Microphysiological Models for Mechanistic-Based Prediction of Idiosyncratic DILI

Area of Science:

• * Pharmacology and Toxicology
• * Immunology
• * Regenerative Medicine

Background:

• * Drug-induced liver injury (DILI) is a significant challenge in drug development, with idiosyncratic DILI (iDILI) posing particular difficulties due to complex pathogenesis.
• * Current preclinical models often fail to accurately predict iDILI, highlighting the need for more relevant and predictive systems.
• * Hepatic inflammation, driven by immune responses, is a critical component of iDILI pathogenesis.

Purpose of the Study:

• * To review and highlight advancements in in vitro co-culture models for investigating immune-mediated iDILI.
• * To focus on human-based 3D multicellular models that incorporate immune cells to better mimic the hepatic microenvironment.
• * To discuss the challenges and future directions for developing more predictive models of iDILI.

Main Methods:

• * Review of in vitro co-culture models, emphasizing human-based 3D multicellular systems.
• * Inclusion of non-parenchymal liver cells (Kupffer cells, stellate cells, dendritic cells, liver sinusoidal endothelial cells) to simulate hepatic microenvironment and immune interactions.
• * Analysis of drugs recalled due to DILI between 1996-2010 and their study in various models.

Main Results:

• * Human-based 3D multicellular models incorporating immune cells offer a promising approach to supplement existing in vivo models for iDILI research.
• * These advanced models facilitate the study of heterotypic cell-cell interactions and immune-mediated mechanisms crucial for iDILI.
• * The review underscores the necessity for harmonizing and comparing characteristics of different iDILI models.

Conclusions:

• * Progress in understanding iDILI pathogenesis through advanced in vitro models is crucial for improving drug safety screening.
• * Immune-mediated mechanisms and the hepatic microenvironment are key targets for developing more predictive hepatotoxicity models.
• * Future research should focus on addressing challenges in model development, including endpoints, 3D architecture, cell sourcing, and multi-stage mechanisms to better predict clinical liver injury.