Machine Learning Approach Gives Insight on Toxin Exposure

Harvard Medical School investigators have developed a machine learning approach using high-quality, large-scale animal model data that sheds new light on the biology of the liver and kidneys after toxin exposure. The findings were recently published in Molecular Systems Biology, and reveal new mechanisms of toxin vulnerability and tolerance that may be broadly relevant to studies of human disease, the authors said. Researchers found nine distinct patterns of response to chemical exposure that the authors termed "disease states." These states shed light on the dynamics of toxin-induced liver and kidney injury, including defense mechanisms and novel biomarkers, and provide insights into molecular signals that cause toxin-induced appetite suppression and weight loss. "We used machine learning to ask a simple question: What can we learn from this rich data set about what happens to the liver and kidneys after exposure to different chemicals?,” lead study author Kenichi Shimada, HMS research fellow in therapeutic science in the Laboratory of Systems Pharmacology, said in a release. Researchers focused on the Open TG-GATEs database, the result of a 10-year effort by a Japanese public-private consortium to assess 170 different compounds with the aim of improving and enhancing drug safety. These compounds represent a wide range of chemicals and medications, including common ones such as ibuprofen and acetaminophen, known for their toxic effects on the liver and kidn...
Source: MDDI - Category: Medical Devices Authors: Tags: Digital Health Source Type: news