Mechanism-based drug safety testing using innovative in vitro liver models: from DILI prediction to idiosyncratic DILI liability assessment

Abstract

Introduction

Idiosyncratic drug-induced liver injury (iDILI) remains unpredictable. As adverse responses arise in a small fraction of patients, drugs often fail in later drug development stages or post-approval, thereby tremendously increasing costs and putting patients at risk, highlighting the need for accurate early identification of iDILI liabilities.

Covered areas

Using articles from the last 5 years (PubMed), iDILI risk factors are described, in vitro liver models and mechanism-based readout strategies are evaluated on their potential to enable iDILI liability assessment.

Expert opinion

Various in vitro liver models are established for disease modeling and DILI prediction. Drawbacks for each of these seem inevitable, making the evaluation of their application domain and iDILI liability assessment potential crucial. A tiered approach could be considered, whereby compounds are initially screened and flagged using simple fit-for-purpose models for DILI prediction, followed by multicellular liver models that integrate the current knowledge of iDILI onset in combination with mechanistic readouts. Multiplexing models within an integrated mechanism-based testing strategy could improve the safety assessment accuracy. Defined in vitro models should integrate critical hepatocyte intrinsic risk factors as well as adaptive immune system components to refine iDILI liability assessment.

Bioactivity Descriptors for in Vivo Toxicity Prediction: Now and the Future

Abstract

Development of a new drug currently takes 10–12 years with costs of around 2 billion EUR. The two main reasons for failures comprise lack of efficacy and unforeseen toxicity. For the latter, a standard process pursued to minimize the risk is the so-called toxicological read across. Briefly, toxicologists query the available literature and databases for compounds, which are structurally similar to their development candidate in order to retrieve information on potential hazards. In addition, computational models might be applied which are either trained for a single protein, such as hERG or P-glycoprotein, or for a respective in vivo endpoint (cholestasis, steatosis, drug-induced liver injury (DILI), ….). In both cases, a proper ‘description’ of the compound of interest is key for the predictive ability of the models. In the following editorial, we will highlight a few general approaches for compound description with a focus on bioactivity-based characterization of compounds.