Pathway-based similarity measurement to quantify transcriptomics similarity between human tissues and preclinical models.

Abstract

Accurate clinical translation of preclinical research remains challenging, primarily due to species-specific differences and disease and patient heterogeneity. An important recent advancement has been development of microphysiological systems that consist of multiple human cell types that recapitulate key characteristics of their respective human systems, allowing essential physiologic processes to be accurately assessed during drug development. However, an unmet need remains regarding a quantitative method to evaluate the similarity between diverse sample types for various contexts of use (CoU)-specific pathways. To address this gap, this study describes the development of pathway-based similarity measurement (PBSM), which leverages RNA-seq data and pathway-based information to assess the human relevance of preclinical models for specific CoU. PBSM offers a quantitative method to compare the transcriptomic similarity of preclinical models to human tissues, shown here as proof of concept for liver and cardiac tissues, enabling improved model selection and validation. Thus, PBSM can successfully support CoU selection for preclinical models, assess the impact of different gene sets on similarity calculations, and differentiate among various in vitro and in vivo models. PBSM has potential to reduce the translational gap in drug development by allowing quantitative evaluation of the similarity of preclinical models to human tissues, facilitating model selection, and improving understanding of context-specific applications. PBSM can serve as a foundation for enhancing the physiological relevance of in vitro models and supporting the development of more effective therapeutic interventions.