Pharmacometric Oncology Research

Lena Friberg, Mats Karlsson

Within the oncology area, we are working on PK and PKPD models describing the time-courses of biomarkers, drug-induced toxicity, tumor size measurements, tumor activity measurements (SUV, standard uptake value) and overall survival. The models are aimed to be mechanism-based and be applicable across different drugs, and thereby valuable in development of new and existing drugs, including individualization of the therapy. 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. The models can also be used to explore different concepts of study design in oncology. This type of modelling framework, including biomarkers, side-effects, tumor response and survival, has been developed for sunitinib in gastrointestinal stromal tumors and for axinitib in renal cell carcinoma.  Different metrics of tumor size, both constant and time-varying, as well as one dimension (diameter) vs. three-dimensional (volume) are evaluated for predicting overall survival. We also have ongoing projects around immuno-therapies, e.g. in quantifying potential biomarkers and their relationship to tumor growth and shrinkage.

Projects are on-going around extensions and applications of a semi-physiological model that can describe neutropenia for numerous anticancer drugs. We have for example quantified the relationship between IL-6, CRP and febrile neutropenia. For TDM-1, an integrated model that includes platelets and the liver enzymes ASAT and ALAT has been developed, which has been applied to explore alternative dosing regimens. For TDM-1, we are also developing models for Patient Reported Outcomes (PRO), which is an increasingly used component for comparison of drug treatments during oncology drug development, by application of Item Response Theory.

Different design aspects are also being evaluated, e.g. how the frequency of neutrophil counts affect the possibility to predict nadir and myelosuppression recovery and how the tumor measurement frequency influence the predictive capacity of OS, as well as how model-based adaptive designs could be valuable for efficient characterization of drug combinations.