For new medicines to be approved for therapeutic use, three internationally accepted criteria have to be met: efficacy, safety, quality. Yet, in drug development and drug therapy, the complex interaction of drug and patient with the disease is rarely fully understood. As a result, patients may not (fully) benefit or suffer from (serious) side effects often due to the lack of recognising biological variability and experimental uncertainty. Pharmacometrics as an interdisciplinary science integrates data and knowledge from various sources, such as clinical trials, in vitro or nonclinical investigations, reported data, routine data as well as patient characteristics in a coherent framework with the aim to generate new knowledge in the underlying mechanisms and processes of the drug‐patient‐disease interaction by analysing e.g. drug concentration, drug effect and disease progression/cure data and developing so called pharmacometric models. These models considering variability and uncertainty can then be used to make predictions for new scenarios, such as different patient populations that have not (yet) been studied in trials: These simulations allow to study and to identify e.g. whether effective and safe concentrations are being reached or the extent of desired/undesired effects and can be performed both on the individual patient and the patient population level. Future advancements in pharmacometric modelling and simulation (as seen in initiatives such as ‘Model-informed drug discovery and development’ (MID3) and ‘Model‐informed patient care’ (MIPC)) will include even more and diverse data to better capture the relevant physiological, pathophysiological pathway and network features of the organism to ultimately contribute to more rational therapeutic drug decision making in patients.
Invited by Wilhelm Huisinga