Abstract
Osimertinib (AZD9291) is a potent, selective, irreversible inhibitor of EGFR-sensitizing (exon 19 and L858R) and T790M-resistant mutation. In vivo, in the mouse, it is metabolized to an active des-methyl metabolite, AZ5104. To understand the therapeutic potential in patients, this study aimed to assess the relationship between osimertinib pharmacokinetics, the pharmacokinetics of the active metabolite, the pharmacodynamics of phosphorylated EGFR reduction, and efficacy in mouse xenograft models of EGFR-driven cancers, including two NSCLC lines. Osimertinib was dosed in xenografted models of EGFR-driven cancers. In one set of experiments, changes in phosphorylated EGFR were measured to confirm target engagement. In a second set of efficacy studies, the resulting changes in tumor volume over time after repeat dosing of osimertinib were observed. To account for the contributions of both molecules, a mathematical modeling approach was taken to integrate the resulting datasets. The model was able to describe the pharmacokinetics, pharmacodynamics, and efficacy in A431, PC9, and NCI-H1975 xenografts, with the differences in sensitivity described by the varying potency against wild-type, sensitizing, and T790M-mutant EGFR and the phosphorylated EGFR reduction required to reduce tumor volume. It was inferred that recovery of pEGFR is slower after chronic dosing due to reduced resynthesis. It was predicted and further demonstrated that although inhibition is irreversible, the resynthesis of EGFR is such that infrequent intermittent dosing is not as efficacious as once daily dosing. Mol Cancer Ther; 15(10); 2378-87. ©2016 AACR.
Highlights
Mathematical modeling has demonstrated considerable value in the design and interpretation of clinical trials [1]
It should be noted that according to the parameter estimates, a significant fraction (70%) of parent is converted to the active metabolite, resulting in similar exposures of both molecules after oral dosing to mice
Pharmacodynamics, and disease modification for preclinical data allows an insight into how target engagement over time results in changes in tumor growth
Summary
Mathematical modeling has demonstrated considerable value in the design and interpretation of clinical trials [1]. There has been less published in the preclinical arena; good examples do exist [2, 3]. Very few of these preclinical modeling reports consider modeling pharmacokinetic/pharmacodynamic (PK/PD) efficacy relationships of anticancer agents across cell lines. There are no examples of modeling the PK/ PD of targeted anticancer agents where there is a significant active metabolite. We will demonstrate here such an example. This article will demonstrate how application of PK/PD/systems pharmacology principles are used to understand the linkage between drug concentration and target engagement, and deconvolute how reduced target expression after repeat dosing of an irreversible inhibitor results in
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