Toxicology Communications

Computational Toxicology for Exposure Science and Risk Assessment: From Mechanistic Modeling to Regulatory Translation

This Article Collection aims to showcase cutting-edge research at the intersection of computational toxicology, exposure assessment, and regulatory decision-making. Emphasis is placed on methods that bridge molecular-level information with real-world exposure data to support hazard identification, dose–response assessment, and risk characterization. Recent developments in physiologically based kinetic (PBK) modeling, quantitative structure–activity relationships (QSARs), read-across approaches, and artificial intelligence (AI) are transforming how toxicological evidence is generated, interpreted, and applied.

The collection welcomes interdisciplinary contributions that advance methodological innovation as well as applied studies demonstrating regulatory relevance. Submissions may draw from toxicology, exposure science, bioinformatics, environmental sciences, and data science, provided they contribute to a more predictive, transparent, and mechanistically informed assessment of chemical safety.

By bringing together leading and emerging research in computational toxicology, this Collection seeks to stimulate scientific dialogue, foster collaboration across disciplines, and support the translation of advanced computational methods into regulatory and public-health practice.

Computational toxicology has rapidly evolved into a cornerstone of modern risk assessment  and exposure science, driven by advances in high-performance computing, machine learning, and mechanistic modeling. Traditional toxicological approaches, while indispensable, are increasingly complemented by in silico methods capable of integrating complex exposure scenarios, biological pathways, and population-level variability. These approaches are particularly critical in the context of emerging chemicals, mixtures, and low-dose chronic exposures, where experimental data are often sparse or unavailable.

A particular focus of this Collection is the integration of computational approaches into next-generation risk assessments (NGRAs) framework. This includes the use of in (quantitative) vitro–in silico extrapolation (qIVIVE), adverse outcome pathway (AOP) networks, and exposure-driven prioritization strategies that align with international efforts to reduce animal testing while maintaining high standards of human health protection. Computational tools also play a pivotal role in addressing uncertainty, inter-individual variability, and cumulative exposure to chemical mixtures—key challenges faced by regulatory agencies worldwide.

Call for Papers: Computational Toxicology for Exposure and Risk Assessment

Toxicology Communications invites submissions to a special Article Collection on “Computational Toxicology for Exposure Science and Risk Assessment.” This Collection focuses on innovative computational approaches that enhance chemical hazard identification, exposure characterization, and risk assessment.

We welcome original research articles, methodological papers, reviews and perspectives addressing (but not limited to):

Key Topics

• Physiologically based kinetic (PBK) and toxicokinetic modeling
• Quantitate-In vitro–in silico extrapolation (qIVIVE) for human exposure assessment
• Machine learning and AI applications in toxicology and exposure science
• QSAR, read-across, and chemical grouping strategies
• Adverse Outcome Pathway (AOP) modeling and network approaches
• Computational approaches for mixture toxicity and cumulative risk
• Exposure-driven chemical prioritization and screening
• Integration of omics data into computational toxicology
• Uncertainty and variability analysis in computational risk assessment
• Regulatory applications of computational toxicology tools

Keywords:

1. qIVIVE
2. Regulatory Toxicology
3. Adverse Outcome Pathway (AOP)
4. Machine learning and AI
5. QSAR

All manuscripts submitted to this Article Collection will undergo desk assessment and peer review if they can pass the desk assessments as part of our standard editorial process; the Guest Advisor for this Collection will not be handling the manuscripts (unless they are an Editorial Board member).

Please contact Catherine Teng at [email protected]  with any queries and discount codes regarding this Article Collection.