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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Pathway-Based Similarity Measurement to Quantify Transcriptomics Similarity Between Human Tissues and Preclinical

Paarth Parekh¹, Jason Sherfey¹, Begum Alaybeyoglu¹

¹Javelin Biotech, Inc., Woburn, Massachusetts, USA.

Clinical Pharmacology and Therapeutics

|October 8, 2024

Summary

This summary is machine-generated.

A new pathway-based similarity measurement (PBSM) quantifies preclinical model relevance to human tissues. This method aids drug development by improving model selection and reducing translation gaps.

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Area of Science:

Biotechnology and Biomedical Engineering
Translational Medicine
Genomics and Transcriptomics

Background:

Clinical translation of preclinical research faces challenges due to species differences and heterogeneity.
Microphysiological systems (MPS) offer improved recapitulation of human physiology but lack quantitative similarity assessment.
A need exists for methods to evaluate preclinical model relevance for specific contexts of use (CoU).

Purpose of the Study:

To develop and validate a quantitative method, pathway-based similarity measurement (PBSM), for assessing preclinical model similarity to human systems.
To leverage RNA-sequencing (RNA-seq) data and pathway information for evaluating human relevance in drug development models.
To demonstrate PBSM's utility in selecting and validating preclinical models for specific CoU.

Main Methods:

Development of pathway-based similarity measurement (PBSM) integrating RNA-seq data with pathway information.
Application of PBSM to assess transcriptomic similarity between preclinical models and human tissues (liver and cardiac).
Evaluation of PBSM's capability to support CoU selection, analyze gene set impacts, and differentiate model types.

Main Results:

PBSM provides a quantitative method to compare transcriptomic similarity between preclinical models and human tissues.
Proof-of-concept demonstrated for liver and cardiac tissues, enabling improved model selection and validation.
PBSM successfully supports CoU selection, assesses gene set influence, and differentiates in vitro and in vivo models.

Conclusions:

PBSM significantly reduces the translational gap in drug development by quantitatively evaluating preclinical model similarity to human systems.
Facilitates informed model selection and enhances understanding of context-specific applications for preclinical models.
Provides a foundation for improving the physiological relevance of in vitro models and advancing therapeutic interventions.