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.

GARD™skin and GARD™potency: A proof-of-concept study investigating applicability domain for agrochemical formulations.

Several New Approach Methodologies (NAMs) for hazard assessment of skin sensitisers have been formally validated. However, data regarding their applicability on certain product classes are limited. The purpose of this project was to provide initial evidence on the applicability domain of GARD™skin and GARD™potency for the product class of agrochemical formulations. For this proof of concept, 30 liquid and 12 solid agrochemical formulations were tested in GARDskin for hazard predictions. Formulations predicted as sensitisers were further evaluated in the GARDpotency assay to determine GHS skin sensitisation category. The selected formulations were of product types, efficacy groups and sensitisation hazard classes representative of the industry's products. The performance of GARDskin was estimated by comparing results to existing in vivo animal data. The overall accuracy, sensitivity, and specificity were 76.2% (32/42), 85.0% (17/20), and 68.2% (15/22), respectively, with the predictivity for liquid formulations being slightly higher compared to the solid formulations. GARDpotency correctly subcategorized 14 out of the 17 correctly predicted sensitisers. Lack of concordance was justifiable by compositional or borderline response analysis. In conclusion, GARDskin and GARDpotency showed satisfactory performance in this initial proof-of-concept study, which supports consideration of agrochemical formulations being within the applicability domain of the test methods.

Use of guinea pig data to obtain starting points for skin sensitisation risk assessment - A commentary.

The increasing drive to understand the likelihood of skin sensitisation from plant protection products (PPPs) in workers and the general public has resulted in recent initiatives to establish a quantitative risk assessment (QRA) methodology applicable to these products and their exposure scenarios. The effective evaluation of skin sensitising substances requires not only the identification of that toxicological hazard, but also determination of relative sensitising potency. Typically, this has been achieved by interpretation of local lymph node assay (LLNA) dose response data, delivering what is known as the EC3 value. This permitted regulatory division of skin sensitisers into defined potency sub-categories, but more importantly enabled derivation of a no expected sensitisation induction level (NESIL) as the point of departure for QRA. However, for many existing substances there is no LLNA data, only older guinea pig results exist. To avoid additional (in vivo) testing, an approach has been outlined to employ guinea pig data and existing regulatory guidelines on the determination of potency sub-categorisation to provide a guinea pig based NESIL. The approach adopts a conservative extrapolation from LLNA NESIL benchmarks to deliver points of departure as the basis for the type of QRA process already in successful use by other industries.