Comprehensive benchmarking of computational tools for predicting toxicokinetic and physicochemical properties of chemicals.

Ensuring the safety of chemicals for environmental and human health involves assessing physicochemical (PC) and toxicokinetic (TK) properties, which are crucial for absorption, distribution, metabolism, excretion, and toxicity (ADMET). Computational methods play a vital role in predicting these properties, given the current trends in reducing experimental approaches, especially those that involve animal experimentation. In the present manuscript, twelve software tools implementing Quantitative Structure-Activity Relationship (QSAR) models were selected for the prediction of 17 relevant PC and TK properties.

Quantitative structure-activity relationships of chemical bioactivity toward proteins associated with molecular initiating events of organ-specific toxicity.

The adverse outcome pathway (AOP) concept has gained attention as a way to explore the mechanism of chemical toxicity. In this study, quantitative structure-activity relationship (QSAR) models were developed to predict compound activity toward protein targets relevant to molecular initiating events (MIE) upstream of organ-specific toxicities, namely liver steatosis, cholestasis, nephrotoxicity, neural tube closure defects, and cognitive functional defects. Utilizing bioactivity data from the ChEMBL 33 database, various machine learning algorithms, chemical features and methods to assess prediction reliability were compared and applied to develop robust models to predict compound activity.

Computational Strategies for Assessing Adverse Outcome Pathways: Hepatic Steatosis as a Case Study.

The evolving landscape of chemical risk assessment is increasingly focused on developing tiered, mechanistically driven approaches that avoid the use of animal experiments. In this context, adverse outcome pathways have gained importance for evaluating various types of chemical-induced toxicity. Using hepatic steatosis as a case study, this review explores the use of diverse computational techniques, such as structure-activity relationship models, quantitative structure-activity relationship models, read-across methods, omics data analysis, and structure-based approaches to fill data gaps within adverse outcome pathway networks.

A review on the structural characterization of nanomaterials for nano-QSAR models.

Quantitative structure-activity relationship (QSAR) models are routinely used to predict the properties and biological activity of chemicals to direct synthetic advances, perform massive screenings, and even to register new substances according to international regulations. Currently, nanoscale QSAR (nano-QSAR) models, adapting this methodology to predict the intrinsic features of nanomaterials (NMs) and quantitatively assess their risks, are blooming. One of the challenges is the characterization of the NMs.

Comparative Binding Study of Gliptins to Bacterial DPP4-like Enzymes for the Treatment of Type 2 Diabetes Mellitus (T2DM).

The role of the gut microbiota and its interplay with host metabolic health, particularly in the context of type 2 diabetes mellitus (T2DM) management, is garnering increasing attention. Dipeptidyl peptidase 4 (DPP4) inhibitors, commonly known as gliptins, constitute a class of drugs extensively used in T2DM treatment. However, their potential interactions with gut microbiota remain poorly understood.

Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans.

Physiologically based kinetic (PBK) modelling offers a mechanistic basis for predicting the pharmaco-/toxicokinetics of compounds and thereby provides critical information for integrating toxicity and exposure data to replace animal testing with in vitro or in silico methods. However, traditional PBK modelling depends on animal and human data, which limits its usefulness for non-animal methods. To address this limitation, high-throughput PBK modelling aims to rely exclusively on in vitro and in silico data for model generation.

Novel clinical phenotypes, drug categorization, and outcome prediction in drug-induced cholestasis: Analysis of a database of 432 patients developed by literature review and machine learning support.

Background: Serum transaminases, alkaline phosphatase and bilirubin are common parameters used for DILI diagnosis, classification, and prognosis. However, the relevance of clinical examination, histopathology and drug chemical properties have not been fully investigated. As cholestasis is a frequent and complex DILI manifestation, our goal was to investigate the relevance of clinical features and drug properties to stratify drug-induced cholestasis (DIC) patients, and to develop a prognosis model to identify patients at risk and high-concern drugs.

Uncovering the toxicity mechanisms of a series of carboxylic acids in liver cells through computational and experimental approaches.

Hepatotoxicity poses a significant concern in drug design due to the potential liver damage that can be caused by new drugs. Among common manifestations of hepatotoxic damage is lipid accumulation in hepatic tissue, resulting in liver steatosis or phospholipidosis. Carboxylic derivatives are prone to interfere with fatty acid metabolism and cause lipid accumulation in hepatocytes.

An extensive review on phenolic compounds and their potential estrogenic properties on skin physiology.

Polyphenolic compounds constitute a diverse group of natural components commonly occurring in various plant species, known for their potential to exert both beneficial and detrimental effects. Additionally, these polyphenols have also been implicated as endocrine-disrupting (ED) chemicals, raising concerns about their widespread use in the cosmetics industry. In this comprehensive review, we focus on the body of literature pertaining to the estrogenic properties of ED chemicals, with a particular emphasis on the interaction of isoflavones with estrogen receptors.

G4-QuadScreen: A Computational Tool for Identifying Multi-Target-Directed Anticancer Leads against G-Quadruplex DNA.

The study presents 'G4-QuadScreen', a user-friendly computational tool for identifying MTDLs against G4s. Also, it offers a few hit MTDLs based on in silico and in vitro approaches. Multi-tasking QSAR models were developed using linear discriminant analysis and random forest machine learning techniques for predicting the responses of interest (G4 interaction, G4 stabilization, G4 selectivity, and cytotoxicity) considering the variations in the experimental conditions (e.

MicotoXilico: An Interactive Database to Predict Mutagenicity, Genotoxicity, and Carcinogenicity of Mycotoxins.

Mycotoxins are secondary metabolites produced by certain filamentous fungi. They are common contaminants found in a wide variety of food matrices, thus representing a threat to public health, as they can be carcinogenic, mutagenic, or teratogenic, among other toxic effects. Several hundreds of mycotoxins have been reported, but only a few of them are regulated, due to the lack of data regarding their toxicity and mechanisms of action.

SH3BP2 Silencing Increases miRNAs Targeting ETV1 and Microphthalmia-Associated Transcription Factor, Decreasing the Proliferation of Gastrointestinal Stromal Tumors.

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors of the gastrointestinal tract. Gain of function in receptor tyrosine kinases type III, KIT, or PDGFRA drives the majority of GIST. Previously, our group reported that silencing of the adaptor molecule SH3 Binding Protein 2 (SH3BP2) downregulated KIT and PDGFRA and microphthalmia-associated transcription factor (MITF) levels and reduced tumor growth.

The microphthalmia-associated transcription factor is involved in gastrointestinal stromal tumor growth.

Gastrointestinal stromal tumors (GISTs) are the most common neoplasms of mesenchymal origin, and most of them emerge due to the oncogenic activation of KIT or PDGFRA receptors. Despite their relevance in GIST oncogenesis, critical intermediates mediating the KIT/PDGFRA transforming program remain mostly unknown. Previously, we found that the adaptor molecule SH3BP2 was involved in GIST cell survival, likely due to the co-regulation of the expression of KIT and Microphthalmia-associated transcription factor (MITF).

The PxIxIT motif of the RCAN3 inhibits angiogenesis and tumor progression in Triple Negative breast cancer in immunocompetent mice.

RCAN proteins are endogenous regulators of the calcineurin-cytosolic nuclear factor of activated T cells (CN-NFATc) pathway that bind CN through similar conserved motifs PxIxIT and LxVP of the NFATc family. RCAN1 and RCAN3 protein levels were reported to correlate with overall survival of breast cancer patients. We additionally provided supporting results about RCAN3 role on cancer showing that overexpression of the native PxIxIT sequence of RCAN3-derived R3 peptide (PSVVVH, EGFP-R3178-210) dramatically inhibits tumor growth and tumor angiogenesis in an orthotopic mouse model of Triple Negative Breast Cancer (TNBC) in nude mice.

Identification of NLRP3 Homo-Oligomerization Inhibitors with Anti-Inflammatory Activity.

Inflammasomes are multiprotein complexes that represent critical elements of the inflammatory response. The dysregulation of the best-characterized complex, the NLRP3 inflammasome, has been linked to the pathogenesis of diseases such as multiple sclerosis, type 2 diabetes mellitus, Alzheimer's disease, and cancer. While there exist molecular inhibitors specific for the various components of inflammasome complexes, no currently reported inhibitors specifically target NLRP3 homo-oligomerization.

MYO1F Regulates IgE and MRGPRX2-Dependent Mast Cell Exocytosis.

The activation and degranulation of mast cells is critical in the pathogenesis of allergic inflammation and modulation of inflammation. Recently, we demonstrated that the unconventional long-tailed myosin, MYO1F, localizes with cortical F-actin and mediates adhesion and migration of mast cells. In this study, we show that knockdown of MYO1F by short hairpin RNA reduces human mast cell degranulation induced by both IgE crosslinking and by stimulation of the Mas-related G protein-coupled receptor X2 (MRGPRX2), which has been associated with allergic and pseudoallergic drug reactions, respectively.

PeptiDesCalculator: Software for computation of peptide descriptors. Definition, implementation and case studies for 9 bioactivity endpoints.

We present a novel Java-based program denominated PeptiDesCalculator for computing peptide descriptors. These descriptors include: redefinitions of known protein parameters to suite the peptide domain, generalization schemes for the global descriptions of peptide characteristics, as well as empirical descriptors based on experimental evidence on peptide stability and interaction propensity. The PeptiDesCalculator software provides a user-friendly Graphical User Interface (GUI) and is parallelized to maximize the use of computational resources available in current work stations.

Activation of Siglec-7 results in inhibition of in vitro and in vivo growth of human mast cell leukemia cells.

Advanced systemic mastocytosis is a rare and still untreatable disease. Blocking antibodies against inhibitory receptors, also known as "immune checkpoints", have revolutionized anti-cancer treatment. Inhibitory receptors are expressed not only on normal immune cells, including mast cells but also on neoplastic cells.

Myo1f, an Unconventional Long-Tailed Myosin, Is a New Partner for the Adaptor 3BP2 Involved in Mast Cell Migration.

Mast cell chemotaxis is essential for cell recruitment to target tissues, where these cells play an important role in adaptive and innate immunity. Stem cell factor (SCF) is a major chemoattractant for mast cells. SCF binds to the KIT receptor, thereby triggering tyrosine phosphorylation in the cytoplasmic domain and resulting in docking sites for SH2 domain-containing molecules, such as Lyn and Fyn, and the subsequent activation of the small GTPases Rac that are responsible for cytoskeletal reorganization and mast cell migration.

MRGPRX2-mediated mast cell response to drugs used in perioperative procedures and anaesthesia.

The study of anaphylactoid reactions during perioperative procedures and anaesthesia represents a diagnostic challenge for allergists, as many drugs are administered simultaneously, and approximately half of them trigger allergic reactions without a verifiable IgE-mediated mechanism. Recently, mast cell receptor MRGPRX2 has been identified as a cause of pseudo-allergic drug reactions. In this study, we analyse the ability of certain drugs used during perioperative procedures and anaesthesia to induce MRGPRX2-dependent degranulation in human mast cells and sera from patients who experienced an anaphylactoid reaction during the perioperative procedure.