Novel QSAR Models for Molecular Initiating Event Modeling in Two Intersecting Adverse Outcome Pathways Based Pulmonary Fibrosis Prediction for Biocidal Mixtures

Multivariate QSAR, similarity search and ADMET studies based in a set of methylamine derivatives described as dopamine transporter inhibitors

The dopamine transporter (DAT), responsible for the regulation of dopaminergic neurotransmission, is implicated in the etiology of several neuropsychiatric disorders which, in turn, have contributed to high rates of disability and numerous deaths in recent years, significantly impacting the global health system. Although the research for new drugs for the treatment of neuropsychiatric disorders has evolved in recent years, the availability of DAT-selective drugs that do not generate the same psychostimulant effects observed in drugs of abuse remains scarce. Therefore, we performed a QSAR study based on a dataset of 36 methylamine derivatives described as DAT inhibitors. The model was obtained based only in descriptors derived from 2D structures, and it was validated and generated satisfactory results considering the metrics used for internal and external validation. Subsequently, a virtual screening step also based on 2D similarity was performed, where it was possible to identify a total of 1157 compounds. After a series of reductions of the set using toxicity filters, applicability domain evaluation, and pharmacokinetic properties in silico assessment, seven hit compounds were selected as the most promising to be used, in future studies, as new scaffolds for the development of new DAT inhibitors.

Investigating fatty liver disease-associated adverse outcome pathways of perfluorooctane sulfonate using a systems toxicology approach

Adverse outcome pathway (AOP) frameworks help elucidate toxic mechanisms and support chemical regulation. AOPs link a molecular initiating event (MIE), key events (KEs), and an adverse outcome by key event relationships (KERs), which assess the biological plausibility, essentiality, and empirical evidence involved. Perfluorooctane sulfonate (PFOS), a hazardous poly-fluoroalkyl substance, demonstrates hepatotoxicity in rodents. PFOS may induce fatty liver disease (FLD) in humans; however, the underlying mechanism remains unclear. In this study, we evaluated the toxic mechanisms of PFOS-associated FLD by developing an AOP using publicly available data. We identified MIE and KEs by performing GO enrichment analysis on PFOS- and FLD-associated target genes collected from public databases. The MIEs and KEs were then prioritized by PFOS-gene-phenotype-FLD networks, AOP-helpFinder, and KEGG pathway analyses. Following a comprehensive literature review, an AOP was then developed. Finally, six KEs for the AOP of FLD were identified. This AOP indicated that toxicological processes initiated by SIRT1 inhibition led to SREBP-1c activation, de novo fatty acid synthesis, and fatty acid and triglyceride accumulation, culminating in liver steatosis. Our study provides insights into the toxic mechanism of PFOS-induced FLD and suggests approaches to assessing the risk of toxic chemicals.

Representing and describing nanomaterials in predictive nanoinformatics

Engineered nanomaterials (ENMs) enable new and enhanced products and devices in which matter can be controlled at a near-atomic scale (in the range of 1 to 100 nm). However, the unique nanoscale properties that make ENMs attractive may result in as yet poorly known risks to human health and the environment. Thus, new ENMs should be designed in line with the idea of safe-and-sustainable-by-design (SSbD). The biological activity of ENMs is closely related to their physicochemical characteristics, changes in these characteristics may therefore cause changes in the ENMs activity. In this sense, a set of physicochemical characteristics (for example, chemical composition, crystal structure, size, shape, surface structure) creates a unique 'representation' of a given ENM. The usability of these characteristics or nanomaterial descriptors (nanodescriptors) in nanoinformatics methods such as quantitative structure-activity/property relationship (QSAR/QSPR) models, provides exciting opportunities to optimize ENMs at the design stage by improving their functionality and minimizing unforeseen health/environmental hazards. A computational screening of possible versions of novel ENMs would return optimal nanostructures and manage ('design out') hazardous features at the earliest possible manufacturing step. Safe adoption of ENMs on a vast scale will depend on the successful integration of the entire bulk of nanodescriptors extracted experimentally with data from theoretical and computational models. This Review discusses directions for developing appropriate nanomaterial representations and related nanodescriptors to enhance the reliability of computational modelling utilized in designing safer and more sustainable ENMs.

Multi-step structure-activity relationship screening efficiently predicts diverse PPARγ antagonists

In discovering the potential antagonist of peroxisome proliferator-activated receptor gamma (PPARγ), the structure-activity relationship (SAR) is a useful in silico method. However, it is difficult for conventional SAR approaches to predict the activities of antagonists owing to the large structural diversity of antagonistic compounds. This study provides evidence that multi-step SAR screening is applicable for predicting PPARγ antagonists by combining different complementary methodologies. We constructed three models: read-across-like SAR, docking-simulation-interpreting SAR, and deep-learning-based SAR. To provide user-customized prediction results, our multi-step SAR screening model combined the three SAR models in a stepwise manner, which subdivided them according to potential levels of the PPARγ antagonist. The read-across-like SAR, which considered specific antagonist scaffolds, revealed the highest positive predictive value (PPV). The docking-simulation-interpreting SAR, which considered the molecular surface features, revealed high statistics for the PPV and the true-positive rate (TPR). The deep-learning-based SAR showed the highest TPR at the last classification step. This multi-step SAR screening covered the antagonists of high reliability provided by a read-across-like SAR, as well as the antagonists of diverse scaffolds provided by docking-simulation-interpreting SAR and deep-learning-based SAR. Therefore, to predict PPARγ antagonists, multi-step SAR screening could be as a useful tool.