Integrating Genome-scale and Pharmacokinetic Models towards Precision Medicine and the Prediction of Tetrahydrocannabinol and Ethanol Metabolism

Abstract

Cannabis and Ethanol are two of the most frequently used recreational substances in the world and many health policies are devoted towards ensuring their safe consumption. In this thesis, the metabolism of tetrahydrocannabinol (THC) and ethanol are modelled to gain further understanding of how these compounds affect the body. First, a physiologically-based pharmacokinetic (PBPK) model was developed to characterize how individuals with different genotypes eliminate THC as well as its effect (in conjunction with ethanol) on driving. Then, a previously developed PBPK model of ethanol metabolism was combined with a whole-body genome-scale model (GEM) to better predict how genetic variations and diseases could affect ethanol metabolism and exposure to the carcinogenic metabolite, acetaldehyde. By gap-filling enzyme-specific PBPK models with general metabolic fluxes from GEMs, we can predict the impact of inter-individual variations on drug metabolism and steer the modelling field towards precision medicine and the development of a “virtual twin”.