Human Exposure and Computational Modeling of Persistent Organic Pollutants (POPs)
A special issue of Toxics (ISSN 2305-6304). This special issue belongs to the section "Novel Methods in Toxicology Research".
Deadline for manuscript submissions: 10 November 2025 | Viewed by 44
Special Issue Editors
Interests: Physiologically based Pharmacokinetic Modeling (PBPK); Systems Biology Modelling (SB); Machine learning (ML); in vitro; human biomonitoring
Interests: system toxicology; biostatistics; big data and data analytics; exposure science; human biomonitoring; epidemiology; environmental and human-health risk assessment; internal dosimetry modeling (PBPK); climate change linked risk assessment
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Persistent Organic Pollutants (PoPs) are toxic chemicals including PCBs, PFAS, and dioxins. These persist in the environment and bioaccumulate in human tissues, causing adverse health effects like cancer, immunosuppression, neurotoxicity, etc. Human exposure occurs mostly through air, food, water, and consumer products, often being detected in blood, urine, or other biological matrices through human biomonitoring (HBM) studies. For improving exposure and risk assessment of PoPs, new approach methodologies (NAMs) can guide regulatory decisions by integrating HBM data and computational models. Computational modeling, like physiologically based pharmacokinetic models (PBPK), integrate HBM and in vitro data to predict the toxicokinetic of the chemical. Further, combining the input from PBPK and other computational models support the quantification of adverse outcome pathways (AOPs), which can help in mapping molecular initiating events such as receptor binding to adverse outcomes, providing dose–response relationships for risk assessment. NAMs, with high throughput in vitro and machine learning based QSAR models, are also increasingly used for predicting toxicity reducing reliance on animal studies. However, challenges still exist, like data gaps for emerging PoPs, population variability, and huge uncertainty when combining data from multiple platforms like transcriptomics, metabolomics, lipidomics, etc. The ongoing advancement in NAMs, the harmonization of data, and predictive modeling integration are crucial steps for improving risk assessment frameworks and protecting long-term health risks.
Dr. Deepika Deepika
Dr. Vikas Kumar
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
- human biomonitoring
- computational modeling
- OMICS analysis
- risk assessment
- mechanism of action
- adverse outcome pathways
- human health predictive modeling machine learning new approach methodology
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.
