Synthesis and Biological Evaluation of 2-Azolylmethylene-3-(2H)-benzofuranone Derivatives as Potent Monoamine Oxidases Inhibitors.

A series of 2-azolylmethylene-3-(2H)-benzofuranone derivatives, 2-indolylmethylene-3-(2H)-benzofuranone and 2-pyrrolylmethylene-3-(2H)-benzofuranone derivatives, were synthesized, and their monoamine oxidase (MAO) A and B inhibitory activities were evaluated. Compounds 1b, 3b, 6b, 7b, and 10b showed strong inhibitory activity against MAO-A, and compound 3b showed the highest potency and selectivity, with an IC50 value of 21 nM and a MAO-A selectivity index of 48. Compounds 3c, 4c, 9a, 9c, 10c, 11a, and 11c showed strong inhibitory activity against MAO-B, and compound 4c showed the highest potency and selectivity, with an IC50 value of 16 nM and a MAO-B selectivity index of >1100. Further analysis of these compounds indicated that compound 3b for MAO-A and compound 4c for MAO-B were competitive inhibitors, with Ki values of 10 and 6.1 nM, respectively. Furthermore, computational analyses, such as quantitative structure-activity relationship (QSAR) analysis of the 2-azolylmethylene-3-(2H)-benzofuranone derivatives conducting their pIC50 values with the Molecular Operating Environment (MOE) and Mordred, and molecular docking analysis using MOE-Dock supported that the 2-azolylmethylene-3-(2H)-benzofuranone derivatives are a privileged scaffold for the design and development of novel MAO inhibitors.

Development of in silico prediction models for drug-induced liver malignant tumors based on the activity of molecular initiating events: Biologically interpretable features.

Liver malignant tumors (LMTs) have recently been reported as severe and life-threatening adverse drug events associated with drug-induced liver injury (DILI). DILIs are the most common adverse drug event and can cause the withdrawal of medicinal products or major regulatory action. To reduce the attrition rate and cost of drug discovery, various quantitative structure-toxicity relationship models have been proposed to predict the probability of a DILI based on the chemical structure of a drug. However, there are many unresolved issues regarding the predictors of LMT-inducing drugs, and biologically interpretable prediction models for LMT have not been developed. Here, we constructed prediction models for whether a drug is LMT-inducing based on the activity of molecular initiating events (MIEs), which are biologically interpretable features and are defined as the initial interaction between a molecule and biosystem. We then constructed five machine learning models (i.e., LightGBM, XGBoost, random forest, neural network, and support vector machine) and evaluated their predictive performances. LightGBM achieved the best performance among the tested models. The MIEs making the highest contribution to the model construction for drug-induced LMT were inducement of Enhanced Level of Genome Instability Gene 1 (human ATAD5), nuclear factor-κ B, and activation of thyrotropin-releasing hormone receptor. These results support the previous literature and can be related to the mechanism onset of drug-induced LMT. Our findings may provide useful knowledge for drug development, research, and regulatory decision-making and will contribute to building more accurate and meaningful DILI prediction models by increasing understanding of biological predictors.

Tumor-Specificity, Neurotoxicity, and Possible Involvement of the Nuclear Receptor Response Pathway of 4,6,8-Trimethyl Azulene Amide Derivatives.

BACKGROUND: Very few papers covering the anticancer activity of azulenes have been reported, as compared with those of antibacterial and anti-inflammatory activity. This led us to investigate the antitumor potential of fifteen 4,6,8-trimethyl azulene amide derivatives against oral malignant cells. METHODS: 4,6,8-Trimethyl azulene amide derivatives were newly synthesized. Anticancer activity was evaluated by tumor-specificity against four human oral squamous cell carcinoma (OSCC) cell lines over three normal oral cells. Neurotoxicity was evaluated by cytotoxicity against three neuronal cell lines over normal oral cells. Apoptosis induction was evaluated by Western blot and cell cycle analyses. RESULTS: Among fifteen derivatives, compounds 7, 9, and 15 showed the highest anticancer activity, and relatively lower neurotoxicity than doxorubicin, 5-fluorouracil (5-FU), and melphalan. They induced the accumulation of a comparable amount of a subG1 population, but slightly lower extent of caspase activation, as compared with actinomycin D, used as an apoptosis inducer. The quantitative structure-activity relationship analysis suggests the significant correlation of tumor-specificity with a 3D shape of molecules, and possible involvement of inflammation and hormone receptor response pathways. CONCLUSIONS: Compounds 7 and 15 can be potential candidates of a lead compound for developing novel anticancer drugs.

Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure-Activity Relationship System.

In silico approaches have been studied intensively to assess the toxicological risk of various chemical compounds as alternatives to traditional in vivo animal tests. Among these approaches, quantitative structure-activity relationship (QSAR) analysis has the advantages that it is able to construct models to predict the biological properties of chemicals based on structural information. Previously, we reported a deep learning (DL) algorithm-based QSAR approach called DeepSnap-DL for high-performance prediction modeling of the agonist and antagonist activity of key molecules in molecular initiating events in toxicological pathways using optimized hyperparameters. In the present study, to achieve high throughput in the DeepSnap-DL system-which consists of the preparation of three-dimensional molecular structures of chemical compounds, the generation of snapshot images from the three-dimensional chemical structures, DL, and statistical calculations-we propose an improved DeepSnap-DL approach. Using this improved system, we constructed 59 prediction models for the agonist and antagonist activity of key molecules in the Tox21 10K library. The results indicate that modeling of the agonist and antagonist activity with high prediction performance and high throughput can be achieved by optimizing suitable parameters in the improved DeepSnap-DL system.

Molecular Initiating Events Associated with Drug-Induced Liver Malignant Tumors: An Integrated Study of the FDA Adverse Event Reporting System and Toxicity Predictions.

Liver malignant tumors (LMTs) represent a serious adverse drug event associated with drug-induced liver injury. Increases in endocrine-disrupting chemicals (EDCs) have attracted attention in recent years, due to their liver function-inhibiting abilities. Exposure to EDCs can induce nonalcoholic fatty liver disease and nonalcoholic steatohepatitis, which are major etiologies of LMTs, through interaction with nuclear receptors (NR) and stress response pathways (SRs). Therefore, exposure to potential EDC drugs could be associated with drug-induced LMTs. However, the drug classes associated with LMTs and the molecular initiating events (MIEs) that are specific to these drugs are not well understood. In this study, using the Food and Drug Administration Adverse Event Reporting System, we detected LMT-inducing drug signals based on adjusted odds ratios. Furthermore, based on the hypothesis that drug-induced LMTs are triggered by NR and SR modulation of potential EDCs, we used the quantitative structure-activity relationship platform for toxicity prediction to identify potential MIEs that are specific to LMT-inducing drug classes. Events related to cell proliferation and apoptosis, DNA damage, and lipid accumulation were identified as potential MIEs, and their relevance to LMTs was supported by the literature. The findings of this study may contribute to drug development and research, as well as regulatory decision making.

Comprehensive study on potent and selective carbonic anhydrase inhibitors: Synthesis, bioactivities and molecular modelling studies of 4-(3-(2-arylidenehydrazine-1-carbonyl)-5-(thiophen-2-yl)-1H-pyrazole-1-yl) benzenesulfonamides.

In this research, rational design, synthesis, carbonic anhydrases (CAs) inhibitory effects, and cytotoxicities of the 4-(3-(2-arylidenehydrazine-1-carbonyl)-5-(thiophen-2-yl)-1H-pyrazole-1-yl)benzenesulfonamides 1-20 were reported. Compound 18 (Ki = 7.0 nM) was approximately 127 times more selective cancer-associated hCA IX inhibitor over hCA I, while compound 17 (Ki = 10.6 nM) was 47 times more selective inhibitor of hCA XI over hCA II compared to the acetazolamide. Compounds 11 (CC50 = 5.2 µM) and 20 (CC50 = 1.6 µM) showed comparative tumor-specificity (TS= > 38.5; >128.2) with doxorubicin (TS > 43.0) towards HSC-2 cancer cell line. Western blot analysis demonstrated that 11 induced slightly apoptosis whereas 20 did not induce detectable apoptosis. A preliminary analysis showed that some correlation of tumor-specificity of 1-20 with the chemical descriptors that reflect hydrophobic volume, dipole moment, lowest hydrophilic energy, and topological structure. Molecular docking simulations were applied to the synthesized ligands to elucidate the predicted binding mode and selectivity profiles towards hCA I, hCA II, and hCA IX.

Analysis of Factors Associated with Hiccups Using the FAERS Database.

In this study, we used the large number of cases in the FDA adverse-event reporting system (FAERS) database to investigate risk factors for drug-induced hiccups and to explore the relationship between hiccups and gender. From 11,810,863 adverse drug reactions reported between the first quarter of 2004 and the first quarter of 2020, we extracted only those in which side effects occurred between the beginning and end of drug administration. Our sample included 1454 adverse reactions for hiccups, with 1159 involving males and 257 involving females (the gender in 38 reports was unknown). We performed univariate analyses of the presence or absence of hiccups for each drug and performed multivariate analysis by adding patient information. The multivariate analysis showed nicotine products to be key suspect drugs for both men and women. For males, the risk factors for hiccups included older age, lower body weight, nicotine, and 14 other drugs. For females, only nicotine and three other drugs were extracted as independent risk factors. Using FAERS, we were thus able to extract new suspect drugs for drug-induced hiccups. Furthermore, this is the first report of a gender-specific analysis of risk factors for hiccups that provides novel insights into drug-induced hiccups, and it suggests that the mechanism responsible is strongly related to gender. Thus, this study can contribute to elucidating the mechanism underlying this phenomenon.

A Toxicity Prediction Tool for Potential Agonist/Antagonist Activities in Molecular Initiating Events Based on Chemical Structures.

Because the health effects of many compounds are unknown, regulatory toxicology must often rely on the development of quantitative structure-activity relationship (QSAR) models to efficiently discover molecular initiating events (MIEs) in the adverse-outcome pathway (AOP) framework. However, the QSAR models used in numerous toxicity prediction studies are publicly unavailable, and thus, they are challenging to use in practical applications. Approaches that simultaneously identify the various toxic responses induced by a compound are also scarce. The present study develops Toxicity Predictor, a web application tool that comprehensively identifies potential MIEs. Using various chemicals in the Toxicology in the 21st Century (Tox21) 10K library, we identified potential endocrine-disrupting chemicals (EDCs) using a machine-learning approach. Based on the optimized three-dimensional (3D) molecular structures and XGBoost algorithm, we established molecular descriptors for QSAR models. Their predictive performances and applicability domain were evaluated and applied to Toxicity Predictor. The prediction performance of the constructed models matched that of the top model in the Tox21 Data Challenge 2014. These advanced prediction results for MIEs are freely available on the Internet.