In vitro and in silico investigation of pharmacokinetics in neonates undergoing therapeutic hypothermia

This PhD project ‘Understanding altered pharmacokinetics in neonates undergoing therapeutic hypothermia: integration of in vitro and in silico research' is a part of the FWO project 'I-PREDICT: Innovative Physiology-based pharmacokinetic model to pREdict Drug exposure In neonates undergoing Cooling Therapy’. It is part of a collaboration between KU Leuven (Prof. Pieter Annaert, Prof. Karel Allegaert), UZ Leuven (Prof. Anne Smits), the proteomics core of the KU Leuven Biomedical Sciences group (SyBioMa, Dr. Sebastien Carpentier), and the University of Antwerp (Prof. Steven Van Cruchten), involving multiple disciplines. The project focuses on both non-clinical and clinical pharmacokinetic research in neonates, with the aim of describing and predicting the impact of cooling on disposition of medicines in asphyxiated neonates. This would allow researchers to make accurate predictions concerning the pharmacokinetics of this population and make decisions accordingly. Asphyxiated neonates are subjected to cooling therapy to decrease the brain damage that often occurs due to birth asphyxia (hypoxic-ischemic encephalopathy). Said hypothermia treatment is theorised to have a relevant impact on the pharmacokinetics of medicines that are administered. Therefore, an innovative physiology-based pharmacokinetic (PBPK) framework will be developed by integrating in vitro, in vivo animal, in silico and clinical research data. The project will have two main components: in vitro laboratory research and computational modelling. The main focus of the in vitro research will be on the accurate determination of the hepatobiliary clearance of several model medicines using established in vitro models such as porcine and human primary hepatocytes. For the in silico part, the project will encompass the development of models for the neonatal population. Special attention will be given to the verification of the model and to the evaluation of the predictive performance of the model concerning the target population.