MARCH 22 - 25, 2026

Exhibitor-Hosted Session
Location: Room 23C
Presenter: Rhiannon N. Hardwick, PhD, DABT, Scientific Director, Bristol Myers Squibb

Abstract: The emergence of complex in vitro models (CIVM), including 3D multicellular tissues and microphysiological systems (MPS) offers new opportunities to better mimic human physiology and improve toxicity predictions in pharmaceutical research. Integrating CIVM into testing pipelines presents challenges, such as the need for specialized expertise, evolving protocols, and sensitive biomarker detection. Careful characterization of baseline function, clear definition of context of use (COU), and rigorous qualification are essential for reliable toxicological assessment.

Poster A128
Location: Hall B
Presenter: Karissa Cottier, PhD, Manager of R&D, BioIVT

Abstract: Preclinical prediction of idiosyncratic drug induced liver injury (iDILI) is important in drug development both to safeguard patients from serious adverse outcomes and to reduce late-stage candidate drug failure. Standard preclinical in vitro models fail to identify DILI potential for several reasons including lack of relevant immune cell types in the model and/or factors that are specific to particular patient populations. Retrospective studies have identified compound interaction with T-cells in patients with specific human leukocyte antigen (HLA) types as a contributing factor for iDILI in some compounds which showed liver toxicity in the clinic. Based on these observations, we developed a co-culture model which incorporates human PBMC into a long-term micropatterned hepatocyte co-culture model (HEPATOPAC) in order to better model peripheral immune system contribution to DILI development. In these studies, culture conditions were optimized to maintain both hepatocyte and PBMC viability and function for up to 7 days.

Poster A150
Location: Hall B
Presenter: Dan Schieffer, Scientific Director, DeNovix Inc.

Abstract: Primary hepatocytes are a critical cell type in toxicity and drug metabolism workflows. Given the sensitive nature of these experiments, accurate and precise hepatocyte counts are essential for achieving reproducible results for downstream applications. We have developed a machine learning-based model for counting hepatocytes and determining viability on an automated cell counter to 1) help standardize results across laboratories and 2) reduce user to user variability. This study aims to test if an automated machine learning model can provide the same level of counting accuracy for hepatocyte quantification against the established gold standard method of manual hemocytometer counts by trained scientists.

Poster A117
Location: Hall B
Presenter: Tina Mueller, PhD, Scientific Advisor, ADMET Services, BioIVT

Abstract: Drug developers have long aimed to design molecules with low metabolic turnover. In vitro assays, such as metabolic stability studies, are commonly used as an initial approach to assess how rapidly compounds are metabolized and to estimate turnover rates to guide early drug development. We therefore used in vitro metabolism study data to examine whether there has been a shift over time toward drug candidates with lower turnover.