Model Informed precision dosing to individualise and optimise pharmacotherapeutic treatment
Generally, patients are all treated with the same dose of a drug, typically established during the licensing process decades ago. In recent years, it has become clear that individual patients are not optimally treated with universal dosing regimens; they are at risk for undertreatment (reduced efficacy) as well as unnecessary toxicity. The process of optimising drug treatment based on individual patient characteristics is called precision dosing. Unfortunately, the use of precision dosing in clinical practice is lagging behind.
Model-informed precision dosing (MIPD) is a next-generation dosing paradigm, where pharmacological information on drugs and diseases is combined with individual patient characteristics and used to calculate the optimal dose of a drug for an individual patient.
With this collaborative MIPD-project, we aim to bring about a significant improvement in the existing drug prescribing and treatment monitoring process by developing advanced precision dosing algorithms for 3 vulnerable patient groups; children after organ transplantation, critically ill, chronically ill nephrology and haematology patients.
To reach this aim, we will first externally validate advanced mathematical pharmacological models with real-world patient data. After evaluation of the predictive performance, we will implement the best suitable models in InsightRX Nova, a user-friendly and intuitive MIPD platform. Next, we will develop and validate limited sampling strategies for these models within Nova. Finally, we will demonstrate and evaluate the use of the developed dosing algorithms within Nova in patient cohorts obtained from clinical practice.
We expect societal impact by lowering the burden of disease in three vulnerable patient groups, by: increased clinical efficacy, reduced toxicity, decreased frequency of outpatient clinic visits and reduced number of blood samples. These aspects will also have an economic impact via reduced hospital stay and visits and decreased treatment of side effects. Further, less blood samples will result in a reduction of laboratory material and personnel, time of nurses and clinicians.
