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New Algorithm Could Save Thousands of Animals From Toxic Testing

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rabbit in testing scenario

Rutgers University researchers announced an algorithm designed to test for chemical toxicity, to keep people safe in various industries, and potentially sparing thousands of animals from being experimental subjects.

The algorithm extracts data from the PubChem chemical database, then compares chemical fragments from tested compounds with those from untested compounds, mathematically grading similarities and differences to predict an untested chemical's toxicity. The researchers trained the algorithm using 7,385 compounds of known toxicity, and presented the program with 600 new compounds. The algorithm had a 62% to 100% success rate in predicting oral toxicity levels for several chemical groups, although this does not suggest animal testing will be completely phased out.

"This model takes an important step toward meeting the needs of industry, in which new chemicals are constantly under development, and for environmental and ecological safety," says Rutgers' Hao Zhu.

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