This presentation will include a comprehensive analysis of translational in vitro models from the perspective of the 3Rs Collaborative, focusing on replacement, reduction, and refinement principles in animal research. We will explore current methodologies, challenges, and opportunities in developing and utilizing these models to enhance biomedical research while minimizing animal use. It delves into the potential impact of these models for future advancements and collaboration in the pursuit of humane and effective research practices.

There is a significant opportunity for a paradigm shift in drug development and discovery by integrating complex in vitro models (CIVM). Stakeholders need to identify areas of unmet need, define questions of interest, and establish criteria for qualification and implementation of CIVM. CIVMs may play an important role in a weight-of-evidence approach when utilizing human-cell CIVMs to address questions when animal models cannot. This talk will focus on approaches for building confidence in CIVMs as a component in a weight-of-evidence approach to advance new therapies into clinical trials.

Human drug absorption is one of the critical parameters to define the bioavailability of drug candidates. Traditional in vitro models have some shortcomings in predicting human fraction absorbed and first pass metabolism. Therefore, we have characterized several novel complex intestinal in vitro models in their suitability to serve as an ADME platform for drugs in discovery and development.

Human-on-a-chip systems for safety and efficacy—with up to 5 organs—have demonstrated up to a 28-day evaluation of compounds. Embodiments are a functional NMJ system for ALS, MG, CMT, and an LTP model for AD. Sanofi has used efficacy data from our conduction velocity model for an IND for CIDP that enabled a clinical trial (#NCT04658472), now in Phase III. Other INDs for phenotypic models have also been filed.

Early translational studies using human-relevant in vitro models can help our understanding of disease pathobiology by simulating hallmark phenotypes in patients. To study these phenotypes, we have developed both 2D and 3D in vitro human models using iPSC-derived cardiomyocytes. We have analyzed the behavior of these models using computational methods like signal processing and computer vision. We will present promising results from this analysis as well as potential caveats.

This presentation will showcase examples from FDA projects in Generative AI, focusing on its application using Generative Adversarial Networks (GANs) in predictive toxicology, translational toxicology, and drug safety assessment. Specifically, these projects apply GANs to: (1) Learn from existing animal study data to generate animal study results without conducting actual experiments; (2) Generate toxicogenomics data based solely on experimental design; and (3) Translate findings from one organ to another. Additionally, the presentation will include a comparative analysis of discriminative and generative approaches, highlighting their complementary roles in toxicology and drug safety. Finally, a framework will be discussed to integrate both discriminative and generative AI to advance the 3Rs (Replacement, Reduction, and Refinement) in toxicology, drug discovery, and safety assessment.

Spatial profiling technologies like hyper-plex immunostaining in tissue, spatial transcriptomics, coupled with non-spatial profiling like scRNAseq have the potential to enable a multi-factorial, multi-modal characterization of the tissue microenvironment. Objective scoring methods inspired by recent advances in statistics and machine learning can serve to aid the interpretation of these datasets, as well as their integration with companion data like bulk and single cell genomics. I will discuss analysis paradigms from machine learning that can be used to integrate and then prioritize gene regulatory programs underlying oncogenesis (as well as therapeutic candidates) using case studies.

Tumors are complex ecosystems where cancer cells are embedded within a complex microenvironment comprising multiple infiltrating cell types and a pathologically remodeled extracellular matrix. While much progress has been made in establishing and interrogating epithelial and stem cell-like models, incorporation of cells from the microenvironment and biophysical changes is challenging. Here, I will describe our efforts to develop and interrogate models of tumor cells and the conscripted microenvironment.

There is a need for neural models that incorporate the physiological complexity of the brain and are compatible with high throughput screening platforms.  We have used iPSC-derived neural cells to make functional brain region-specific neural 3D cellular models in multi well plate format for drug screening.  We will show data demonstrating neural spheroids with spontaneous and synchronized calcium oscillations and spatially defined neural circuit models produced using bioprinting techniques.

Translation throughout the discovery/development pipeline into the clinic requires assays and model systems with physiological and pharmacological relevance. The ability to incorporate patient-derived tumor cells into assays with sufficient complexity to predict patient tumor responses could accelerate the development of new therapies. This presentation will describe the use of various patient-derived tumor models from the National Cancer Institute’s Patient-Derived Models Repository for the evaluation of approved and investigational anticancer agents.