In silico prediction of dermal absorption of pesticides – an evaluation of selected models against results from in vitro testing. Issue 8 (3rd August 2019)

• Record Type: Journal Article
• Authors: Eleftheriadou, D.; Luette, S.; Kneuer, C.
• Is Part Of: SAR and QSAR in environmental research. Volume 30:Issue 8(2019)
• Page Start: 561
• Page End: 585
• Publication Date: 2019-08-03
• Subjects: Dermal absorption -- in silico model -- pesticides -- QSAR -- skin; Structure-activity relationships (Biochemistry) -- Periodicals; QSAR (Biochemistry) -- Periodicals; 572.4
• DOI: 10.1080/1062936X.2019.1644533
• Languages: English
• ISSNs: 1062-936X

Abstract

ABSTRACT: Current guidance for the estimation of dermal absorption (DA) of pesticides recommends the use of default values, read-across of information between formulations and in vitro testing. While QSARs exist to estimate percutaneous absorption, their use is currently not encouraged. Therefore, the potential of publicly available models for DA estimation was investigated based on data from 564 human in vitro DA experiments on pesticides. The classic Potts Guy model, the correction of Cleek Bunge for highly lipophilic chemicals, the mechanistic model of Mitragotri, and the COSMOS model were used to estimate the permeability coefficient kp . Different approaches were explored to calculate the percentage of external dose absorbed. IH SkinPerm was examined as stand-alone model. The models generally failed to accurately predict experimental values. For 30–40% of the predictions, there was overestimation by one order of magnitude. Three models underpredicted >10% of the cases, the remaining models <5%. DA of hydrophilic substances was typically underpredicted. Overprediction was more prominent for solid preparations and suspensions. The molecular weight, irritation potential and skin thickness did not correlate with the models' predictivity. Of the models investigated, IH SkinPerm performed best with 38% of the predictions within one order of magnitude and 2% underpredicted cases.