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.

Structure-Based Design and Synthesis of Stapled 10Panx1 Analogues for Use in Cardiovascular Inflammatory Diseases.

Following a rational design, a series of macrocyclic ("stapled") peptidomimetics of 10Panx1, the most established peptide inhibitor of Pannexin1 (Panx1) channels, were developed and synthesized. Two macrocyclic analogues SBL-PX1-42 and SBL-PX1-44 outperformed the linear native peptide. During in vitro adenosine triphosphate (ATP) release and Yo-Pro-1 uptake assays in a Panx1-expressing tumor cell line, both compounds were revealed to be promising bidirectional inhibitors of Panx1 channel function, able to induce a two-fold inhibition, as compared to the native 10Panx1 sequence. The introduction of triazole-based cross-links within the peptide backbones increased helical content and enhanced in vitro proteolytic stability in human plasma (>30-fold longer half-lives, compared to 10Panx1). In adhesion assays, a "double-stapled" peptide, SBL-PX1-206 inhibited ATP release from endothelial cells, thereby efficiently reducing THP-1 monocyte adhesion to a TNF-α-activated endothelial monolayer and making it a promising candidate for future in vivo investigations in animal models of cardiovascular inflammatory disease.

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.

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.

Determination of structural features that underpin the pannexin1 channel inhibitory activity of the peptide 10Panx1.

Pannexin1 channels facilitate paracrine communication and are involved in a broad spectrum of diseases. Attempts to find appropriate pannexin1 channel inhibitors that showcase target-selective properties and in vivo applicability remain nonetheless scarce. However, a promising lead candidate, the ten amino acid long peptide mimetic 10Panx1 (H-Trp1-Arg2-Gln3-Ala4-Ala5-Phe6-Val7-Asp8-Ser9-Tyr10-OH), has shown potential as a pannexin1 channel inhibitor in both in vitro and in vivo studies. Nonetheless, structural optimization is critical for clinical use. One of the main hurdles to overcome along the optimization process consists of subduing the low biological stability (10Panx1 t1/2 = 2.27 ± 0.11 min). To tackle this issue, identification of important structural features within the decapeptide structure is warranted. For this reason, a structure-activity relationship study was performed to proteolytically stabilize the sequence. Through an Alanine scan, this study demonstrated that the side chains of Gln3 and Asp8 are crucial for 10Panx1's channel inhibitory capacity. Guided by plasma stability experiments, scissile amide bonds were identified and stabilized, while extracellular adenosine triphosphate release experiments, indicative of pannexin1 channel functionality, allowed to enhance the in vitro inhibitory capacity of 10Panx1.

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.

Computational strategies for the discovery of biological functions of health foods, nutraceuticals and cosmeceuticals: a review.

Scientific and consumer interest in healthy foods (also known as functional foods), nutraceuticals and cosmeceuticals has increased in the recent years, leading to an increased presence of these products in the market. However, the regulations across different countries that define the type of claims that may be made, and the degree of evidence required to support these claims, are rather inconsistent. Moreover, there is also controversy on the effectiveness and biological mode of action of many of these products, which should undergo an exhaustive approval process to guarantee the consumer rights. Computational approaches constitute invaluable tools to facilitate the discovery of bioactive molecules and provide biological plausibility on the mode of action of these products. Indeed, methodologies like QSAR, docking or molecular dynamics have been used in drug discovery protocols for decades and can now aid in the discovery of bioactive food components. Thanks to these approaches, it is possible to search for new functions in food constituents, which may be part of our daily diet, and help to prevent disorders like diabetes, hypercholesterolemia or obesity. In the present manuscript, computational studies applied to this field are reviewed to illustrate the potential of these approaches to guide the first screening steps and the mechanistic studies of nutraceutical, cosmeceutical and functional foods.

Safer chemicals using less animals: kick-off of the European ONTOX project.

The 3Rs concept, calling for replacement, reduction and refinement of animal experimentation, is receiving increasing attention around the world, and has found its way to legislation, in particular in the European Union. This is aligned by continuing high-level efforts of the European Commission to support development and implementation of 3Rs methods. In this respect, the European project called "ONTOX: ontology-driven and artificial intelligence-based repeated dose toxicity testing of chemicals for next generation risk assessment" was recently initiated with the goal to provide a functional and sustainable solution for advancing human risk assessment of chemicals without the use of animals in line with the principles of 21st century toxicity testing and next generation risk assessment. ONTOX will deliver a generic strategy to create new approach methodologies (NAMs) in order to predict systemic repeated dose toxicity effects that, upon combination with tailored exposure assessment, will enable human risk assessment. For proof-of-concept purposes, focus is put on NAMs addressing adversities in the liver, kidneys and developing brain induced by a variety of chemicals. The NAMs each consist of a computational system based on artificial intelligence and are fed by biological, toxicological, chemical and kinetic data. Data are consecutively integrated in physiological maps, quantitative adverse outcome pathway networks and ontology frameworks. Supported by artificial intelligence, data gaps are identified and are filled by targeted in vitro and in silico testing. ONTOX is anticipated to have a deep and long-lasting impact at many levels, in particular by consolidating Europe's world-leading position regarding the development, exploitation, regulation and application of animal-free methods for human risk assessment of chemicals.

Assessing the chemical-induced estrogenicity using in silico and in vitro methods.

Multiple substances are considered endocrine disrupting chemicals (EDCs). However, there is a significant gap in the early prioritization of EDC's effects. In this work, in silico and in vitro methods were used to model estrogenicity. Two Quantitative Structure-Activity Relationship (QSAR) models based on Logistic Regression and REPTree algorithms were built using a large and diverse database of estrogen receptor (ESR) agonism. A 10-fold external validation demonstrated their robustness and predictive capacity. Mechanistic interpretations of the molecular descriptors (C-026, nArOH,PW5, B06[Br-Br]) used for modelling suggested that the heteroatomic fragments, aromatic hydroxyls, and bromines, and the relative bond accessibility areas of molecules, are structural determinants in estrogenicity. As validation of the QSARs, ESR transactivity of thirteen persistent organic pollutants (POPs) and suspected EDCs was tested in vitro using the MMV-Luc cell line. A good correspondence between predictions and experimental bioassays demonstrated the value of the QSARs for prioritization of ESR agonist compounds.

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.

Targeting the aryl hydrocarbon receptor with a novel set of triarylmethanes.

The aryl hydrocarbon receptor (AhR) is a chemical sensor upregulating the transcription of responsive genes associated with endocrine homeostasis, oxidative balance and diverse metabolic, immunological and inflammatory processes, which have raised the pharmacological interest on its modulation. Herein, a novel set of 32 unsymmetrical triarylmethane (TAM) class of structures has been synthesized, characterized and their AhR transcriptional activity evaluated using a cell-based assay. Eight of the assayed TAM compounds (14, 15, 18, 19, 21, 22, 25, 28) exhibited AhR agonism but none of them showed antagonist effects. TAMs bearing benzotrifluoride, naphthol or heteroaromatic (indole, quinoline or thiophene) rings seem to be prone to AhR activation unlike phenyl substituted or benzotriazole derivatives. A molecular docking analysis with the AhR ligand binding domain (LBD) showed similarities in the binding mode and in the interactions of the most potent TAM identified 4-(pyridin-2-yl (thiophen-2-yl)methyl)phenol (22) compared to the endogenous AhR agonist 5,11-dihydroindolo[3,2-b]carbazole-12-carbaldehyde (FICZ). Finally, in silico predictions of physicochemical and biopharmaceutical properties for the most potent agonistic compounds were performed and these exhibited acceptable druglikeness and good ADME profiles. To our knowledge, this is the first study assessing the AhR modulatory effects of unsymmetrical TAM class of compounds.

Computational Evaluation and In Vitro Validation of New Epidermal Growth Factor Receptor Inhibitors.

BACKGROUND: The Epidermal Growth Factor Receptor (EGFR) is a transmembrane protein that acts as a receptor of extracellular protein ligands of the epidermal growth factor (EGF/ErbB) family. It has been shown that EGFR is overexpressed by many tumours and correlates with poor prognosis. Therefore, EGFR can be considered as a very interesting therapeutic target for the treatment of a large variety of cancers such as lung, ovarian, endometrial, gastric, bladder and breast cancers, cervical adenocarcinoma, malignant melanoma and glioblastoma. METHODS: We have followed a structure-based virtual screening (SBVS) procedure with a library composed of several commercial collections of chemicals (615,462 compounds in total) and the 3D structure of EGFR obtained from the Protein Data Bank (PDB code: 1M17). The docking results from this campaign were then ranked according to the theoretical binding affinity of these molecules to EGFR, and compared with the binding affinity of erlotinib, a well-known EGFR inhibitor. A total of 23 top-rated commercial compounds displaying potential binding affinities similar or even better than erlotinib were selected for experimental evaluation. In vitro assays in different cell lines were performed. A preliminary test was carried out with a simple and standard quick cell proliferation assay kit, and six compounds showed significant activity when compared to positive control. Then, viability and cell proliferation of these compounds were further tested using a protocol based on propidium iodide (PI) and flow cytometry in HCT116, Caco-2 and H358 cell lines. RESULTS: The whole six compounds displayed good effects when compared with erlotinib at 30 µM. When reducing the concentration to 10µM, the activity of the 6 compounds depends on the cell line used: the six compounds showed inhibitory activity with HCT116, two compounds showed inhibition with Caco-2, and three compounds showed inhibitory effects with H358. At 2 µM, one compound showed inhibiting effects close to those from erlotinib. CONCLUSION: Therefore, these compounds could be considered as potential primary hits, acting as promising starting points to expand the therapeutic options against a wide range of cancers.

Predictive modeling of aryl hydrocarbon receptor (AhR) agonism.

The aryl hydrocarbon receptor (AhR) plays a key role in the regulation of gene expression in metabolic machinery and detoxification systems. In the recent years, this receptor has attracted interest as a therapeutic target for immunological, oncogenic and inflammatory conditions. In the present report, in silico and in vitro approaches were combined to study the activation of the AhR. To this end, a large database of chemical compounds with known AhR agonistic activity was employed to build 5 classifiers based on the Adaboost (AdB), Gradient Boosting (GB), Random Forest (RF), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) algorithms, respectively. The built classifiers were examined, following a 10-fold external validation procedure, demonstrating adequate robustness and predictivity. These models were integrated into a majority vote based ensemble, subsequently used to screen an in-house library of compounds from which 40 compounds were selected for prospective in vitro experimental validation. The general correspondence between the ensemble predictions and the in vitro results suggests that the constructed ensemble may be useful in predicting the AhR agonistic activity, both in a toxicological and pharmacological context. A preliminary structure-activity analysis of the evaluated compounds revealed that all structures bearing a benzothiazole moiety induced AhR expression while diverse activity profiles were exhibited by phenolic derivatives.

A new split-luciferase complementation assay identifies pentachlorophenol as an inhibitor of apoptosome formation.

The expense and time required for in vivo reproductive and developmental toxicity studies have driven the development of in vitro alternatives. Here, we used a new in vitro split luciferase-based assay to screen a library of 177 toxicants for inhibitors of apoptosome formation. The apoptosome contains seven Apoptotic Protease-Activating Factor-1 (Apaf-1) molecules and induces cell death by activating caspase-9. Apaf-1-dependent caspase activation also plays an important role in CNS development and spermatogenesis. In the in vitro assay, Apaf-1 fused to an N-terminal fragment of luciferase binds to Apaf-1 fused to a C-terminal fragment of luciferase and reconstitutes luciferase activity. Our assay indicated that pentachlorophenol (PCP) inhibits apoptosome formation, and further investigation revealed that PCP binds to cytochrome c. PCP is a wood preservative that reduces male fertility by ill-defined mechanisms. Although the data show that PCP inhibited apoptosome formation, the concentration required suggests that other mechanisms may be more important for PCP's effects on spermatogenesis. Nonetheless, the data demonstrate the utility of the new assay in identifying apoptosome inhibitors, and we suggest that the assay may be useful in screening for reproductive and developmental toxicants.

Discriminating Drug-Like Compounds by Partition Trees with Quantum Similarity Indices and Graph Invariants.

The search for new drug candidates in databases is of paramount importance in pharmaceutical chemistry. The selection of molecular subsets is greatly optimized and much more promising when potential drug-like molecules are detected a priori. In this work, about one hundred thousand molecules are ranked following a new methodology: a drug/non-drug classifier constructed by a consensual set of classification trees. The classification trees arise from the stochastic generation of training sets, which in turn are used to estimate probability factors of test molecules to be drug-like compounds. Molecules were represented by Topological Quantum Similarity Indices and their Graph Theoretical counterparts. The contribution of the present paper consists of presenting an effective ranking method able to improve the probability of finding drug-like substances by using these types of molecular descriptors.

Small Molecule Inhibition of ERK Dimerization Prevents Tumorigenesis by RAS-ERK Pathway Oncogenes.

Nearly 50% of human malignancies exhibit unregulated RAS-ERK signaling; inhibiting it is a valid strategy for antineoplastic intervention. Upon activation, ERK dimerize, which is essential for ERK extranuclear, but not for nuclear, signaling. Here, we describe a small molecule inhibitor for ERK dimerization that, without affecting ERK phosphorylation, forestalls tumorigenesis driven by RAS-ERK pathway oncogenes. This compound is unaffected by resistance mechanisms that hamper classical RAS-ERK pathway inhibitors. Thus, ERK dimerization inhibitors provide the proof of principle for two understudied concepts in cancer therapy: (1) the blockade of sub-localization-specific sub-signals, rather than total signals, as a means of impeding oncogenic RAS-ERK signaling and (2) targeting regulatory protein-protein interactions, rather than catalytic activities, as an approach for producing effective antitumor agents.

Hit identification of novel heparanase inhibitors by structure- and ligand-based approaches.

Heparanase is a key enzyme involved in the dissemination of metastatic cancer cells. In this study a combination of in silico techniques and experimental methods was used to identify new potential inhibitors against this target. A 3D model of heparanase was built from sequence homology and applied to the virtual screening of a library composed of 27 known heparanase inhibitors and a commercial collection of drugs and drug-like compounds. The docking results from this campaign were combined with those obtained from a pharmacophore model recently published based in the same set of chemicals. Compounds were then ranked according to their theoretical binding affinity, and the top-rated commercial drugs were selected for further experimental evaluation. Biophysical methods (NMR and SPR) were applied to assess experimentally the interaction of the selected compounds with heparanase. The binding site was evaluated via competition experiments, using a known inhibitor of heparanase. Three of the selected drugs were found to bind to the active site of the protein and their KD values were determined. Among them, the antimalarial drug amodiaquine presented affinity towards the protein in the low-micromolar range, and was singled out for a SAR study based on its chemical scaffold. A subset of fourteen 4-arylaminoquinolines from a global set of 249 analogues of amodiaquine was selected based on the application of in silico models, a QSAR solubility prediction model and a chemical diversity analysis. Some of these compounds displayed binding affinities in the micromolar range.

QSAR-based permeability model for drug-like compounds.

A QSAR model was developed for predicting intestinal drug permeability, one of the most important parameters when evaluating compounds in drug discovery projects. First, a set of relevant properties for establishing a drug-like chemical space was applied to a database of compounds with Caco-2 permeability values obtained from previous studies. Several QSAR regression models were then developed from this set of drug-like structures. The best model was selected based on the accuracy of correct classifications obtained for training and validation subsets previously defined, including 17 structures from the FDA Biopharmaceutics Classification System (BCS). Further validation of the QSAR model was performed by applying it to 21 drugs for which Caco-2 permeability values were experimentally determined by us. The good agreement between predictions and experimental values in all cases confirmed the reliability of the equation. Since the model was developed with very simple descriptors, easy to calculate, its applicability to large collections of in silico chemicals is guaranteed.

Small molecule databases and chemical descriptors useful in chemoinformatics: an overview.

Chemoinformatics is a scientific discipline at the interface between chemistry and computer science, which nowadays is currently implemented in pharmaceutical companies as a part of the usual drug discovery pathway. Furthermore, taking into account the vast amount of experimental and computational data currently generated on drug discovery projects, the use of chemoinformatics tools has become increasingly necessary. Most of chemoinformatics projects are initiated from information stored in large databases of chemicals. The compounds are systematically characterized by numerical descriptors in order to manage this information and obtain some kind of chemical-biological relationships by similarity/diversity analysis, QSAR development for ADMET predictions, chemical space navigation for the selection of drug subspaces, drug-like and lead-like selection, or substructure searching. In this paper, we will review some of the more important chemical databases of small molecules and the descriptors that can be used to describe them, as well as their applications to the specific area of design of focused/targeted libraries.