The growing demand for consumer products that are safe and effective while also less harmful to the environment is driving manufacturers to identify new and alternative chemicals and chemical combinations that can support the development of innovative products that meet that demand. At the same time, the cost and time required to evaluate new chemical substances using conventional toxicological methods is increasingly incompatible with the need for rapid and continuous product innovation. Today, scientists, chemists and toxicologists require access to more advanced tools and technologies to efficiently and effectively assess new chemical substances for their potentially harmful effects.
Working with researchers at the Bloomberg School of Public Health at the Johns Hopkins University, UL has developed an innovative, cheminformatics software-based tool to predict chemical hazards that can be used wherever chemical hazard data is needed. The first module in the UL cheminformatics suite, REACHAcross, utilizes an advanced, predictive algorithm, as well as machine learning, to assess the endpoint behavior of any chemical of interest. By analyzing millions of chemical combinations, REACHAcross can predict potentially-harmful health and environmental outcomes associated with chemical substances, including skin sensitization, acute oral- and dermal-toxicity, eye-and dermal-irritation, mutagenicity and acute- and chronic-aquatic toxicity. Additional modules in the cheminformatics suite are expected to follow in the near future to take full advantage of the increasing availability and use of big data in predictive toxicology.