The Pharmacy research group focusses on the development of predictive models to improve clinical drug efficacy and safety. We work on clinical problems that require further mechanistic understanding and strive for ultimate benefit to patients.

Treatment failure and serious side effects due to ‘one size fits all’ drug treatment approaches are common and have a major impact on patient quality-of-life and pose a high economic burden. In particular in special patient populations such as neonates, elderly, (pregnant) women, obese patients or critically ill patients, selecting the optimal drug treatment strategy is complex. This can be attributed to the multifactorial heterogeneity of these patient populations, in which differences in co-morbidities, organ function(s), genetics, and drug-use modulate the pharmacokinetics (PK) and pharmacodynamics (PD) of drugs and hence affect treatment efficacy. 

Personalized medicine approaches aim to select the right drug and the right dose for each patient. However, there is a high unmet need for better predictive models to guide personalised pharmacotherapy, integrating these multifactorial drug- and patient-specific factors contributing to differences in treatment response. The increasing availability of real-world data from routine patient care offers unique opportunities to identify drivers of currently unexplained inter-patient variability, which forms the basis for personalised medicine, but which is also associated with major data analytical challenges. Identification of potential drivers in turn requires establishment of causal relationships via mechanistic studies using (pre)clinical and computational models. 

As a team of researchers with complementary expertise in pharmacoepidemiology, pharmacodynamic pharmacokinetic modelling, and expertise in clinical pharmacology and translational preclinical models, we address these major challenges with a multidisciplinary research approach. For our research we closely collaborate with both clinical and community pharmacies.