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Analyzing high-throughput assay data to advance the rapid screening of environmental chemicals for human reproductive toxicity.
Varshavsky, Julia R; Lam, Juleen; Cooper, Courtney; Allard, Patrick; Fung, Jennifer; Oke, Ashwini; Kumar, Ravinder; Robinson, Joshua F; Woodruff, Tracey J.
Afiliación
  • Varshavsky JR; Department of Health Sciences and Department of Civil and Environmental Engineering, Northeastern University, Boston, MA.
  • Lam J; Department of Public Health, California State University, East Bay, Hayward, CA, USA.
  • Cooper C; University of California, San Francisco (UCSF), Program on Reproductive Health and the Environment, Department of Obstetrics, Gynecology and Reproductive Sciences, San Francisco, CA, USA.
  • Allard P; Institute for Society and Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA, USA.
  • Fung J; Center for Reproductive Sciences and Department of Obstetrics, Gynecology & Reproductive Sciences, UCSF, San Francisco, CA, USA.
  • Oke A; Center for Reproductive Sciences and Department of Obstetrics, Gynecology & Reproductive Sciences, UCSF, San Francisco, CA, USA.
  • Kumar R; Center for Reproductive Sciences and Department of Obstetrics, Gynecology & Reproductive Sciences, UCSF, San Francisco, CA, USA.
  • Robinson JF; University of California, San Francisco (UCSF), Program on Reproductive Health and the Environment, Department of Obstetrics, Gynecology and Reproductive Sciences, San Francisco, CA, USA.
  • Woodruff TJ; Center for Reproductive Sciences and Department of Obstetrics, Gynecology & Reproductive Sciences, UCSF, San Francisco, CA, USA.
bioRxiv ; 2024 May 22.
Article en En | MEDLINE | ID: mdl-38826231
ABSTRACT
While high-throughput (HTP) assays have been proposed as platforms to rapidly assess reproductive toxicity, there is currently a lack of established assays that specifically address germline development/function and fertility. We assessed the applicability domains of yeast (S. cerevisiae) and nematode (C. elegans) HTP assays in toxicity screening of 124 environmental chemicals, determining their agreement in identifying toxicants and their concordance with reproductive toxicity in vivo. We integrated data generated in the two models and compared results using a streamlined, semi-automated benchmark dose (BMD) modeling approach. We then extracted and modeled relevant mammalian in vivo data available for the matching chemicals included in the Toxicological Reference Database (ToxRefDB). We ranked potencies of common compounds using the BMD and evaluated correlation between the datasets using Pearson and Spearman correlation coefficients. We found moderate to good correlation across the three data sets, with r = 0.48 (95% CI 0.28-1.00, p<0.001) and rs = 0.40 (p=0.002) for the parametric and rank order correlations between the HTP BMDs; r = 0.95 (95% CI 0.76-1.00, p=0.0005) and rs = 0.89 (p=0.006) between the yeast assay and ToxRefDB BMDs; and r = 0.81 (95% CI 0.28-1.00, p=0.014) and rs = 0.75 (p=0.033) between the worm assay and ToxRefDB BMDs. Our findings underscore the potential of these HTP assays to identify environmental chemicals that exhibit reproductive toxicity. Integrating these HTP datasets into mammalian in vivo prediction models using machine learning methods could further enhance the predictive value of these assays in future rapid screening efforts.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: BioRxiv Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: BioRxiv Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos