Animal testing exercise will be put to an end with this new algorithm.
Washington: A new study claims to have developed a new high-speed algorithm, which can put an end on the practice of using animals to test the toxicity of chemicals. The algorithm compares tested and untested chemical fragment compounds in order to predict an untested chemical's toxicity.
The details were published in the Journal of Environmental Health Perspectives. Out of the 85,000 compounds used currently in consumer products, the majority have not been comprehensively tested for safety.
"There is an urgent, worldwide need for an accurate, cost-effective and rapid way to test the toxicity of chemicals, in order to ensure the safety of the people who work with them and of the environments in which they are used. Animal testing alone cannot meet this need." said, lead researcher Daniel Russo. "
Previous efforts to solve this problem used computers to compare untested chemicals with structurally similar compounds whose toxicity is already known. But those methods were unable to assess structurally unique chemicals and were confounded by the fact that some structurally similar chemicals have very different levels of toxicity.
The researchers overcame these challenges by developing a first-of-its-kind algorithm that automatically extracts data from PubChem, a National Institutes of Health database of information on millions of chemicals.
"The algorithm developed by Daniel and the Zhu laboratory mines massive amounts of data, and discerns relationships between fragments of compounds from different chemical classes, exponentially faster than a human could. This model is efficient and provides companies and regulators with a tool to prioritize chemicals that may need more comprehensive testing in animals before use in commerce." said co-author Lauren Aleksunes,
To fine-tune the algorithm, the researchers began with 7,385 compounds for which toxicity data is known and compared it with data on the same chemicals in PubChem. By comparing relationships between sets of chemicals, they shed light on new factors that can determine the toxicity of a chemical.
Although the algorithm was directed only to assess the chemicals' level of toxicity when consumed orally, the researchers conclude that their strategy can be extended to predict other types of toxicity.
"While the complete replacement of animal testing is still not feasible, 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," said the corresponding author Hao Zhu.