Database analysis more reliable than animal testing at predicting chemical toxicity in consumer products
A study led by scientists at Johns Hopkins Bloomberg School of Public Health suggests that advanced algorithms working from large chemical databases can predict a new chemical’s toxicity better than standard animal tests. The computer-based approach could replace many animal tests commonly used during consumer product testing. It could also evaluate more chemicals than animal testing, a change that could lead to wider safety assessments.
For the study, which appears online today in the journal Toxicological Sciences, the researchers mined a large database of known chemicals they developed to map the relationships between chemical structures and toxic properties. They then showed that one can use the map to automatically predict the toxic properties of any chemical compound more accurately than a single animal test would do.
“These results are a real eye-opener—they suggest that we can replace many animal tests with computer-based predictions and get more reliable results,” says principal investigator Thomas Hartung from the Department of Environmental Health and Engineering. He directs the Center for Alternatives to Animal Testing, which seeks to promote humane science by finding and developing alternatives to the use of animals in research, product safety testing, and education.