About this Research Topic
Risk assessment relies on the experimental data from mammalian studies to quantify the effect under the given exposure conditions. Animal studies are often expensive and time-consuming, as thousands of chemicals are produced and used in our daily life. Our understanding of toxicities for numerous chemicals remains a critical data gap in health risk assessment and chemical prioritization. High-throughput bioactivity data generated from new alternative approaches, in combination with computational tools, has created a great opportunity to fill in this knowledge gap. However, scientists have faced several challenges when integrating high-throughput data with computational tools and predictive models.
There is a rapid growth in knowledge of applications combining computational approach in biology and other related fields. These approaches are expected to help researchers accelerate the process of identifying chemicals that might be harmful and should be prioritized in the advanced investigation and regulation. This Research Topic aims to collect research from scientists working in different fields (e.g., academia, government, or pharmaceutical company) with the same interest. This is to shed light on the novel development of computational tools and workflows that can be used in toxicity prediction and risk assessment.
The Research Topic will bring together the modern high-throughput toxicity data and advanced modeling approach in toxicology and pharmacology. We would like to focus on non-animal approaches that utilize in vitro and in silico systems and further inform regulatory decision making or risk assessment. Aside from studying the toxicity effect of the individual chemicals, this Topic will focus on the adverse effect of the complex mixtures, which may cause unexpected outcomes despite having a safe chemical exposure level theoretically. This Topic includes but is not limited to some modern issue in computational modeling:
- Pharmacokinetic/pharmacodynamic (PK/PD) modeling
- Physiologically based pharmacokinetic (PBPK) modeling
- Quantitative in vitro to in vivo extrapolation
- Probabilistic risk assessment and chemical prioritization
- Database tool and open-source software
There is a rapid growth in knowledge of applications combining computational approach in biology and other related fields. These approaches are expected to help researchers accelerate the process of identifying chemicals that might be harmful and should be prioritized in the advanced investigation and regulation. This Research Topic aims to collect research from scientists working in different fields (e.g., academia, government, or pharmaceutical company) with the same interest. This is to shed light on the novel development of computational tools and workflows that can be used in toxicity prediction and risk assessment.
The Research Topic will bring together the modern high-throughput toxicity data and advanced modeling approach in toxicology and pharmacology. We would like to focus on non-animal approaches that utilize in vitro and in silico systems and further inform regulatory decision making or risk assessment. Aside from studying the toxicity effect of the individual chemicals, this Topic will focus on the adverse effect of the complex mixtures, which may cause unexpected outcomes despite having a safe chemical exposure level theoretically. This Topic includes but is not limited to some modern issue in computational modeling:
- Pharmacokinetic/pharmacodynamic (PK/PD) modeling
- Physiologically based pharmacokinetic (PBPK) modeling
- Quantitative in vitro to in vivo extrapolation
- Probabilistic risk assessment and chemical prioritization
- Database tool and open-source software
Keywords: Computational tools, Chemical risk assessment, in-vitro to in-vivo extrapolation, Quantitative systems toxicology, Mixture toxicity
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
