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Experts list 30 questions to assess read-across uncertainty

Concept - Data ©Julien Eichinger

Computational toxicologists have devised a list of 30 questions to assess the level of uncertainty in read-across cases, which predict toxicity based on a chemical's similarity to other data-rich compounds.

Although read-across is often used to fill data gaps, it is widely agreed that boosting confidence in predictions would improve regulatory acceptance. Different levels of uncertainty fit different regulatory needs. For example, acceptable levels are lower for risk assessment purposes than for prioritisation and screening chemicals.

Terry Schultz from the University of Tennessee, US, Andrea-Nicole Richarz from the European Commission's Joint Research Centre (JRC) and Mark Cronin from Liverpool John Moores University, UK, set out to review uncertainty issues for repeated dose toxicity.

They assessed six case studies for alkanols, esters, and alcohols, all of which related to reading across a 'no observed adverse effect' level. "This is considered to be one of the most challenging applications of read-across," the researchers wrote in the journal Computational Toxicology.

The team defined four main categories of uncertainty for read-acoss. These link to:

  • regulatory use of the read-across prediction;
  • quality and relevance of the data being read across;
  • arguments for read-across; and
  • similarity justification.

The 30 questions spread across each of the categories. Trying out the questions on the six case studies showed that uncertainty is often associated with data quality. Understanding the chemical and biological mechanisms that result in toxicity in also key to assessing uncertainty.

Meanwhile, confidence can also be enhanced by using appropriate toxicokinetic properties such as absorption, distribution, metabolism and excretion (ADME) information, they suggest.

Overall, the team reports that analysing read-across predictions using the questions is a "rapid and efficient" way to determine and analyse uncertainties.

"Greater understanding of uncertainty through schemes such as this will assist regulatory acceptance," Professor Cronin said.

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