Statistics framework to remove bias from debate about how well mouse models mimic human disease

lab mouse
Credit: Pixabay/CC0 Public Domain

Mice and other animals have actually been essential to a few of the greatest medical advancements in human history. Animals aren’t constantly great designs of human illness, leading to stopped working experiments and debate over their effectiveness.

A group of biostatisticians led by University of Pittsburgh School of Public Health researchers revealed today in PNAS that they’ve established a structure to identify just how much congruence and discordance lab animals have with particular human illness. The tool eliminates possible predisposition from clinical analysis of how translational animal information is for human conditions.

” There have actually been years of dispute about whether animal designs imitate people well and whether they work for translational or medical research study,” stated senior author George Tseng, Sc.D., teacher and vice chair for research study in Pitt Public Health’s Department of Biostatistics. “Our structure is the very first to supply quantitative techniques and bioinformatic workflow to appropriately resolve that argument.”

Tseng and his group took on the subject after 2 documents released in PNAS— one in 2013 and one in 2014— that utilized the very same datasets provided inconsistent conclusions on the effectiveness of mice as designs of human illness that include swelling, such as sepsis and burns.

The group reanalyzed the datasets in the inconsistent PNAS documents with their Congruence Analysis for Model Organisms (CAMO) structure. It discovered that for the 6 human inflammatory conditions studied, 2 were well simulated by mice; 2 were not, and 2 did not have sufficient information to reason. Tseng’s group identified that the previous research studies reached various endpoints due to the fact that the clinical groups– one primarily made up of laboratory-based researchers and the other mainly of clinicians– had actually utilized various limits, or cut-off points, for their analyses.

” The conclusion drawn by our impartial, threshold-free structure is far more practical,” Tseng stated. “In the end, you can not state that the mouse design is absolutely worthless or completely best. A mouse design can imitate some biological systems well however others inadequately. The concern is whether it imitates the system of interest, such as the drug target. And it even exposed that the information aren’t ideal in some circumstances– if you have actually restricted info, you can’t draw a conclusion.”

The group is extending their research study into cancer to analyze which cell-cultured designs are excellent mimics for growths and psychiatric conditions to discover, for instance, whether mice imitate the human body clock.

” We expect CAMO ending up being a crucial part of preclinical research studies to resolve all way of human illness,” Tseng stated.

More details: Wei Zong et al, Transcriptomic congruence analysis for examining design organisms, Proceedings of the National Academy of Sciences(2023). DOI: 10.1073/ pnas.2202584120

Citation: Statistics structure to eliminate predisposition from dispute about how well mouse designs simulate human illness (2023, February 2) recovered 3 February 2023 from statistics-framework-bias-debate-mouse. html

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