Pharmacometric Models for Biomarkers, Side Effects and Efficacy in Anticancer Drug Therapy

Abstract

New approaches quantifying the effect of treatment are needed in oncology to improve the drug development process and to enable treatment optimization for existing therapies. This thesis focuses on the development of pharmacometric models for biomarkers, side effects and efficacy in order to identify predictors of clinical response in anti-cancer drug therapy. The variability in myelosuppression was characterized in six different cytotoxic anticancer treatments to evaluate a model-based dose individualization approach utilizing neutrophil counts as a biomarker. The estimated impact of inter-occasion variability was relatively low in relation to the inter-individual variability, indicating that myelosuppression is predictable from one treatment course to another. The approach may thereby be useful for dose optimization within an individual. To further study and to identify predictors for the severe side effect febrile neutropenia (FN), the relationship between the shape of the myelosuppression time-course and the probability of FN was characterized. Patients with a rapid decline in neutrophil counts and high drug sensitivity were identified to have a higher probability of developing FN compared with other patients who experience grade 4 neutropenia. Predictors of clinical response in patients receiving sunitinib for the treatment of gastro-intestinal stromal tumor (GIST) were identified by the development of an integrated modeling framework. Drug exposure, biomarkers, tumor dynamics, side effects and overall survival (OS) were linked in a unified structure, and univariate and multivariate exposure variables were tested for their predictive capacities. The soluble biomarker, sVEGFR-3 and tumor size at start of treatment were found to be promising predictors of overall survival, with decreased sVEGFR-3 levels and smaller baseline tumor size being predictive of longer OS. Also hypertension and neutropenia was identified as predictors of OS. The developed modeling framework may be useful to monitor clinical response, optimize dosing in sunitinib and to facilitate dose individualization