Reproductive toxicology: keeping up with our changing world.

Front Toxicol

Department of Veterinary, Clinical and Life Sciences, College of Veterinary Medicine, Utah State University, Logan, UT, United States.

Published: October 2024

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Article Abstract

Reproductive toxicology testing is essential to safeguard public health of current and future generations. Traditional toxicological testing of male reproduction has focused on evaluating substances for acute toxicity to the reproductive system, with fertility assessment as a main endpoint and infertility a main adverse outcome. Newer studies in the last few decades have significantly widened our understanding of what represents an adverse event in reproductive toxicology, and thus changed our perspective of what constitutes a reproductive toxicant, such as endocrine disrupting chemicals that affect fertility and offspring health in an intergenerational manner. Besides infertility or congenital abnormalities, adverse outcomes can present as increased likelihood for various health problems in offspring, including metabolic syndrome, neurodevelopmental problems like autism and increased cancer predisposition, among others. To enable toxicologic studies to accurately represent the population, toxicologic testing designs need to model changing population characteristics and exposure circumstances. Current trends of increasing importance in human reproduction include increased paternal age, with an associated decline of nicotinamide adenine dinucleotide (NAD), and a higher prevalence of obesity, both of which are factors that toxicological testing study design should account for. In this perspective article, we highlighted some limitations of standard testing protocols, the need for expanding the assessed reproductive endpoint by including genetic and epigenetic sperm parameters, and the potential of recent developments, including mixture testing, novel animal models, systems like organoids, multigenerational testing protocols, as well as modelling, machine learning and artificial intelligence.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11502475PMC
http://dx.doi.org/10.3389/ftox.2024.1456687DOI Listing

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