Understanding and overcoming the technical challenges in using in silico predictions in regulatory decisions of complex toxicological endpoints - A pesticide perspective for regulatory toxicologists with a focus on machine learning models

Advancements in the Developmental Zebrafish Model for Predictive Human Toxicology

The rapid assessment of chemical hazards to human health, with reduced reliance on mammalian testing, is essential in the 21st century. Early life stage zebrafish have emerged as a leading model in the field due to their amenability to high throughput developmental toxicity testing while retaining the benefits of using a whole vertebrate organism with high homology with humans. Zebrafish are particularly well suited for a variety of study areas that are more challenging in other vertebrate model systems including microbiome work, transgenerational studies, gene-environment interactions, molecular responses, and mechanisms of action. The high volume of data generated from zebrafish screening studies is highly valuable for QSAR modeling and dose modeling for use in predictive hazard assessment. Recent advancements and challenges in using early life stage zebrafish for predictive human toxicology are reviewed.

From Pipeline to Plant Protection Products: Using New Approach Methodologies (NAMs) in Agrochemical Safety Assessment

The human population will be approximately 9.7 billion by 2050, and food security has been identified as one of the key issues facing the global population. Agrochemicals are an important tool available to farmers that enable high crop yields and continued access to healthy foods, but the average new agrochemical active ingredient takes more than ten years, 350 million dollars, and 20,000 animals to develop and register. The time, monetary, and animal costs incentivize the use of New Approach Methodologies (NAMs) in early-stage screening to prioritize chemical candidates. This review outlines NAMs that are currently available or can be adapted for use in early-stage screening agrochemical programs. It covers new in vitro screens that are on the horizon in key areas of regulatory concern. Overall, early-stage screening with NAMs enables the prioritization of development for agrochemicals without human and environmental health concerns through a more directed, agile, and iterative development program before animal-based regulatory testing is even considered.

Systematic approaches to machine learning models for predicting pesticide toxicity

Pesticides play an important role in modern agriculture by protecting crops from pests and diseases. However, the negative consequences of pesticides, such as environmental contamination and adverse effects on human and ecological health, underscore the importance of accurate toxicity predictions. To address this issue, artificial intelligence models have emerged as valuable methods for predicting the toxicity of organic compounds. In this review article, we explore the application of machine learning (ML) for pesticide toxicity prediction. This review provides a detailed summary of recent developments, prediction models, and datasets used for pesticide toxicity prediction. In this analysis, we compared the results of several algorithms that predict the harmfulness of various classes of pesticides. Furthermore, this review article identified emerging trends and areas for future direction, showcasing the transformative potential of machine learning in promoting safer pesticide usage and sustainable agriculture.

Difficulties in translating in vitro hazards of local acute irritants to relevant human risk: Learnings from Captan and Folpet

In order to assess the regulatory value of New Approach Methodologies (NAMs), authors should provide their opinion on the physiological and exposure relevance of observed in vitro effects for correlation with predicted in vivo effects. Further, peer-reviewers should be encouraged to request such information during review. This is critical to scientifically transition to animal-free, reliable, robust and -- most importantly -- relevant regulatory toxicology and risk assessment approaches. Recently published studies using NAMs for the fungicides Captan and Folpet illustrate the difficulties and limitations of applying NAMs to adequately assess the toxicological relevance of these substances.

Practical guidance to evaluate in vitro dermal absorption studies for pesticide registration: An industry perspective

While there are some regulatory assessment criteria available on how to generally evaluate dermal absorption (DA) studies for risk assessment purposes, practical guidance and examples are lacking. The current manuscript highlights the challenges in interpretating data from in vitro assays and proposes holistic data-based assessment strategies from an industry perspective. Inflexible decision criteria may be inadequate for real data and may lead to irrelevant DA estimates. We recommend the use of mean values for reasonably conservative DA estimates from in vitro studies. In cases where additional conservatism is needed, e.g., due to non-robust data and acute exposure scenarios, the upper 95% confidence interval of the mean may be appropriate. It is critical to review the data for potential outliers and we provide some example cases and strategies to identify aberrant responses. Some regional regulatory authorities require the evaluation of stratum corneum (SC) residue, but here, as a very simple pro-rata approach, we propose to review whether the predicted post 24-h absorption flux exceeds the predicted elimination flux by desquamation because otherwise it is not possible for the SC residue to contribute to systemic dose. Overall, the adjustment of DA estimates due to mass balance (normalization) is not recommended.