Benchmarking of BMDC assay and related QSAR study for identifying sensitizing chemicals.

The Bone-Marrow derived Dendritic Cell (BMDC) test is a promising assay for identifying sensitizing chemicals based on the 3Rs (Replace, Reduce, Refine) principle. This study expanded the BMDC benchmarking to various in vitro, in chemico, and in silico assays targeting different key events (KE) in the skin sensitization pathway, using common substances datasets. Additionally, a Quantitative Structure-Activity Relationship (QSAR) model was developed to predict the BMDC test outcomes for sensitizing or non-sensitizing chemicals. The modeling workflow involved ISIDA (In Silico Design and Data Analysis) molecular fragment descriptors and the SVM (Support Vector Machine) machine-learning method. The BMDC model's performance was at least comparable to that of all ECVAM-validated models regardless of the KE considered. Compared with other tests targeting KE3, related to dendritic cell activation, BMDC assay was shown to have higher balanced accuracy and sensitivity concerning both the Local Lymph Node Assay (LLNA) and human labels, providing additional evidence for its reliability. The consensus QSAR model exhibits promising results, correlating well with observed sensitization potential. Integrated into a publicly available web service, the BMDC-based QSAR model may serve as a cost-effective and rapid alternative to lab experiments, providing preliminary screening for sensitization potential, compound prioritization, optimization and risk assessment.

An update of skin permeability data based on a systematic review of recent research.

The cutaneous absorption parameters of xenobiotics are crucial for the development of drugs and cosmetics, as well as for assessing environmental and occupational chemical risks. Despite the great variability in the design of experimental conditions due to uncertain international guidelines, datasets like HuskinDB have been created to report skin absorption endpoints. This review updates available skin permeability data by rigorously compiling research published between 2012 and 2021. Inclusion and exclusion criteria have been selected to build the most harmonized and reusable dataset possible. The Generative Topographic Mapping method was applied to the present dataset and compared to HuskinDB to monitor the progress in skin permeability research and locate chemotypes of particular concern. The open-source dataset (SkinPiX) includes steady-state flux, maximum flux, lag time and permeability coefficient results for the substances tested, as well as relevant information on experimental parameters that can impact the data. It can be used to extract subsets of data for comparisons and to build predictive models.

Kinetic solubility: Experimental and machine-learning modeling perspectives.

Kinetic aqueous or buffer solubility is important parameter measuring suitability of compounds for high throughput assays in early drug discovery while thermodynamic solubility is reserved for later stages of drug discovery and development. Kinetic solubility is also considered to have low inter-laboratory reproducibility because of its sensitivity to protocol parameters [1]. Presumably, this is why little efforts have been put to build QSPR models for kinetic in comparison to thermodynamic aqueous solubility. Here, we investigate the reproducibility and modelability of kinetic solubility assays. We first analyzed the relationship between kinetic and thermodynamic solubility data, and then examined the consistency of data from different kinetic assays. In this contribution, we report differences between kinetic and thermodynamic solubility data that are consistent with those reported by others [1, 2] and good agreement between data from different kinetic solubility campaigns in contrast to general expectations. The latter is confirmed by achieving high performing QSPR models trained on merged kinetic solubility datasets. The poor performance of QSPR model trained on thermodynamic solubility when applied to kinetic solubility dataset reinforces the conclusion that kinetic and thermodynamic solubilities do not correlate: one cannot be used as an ersatz for the other. This encourages for building predictive models for kinetic solubility. The kinetic solubility QSPR model developed in this study is freely accessible through the Predictor web service of the Laboratory of Chemoinformatics (https://chematlas.chimie.unistra.fr/cgi-bin/predictor2.cgi).

DMSO Solubility Assessment for Fragment-Based Screening.

In this paper, we report comprehensive experimental and chemoinformatics analyses of the solubility of small organic molecules ("fragments") in dimethyl sulfoxide (DMSO) in the context of their ability to be tested in screening experiments. Here, DMSO solubility of 939 fragments has been measured experimentally using an NMR technique. A Support Vector Classification model was built on the obtained data using the ISIDA fragment descriptors. The analysis revealed 34 outliers: experimental issues were retrospectively identified for 28 of them. The updated model performs well in 5-fold cross-validation (balanced accuracy = 0.78). The datasets are available on the Zenodo platform (DOI:10.5281/zenodo.4767511) and the model is available on the website of the Laboratory of Chemoinformatics.