
Area of Science:
- Cancer research
- Computational biology
- Pharmacology
Background:
- Drug resistance is a major challenge in cancer therapy.
- Combination therapies complicate predicting cancer evolution and treatment outcomes.
- Understanding determinants of cancer evolution under combination therapy is crucial.
Purpose of the Study:
- To develop a computational strategy for exploring pharmacokinetic and drug interaction effects in cancer evolutionary models.
- To predict optimal drug administration schedules for combination therapies.
- To make clinically relevant predictions for cancer treatment.
Main Methods:
- Developed a multiscale stochastic modeling framework.
- Incorporated pharmacokinetic and drug interaction effects into evolutionary models.
- Applied the approach to a phase Ib clinical trial (TATTON) for EGFR-mutant lung cancer.
Main Results:
- Computational models significantly alter predicted optimal drug administration schedules, often suggesting nonintuitive strategies.
- Current schedules in the TATTON trial show similar efficacy, but slight dosing frequency modifications can enhance tumor cell eradication.
- Lower drug concentrations than the maximum tolerated dose (MTD) may be as efficacious, potentially reducing toxicity.
Conclusions:
- Quantitative understanding of pharmacokinetic, drug interaction, and evolutionary processes is essential for effective cancer intervention strategies.
- The developed computational approach enables rational design of clinical trials for diverse cancer and treatment types.
- Modified drug schedules and potentially lower doses can improve combination therapy outcomes in EGFR-mutant lung cancer.
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