Enhancing hERG Risk Assessment with Interpretable Classificatory and Regression Models.

The human Ether-à-go-go-Related Gene (hERG) is a transmembrane protein that regulates cardiac action potential, and its inhibition can induce a potentially deadly cardiac syndrome. In vitro tests help identify hERG blockers at early stages; however, the high cost motivates searching for alternative, cost-effective methods. The primary goal of this study was to enhance the Pred-hERG tool for predicting hERG blockage. To achieve this, we developed new QSAR models that incorporated additional data, updated existing classificatory and multiclassificatory models, and introduced new regression models. Notably, we integrated SHAP (SHapley Additive exPlanations) values to offer a visual interpretation of these models. Utilizing the latest data from ChEMBL v30, encompassing over 14,364 compounds with hERG data, our binary and multiclassification models outperformed both the previous iteration of Pred-hERG and all publicly available models. Notably, the new version of our tool introduces a regression model for predicting hERG activity (pIC50). The optimal model demonstrated an R2 of 0.61 and an RMSE of 0.48, surpassing the only available regression model in the literature. Pred-hERG 5.0 now offers users a swift, reliable, and user-friendly platform for the early assessment of chemically induced cardiotoxicity through hERG blockage. The tool provides versatile outcomes, including (i) classificatory predictions of hERG blockage with prediction reliability, (ii) multiclassificatory predictions of hERG blockage with reliability, (iii) regression predictions with estimated pIC50 values, and (iv) probability maps illustrating the contribution of chemical fragments for each prediction. Furthermore, we implemented explainable AI analysis (XAI) to visualize SHAP values, providing insights into the contribution of each feature to binary classification predictions. A consensus prediction calculated based on the predictions of the three developed models is also present to assist the user's decision-making process. Pred-hERG 5.0 has been designed to be user-friendly, making it accessible to users without computational or programming expertise. The tool is freely available at http://predherg.labmol.com.br.

Cheminformatics-driven discovery of hit compounds against Paracoccidioides spp.

Aims: The development of safe and effective therapies for treating paracoccidioidomycosis using computational strategies were employed to discover anti-Paracoccidioides compounds. Materials & methods: We 1) collected, curated and integrated the largest library of compounds tested against Paracoccidioides spp.; 2) employed a similarity search to virtually screen the ChemBridge database and select nine compounds for experimental evaluation; 3) performed an experimental evaluation to determine the minimum inhibitory concentration and minimum fungicidal concentration as well as cytotoxicity; and 4) employed computational tools to identify potential targets for the most active compounds. Seven compounds presented activity against Paracoccidioides spp. Conclusion: These compounds are new hits with a predicted mechanisms of action, making them potentially attractive to develop new compounds.

Hemodynamic responses to neuromuscular electrical stimulation and to metaboreflex activation.

BACKGROUND: Metabolites produced during muscle exercise can sensitize types III and IV fibers, which account for increasing blood pressure (BP) and vascular resistance in non-exercising limbs, as well as for redistributing the blood flow to active muscles; reflex response is called metaboreflex. Neuromuscular electrical stimulation (NMES) induces greater local muscle metabolic demand than voluntary isometric contractions. Metabolic accumulation is essential to activate muscle metaboreflex; thus, the hypothesis of the current study is that one NMES session can induce metaboreflex with different hemodynamic responses in upper and lower limbs. Objective - to investigate whether one acute NMES session could activate metaboreflex by inducing different hemodynamic responses between arms and legs. METHODS: Twenty (20) healthy subjects (mean age = 47.7±9.4 years, 13 women, mean body mass index = 26 ± 3 kg/m2) participated in this randomized crossover study. All participants were subjected to two NMES interventions, one in the upper limbs (UPL) and the other in the lower limbs (LL). Mean blood pressure (MBP), blood flow (BF) and vascular resistance (VR) were used to selectively evaluate metaboreflex responses at baseline, during NMES interventions, and recovery periods with, and without, postexercise circulatory occlusion (PECO+ and PECO-, respectively) through the area under the curve (AUC) in VR. RESULTS: MBP increased by 13% during UPL interventions and only remained high during PECO+. Changes in MBP were not observed in LL, although BF in the contralateral leg has decreased by 14% during PECO+ protocol. Muscle metaboreflex activation (AUC differences in VR between PECO+ and PECO-) was not different between UPL and LL (P=0.655). CONCLUSIONS: Acute NMES session has induced similar metaboreflex activation in both arms and legs, although hemodynamic responses differed between interventions.

Chemical safety assessment of transformation products of landfill leachate formed during the Fenton process.

Organic chemicals identified in raw landfill leachate (LL) and their transformation products (TPs), formed during Fenton treatment, were analyzed for chemical safety following REACH guidelines. The raw LL was located in the metropolitan region of Campina Grande, in northeast Brazil. We elucidated 197 unique chemical structures, including 154 compounds that were present in raw LL and 82 compounds that were detected in the treated LL, totaling 39 persistent compounds and 43 TPs. In silico models were developed to identify and prioritize the potential level of hazard/risk these compounds pose to the environment and society. The models revealed that the Fenton process improved the biodegradability of TPs. Still, a slight increase in ecotoxicological effects was observed among the compounds in treated LL compared with those present in raw LL. No differences were observed for aryl hydrocarbon receptor (AhR) and antioxidant response element (ARE) mutagenicity. Similar behavior among both raw and treated LL samples was observed for biodegradability; Tetrahymena pyriformis, Daphnia magna, Pimephales promelas and ARE, AhR, and Ames mutagenicity. Overall, our results suggest that raw and treated LL samples have similar activity profiles for all endpoints other than biodegradability.

Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals.

Eye irritation and corrosion are fundamental considerations in developing chemicals to be used in or near the eye, from cleaning products to ophthalmic solutions. Unfortunately, animal testing is currently the standard method to identify compounds that cause eye irritation or corrosion. Yet, there is growing pressure on the part of regulatory agencies both in the USA and abroad to develop New Approach Methodologies (NAMs) that help reduce the need for animal testing and address unmet need to modernize safety evaluation of chemical hazards. In furthering the development and applications of computational NAMs in chemical safety assessment, in this study we have collected the largest expertly curated dataset of compounds tested for eye irritation and corrosion, and employed this data to build and validate binary and multi-classification Quantitative Structure-Activity Relationships (QSAR) models that can reliably assess eye irritation/corrosion potential of novel untested compounds. QSAR models were generated with Random Forest (RF) and Multi-Descriptor Read Across (MuDRA) machine learning (ML) methods, and validated using a 5-fold external cross-validation protocol. These models demonstrated high balanced accuracy (CCR of 0.68-0.88), sensitivity (SE of 0.61-0.84), positive predictive value (PPV of 0.65-0.90), specificity (SP of 0.56-0.91), and negative predictive value (NPV of 0.68-0.85). Overall, MuDRA models outperformed RF models and were applied to predict compounds' irritation/corrosion potential from the Inactive Ingredient Database, which contains components present in FDA-approved drug products, and from the Cosmetic Ingredient Database, the European Commission source of information on cosmetic substances. All models built and validated in this study are publicly available at the STopTox web portal (https://stoptox.mml.unc.edu/). These models can be employed as reliable tools for identifying potential eye irritant/corrosive compounds.

Pred-Skin: A Web Portal for Accurate Prediction of Human Skin Sensitizers.

Safety assessment is an essential component of the regulatory acceptance of industrial chemicals. Previously, we have developed a model to predict the skin sensitization potential of chemicals for two assays, the human patch test and murine local lymph node assay, and implemented this model in a web portal. Here, we report on the substantially revised and expanded freely available web tool, Pred-Skin version 3.0. This up-to-date version of Pred-Skin incorporates multiple quantitative structure-activity relationship (QSAR) models developed with in vitro, in chemico, and mice and human in vivo data, integrated into a consensus naïve Bayes model that predicts human effects. Individual QSAR models were generated using skin sensitization data derived from human repeat insult patch tests, human maximization tests, and mouse local lymph node assays. In addition, data for three validated alternative methods, the direct peptide reactivity assay, KeratinoSens, and the human cell line activation test, were employed as well. Models were developed using open-source tools and rigorously validated according to the best practices of QSAR modeling. Predictions obtained from these models were then used to build a naïve Bayes model for predicting human skin sensitization with the following external prediction accuracy: correct classification rate (89%), sensitivity (94%), positive predicted value (91%), specificity (84%), and negative predicted value (89%). As an additional assessment of model performance, we identified 11 cosmetic ingredients known to cause skin sensitization but were not included in our training set, and nine of them were accurately predicted as sensitizers by our models. Pred-Skin can be used as a reliable alternative to animal tests for predicting human skin sensitization.

Deep Learning-driven research for drug discovery: Tackling Malaria.

Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC50 <500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates.

QSAR-Driven Design and Discovery of Novel Compounds With Antiplasmodial and Transmission Blocking Activities.

Malaria is a life-threatening infectious disease caused by parasites of the genus Plasmodium, affecting more than 200 million people worldwide every year and leading to about a half million deaths. Malaria parasites of humans have evolved resistance to all current antimalarial drugs, urging for the discovery of new effective compounds. Given that the inhibition of deoxyuridine triphosphatase of Plasmodium falciparum (PfdUTPase) induces wrong insertions in plasmodial DNA and consequently leading the parasite to death, this enzyme is considered an attractive antimalarial drug target. Using a combi-QSAR (quantitative structure-activity relationship) approach followed by virtual screening and in vitro experimental evaluation, we report herein the discovery of novel chemical scaffolds with in vitro potency against asexual blood stages of both P. falciparum multidrug-resistant and sensitive strains and against sporogonic development of P. berghei. We developed 2D- and 3D-QSAR models using a series of nucleosides reported in the literature as PfdUTPase inhibitors. The best models were combined in a consensus approach and used for virtual screening of the ChemBridge database, leading to the identification of five new virtual PfdUTPase inhibitors. Further in vitro testing on P. falciparum multidrug-resistant (W2) and sensitive (3D7) parasites showed that compounds LabMol-144 and LabMol-146 demonstrated fair activity against both strains and presented good selectivity versus mammalian cells. In addition, LabMol-144 showed good in vitro inhibition of P. berghei ookinete formation, demonstrating that hit-to-lead optimization based on this compound may also lead to new antimalarials with transmission blocking activity.

Evidence of the Presence of Thyroid Hormones in Achatina fulica Snails.

The objective of this study was to identify thyroid hormones and to examine their putative site of synthesis in Achatina fulica snails. For this purpose, radioimmunoassays were performed for T3 and T4 before and after long starvation with or without hemolymph deproteinization. Sodium/iodide symporter activity in vivo was analyzed through 125I administration with and without KClO4 pretreatment. Only T4 was detected, and its concentration decreased due to starvation or deproteinization. However, high-performance liquid chromatography analysis also showed the presence of T2 and T3 apart from T4, but rT3 was not detected in the A. fulica hemolymph. The sodium/iodide symporter activity was greater in cerebral ganglia than digestive gland, but KClO4 treatment did not inhibit iodide uptake in any of the tissues analyzed. Altogether, our data confirm for the first time the presence of thyroid hormones in A. fulica snails and suggest their participation in the metabolism control in this species, although the putative site of hormone biosynthesis remains to be elucidated.

Pred-Skin: A Fast and Reliable Web Application to Assess Skin Sensitization Effect of Chemicals.

Chemically induced skin sensitization is a complex immunological disease with a profound impact on quality of life and working ability. Despite some progress in developing alternative methods for assessing the skin sensitization potential of chemical substances, there is no in vitro test that correlates well with human data. Computational QSAR models provide a rapid screening approach and contribute valuable information for the assessment of chemical toxicity. We describe the development of a freely accessible web-based and mobile application for the identification of potential skin sensitizers. The application is based on previously developed binary QSAR models of skin sensitization potential from human (109 compounds) and murine local lymph node assay (LLNA, 515 compounds) data with good external correct classification rate (0.70-0.81 and 0.72-0.84, respectively). We also included a multiclass skin sensitization potency model based on LLNA data (accuracy ranging between 0.73 and 0.76). When a user evaluates a compound in the web app, the outputs are (i) binary predictions of human and murine skin sensitization potential; (ii) multiclass prediction of murine skin sensitization; and (iii) probability maps illustrating the predicted contribution of chemical fragments. The app is the first tool available that incorporates quantitative structure-activity relationship (QSAR) models based on human data as well as multiclass models for LLNA. The Pred-Skin web app version 1.0 is freely available for the web, iOS, and Android (in development) at the LabMol web portal ( http://labmol.com.br/predskin/ ), in the Apple Store, and on Google Play, respectively. We will continuously update the app as new skin sensitization data and respective models become available.

Evidence of the Presence of Thyroid Hormones in Achatina fulica Snails

ABSTRACT The objective of this study was to identify thyroid hormones and to examine their putative site of synthesis in Achatina fulica snails. For this purpose, radioimmunoassays were performed for T3 and T4 before and after long starvation with or without hemolymph deproteinization. Sodium/iodide symporter activity in vivo was analyzed through 125I administration with and without KClO4 pretreatment. Only T4 was detected, and its concentration decreased due to starvation or deproteinization. However, high-performance liquid chromatography analysis also showed the presence of T2 and T3 apart from T4, but rT3 was not detected in the A. fulica hemolymph. The sodium/iodide symporter activity was greater in cerebral ganglia than digestive gland, but KClO4 treatment did not inhibit iodide uptake in any of the tissues analyzed. Altogether, our data confirm for the first time the presence of thyroid hormones in A. fulica snails and suggest their participation in the metabolism control in this species, although the putative site of hormone biosynthesis remains to be elucidated.

Genomic profile of a Li-Fraumeni-like syndrome patient with a 45,X/46,XX karyotype, presenting neither mutations in TP53 nor clinical stigmata of Turner syndrome.

Li-Fraumeni syndrome (LFS) is a hereditary disorder that predisposes patients to several types of cancer and is associated with TP53 germline mutations. Turner syndrome (TS) is one of the most common aneuploidies in women. Patients with TS have a higher risk of developing cancer, although multiple malignant tumors are extremely rare. Herein, we describe a patient with a 45,X/46,XX karyotype with no classic phenotype of TS. She presented with a clinical diagnosis of Li-Fraumeni-like syndrome (LFL), showing papillary thyroid carcinoma and fibrosarcoma of the left flank, and had no TP53 germline mutations. Genome-wide analysis of copy number variations (CNVs) was assessed in DNA from peripheral blood cells and saliva. A total of 109 rare CNVs in the blood cells, including mosaic loss of the X chromosome (76% of cells), were identified. In saliva, three rare CNVs were detected, all of them were also detected in the blood cells: loss of 8q24.11 (EXT1), gain of 16q24.3 (PRDM7 and GAS8), and the mosaic loss of the X chromosome (50% of cells). Results of conventional G-banding confirmed the 45,X/46,XX karyotype. Surprisingly, the patient presented with an apparently normal phenotype. The PRDM and GAS8 genes are potential candidates to be associated with the risk of developing cancer in this LFL/TS patient.

Combination of docking, molecular dynamics and quantum mechanical calculations for metabolism prediction of 3,4-methylenedioxybenzoyl-2-thienylhydrazone.

In modern drug discovery process, ADME/Tox properties should be determined as early as possible in the test cascade to allow a timely assessment of their property profiles. To help medicinal chemists in designing new compounds with improved pharmacokinetics, the knowledge of the soft spot position or the site of metabolism (SOM) is needed. In silico methods based on docking, molecular dynamics and quantum chemical calculations can bring us closer to understand drug metabolism and predict drug-drug interactions. We report herein on a combined methodology to explore the site of metabolism prediction of a new cardioactive drug prototype, LASSBio-294 (1), using MetaPrint2D to predict the most likely metabolites, combined with structure-based tools using docking, molecular dynamics and quantum mechanical calculations to predict the binding of the substrate to CYP2C9 enzyme, to estimate the binding free energy and to study the energy profiles for the oxidation of (1). Additionally, the computational study was correlated with a metabolic fingerprint profiling using LC-MS analysis. The results obtained using the computational methods gave valuable information about the probable metabolites of (1) (qualitatively) and also about the important interactions of this lead compound with the amino acid residues of the active site of CYP2C9. Moreover, using a combination of different levels of theory sheds light on the understanding of (1) metabolism by CYP2C9 and its mechanisms. The metabolic fingerprint profiling of (1) has shown that the metabolites founded in highest concentration in different species were metabolites M1, M2 and M3, whereas M8 was found to be a minor metabolite. Therefore, our computational study allowed a qualitative prediction for the metabolism of (1). The approach presented here has afforded new opportunities to improve metabolite identification strategies, mediated by not only CYP2C9 but also other CYP450 family enzymes.

Biochemical profile of Biomphalaria glabrata (Mollusca: Gastropoda) after infection by Echinostoma paraensei (Trematoda: Echinostomatidae).

The effect of infection by Echinostoma paraensei on the activity of the enzymes alanine aminotransferase (ALT) and aspartate aminotransferase (AST) and the concentration of total proteins, uric acid and urea in the hemolymph of Biomphalaria glabrata were investigated after exposure to five or 50 miracidia. The biochemical concentrations were measured weekly until the end of the fourth week after exposure. There was a significant decrease in the concentrations of total proteins in the snails exposed both to five and 50 miracidia, as well as an increase in the nitrogenous products of excretion, ALT and AST activities. The higher ALT activity in the hemolymph of the snails after infection with 50 miracidia suggests highest energetic requirement in these snails in relation to snails exposed to five miracidia. The results also suggest an increase in the use of total proteins, since there was increased formation of nitrogenous catabolites, in conformity with an increase in the aminotransferase activities, frequently associated with tissue damages. This can be explained by damage due to penetration by the miracidia and subsequent development of intramolluscan sporocysts and rediae.