Study recognizes biomarker gene mark that demonstrates likely liver toxicity

Overview

• Post By : Kumar Jeetendra


• Source: University of Illinois College of Agricultural, Consumer and Environmental Sciences


• Date: 19 Dec,2020

When agrochemical and pharmaceutical companies develop new products, they must test extensively for potential toxicity before obtaining regulatory approval. This testing usually involves lengthy and expensive animal research.

A research team at University of Illinois has developed a gene biomarker identification technique that cuts the testing process down to a couple of days while still maintaining a high level of accuracy.

Usually, companies do so through long-term animal experiments, ” she adds. They track animals for up to a year to find out if they develop liver cancer after exposure to these compounds. The research require thousands of mice or rats, and a lot of human time taking care of these animals, collecting samples, and analyzing the information.

The aim of this research was to identify the smallest set of indicators from the liver to predict toxicity and potential liver cancer.

The agrochemical industry has a pipeline where they test new compounds in terms of toxicity-related endpoints. Liver toxicity is one of the most important endpoints, because the liver is the organ that receives the blood supply and cleans it, making it one of the biggest targets in terms of environmental toxic action.”

Zeynep Madak-Erdogan, Associate Professor, Department of Food Science and Human Nutrition, U of I and Lead Author on the Study

The study, published in Scientific Reports, identifies a biomarker gene signature that indicates potential liver toxicity only 24 hours after exposure.

Madak-Erdogan and her colleagues analyzed information from a large database maintained by the National Institute of Environmental Health Sciences. In cooperation with scientists at the U of I National Center for Supercomputing Applications (NCSA), they used machine learning approaches to identify chemical biomarkers in messenger RNA to predict future toxicity.

“From designing new molecules into identifying novel biological targets, machine learning approaches are playing a crucial role in accelerating drug target identification and validation,” explains Colleen Bushell, director of NCSA’s Healthcare Innovation Program Office and co-author on the analysis.

While this study is not the first to employ these methods, it’s the most comprehensive, Madak-Erdogan says. The researchers used a large amount of data and multiple machine learning techniques in order to identify the methods that provide the quickest and most accurate results.

“We’re assessing the best prediction techniques and finding the best indicators for liver toxicity. Instead of going for months or years, we can only treat a few mice for 24 hours, collect livers, examine the biomarkers we identified, and predict whether the animal will potentially develop liver cancer or not,” she explains.

The study’s results can be used broadly by toxicologists and other scientists, and will help the agrochemical and pharmaceutical sector improve their testing capabilities.

“Our findings reveal machine learning approaches are unquestionably very valuable in analyzing the huge quantity of biological data that we produce in our research activities. Collaboration between life sciences and computer sciences is very important for this work,” Madak-Erdogan concludes.