Oncology

We develop PKPD models describing the time-courses for a range of variables of interest for cytotoxic, target and immunotherapies, and characterize relationships between them; biomarkers, drug-induced toxicity, tumor size measurements (diameters, volumes), tumor and immune response activity, patient reported outcomes (PRO) as well as overall survival. The models are aimed at being of value to support development of new and existing drug therapies, including being a tool for individualized dose-adaptations. By integrating information of different variables into a modelling framework the variables’ relations and predictive value can be tested, and a better overview of both desired and adverse effects from a changed dosing regimen can be obtained. For example, a modelling framework, including biomarkers, side-effects, tumor response and survival, has been developed for sunitinib in gastrointestinal stromal tumors that can be used to explored consequences on survival and different adverse effect from a changed dose. We also explore what metrics of tumor size, constant and time-varying, are predictive of the hazard of death, and if changes in individual lesions, and their location, are predictive of patient outcomes and better than the standard measure sum of longest diameters (SLD). The models can also be used to explore different concepts of study design in oncology, for example to identify a strategy to define an optimal combination of drugs.