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. Here, we evaluate a variety of in silico tools and different strategies to parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000 + publicly available human in vivo concentration-time profiles of 200 + compounds (IV and oral administration), as well as in silico, in vitro and in vivo determined compound-specific parameters required for the PBK modelling of these compounds. Then, we systematically evaluate all possible PBK model parametrisation strategies in PK-Sim and quantify their prediction accuracy against the collected in vivo concentration-time profiles. Our results show that even simple, generic high-throughput PBK modelling can provide accurate predictions of the pharmacokinetics of most compounds (87% of Cmax and 84% of AUC within tenfold). Nevertheless, we also observe major differences in prediction accuracies between the different parameterisation strategies, as well as between different compounds. Finally, we outline a strategy for high-throughput PBK modelling that relies exclusively on freely available tools. Our findings contribute to a more robust understanding of the reliability of high-throughput PBK modelling, which is essential to establish the confidence necessary for its utilisation in Next-Generation Risk Assessment.

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. In this study, we employed computational methodologies to investigate the binding affinities of various gliptins to DPP4-like homologs produced by intestinal bacteria. The 3D structures of DPP4 homologs from gut microbiota species, including Segatella copri, Phocaeicola vulgatus, Bacteroides uniformis, Parabacteroides merdae, and Alistipes sp., were predicted using computational modeling techniques. Subsequently, molecular dynamics simulations were conducted for 200 ns to ensure the stability of the predicted structures. Stable structures were then utilized to predict the binding interactions with known gliptins through molecular docking algorithms. Our results revealed binding similarities of gliptins toward bacterial DPP4 homologs compared to human DPP4. Specifically, certain gliptins exhibited similar binding scores to bacterial DPP4 homologs as they did with human DPP4, suggesting a potential interaction of these drugs with gut microbiota. These findings could help in understanding the interplay between gliptins and gut microbiota DPP4 homologs, considering the intricate relationship between the host metabolism and microbial communities in the gut.

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. Defective degranulation was accompanied by a reduced reassembly of the cortical actin ring after activation but reversed by inhibition of actin polymerization. Our data show that MYO1F is required for full Cdc42 GTPase activation, a critical step in exocytosis. Furthermore, MYO1F knockdown resulted in less granule localization in the cell membrane and fewer fissioned mitochondria along with deficient mitochondria translocation to exocytic sites. Consistent with that, AKT and DRP1 phosphorylation are diminished in MYO1F knockdown cells. Altogether, our data point to MYO1F as an important regulator of mast cell degranulation by contributing to the dynamics of the cortical actin ring and the distribution of both the secretory granules and mitochondria.

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. On the other hand, RCAN3 protein and its derived peptide EGFP-R3178-210 bind to CN and inhibit NFAT-mediated cytokine gene expression without affecting CN phosphatase activity suggesting that RCAN3 and EGFP-R3178-210 peptide have tumor suppressor and immunosuppressant activity. Due to the known relationship between tumor development and immune system, as well as the relevance of CN-NFATc in the regulation of the immune system, in the present study we decided to assess the effect of EGFP-R3178-210 peptide in an orthotopic syngeneic TNBC mouse model, in order to ensure that the role of RCAN3 as immunosuppressant do not override its tumor suppressor activity. Our results evidence that EGFP-R3178-210 peptide displays an inhibitory potential on tumor growth and tumor angiogenesis similar to those obtained in the previous orthotopic TNBC model. These results highlight the importance of the RCAN3 peptide as a tumor suppressor protein and totally complement our previous results, indicating that this antitumor activity role is maintained in the presence of a complete functional immune system.

Identification of NLRP3PYD 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 NLRP3PYD homo-oligomerization. In the present study, we describe the identification of QM380 and QM381 as NLRP3PYD homo-oligomerization inhibitors after screening small molecules from the MyriaScreen library using a split-luciferase complementation assay. Our results demonstrate that these NLRP3PYD inhibitors interfere with ASC speck formation, inhibit pro-inflammatory cytokine IL1-ß release, and decrease pyroptotic cell death. We employed spectroscopic techniques and computational docking analyses with QM380 and QM381 and the PYD domain to confirm the experimental results and predict possible mechanisms underlying the inhibition of NLRP3PYD homo-interactions.

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. The PeptiDesCalculator indices are employed in modeling 8 peptide bioactivity endpoints demonstrating satisfactory behavior. Moreover, we compare the performance of a support vector machine (SVM) classifier built using 15 PeptiDesCalculator indices with that of a recently reported deep neural network (DNN) antimicrobial activity classifier, demonstrating comparable test set performance notwithstanding the remarkably lower degree of freedom for the former. This software will facilitate the development of in silico models for the prediction of peptide properties.

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. Whether activation of inhibitory receptors through monoclonal antibodies can lead to tumor growth inhibition remains mostly unknown. Here we show that the inhibitory receptor Siglec-7 is expressed by primary neoplastic mast cells in patients with systemic mastocytosis and by mast cell leukemia cell lines. Activation of Siglec-7 by anti-Siglec-7 monoclonal antibody caused phosphorylation of Src homology region 2 domain-containing phosphatase-1 (SHP-1), reduced phosphorylation of KIT and induced growth inhibition in mast cell lines. In SCID-beige mice injected with either the human mast cell line HMC-1.1 and HMC-1.2 or with Siglec-7 transduced B cell lymphoma cells, anti-Siglec-7 monoclonal antibody reduced tumor growth by a mechanism involving Siglec-7 cytoplasmic domains in "preventive" and "treatment" settings. These data demonstrate that activation of Siglec-7 on mast cell lines can inhibit their growth in vitro and in vivo. This might pave the way to additional treatment strategies for mastocytosis.

The adaptor 3BP2 is required for KIT receptor expression and human mast cell survival.

SH3-binding protein 2 (3BP2) is a cytoplasmic adaptor protein that acts as a positive regulator in mast cell FcεRI-dependent signaling. The KIT receptor whose ligand is the stem cell factor is necessary for mast cell development, proliferation, and survival as well as for optimal IgE-dependent signal. Activating mutations in KIT have been associated with several diseases including mastocytosis. In the present work, we found that 3BP2 silencing impairs KIT signaling pathways, thus affecting phosphoinositide 3-kinase and MAPK pathways in human mast cells (huMCs) from HMC-1, LAD2 (huMC lines), and CD34(+)-derived mast cells. Unexpectedly, silencing of 3BP2 reduces KIT expression in normal huMCs as well as in HMC-1 cells where KIT is mutated, thus increasing cellular apoptosis and caspase-3/7 activity. 3BP2 silencing reduces KIT transcription expression levels. Interestingly, 3BP2 silencing decreased microphthalmia-associated transcription factor (MITF) expression, a transcription factor involved in KIT expression. Reconstitution of 3BP2 in knockdown cells leads to reversal of KIT expression as well as survival phenotype. Accordingly MITF reconstitution enhances KIT expression levels in 3BP2-silenced cells. Moreover, downregulation of KIT expression by miRNA-221 overexpression or the proteasome inhibitor bortezomib also reduced 3BP2 and MITF expression. Furthermore, KIT tyrosine activity inhibition reduced 3BP2 and MITF expression, demonstrating again a tight and reciprocal relationship between these molecules. Taken together, our results show that 3BP2 regulates huMC survival and participates in KIT-mediated signal transduction by directly controlling KIT receptor expression, suggesting its potential as a therapeutic target in mast cell-mediated inflammatory diseases and deregulated KIT disorders.

A novel role for an RCAN3-derived peptide as a tumor suppressor in breast cancer.

The members of the human regulators of calcineurin (RCAN) protein family are endogenous regulators of the calcineurin (CN)-cytosolic nuclear factor of activated T-cells (NFATc) pathway activation. This function is explained by the presence of a highly conserved calcipressin inhibitor of calcineurin (CIC) motif in RCAN proteins, which has been shown to compete with NFATc for the binding to CN and therefore are able to inhibit NFATc dephosphorylation and activation by CN. Very recently, emerging roles for NFATc proteins in transformation, tumor angiogenesis and metastasis have been described in different cancer cell types. In this work, we report that the overexpression of RCAN3 dramatically inhibits tumor growth and tumor angiogenesis in an orthotopic human breast cancer model. We suggest that RCAN3 exerts these effects in a CN-dependent manner, as mutation of the CIC motif in RCAN3 abolishes the tumor suppressor effect. Moreover, the expression of the EGFP-R3(178-210) peptide, spanning the CIC motif of RCAN3, is able to reproduce all the antitumor effects of RCAN3 full-length protein. Finally, we show that RCAN3 and the EGFP-R3(178-210) peptide inhibit the CN-NFATc signaling pathway and the induction of the NFATc-dependent gene cyclooxygenase-2. Our work suggests that the EGFP-R3(178-210) peptide possess potent tumor suppressor properties and therefore constitutes a novel lead for the development of potent and specific antitumoral agents. Moreover, we propose the targeting of the CN-NFATc pathway in the tumor cells constitutes an effective way to hamper tumor progression by impairing the paracrine network among tumor, endothelial and polymorphonucleated cells.

RCAN 1 and 3 proteins regulate thymic positive selection.

Cooperation between calcineurin (CN)-NFATc and RAF-MEK-ERK signaling pathways is essential in thymocyte positive selection. It is known that the Regulators of Calcineurin (RCAN) proteins can act either facilitating or suppressing CN-dependent signaling events. Here, we show that RCAN genes are expressed in lymphoid tissues, and address the role of RCAN proteins in T cell development. Overexpression of human RCAN3 and RCAN1 can modulate T cell development by increasing positive selection-related surface markers, as well as the "Erk(hi) competence state" in double positive thymocytes, a characteristic molecular signature of positive selection, without affecting CN activity. We also found that RCAN1/3 interact with RAF kinases and CN in a non-exclusive manner. Our data suggests that the balance of RCAN interactions with CN and/or RAF kinases may influence T cell positive selection.

Protein kinase CK2-dependent phosphorylation of the human Regulators of Calcineurin reveals a novel mechanism regulating the calcineurin-NFATc signaling pathway.

Cyclosporine A and FK506 produce immunosuppression by blocking calcineurin phosphatase activity and consequently activation of cytosolic Nuclear Factor of Activated T-cell (NFATc) transcription factor. Due to the chronic toxicity associated with their administration, the development of more specific immunosuppressants is currently an important unmet medical need. In this context, an immunosuppressant peptide derived from the CIC motif of the human Regulators of Calcineurin (RCAN) proteins has been shown to inhibit NFATc signaling without affecting general phosphatase activity of calcineurin. Here we show that protein kinase CK2 phosphorylates a conserved serine residue within the CIC motif of vertebrate RCANs, which increases its affinity for calcineurin and consequently its inhibition of NFATc-dependent gene expression in activated T-cells. Molecular modeling studies have led us to identify a positively charged interaction site on the surface of calcineurin where the phosphorylated serine residue of the CIC motif would normally locate. Finally, we have also identified RCAN3 as a new phosphoprotein with multiple phosphorylation sites. Therefore, our findings reveal for the first time a novel molecular mechanism underlying the regulation of calcineurin-NFATc signaling by means of phosphorylation of the CIC motif of RCAN proteins. The knowledge of how RCAN proteins modulate the calcineurin-NFATc pathway paves the way for the development of potent novel selective immunosuppressant drugs.

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. This cannot be done with a simple SMILES representation, as for organic molecules, because their chemical structure is complex, including several layers and many inorganic materials, and their size and geometry are key features. In this review, we survey the literature for existing predictive models for NMs and discuss the variety of calculated and experimental features used to define and describe NMs. In the light of this research, we propose a classification of the descriptors including those that directly describe a component of the nanoform (core, surface, or structure) and also experimental features (related to the nanomaterial's behavior, preparation, or test conditions) that indirectly reflect its structure.

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. This study investigates the toxic behaviour of 24 structurally related carboxylic acids in hepatocytes, specifically their ability to cause accumulation of fatty acids and phospholipids. Using high-content screening (HCS) assays, we identified two distinct lipid accumulation patterns. Subsequently, we developed structure-activity relationship (SAR) and quantitative structure-activity relationship (QSAR) models to determine relevant molecular substructures and descriptors contributing to these adverse effects. Additionally, we calculated physicochemical properties associated with lipid accumulation in hepatocytes and examined their correlation with our chemical structure characteristics. To assess the applicability of our findings to a wide range of chemical compounds, we employed two external datasets to evaluate the distribution of our QSAR descriptors. Our study highlights the significance of subtle molecular structural variations in triggering hepatotoxicity, such as the presence of nitrogen or the specific arrangement of substitutions within the carbon chain. By employing our comprehensive approach, we pinpointed specific molecules and elucidated their mechanisms of toxicity, thus offering valuable insights to guide future toxicology investigations.

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. METHODS: DIC-related articles were searched by keywords and Boolean operators in seven databases. Relevant articles were uploaded onto Sysrev, a machine-learning based platform for article review and data extraction. Demographic, clinical, biochemical, and liver histopathological data were collected. Drug properties were obtained from databases or QSAR modelling. Statistical analyses and logistic regressions were performed. RESULTS: Data from 432 DIC patients associated with 52 drugs were collected. Fibrosis strongly associated with fatality, whereas canalicular paucity and ALP associated with chronicity. Drugs causing cholestasis clustered in three major groups. The pure cholestatic pattern divided into two subphenotypes with differences in prognosis, canalicular paucity, fibrosis, ALP and bilirubin. A predictive model of DIC outcome based on non-invasive parameters and drug properties was developed. Results demonstrate that physicochemical (pKa-a) and pharmacokinetic (bioavailability, CYP2C9) attributes impinged on the DIC phenotype and allowed the identification of high-concern drugs. CONCLUSIONS: We identified novel associations among DIC manifestations and disclosed novel DIC subphenotypes with specific clinical and chemical traits. The developed predictive DIC outcome model could facilitate DIC prognosis in clinical practice and drug categorization.

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). Remarkably, MITF reconstitution restored KIT expression levels in SH3BP2 silenced cells and restored cell viability. This study aimed to analyze MITF as a novel driver of KIT transforming program in GIST. Firstly, MITF isoforms were characterized in GIST cell lines and GIST patients' samples. MITF silencing decreases cell viability and increases apoptosis in GIST cell lines irrespective of the type of KIT primary or secondary mutation. Additionally, MITF silencing leads to cell cycle arrest and impaired tumor growth in vivo. Interestingly, MITF silencing also affects ETV1 expression, a linage survival factor in GIST that promotes tumorigenesis and is directly regulated by KIT signaling. Altogether, these results point to MITF as a key target of KIT/PDGFRA oncogenic signaling for GIST survival and tumor growth.

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.g., G4 sequences, endpoints, cell lines, buffers, and assays). A virtual screening with G4-QuadScreen and molecular docking using YASARA (AutoDock-Vina) was performed. G4 activities were confirmed via FRET melting, FID, and cell viability assays. Validation metrics demonstrated the high discriminatory power and robustness of the models (the accuracy of all models is ~>90% for the training sets and ~>80% for the external sets). The experimental evaluations showed that ten screened MTDLs have the capacity to selectively stabilize multiple G4s. Three screened MTDLs induced a strong inhibitory effect on various human cancer cell lines. This pioneering computational study serves a tool to accelerate the search for new leads against G4s, reducing false positive outcomes in the early stages of drug discovery. The G4-QuadScreen tool is accessible on the ChemoPredictionSuite website.

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. Within this review, we aim to elucidate the multifaceted roles and effects of polyphenols on the skin, exploring their potential benefits as well as their capacity to act as ED agents. By delving into this intricate subject matter, we intend to provoke thoughtful consideration, effectively opening a Pandora's box of questions for the reader to ponder. Ultimately, we invite the reader to contemplate whether polyphenols should be regarded as friends or foes in the realm of skincare and endocrine disruption.

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. Thus, a more comprehensive evaluation of the toxicity of mycotoxins found in foodstuffs is required. In silico toxicology approaches, such as Quantitative Structure-Activity Relationship (QSAR) models, can be used to rapidly assess chemical hazards by predicting different toxicological endpoints. In this work, for the first time, a comprehensive database containing 4360 mycotoxins classified in 170 categories was constructed. Then, specific robust QSAR models for the prediction of mutagenicity, genotoxicity, and carcinogenicity were generated, showing good accuracy, precision, sensitivity, and specificity. It must be highlighted that the developed QSAR models are compliant with the OECD regulatory criteria, and they can be used for regulatory purposes. Finally, all data were integrated into a web server that allows the exploration of the mycotoxin database and toxicity prediction. In conclusion, the developed tool is a valuable resource for scientists, industry, and regulatory agencies to screen the mutagenicity, genotoxicity, and carcinogenicity of non-regulated mycotoxins.