Application of In Vitro Toxicity Evaluation Models for Environmental Pollutants

A special issue of Toxics (ISSN 2305-6304).

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1457

Special Issue Editors


E-Mail Website
Guest Editor
NCCU-RTI Center for Applied Research in Environmental Sciences (CARES), RTI International, Durham, NC 27707, USA
Interests: environmental contaminants; toxicology, molecular biology
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Center for Food Safety & Applied Nutrition, US Food and Drug Administration, College Park, MD 20740, USA
Interests: predictive toxicology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Environmental pollution is a significant source of contaminants that impacts both human health and ecosystems. This Special Issue will explore a variety of topics related to the effects of pollutants and their metabolites on human health, utilizing physiologically relevant in vitro models and exposure systems to predict human health outcomes. Considering the ubiquitous exposure to environmental pollutants such as PFASs and microplastics, there is a significant gap in our knowledge and understanding of related long-term health effects in humans. This Issue will provide insight into cutting-edge in vitro modeling in toxicology for the evaluation of environmental pollutants.

Dr. Leslimar Rios-Colon
Dr. Suzanne Compton Fitzpatrick
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Toxics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • in vitro techniques
  • environmental contaminants
  • new approach methodologies (NAMSs)
  • per- and polyfluoroalkyl substances (PFASs)
  • microplastics
  • pesticides
  • endocrine-disrupting chemicals
  • complex in vitro models

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

24 pages, 2088 KB  
Article
Comparative Analysis of Chemical Distribution Models for Quantitative In Vitro to In Vivo Extrapolation
by Hsing-Chieh Lin, Lucie C. Ford, Ivan Rusyn and Weihsueh A. Chiu
Toxics 2025, 13(6), 439; https://doi.org/10.3390/toxics13060439 - 26 May 2025
Cited by 1 | Viewed by 1091
Abstract
Quantitative in vitro to in vivo extrapolation (QIVIVE) utilizes in vitro data to predict in vivo toxicity. However, there may be differences between reported nominal concentrations and the biologically effective free concentrations in media or cells. This study evaluated the performance of four [...] Read more.
Quantitative in vitro to in vivo extrapolation (QIVIVE) utilizes in vitro data to predict in vivo toxicity. However, there may be differences between reported nominal concentrations and the biologically effective free concentrations in media or cells. This study evaluated the performance of four in vitro mass balance models for predicting free media or cellular concentrations. Comparing model predictions to experimentally measured values for a wide range of chemicals and test systems, we found that predictions of media concentrations were more accurate than those for cells, and that the Armitage model had slightly better performance overall. Through sensitivity analyses, we found that chemical property-related parameters were most influential for media predictions, while cell-related parameters were also important for cellular predictions. Assessing the impact of these models on QIVIVE accuracy for a small dataset of 15 chemicals with both in vitro and regulatory in vivo points-of-departure, we found that incorporating in vitro and in vivo bioavailability resulted in at best modest improvements to in vitro–in vivo concordance. Based on these results, we conclude that a reasonable first-line approach for incorporating in vitro bioavailability into QIVIVE would be to use the Armitage model to predict media concentrations, while prioritizing accurate chemical property data as input parameters. Full article
Show Figures

Graphical abstract

Back to TopTop