Improved clearance prediction using ex vivo and new in silico tools to bridge the in vitro to in vivo extrapolation (IVIVE) gap

Despite the implementation of new technologies and approaches in drug discovery over the last years, 90% of drug candidates fail in clinical trials due to insufficient efficacy, toxicity or poor pharmacokinetics (PK) properties. To reduce this attrition rate due to PK, an accurate prediction of (hepatic) clearance (CL(H)), which determines drug exposure and dose prediction, at an early stage is indispensable. Although progress in the field has been made, many drug discovery programs fail to predict an accurate CLH. Especially, reliable prediction for higher (>50%) extraction ratio (ER) drugs, substrates of Phase 2 metabolism, and substrates for hepatic uptake and efflux transporters is challenging. For decades, Janssen and other pharmaceutical companies have used in vitro-in vivo extrapolation (IVIVE) as a method to predict human CLH. However, despite much methodology research to improve the predictivity of in vitro models, IVIVE entails a significant underprediction of in vivo CLH for many small molecule discovery programs. Knowledge about the underlying cause of clearance underprediction is still missing. In all likelihood, an interplay of several factors is the root cause of this underprediction. Among other reasons, immature in vitro models not capturing the dynamic in vivo context together with a substandard mechanistic understanding of liver disposition and the acceptance of the liver as a homogenous WSM hinders progress in the field. The novelty of this project arises from two main aspects. (i) The ex vivo isolated perfused rat liver (IPRL) model will be used as an intermediate model to provide unique mechanistic insights and to bridge the current in vitro-in vivo “CL prediction gap”. The mechanistic ‘defects’ of the in vitro models that cause mispredictions can be identified using the IPRL; and (ii) In vitro systems that recapitulate the inhomogeneous distribution of unbound intracellular concentrations should lead to better clearance predictions. Computational modeling of this inhomogeneity will be explored to advance the prediction of in vivo CLH for compound classes where pronounced inhomogeneity in intracellular distribution applies. The anticipated outcome of this project may be a paradigm shift in IVIVE-PBPK based predictions of hepatically cleared drug(s) in humans.