N-substituted phenylbenzamides of the niclosamide chemotype attenuate obesity related changes in high fat diet fed mice

AMPred-CNN: Ames mutagenicity prediction model based on convolutional neural networks

Mutagenicity assessment plays a pivotal role in the safety evaluation of chemicals, pharmaceuticals, and environmental compounds. In recent years, the development of robust computational models for predicting chemical mutagenicity has gained significant attention, driven by the need for efficient and cost-effective toxicity assessments. In this paper, we proposed AMPred-CNN, an innovative Ames mutagenicity prediction model based on Convolutional Neural Networks (CNNs), uniquely employing molecular structures as images to leverage CNNs' powerful feature extraction capabilities. The study employs the widely used benchmark mutagenicity dataset from Hansen et al. for model development and evaluation. Comparative analyses with traditional ML models on different molecular features reveal substantial performance enhancements. AMPred-CNN outshines these models, demonstrating superior accuracy, AUC, F1 score, MCC, sensitivity, and specificity on the test set. Notably, AMPred-CNN is further benchmarked against seven recent ML and DL models, consistently showcasing superior performance with an impressive AUC of 0.954. Our study highlights the effectiveness of CNNs in advancing mutagenicity prediction, paving the way for broader applications in toxicology and drug development.

Effects of environmentally relevant concentrations of niclosamide on lipid metabolism and steroid hormone synthesis in adult female zebrafish

Niclosamide (NIC) is a commonly used molluscicide that reportedly disrupts the endocrine system and may lead to lipid metabolism disorders. However, few studies have investigated the mechanism by which NIC affects the endocrine system from the perspective of lipid metabolism. Adult female zebrafish were fed either a normal-fat diet (NFD) or a high-fat diet (HFD) and then exposed for 28 days to environmentally relevant concentrations of NIC. NIC accumulated most in the liver followed by the brain and then the gonads in both feeding conditions. Somatic index changes confirmed that HFD promotes fish growth, and NIC administration inhibits it. Lipid metabolites were decreased by NIC, as were levels of pregnenolone, androstenedione, estrogen, testosterone, and estradiol, suggesting that NIC impacted steroidogenesis. In addition, gene transcription changes related to the hypothalamic-pituitary-gonad-liver (HPGL) axis and altered ovarian histology strongly suggest that environmental relevant concentrations of NIC exposure may disrupt endocrine function. These findings highlighted that NIC exposure at environmentally relevant concentrations elicited endocrine-disruption effects may through impairing of steroid hormone synthesis.

Pregnancy-associated Steroid Effects on Insulin Sensitivity, Adipogenesis, and Lipogenesis: Role of Wnt/β-Catenin Pathway

Context

The shift in maternal energy metabolism characteristic of pregnancy is thought to be driven by various hormonal changes, especially of ovarian and placental steroids. Imbalances in circulating estradiol (E2) and progesterone (P4) levels during this period are often associated with metabolic disturbances leading to the development of gestational diabetes mellitus (GDM). Since abnormalities in the Wnt pathway effector transcription factor 7-like 2 (TCF7L2) are commonly associated with the occurrence of GDM, we hypothesized that the canonical or β-catenin-dependent Wnt signaling pathway mediates the metabolic actions of E2 and P4.

Objective

Our study was aimed at elucidating the metabolic function of the steroids E2 and P4, and examining the role of the canonical Wnt signaling pathway in mediating the actions of these steroids.

Methods

The ovariectomized (OVX) rat was used as a model system to study the effect of known concentrations of exogenously administered E2 and P4. Niclosamide (Nic) was administered to block Wnt signaling. 3T3-L1 cells were used to analyze changes in differentiation in the presence of the steroids or niclosamide.

Results

In the present study, we observed that E2 enhanced insulin sensitivity and inhibited lipogenesis while P4 increased lipogenic gene expression-in 3T3-L1 adipocytes, and in adipose tissue and skeletal muscle of OVX rats when the dosage of E2 and P4 mimicked that of pregnancy. Both E2 and P4 were also found to upregulate Wnt signaling. Nic nhibited the steroid-mediated increase in Wnt signaling in adipocytes and OVX rats. The insulin-sensitizing and antilipogenic actions of E2 were found to be mediated by the canonical Wnt pathway, but the effects of P4 on lipogenesis appeared to be independent of it. Additionally, it was observed that inhibition of Wnt signaling by Nic hastened adipogenic differentiation, and the inhibitory effect of E2 on differentiation was prevented by Nic.

Conclusion

The findings presented in this study highlight the role of steroids and Wnt pathway in glucose and lipid metabolism and are relevant to understanding the pathophysiology of metabolic disorders arising from hormonal disturbances.

Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives

Toxicity prediction is a critical step in the drug discovery process that helps identify and prioritize compounds with the greatest potential for safe and effective use in humans, while also reducing the risk of costly late-stage failures. It is estimated that over 30% of drug candidates are discarded owing to toxicity. Recently, artificial intelligence (AI) has been used to improve drug toxicity prediction as it provides more accurate and efficient methods for identifying the potentially toxic effects of new compounds before they are tested in human clinical trials, thus saving time and money. In this review, we present an overview of recent advances in AI-based drug toxicity prediction, including the use of various machine learning algorithms and deep learning architectures, of six major toxicity properties and Tox21 assay end points. Additionally, we provide a list of public data sources and useful toxicity prediction tools for the research community and highlight the challenges that must be addressed to enhance model performance. Finally, we discuss future perspectives for AI-based drug toxicity prediction. This review can aid researchers in understanding toxicity prediction and pave the way for new methods of drug discovery.

In Vitro Evaluation and Mitigation of Niclosamide's Liabilities as a COVID-19 Treatment

Niclosamide, an FDA-approved oral anthelmintic drug, has broad biological activity including anticancer, antibacterial, and antiviral properties. Niclosamide has also been identified as a potent inhibitor of SARS-CoV-2 infection in vitro, generating interest in its use for the treatment or prevention of COVID-19. Unfortunately, there are several potential issues with using niclosamide for COVID-19, including low bioavailability, significant polypharmacology, high cellular toxicity, and unknown efficacy against emerging SARS-CoV-2 variants of concern. In this study, we used high-content imaging-based immunofluorescence assays in two different cell models to assess these limitations and evaluate the potential for using niclosamide as a COVID-19 antiviral. We show that despite promising preliminary reports, the antiviral efficacy of niclosamide overlaps with its cytotoxicity giving it a poor in vitro selectivity index for anti-SARS-CoV-2 inhibition. We also show that niclosamide has significantly variable potency against the different SARS-CoV-2 variants of concern and is most potent against variants with enhanced cell-to-cell spread including the B.1.1.7 (alpha) variant. Finally, we report the activity of 33 niclosamide analogs, several of which have reduced cytotoxicity and increased potency relative to niclosamide. A preliminary structure-activity relationship analysis reveals dependence on a protonophore for antiviral efficacy, which implicates nonspecific endolysosomal neutralization as a dominant mechanism of action. Further single-cell morphological profiling suggests niclosamide also inhibits viral entry and cell-to-cell spread by syncytia. Altogether, our results suggest that niclosamide is not an ideal candidate for the treatment of COVID-19, but that there is potential for developing improved analogs with higher clinical translational potential in the future.

In Vitro Evaluation and Mitigation of Niclosamide’s Liabilities as a COVID-19 Treatment.. [PMID: 35860224 PMCID: PMC9298134 DOI: 10.1101/2022.06.24.497526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Niclosamide, an FDA-approved oral anthelmintic drug, has broad biological activity including anticancer, antibacterial, and antiviral properties. Niclosamide has also been identified as a potent inhibitor of SARS-CoV-2 infection in vitro, generating interest in its use for the treatment or prevention of COVID-19. Unfortunately, there are several potential issues with using niclosamide for COVID-19, including low bioavailability, significant polypharmacology, high cellular toxicity, and unknown efficacy against emerging SARS-CoV-2 variants of concern. In this study, we used high-content imaging-based immunofluorescence assays in two different cell models to assess these limitations and evaluate the potential for using niclosamide as a COVID-19 antiviral. We show that despite promising preliminary reports, the antiviral efficacy of niclosamide overlaps with its cytotoxicity giving it a poor in vitro selectivity index for anti-SARS-CoV-2 inhibition. We also show that niclosamide has significantly variable potency against the different SARS-CoV-2 variants of concern and is most potent against variants with enhanced cell-to-cell spread including B.1.1.7. Finally, we report the activity of 33 niclosamide analogs, several of which have reduced cytotoxicity and increased potency relative to niclosamide. A preliminary structure-activity relationship analysis reveals dependence on a protonophore for antiviral efficacy, which implicates nonspecific endolysosomal neutralization as a dominant mechanism of action. Further single-cell morphological profiling suggests niclosamide also inhibits viral entry and cell-to-cell spread by syncytia. Altogether, our results suggest that niclosamide is not an ideal candidate for the treatment of COVID-19, but that there is potential for developing improved analogs with higher clinical translational potential in the future.

Nitro-Deficient Niclosamide Confers Reduced Genotoxicity and Retains Mitochondrial Uncoupling Activity for Cancer Therapy

Niclosamide is an oral anthelmintic drug, approved for use against tapeworm infections. Recent studies suggest however that niclosamide may have broader clinical applications in cancers, spurring increased interest in the functions and mechanisms of niclosamide. Previously, we reported that niclosamide targets a metabolic vulnerability in p53-deficient tumours, providing a basis for patient stratification and personalised treatment strategies. In the present study, we functionally characterised the contribution of the aniline 4'-NO2 group on niclosamide to its cellular activities. We demonstrated that niclosamide induces genome-wide DNA damage that is mechanistically uncoupled from its antitumour effects mediated through mitochondrial uncoupling. Elimination of the nitro group in ND-Nic analogue significantly reduced γH2AX signals and DNA breaks while preserving its antitumour mechanism mediated through a calcium signalling pathway and arachidonic acid metabolism. Lipidomics profiling further revealed that ND-Nic-treated cells retained a metabolite profile characteristic of niclosamide-treated cells. Notably, quantitative scoring of drug sensitivity suggests that elimination of its nitro group enhanced the target selectivity of niclosamide against p53 deficiency. Importantly, the results also raise concern that niclosamide may impose a pleiotropic genotoxic effect, which limits its clinical efficacy and warrants further investigation into alternative drug analogues that may ameliorate any potential unwanted side effects.

A deep neural network-based approach for prediction of mutagenicity of compounds

We are exposed to various chemical compounds present in the environment, cosmetics, and drugs almost every day. Mutagenicity is a valuable property that plays a significant role in establishing a chemical compound's safety. Exposure and handling of mutagenic chemicals in the environment pose a high health risk; therefore, identification and screening of these chemicals are essential. Considering the time constraints and the pressure to avoid laboratory animals' use, the shift to alternative methodologies that can establish a rapid and cost-effective detection without undue over-conservation seems critical. In this regard, computational detection and identification of the mutagens in environmental samples like drugs, pesticides, dyes, reagents, wastewater, cosmetics, and other substances is vital. From the last two decades, there have been numerous efforts to develop the prediction models for mutagenicity, and by far, machine learning methods have demonstrated some noteworthy performance and reliability. However, the accuracy of such prediction models has always been one of the major concerns for the researchers working in this area. The mutagenicity prediction models were developed using deep neural network (DNN), support vector machine, k-nearest neighbor, and random forest. The developed classifiers were based on 3039 compounds and validated on 1014 compounds; each of them encoded with 1597 molecular feature vectors. DNN-based prediction model yielded highest prediction accuracy of 92.95% and 83.81% with the training and test data, respectively. The area under the receiver's operating curve and precision-recall curve values were found to be 0.894 and 0.838, respectively. The DNN-based classifier not only fits the data with better performance as compared to traditional machine learning algorithms, viz., support vector machine, k-nearest neighbor, and random forest (with and without feature reduction) but also yields better performance metrics. In current work, we propose a DNN-based model to predict mutagenicity of compounds.

Lipophilic Cations Rescue the Growth of Yeast under the Conditions of Glycolysis Overflow

Chemicals inducing a mild decrease in the ATP/ADP ratio are considered as caloric restriction mimetics as well as treatments against obesity. Screening for such chemicals in animal model systems requires a lot of time and labor. Here, we present a system for the rapid screening of non-toxic substances causing such a de-energization of cells. We looked for chemicals allowing the growth of yeast lacking trehalose phosphate synthase on a non-fermentable carbon source in the presence of glucose. Under such conditions, the cells cannot grow because the cellular phosphate is mostly being used to phosphorylate the sugars in upper glycolysis, while the biosynthesis of bisphosphoglycerate is blocked. We reasoned that by decreasing the ATP/ADP ratio, one might prevent the phosphorylation of the sugars and also boost bisphosphoglycerate synthesis by providing the substrate, i.e., inorganic phosphate. We confirmed that a complete inhibition of oxidative phosphorylation alleviates the block. As our system includes a non-fermentable carbon source, only the chemicals that did not cause a complete block of mitochondrial ATP synthesis allowed the initial depletion of glucose followed by respiratory growth. Using this system, we found two novel compounds, dodecylmethyl diphenylamine (FS1) and diethyl (tetradecyl) phenyl ammonium bromide (Kor105), which possess a mild membrane-depolarizing activity.

Prenatal High-Salt Diet-Induced Metabolic Disorders via Decreasing Peroxisome Proliferator-Activated Receptor Gamma Coactivator 1α in Adult Male Rat Offspring

SCOPE

Although prenatal high-salt (HS) intake leads to physiological complications in the offspring, little is known regarding its effects on the offspring's glucose metabolism. Therefore, the objectives of this study are to determine the consequences of prenatal HS diet on the offspring's metabolism and to test a potential therapy.

METHODS AND RESULTS

Pregnant rats are fed either a normal-salt (1% NaCl) or high-salt (8% NaCl) diet during the whole pregnancy. Experiments are conducted in five-month-old male offspring. It is found that the prenatal HS diet reduced the glucose tolerance and insulin sensitivity of the offspring. Additionally, there is down-regulation of peroxisome proliferator-activated receptor gamma coactivator 1 alpha (Ppargc1a/PPARGC1A) at the transcript and protein level, which leads to decreased mitochondrial biogenesis and oxidative respiration in skeletal muscle. Moreover, the down-regulation of Ppargc1a is accompanied by decreases in the expression of glucose transporter type 4 (Glut4). With endurance exercise training, these changes are mitigated, which ultimately resulted in improved insulin resistance.

CONCLUSION

These findings suggest that prenatal HS intake induces metabolic disorders via the decreased expression of Ppargc1a in the skeletal muscle of adult offspring, providing novel information concerning the mechanisms and early prevention of metabolic diseases of fetal origins.

PGC-1α activator ZLN005 promotes maturation of cardiomyocytes derived from human embryonic stem cells

Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) have great potential in biomedical applications. However, the immature state of cardiomyocytes obtained using existing protocols limits the application of hPSC-CMs. Unlike adult cardiac myocytes, hPSC-CMs generate ATP through an immature metabolic pathway—aerobic glycolysis, instead of mitochondrial oxidative phosphorylation (OXPHOS). Hence, metabolic switching is critical for functional maturation in hPSC-CMs. Peroxisome proliferator-activated receptor gamma coactivator 1α (PGC-1α) is a key regulator of mitochondrial biogenesis and metabolism, which may help promote cardiac maturation during development. In this study, we investigated the effects of PGC-1α and its activator ZLN005 on the maturation of human embryonic stem cell-derived cardiomyocyte (hESC-CM). hESC-CMs were generated using a chemically defined differentiation protocol and supplemented with either ZLN005 or DMSO (control) on differentiating days 10 to 12. Biological assays were then performed around day 30. ZLN005 treatment upregulated the expressions of PGC-1α and mitochondrial function-related genes in hESC-CMs and induced more mature energy metabolism compared with the control group. In addition, ZLN005 treatment increased cell sarcomere length, improved cell calcium handling, and enhanced intercellular connectivity. These findings support an effective approach to promote hESC-CM maturation, which is critical for the application of hESC-CM in disease modeling, drug screening, and engineering cardiac tissue.