Liver three-dimensional cellular models for high-throughput chemical testing

Drug-induced hepatotoxicity is a leading cause of drug withdrawal from the market. High-throughput screening utilizing in vitro liver models is critical for early-stage liver toxicity testing. Traditionally, monolayer human hepatocytes or immortalized liver cell lines (e.g., HepG2, HepaRG) have been used to test compound liver toxicity. However, monolayer-cultured liver cells sometimes lack the metabolic competence to mimic the in vivo condition and are therefore largely appropriate for short-term toxicological testing. They may not, however, be adequate for identifying chronic and recurring liver damage caused by drugs. Recently, several three-dimensional (3D) liver models have been developed. These 3D liver models better recapitulate normal liver function and metabolic capacity. This review describes the current development of 3D liver models that can be used to test drugs/chemicals for their pharmacologic and toxicologic effects, as well as the advantages and limitations of using these 3D liver models for high-throughput screening.

Artificial Intelligence in Drug Safety and Metabolism

The use of artificial intelligence methods in drug safety began in the early 2000s with applications such as predicting bacterial mutagenicity and hERG inhibition. The field has been endlessly expanding ever since and the models have become more complex. These approaches are now integrated into molecule risk assessment processes along with in vitro and in vivo methods. Today, artificial intelligence can be used in every phase of drug discovery and development, from profiling chemical libraries in early discovery, to predicting off-target effects in the mid-discovery phase, to assessing potential mutagenic impurities in development and degradants as part of life cycle management. This chapter provides an overview of artificial intelligence in drug safety and describes its application throughout the entire discovery and development process.