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Review
. 2018 Feb 15:395:1-8.
doi: 10.1016/j.tox.2017.12.007. Epub 2017 Dec 21.

Incorporating population-level genetic variability within laboratory models in toxicology: From the individual to the population

Peter Dornbos  1 , John J LaPres  2
Affiliations
Review

Incorporating population-level genetic variability within laboratory models in toxicology: From the individual to the population

Peter Dornbos et al. Toxicology. .

Abstract

Humans respond to chemical exposures differently due to many factors, such as previous and concurrent stressors, age, sex, and genetic background. The vast majority of laboratory-based toxicology studies, however, have not considered the impact of population-level variability within dose-response relationships. The lack of data dealing with the influence of genetic diversity on the response to chemical exposure provides a difficult challenge for risk assessment as individuals within the population will display a wide-range of responses following toxicant challenge. Notably, the genetic background of individuals plays a major role in the variability seen in a population-level response to a drug or chemical and, thus, there is growing interest in including genetic diversity into laboratory-models. Here we outline several laboratory-based models that can be used to assay the influence of genetic variability on an individual's response to chemicals: 1) genetically-diverse cell lines, 2) human primary cells, 3) and genetically-diverse mouse panels. We also provide a succinct review for several seminal studies to highlight the capability, feasibility, and power of each of these models. This article is intended to highlight the need to include population-level genetic diversity into toxicological study designs via laboratory-based models with the goal to provide and supplement evidence in assessing the risk posed by chemicals to the human population. As such, incorporation of genetic variability will positively impact human-based risk assessment and provide empirical data to aid and influence decision-making processes in relation to chemical exposures.

Keywords: Genetic variability; Population-Based models; Risk assessment.

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Conflict of interest statement

4. Conflicts of Interest.

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1. The National Research Council’s (NRC) Low-Dose Linearity Hypothesis
As published in a report in 2009 call “Science and Decisions: Advancing Risk Assessment,” the NRC suggest that, in considering population-level genetic variability, the low-dose region of non-cancer dose-response relationships will linearize. As such, the NRC recommends that there are no safe exposures of chemicals that induced adverse, non-cancer endpoints.
See this image and copyright information in PMC

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