Small Business Grant Aims to Cut Animals in Toxicity Testing

(tiburi, Pixabay)

10 Oct. 2018. A grant from National Institute of Health supports work by a start-up enterprise in Indiana to better estimate doses needed to test drugs for toxicity and reduce the number of animals needed for those tests. The $245,000 award from National Center for Advancing Translational Sciences, or NCATS, went to Dream Tech LLC, a spin-off company from Indiana University in Bloomington.

Dream Tech LLC is the creation of environmental engineer Kan Shao who heads the Computational Risk Assessment Laboratory in the university’s school of public health. The company, originally named KS and Associates, licenses and commercializes Shao’s work in statistical modeling for chemical risk assessments, particularly for regulatory decision making. A federal law enacted in 2016 calls for new methods for testing the toxicity of chemicals, with one of those methods called the benchmark dose modeling method.

A benchmark dose is a dose or concentration of a drug that produces a predetermined response rate for adverse effects. Models for calculating benchmark doses usually return a range, with the lower limits indicating a safe human dosage level. While Shao says benchmark dose models are an improvement over previous statistical methods, current modeling methods and software are still out of date. He specifically cites software provided by the U.S. Environmental Protection Agency and a public health institute in the Netherlands.

“Dream Tech,” says Shao in a university statement, “is developing a method to incorporate existing information to enhance the efficiency and effectiveness of dose-response assessment, which addresses a chemical’s toxicity.” His approach uses adaptive statistical models that incorporate real-world data to make dose-response assessments. These models, often called Bayesian statistics, calculate probabilities in dynamic environments, where adding new or updated data alter or increase confidence in the outcomes. Bayesian models are the basis of many machine learning algorithms that calculate the changing probability of various outcomes with addition of new data.

In the new 1-year project, Shao and Dream Tech propose developing a web-based program based on Bayesian statistics for calculating benchmark doses for toxicology testing. Current real-world data and historical data, says Shao, will be used to perform online calculations of plausible dosage targets. In addition, Shao plans to demonstrate that these Bayesian models are more precise than current computational methods, with one of the outcomes being less need for toxicology tests with live animals.

“Dream Tech’s online dose-response modeling system,” he notes in a university statement in December 2017, “also provides probabilistic estimates for the most important quantities, which cannot be achieved by current dose-response modeling systems.”

The NCATS award was made under NIH’s Small Business Technology Transfer or STTR grant program. STTR grants are funds set aside from the agency’s research budget for collaborations between small businesses and academic or not-for-profit research labs. In STTR and related Small Business Innovation Research, or SBIR grants awarded to small businesses alone, the first phase of a typical project establishes its technical and commercial feasibility — as is the case with this grant — and if successful, can be extended in a second phase to develop the technology into working prototypes or for clinical trials.

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