Research lines within the Pharmacy group:

Pharmacology of drugs in special populations (Swantje Völler)

To date, two thirds of all drugs used in term and preterm neonates is used off-label in this extremely vulnerable population. This is mostly due to the challenges in conducting clinical trials in neonates. In our research group, we use the sparse available data from clinical practice to build population PK/PD and physiology-based models. These models enable us to understand what the most important drivers of drug concentration (PK) and effect (PD)are.

With this knowledge we can develop rational dosing schemes that help to ensure therapy success and prevent side effects. Another focus is the implementation of model-based dose advice in the clinic. Over the past two decades a multitude of PK and PD models have been developed but only few make it into daily practise due to their complexity. We strive for enabling and facilitating the use of models to improve patient treatment.

Phenoconversion by inflammation and drug-drug interactions (Martijn Manson)

Pharmacogenetics is increasingly incorporated in the clinic to better predict pharmacokinetics and optimize dosing regimens of drug treatments. While this approach has improved our prediction of drug metabolism, a mismatch between the genotype-based prediction of drug metabolism and the true capacity of an individual to metabolize drugs (phenotype) is unfortunately commonly observed in patients. This mismatch better known as phenoconversion is a consequence of non-genetic factors and remains an issue for the clinic.

To improve our understanding of the impact of phenoconversion we are currently investigating how inflammation & immuno-modulatory drugs affect drug metabolism. Secondly, we examine how the outcome of drug-drug-interactions are affected by pharmacogenetics (drug-drug-gene interactions). For this research we are using human liver biopsies and human hepatocyte models (2D/3D) in which human drug metabolism can be adequately studied and (endogenous) genetic variation is conserved or introduced. This project takes place in close contact with collaborators of the hospital pharmacy of the Leiden University Medical Center (LUMC) to facilitate translation of these findings to- and from the clinic.  

Pharmacoepidemiology for better understanding and prediction of (adverse) drug reactions (Fouzia Lghoul-Oulad Said)

Data is becoming more and more valuable and medical data does not fall short. With database pharmacoepidemiologic research we try to put this data to good use by looking for predictors for adverse drug reactions. We strive to contribute to a better and safer drug use by predicting and increasing understanding on the mechanisms that cause these reactions.

With an aging society more and more diversity in older patients is present. No two patients are the same and can therefore not be treated the same. By determining the factors which contribute to the occurrence of side effects such as hospitalisations or even death with predictive models we aim for personalisation of treatment based on the safety of drugs in addition to the efficacy of it.