Characterizing population variability, including identifying susceptible populations and quantifying their increased susceptibility, is an important aspect of chemical risk assessment, but one that is challenging with traditional experimental models and risk assessment methods. New models and methods to address population variability can be used to advance the human health assessments of chemicals in three key areas. First, with respect to hazard identification, evaluating toxicity using population-based in vitro and in vivo models can potentially reduce both false positive and false negative signals. Second, with respect to evaluating mechanisms of toxicity, enhanced ability to do genetic mapping using these models allows for the identification of key biological pathways and mechanisms that may be involved in toxicity and/or susceptibility. Third, with respect to dose–response assessment, population-based toxicity data can serve as a surrogate for human variability, and thus be used to quantitatively estimate the degree of human toxicokinetic/toxicodynamic variability and thereby increase confidence in setting health-protective exposure limits. A number of case studies have been published that demonstrate the potential opportunities for improving risk assessment and decision-making, and include studies using Collaborative Cross and Diversity Outbred mice, as well as populations of human cell lines from the 1000 Genomes project. Key challenges include the need to apply more sophisticated computational and statistical models analyzing population-based toxicity data, and the need to integrate these more complex analyses into risk assessments and decision-making.
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This paper was supported, in part, by an NIH grant #P42 ES027704. Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the NIH. Further, the NIH does not endorse the purchase of any commercial products or services mentioned in this paper.
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On behalf of all the authors, the corresponding author states that there is no conflict of interest.
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Chiu, W.A., Rusyn, I. Advancing chemical risk assessment decision-making with population variability data: challenges and opportunities. Mamm Genome 29, 182–189 (2018). https://doi.org/10.1007/s00335-017-9731-6
- Dose-response Assessment
- Human Variability
- Collaborative Cross (CC)
- Hazard Identification
- Hybrid Mouse Diversity Panel (HMDP)