Introduction: Erlotinib (Tarceva) is an epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor, which effectively targets EGFR-mutant driven non-small-cell lung cancer. However, the evolution of acquired resistance because of a second-site mutation (T790M) within EGFR remains an obstacle to successful treatment. Methods: We used mathematical modeling and available clinical trial data to predict how different pharmacokinetic parameters (fast versus slow metabolism) and dosing schedules (low dose versus high dose; missed doses with and without make-up doses) might affect the evolution of T790M-mediated resistance in mixed populations of tumor cells. Results: We found that high-dose pulses with low-dose continuous therapy impede the development of resistance to the maximum extent, both pre- and post-emergence of resistance. The probability of resistance is greater in fast versus slow drug metabolizers, suggesting a potential mechanism, unappreciated to date, influencing acquired resistance in patients. In case of required dose modifications because of toxicity, little difference is observed in terms of efficacy and resistance dynamics between the standard daily dose (150 mg/d) and 150 mg/d alternating with 100 mg/d. Missed doses are expected to lead to resistance faster, even if make-up doses are attempted. Conclusions: For existing and new kinase inhibitors, this novel framework can be used to rationally and rapidly design optimal dosing strategies to minimize the development of acquired resistance.
Bibliographical noteFunding Information:
The authors acknowledge support from the National Institutes of Health/National Cancer Institute grants R01-CA121210 (William Pao), P01-CA129243 (William Pao), and the Dana-Farber Cancer Institute Physical Sciences-Oncology Center U54-CA143798 (Jasmine Foo, William Pao, and Franziska Michor). William Pao also received additional support from the Vanderbilt-Ingram Cancer Center Core grant (P30-CA68485).
- Acquired resistance
- EGFR T790M mutation
- EGFR-mutant lung cancer
- Evolutionary cancer modeling
- Pharmacokinetic modeling