In silico prediction of dermal absorption of pesticides - an evaluation of selected models against results from in vitro testing

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

Pesticides must not pose unacceptable risks to human health, so risk assessments are conducted before products are authorised. Dermal exposure is often the main route of intake, so estimating realistic and trustworthy dermal absorption values is crucial for risk assessment. Although there are agreed test guidelines for in vitro dermal absorption studies, not every product is tested due to cost reasons. The present dataset consists of 945 individual in vitro experiments on the dermal absorption of human skin with 179 active substances of pesticides in 353 different mixtures, including concentrates and dilutions. The dataset was evaluated to identify the possible impacts of experimental conditions and physico-chemical properties on dermal absorption. The dataset was also analysed to assess the appropriateness of the pro rata correction for untested dilutions, and the set concentration cut-off to decide on the dilution status for choosing a default value on dermal absorption. The study found that the implementation of specific guidelines improved the harmonisation of study conduct, with support for approaches such as pro rata correction and default values. Further analysis of the specific co-formulants may identify influencing factors that may be more important than the experimental variables.

Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state

In this study, we use machine learning algorithms with QM-derived COSMO-RS descriptors, along with Morgan fingerprints, to predict the absolute solubility of drug-like compounds. The QM-derived descriptors account for the molecular properties of the solute, i.e., the solute-solute interactions in an artificial-liquid-state (super-cooled liquid), and the solute-solvent interactions in solution. We employ two main approaches to predict solubility: (i) a hypothetical pathway that involves melting the solute at room temperature T = T¯ ([Formula: see text]) and mixing the artificially liquid solute into the solvent ([Formula: see text]). In this approach [Formula: see text] is predicted using machine learning models, and the [Formula: see text] is obtained from COSMO-RS calculations; (ii) direct solubility prediction using machine learning algorithms. The models were trained on a large number of Bayer in-house compounds for which water solubility data is available at physiological pH of 6.5 and ambient temperature. We also evaluated our models using external datasets from a solubility challenge. Our models present great improvements compared to the absolute solubility prediction with the QSAR model for the artificial liquid state as implemented in the COSMOtherm software, for both in-house and external datasets. We are furthermore able to demonstrate the superiority of QM-derived descriptors compared to cheminformatics descriptors. We finally present low-cost alternative models using fragment-based COSMOquick calculations with only marginal reduction in the quality of predicted solubility.

In silico assessment of risks associated with pesticides exposure during pregnancy

Novel Quantitative Structure-Activity Relationship (QSAR) models of compounds' placenta (PL) permeability expressed as their log FM (fetus-to-mother blood concentration) values or binary PL1/0 (crossing/non-crossing) score were generated using a number of statistical tools: Multiple Linear Regression, Boosted Trees, Principal Component Analysis and Artificial Neural Networks, on the basis of molecular descriptors calculated by Mordred software and selected using Partial Least Squares (PLS) analysis. It was established that the most important predictor of both log FM and the binary PL1/0 score is Lipinski - a binary variable reflecting the compounds' ability to satisfy the criteria of drug-likeness according to the Lipinski's "Rule of 5". The quantitative (log FM) and qualitative (PL1/0) models of PL permeability were applied to 345 pesticides from different chemical families (triazines, carbamates, pyrethroids, organochlorine, organophosphorus and miscellaneous compounds). The ability of studied pesticides to cross the placenta was assessed; the basic physico-chemical parameters responsible for good or poor placenta transport of pesticides were identified and the relationships between the pesticides' PL permeability, blood-brain barrier (BBB) transfer and gastro-intestinal (GI) absorption were investigated. It was found (on the basis of logistic regression analysis) that the probability of a compound crossing the placenta (PL1) is inversely correlated with its lipophilicity and molar refractivity and positively correlated with the total count of oxygen and nitrogen atoms.

Semiquantitative sensitization safety assessment of extractable and leachables associated with parenteral pharmaceutical products

Extractable and leachables (E&Ls) associated with parenteral pharmaceutical products should be assessed for patient safety. One essential safety endpoint is local or systemic sensitization. However, there are no regulatory guidelines for quantitative sensitization safety assessment of E&Ls. A semiquantitative sensitization safety assessment workflow is developed to refine the sensitization safety assessment of E&Ls associated with parenteral pharmaceutical products. The workflow is composed of two sequential steps: local skin sensitization and systemic sensitization safety assessment. The local skin sensitization step has four tiers. The output from this step is the acceptable exposure level for local sensitization (AEL_ls) and this safety threshold can be used for local sensitization safety assessment. From the derived AEL_ls, the systemic sensitization safety assessment at step 2 proceeds in 2 tiers. The output from this workflow is the derivation of acceptable exposure level for systemic sensitization (AEL_ss). When the estimated human daily exposure (HDE) is compared with the AEL_ss, the margin of exposure is calculated to determine the sensitization safety of E&Ls following parenteral administration. The current work represents an initial effort to develop a scientifically robust process for sensitization safety assessment of E&Ls associated with parenteral pharmaceutical products.