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 13
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
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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
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Keywords
- human biomonitoring
- computational modeling
- OMICS analysis
- risk assessment
- mechanism of action
- adverse outcome pathways
- human health predictive modeling machine learning new approach methodology
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