Applicability of in silico tools for the prediction of dermal absorption for pesticides

Abstract

Based on the “Human in vitro dermal absorption datasets” published as supporting information to the revised EFSA Guidance on Dermal Absorption, in silico models for prediction of absorption across the skin have been evaluated. For this evaluation, a systematic literature search and review was performed, identifying 288 publications describing mathematical models for prediction of dermal absorption. Eleven models potentially relevant to the regulatory assessment of pesticides and which cover a range of approaches were selected for in depth evaluation. This included three mixture models taking into account physicochemical properties of the co‐formulants such as polar surface area, hydrogen bonding or octanol‐water partition coefficients. Additional data on the pesticidal active substances and information on the composition of some of the formulations covered in the dermal absorption dataset were gathered, as these were required as input parameters for the selected models. The models were implemented with settings reflecting as much as possible realistic exposure scenarios and the experimental conditions under which the measured data were obtained. As the majority of the models predicted either maximum flux or the permeation coefficient, further combination with a model achieving translation into percentage absorption was required. This was done with and without consideration of the lag time. Only one model directly predicted percentage dermal absorption, which was a spreadsheet‐based single substance model taking into consideration several skin parameters, experimental conditions, various physicochemical properties of the active substance and the type of vehicle. Statistical analysis of model predictions revealed overall low concordance with measured values, thereby limiting regulatory acceptance. Additional analysis was performed on the results of two mixture models and the above mentioned complex single substance model which showed moderate correlation between predicted and measured data. Options to improve model performance were discussed and Bayesian random effects modelling was explored to adjust predicted percentage dermal absorption to measured data as was model combination. When taking into account observed uncertainties of predictions, one of the models may provide a Tier 2 tool to estimate dermal absorption value in the absence of adequate experimental data when the predicted values are in the range of 10 to 70%. However, further work is needed to better understand the effect of co‐formulants on dermal absorption of pesticides and to improve model predicitivity.

In Vitro Human Dermal Absorption Studies on Pesticides in Complex Mixtures: Investigation of Guidance Criteria and Possible Impact Parameters

“…Kneuer et al [ 22 ] investigated available in silico models for the prediction of absorption across the skin. For this, suitable models potentially relevant to the regulatory assessment of pesticides were selected from the literature to compare predicted dermal absorption values versus measured data of plant protection products using a large dataset of in vitro dermal absorption studies.…”

“…Nonetheless, the specific combination of co-formulants was not part of the selected models. Kneuer et al [ 22 ] highlighted that one model is worthy for further investigations to estimate the dermal absorption value in the absence of adequate experimental data, but it was noted that the influence of physico-chemical properties had to be investigated further. Furthermore, none of the selected models took into account the possible co-formulant’s effect on the stratum corneum.…”

Integrated Translation Framework for Endocrine Disruptors in the area of Computational Toxicology

“…7 Recent, European Food Safety Authority (EFSA) projects on in silico methods for evaluating dermal absorption of pesticides and protein toxicity confirmed that the computational approach has been widely accepted. 8 In parallel to this, the recent development of systems biology and multiscale modelling has increased the understanding of the physiological endogenous pathway and effects of toxicants on the temporal behaviour of the cell, tissue, and whole-organ systems.…”