Curr Health Sci J. 2023 Apr-Jun;49(2):210-219. doi: 10.12865/CHSJ.49.02.210. Epub 2023 Jun 30.

ABSTRACT

Stroke is a major cause of death and disability worldwide. Between 1990 and 2010, its global burden increased notably with reference to the absolute number of incident events, number of deaths, and disability-adjusted life-years lost. Trazodone is a triazolopyridine derivative that was approved for more than 40 years as monotherapy or in combination with other antidepressant drugs for the treatment of major depressive disorder in adult patients. The aim was investigated if trazodone can improve behavioural outcome after stroke in a mice model of middle cerebral artery occlusion (MCAo) due to the potential neuroprotective and neurodegenerative effects by using three behavioural tests: adhesive tape test, beam test and hole board test. Trazodone administration show modest improvements regarding the motor-sensorial function after stroke especially in the acute post-stroke phase in aged and young animals. The antidepressant effect of the drug was observed in the post-stroke period in aged animals and to a lesser extent in young animals. Future research is needed to evaluate the effects of trazodone at the cellular level to be sure that it has no benefit in stroke patients who do not suffer from depression.

PMID:37786617 | PMC:PMC10541511 | DOI:10.12865/CHSJ.49.02.210

Arch Biochem Biophys. 2023 Sep 30:109770. doi: 10.1016/j.abb.2023.109770. Online ahead of print.

ABSTRACT

Angiotensin receptor blockers (ARBs) have been reported to be beneficial of renal fibrosis, but the molecular and cellular mechanisms are still unclear. In this study, we investigated the effectiveness and relevant mechanism of ARBs in alleviating renal fibrosis, especially by focusing on biomechanical stress-induced epithelial to mesenchymal transition (EMT) of renal epithelial cells. Unilateral ureteral obstruction (UUO) renal fibrosis model was established in mice by ligating the left ureter, and then randomly received losartan at a low dose (1 mg/kg) or a regular dose (3 mg/kg) for 2 weeks. Compared to the control, histological analysis showed that losartan treatment at either a low dose or a regular dose effectively attenuated renal fibrosis in the UUO model. To further understand the mechanism, we ex vivo loaded primary human renal epithelial cells to 50 mmHg hydrostatic pressure. Western blot and immunostaining analyses indicated that the loading to 50 mmHg hydrostatic pressure for 24 h significantly upregulated vimentin, β-catenin and α-SMA, but downregulated E-cadherin in renal epithelial cells, suggesting the EMT. The addition of 10 or 100 nM losartan in medium effectively attenuated the EMT of renal epithelial cells induced by 50 mmHg hydrostatic pressure loading. Our in vivo and ex vivo experimental data suggest that losartan treatment, even at a low dose can effectively alleviate renal fibrosis in mouse UUO model, at least partly by inhibiting the biomechanical stress-induced EMT of renal epithelial cells. A low dose of ARBs may repurpose for renal fibrosis treatment.

PMID:37783367 | DOI:10.1016/j.abb.2023.109770

ACS Omega. 2023 Sep 15;8(38):34583-34596. doi: 10.1021/acsomega.3c03471. eCollection 2023 Sep 26.

ABSTRACT

Cancer remains a major cause of morbidity and mortality worldwide, and while current therapies, such as chemotherapy, immunotherapy, and cell therapy, have been effective in many patients, the development of novel therapeutic options remains an urgent priority. Mouse double minute 2 (MDM2) is a key regulator of the tumor suppressor protein p53, which plays a critical role in regulating cellular growth, apoptosis, and DNA repair. Consequently, MDM2 has been the subject of extensive research aimed at developing novel cancer therapies. In this study, we employed a machine learning-based approach to establish a quantitative structure-activity relationship model capable of predicting the potential in vitro efficacy of small molecules as MDM2 inhibitors. Our model was used to screen 5883 FDA-approved drugs, resulting in the identification of promising hits that were subsequently evaluated using molecular docking and molecular dynamics simulations. Two antihistamine drugs, cetirizine (CZ) and rupatadine (RP), exhibited particularly favorable results in the initial in silico analyses. To further assess their potential use as the activators of the p53 pathway, we investigated the antiproliferative capability of the abovementioned drugs on human glioblastoma and neuroblastoma cell lines. Both the compounds exhibited significant antiproliferative effects on the abovementioned cell lines in a dose-dependent manner. The half-maximal inhibitory concentration (IC50) of CZ was found to be 697.87 and 941.37 μM on U87 and SH-SY5Y cell lines, respectively, while the IC50 of RP was found to be 524.28 and 617.07 μM on the same cell lines, respectively. Further investigation by quantitative reverse transcriptase polymerase chain reaction analysis revealed that the CZ-treated cell lines upregulate the expression of the p53-regulated genes involved in cell cycle arrest, apoptosis, and DNA damage response compared to their respective vehicle controls. These findings suggest that CZ activates the p53 pathway by inhibiting MDM2. Our results provide compelling preclinical evidence supporting the potential use of CZ as a modulator of the MDM2-p53 axis and its plausible repurposing for cancer treatment.

PMID:37779953 | PMC:PMC10536845 | DOI:10.1021/acsomega.3c03471

Cureus. 2023 Aug 30;15(8):e44359. doi: 10.7759/cureus.44359. eCollection 2023 Aug.

ABSTRACT

Artificial intelligence (AI) has transformed pharmacological research through machine learning, deep learning, and natural language processing. These advancements have greatly influenced drug discovery, development, and precision medicine. AI algorithms analyze vast biomedical data identifying potential drug targets, predicting efficacy, and optimizing lead compounds. AI has diverse applications in pharmacological research, including target identification, drug repurposing, virtual screening, de novo drug design, toxicity prediction, and personalized medicine. AI improves patient selection, trial design, and real-time data analysis in clinical trials, leading to enhanced safety and efficacy outcomes. Post-marketing surveillance utilizes AI-based systems to monitor adverse events, detect drug interactions, and support pharmacovigilance efforts. Machine learning models extract patterns from complex datasets, enabling accurate predictions and informed decision-making, thus accelerating drug discovery. Deep learning, specifically convolutional neural networks (CNN), excels in image analysis, aiding biomarker identification and optimizing drug formulation. Natural language processing facilitates the mining and analysis of scientific literature, unlocking valuable insights and information. However, the adoption of AI in pharmacological research raises ethical considerations. Ensuring data privacy and security, addressing algorithm bias and transparency, obtaining informed consent, and maintaining human oversight in decision-making are crucial ethical concerns. The responsible deployment of AI necessitates robust frameworks and regulations. The future of AI in pharmacological research is promising, with integration with emerging technologies like genomics, proteomics, and metabolomics offering the potential for personalized medicine and targeted therapies. Collaboration among academia, industry, and regulatory bodies is essential for the ethical implementation of AI in drug discovery and development. Continuous research and development in AI techniques and comprehensive training programs will empower scientists and healthcare professionals to fully exploit AI's potential, leading to improved patient outcomes and innovative pharmacological interventions.

PMID:37779744 | PMC:PMC10539991 | DOI:10.7759/cureus.44359

Diabetes Obes Metab. 2023 Oct 1. doi: 10.1111/dom.15298. Online ahead of print.

ABSTRACT

AIM: Genome-wide association studies (GWAS) have identified multiple susceptibility loci associated with insulin resistance (IR)-relevant phenotypes. However, the genes responsible for these associations remain largely unknown. We aim to identify susceptibility genes for IR-relevant phenotypes via a transcriptome-wide association study.

MATERIALS AND METHODS: We conducted a large-scale multi-tissue transcriptome-wide association study for IR (Insulin Sensitivity Index, homeostasis model assessment-IR, fasting insulin) and lipid-relevant traits (high-density lipoprotein cholesterol, triglycerides, low-density lipoprotein cholesterol and total cholesterol) using the largest GWAS summary statistics and precomputed gene expression weights of 49 human tissues. Conditional and joint analyses were implemented to identify significantly independent genes. Furthermore, we estimated the causal effects of independent genes by Mendelian randomization causal inference analysis.

RESULTS: We identified 1190 susceptibility genes causally associated with IR-relevant phenotypes, including 58 genes that were not implicated in the original GWAS. Among them, 11 genes were further supported in differential expression analyses or a gene knockout mice database, such as KRIT1 showed both significantly differential expression and IR-related phenotypic effects in knockout mice. Meanwhile, seven proteins encoded by susceptibility genes were targeted by clinically approved drugs, and three of these genes (H6PD, CACNB2 and DRD2) have been served as drug targets for IR-related diseases/traits. Moreover, drug repurposing analysis identified four compounds with profiles opposing the expression of genes associated with IR risk.

CONCLUSIONS: Our study provided new insights into IR aetiology and avenues for therapeutic development.

PMID:37779362 | DOI:10.1111/dom.15298

Methods. 2023 Sep 29:S1046-2023(23)00164-0. doi: 10.1016/j.ymeth.2023.09.010. Online ahead of print.

ABSTRACT

Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to discover and develop innovative drugs. The use of AI in drug discovery is still in its early stages, but it has the potential to revolutionize the way new drugs are discovered and developed. As AI technology continues to evolve, it is likely that AI will play an even greater role in the future of drug discovery. AI is used to identify new drug targets, design new molecules, and predict the efficacy and safety of potential drugs. For example, the AI-powered platform ATOM developed by Atom wise can screen millions of compounds in a matter of hours, identifying potential drug candidates that would have taken years to find using traditional methods. AI is highly utilized in the pharmaceutical industry by optimizing processes, reducing waste, and ensuring quality control. This review covers much-needed topics, including the different types of machine-learning techniques, their applications in drug discovery, and the challenges and limitations of using machine learning in this field. The state-of-the-art of AI-assisted pharmaceutical discovery is described, covering applications in structure- and ligand-based virtual screening, de novo drug creation, prediction of physicochemical and pharmacokinetic properties, drug repurposing, and related topics. Finally, many obstacles and limits of present approaches are outlined, with an eye on potential future avenues for AI-assisted drug discovery and design.

PMID:37778659 | DOI:10.1016/j.ymeth.2023.09.010

BMC Bioinformatics. 2023 Sep 30;24(1):367. doi: 10.1186/s12859-023-05497-5.

ABSTRACT

BACKGROUND: Obtaining accurate drug-target binding affinity (DTA) information is significant for drug discovery and drug repositioning. Although some methods have been proposed for predicting DTA, the features of proteins and drugs still need to be further analyzed. Recently, deep learning has been successfully used in many fields. Hence, designing a more effective deep learning method for predicting DTA remains attractive.

RESULTS: Dynamic graph DTA (DGDTA), which uses a dynamic graph attention network combined with a bidirectional long short-term memory (Bi-LSTM) network to predict DTA is proposed in this paper. DGDTA adopts drug compound as input according to its corresponding simplified molecular input line entry system (SMILES) and protein amino acid sequence. First, each drug is considered a graph of interactions between atoms and edges, and dynamic attention scores are used to consider which atoms and edges in the drug are most important for predicting DTA. Then, Bi-LSTM is used to better extract the contextual information features of protein amino acid sequences. Finally, after combining the obtained drug and protein feature vectors, the DTA is predicted by a fully connected layer. The source code is available from GitHub at https://github.com/luojunwei/DGDTA .

CONCLUSIONS: The experimental results show that DGDTA can predict DTA more accurately than some other methods.

PMID:37777712 | DOI:10.1186/s12859-023-05497-5

Med Oncol. 2023 Sep 30;40(11):312. doi: 10.1007/s12032-023-02183-7.

ABSTRACT

Immunotherapies are promising therapeutic options for the management of triple-negative breast cancer because of its high mutation rate and genomic instability. Of note, the blockade of the immune checkpoint protein PD-1 and its ligand PD-L1 has been proven to be an efficient and potent strategy to combat triple-negative breast cancer. To date, various anti-PD-1/anti-PD-L1 antibodies have been approved. However, the intrinsic constraints of these therapeutic antibodies significantly limit their application, making small molecules a potentially significant option for PD-1/PD-L1 inhibition. In light of this, the current study aims to use a high-throughput virtual screening technique to identify potential repurposed candidates as PD-L1 inhibitors. Thus, the present study explored binding efficiency of 2509 FDA-approved compounds retrieved from the drug bank database against PD-L1 protein. The binding affinity of the compounds was determined using the glide XP docking programme. Furthermore, prime-MM/GBSA, DFT calculations, and RF score were used to precisely re-score the binding free energy of the docked complexes. In addition, the ADME and toxicity profiles for the lead compounds were also examined to address PK/PD characteristics. Altogether, the screening process identified three molecules, namely DB01238, DB06016 and DB01167 as potential therapeutics for the PD-L1 protein. To conclude, a molecular dynamic simulation of 100 ns was run to characterise the stability and inhibitory action of the three lead compounds. The results from the simulation study confirm the robust structural and thermodynamic stability of DB01238 than other investigated molecules. Thus, our findings hypothesize that DB01238 could serve as potential PD-L1 inhibitor in the near future for triple-negative breast cancer patients.

PMID:37777635 | DOI:10.1007/s12032-023-02183-7

J Comput Aided Mol Des. 2023 Sep 30. doi: 10.1007/s10822-023-00533-1. Online ahead of print.

ABSTRACT

Drug discovery, especially virtual screening and drug repositioning, can be accelerated through deeper understanding and prediction of Drug Target Interactions (DTIs). The advancement of deep learning as well as the time and financial costs associated with conventional wet-lab experiments have made computational methods for DTI prediction more popular. However, the majority of these computational methods handle the DTI problem as a binary classification task, ignoring the quantitative binding affinity that determines the drug efficacy to their target proteins. Moreover, computational space as well as execution time of the model is often ignored over accuracy. To address these challenges, we introduce a novel method, called Time-efficient Multimodal Drug Target Binding Affinity (TeM-DTBA), which predicts the binding affinity between drugs and targets by fusing different modalities based on compound structures and target sequences. We employ the Lasso feature selection method, which lowers the dimensionality of feature vectors and speeds up the proposed model training time by more than 50%. The results from two benchmark datasets demonstrate that our method outperforms state-of-the-art methods in terms of performance. The mean squared errors of 18.8% and 23.19%, achieved on the KIBA and Davis datasets, respectively, suggest that our method is more accurate in predicting drug-target binding affinity.

PMID:37777631 | DOI:10.1007/s10822-023-00533-1

Sci Rep. 2023 Sep 30;13(1):16443. doi: 10.1038/s41598-023-43219-2.

ABSTRACT

Neuroblastoma, the most common type of pediatric extracranial solid tumor, causes 10% of childhood cancer deaths. Despite intensive multimodal treatment, the outcomes of high-risk neuroblastoma remain poor. We urgently need to develop new therapies with safe long-term toxicity profiles for rapid testing in clinical trials. Drug repurposing is a promising approach to meet these needs. Here, we investigated disulfiram, a safe and successful chronic alcoholism treatment with known anticancer and epigenetic effects. Disulfiram efficiently induced cell cycle arrest and decreased the viability of six human neuroblastoma cell lines at half-maximal inhibitory concentrations up to 20 times lower than its peak clinical plasma level in patients treated for chronic alcoholism. Disulfiram shifted neuroblastoma transcriptome, decreasing MYCN levels and activating neuronal differentiation. Consistently, disulfiram significantly reduced the protein level of lysine acetyltransferase 2A (KAT2A), drastically reducing acetylation of its target residues on histone H3. To investigate disulfiram's anticancer effects in an in vivo model of high-risk neuroblastoma, we developed a disulfiram-loaded emulsion to deliver the highly liposoluble drug. Treatment with the emulsion significantly delayed neuroblastoma progression in mice. These results identify KAT2A as a novel target of disulfiram, which directly impacts neuroblastoma epigenetics and is a promising candidate for repurposing to treat pediatric neuroblastoma.

PMID:37777587 | DOI:10.1038/s41598-023-43219-2

Nat Cardiovasc Res. 2023 Jun;2(6):550-571. doi: 10.1038/s44161-023-00278-y. Epub 2023 Jun 8.

ABSTRACT

The development of new immunotherapies to treat the inflammatory mechanisms that sustain atherosclerotic cardiovascular disease (ASCVD) is urgently needed. Herein, we present a path to drug repurposing to identify immunotherapies for ASCVD. The integration of time-of-flight mass cytometry and RNA sequencing identified unique inflammatory signatures in peripheral blood mononuclear cells stimulated with ASCVD plasma. By comparing these inflammatory signatures to large-scale gene expression data from the LINCS L1000 dataset, we identified drugs that could reverse this inflammatory response. Ex vivo screens, using human samples, showed that saracatinib-a phase 2a-ready SRC and ABL inhibitor-reversed the inflammatory responses induced by ASCVD plasma. In Apoe-/- mice, saracatinib reduced atherosclerosis progression by reprogramming reparative macrophages. In a rabbit model of advanced atherosclerosis, saracatinib reduced plaque inflammation measured by [18F] fluorodeoxyglucose positron emission tomography-magnetic resonance imaging. Here we show a systems immunology-driven drug repurposing with a preclinical validation strategy to aid the development of cardiovascular immunotherapies.

PMID:37771373 | PMC:PMC10538622 | DOI:10.1038/s44161-023-00278-y

ESMO Open. 2023 Sep 26;8(5):101642. doi: 10.1016/j.esmoop.2023.101642. Online ahead of print.

ABSTRACT

Precision medicine for cancer is rapidly moving to an approach that integrates multiple dimensions of the biology in order to model mechanisms of cancer progression in each patient. The discovery of multiple drivers per tumor challenges medical decision that faces several treatment options. Drug sensitivity depends on the actionability of the target, its clonal or subclonal origin and coexisting genomic alterations. Sequencing has revealed a large diversity of drivers emerging at treatment failure, which are potential targets for clinical trials or drug repurposing. To effectively prioritize therapies, it is essential to rank genomic alterations based on their proven actionability. Moving beyond primary drivers, the future of precision medicine necessitates acknowledging the intricate spatial and temporal heterogeneity inherent in cancer. The advent of abundant complex biological data will make artificial intelligence algorithms indispensable for thorough analysis. Here, we will discuss the advancements brought by the use of high-throughput genomics, the advantages and limitations of precision medicine studies and future perspectives in this field.

PMID:37769400 | DOI:10.1016/j.esmoop.2023.101642

Int J Mol Sci. 2023 Sep 7;24(18):13826. doi: 10.3390/ijms241813826.

ABSTRACT

The identification of novel drug targets is needed to improve the outcomes of heart failure (HF). G-protein-coupled receptors (GPCRs) represent the largest family of targets for already approved drugs, thus providing an opportunity for drug repurposing. Here, we aimed (i) to investigate the differential expressions of 288 cardiac GPCRs via droplet digital PCR (ddPCR) and bulk RNA sequencing (RNAseq) in a rat model of left ventricular pressure-overload; (ii) to compare RNAseq findings with those of ddPCR; and (iii) to screen and test for novel, translatable GPCR drug targets in HF. Male Wistar rats subjected to transverse aortic constriction (TAC, n = 5) showed significant systolic dysfunction vs. sham operated animals (SHAM, n = 5) via echocardiography. In TAC vs. SHAM hearts, RNAseq identified 69, and ddPCR identified 27 significantly differentially expressed GPCR mRNAs, 8 of which were identified using both methods, thus showing a correlation between the two methods. Of these, Prostaglandin-F2α-receptor (Ptgfr) was further investigated and localized on cardiomyocytes and fibroblasts in murine hearts via RNA-Scope. Antagonizing Ptgfr via AL-8810 reverted angiotensin-II-induced cardiomyocyte hypertrophy in vitro. In conclusion, using ddPCR as a novel screening method, we were able to identify GPCR targets in HF. We also show that the antagonism of Ptgfr could be a novel target in HF by alleviating cardiomyocyte hypertrophy.

PMID:37762130 | DOI:10.3390/ijms241813826

F1000Res. 2022 Mar 23;11:Chem Inf Sci-217. doi: 10.12688/f1000research.109017.2. eCollection 2022.

ABSTRACT

Background: Drug repurposing is an alternative strategy to traditional drug discovery that aims at predicting new uses for already existing drugs or clinical candidates. Drug repurposing has many advantages over traditional drug development, such as reduced attrition rates, time and costs. This is especially the case considering that most drugs investigated for repurposing have already been assessed for their safety in clinical trials. Repurposing campaigns can also be designed for libraries of already synthesized molecules at different levels of biological experimentation, from null to in vitro and in vivo. Such an extension of the "repurposing" concept is expected to provide significant advantages for the identification of novel drugs, as the synthetic accessibility of the desired compounds is often one of the limiting factors in the traditional drug discovery pipeline. Methods: In this work, we performed a computational repurposing campaign on a library of previously synthesized oxindole-based compounds, in order to identify potential new targets for this versatile scaffold. To this aim, ligand-based approaches were firstly applied to evaluate the similarity degree of the investigated compound library, with respect to ligands extracted from the DrugBank, Protein Data Bank (PDB) and ChEMBL databases. In particular, the 2D fingerprint-based and 3D shape-based similarity profiles were evaluated and compared for the oxindole derivates. Results: The analyses predicted a set of potential candidate targets for repurposing, some of them emerging by consensus of different computational analyses. One of the identified targets, i.e., the vascular endothelial growth factor receptor 2 (VEGFR-2) kinase, was further investigated by means of docking calculations, followed by biological testing of one candidate. Conclusions: While the compound did not show potent inhibitory activity towards VEGFR-2, the study highlighted several other possibilities of therapeutically relevant targets that may be worth of consideration for drug repurposing.

PMID:37767081 | PMC:PMC10521104 | DOI:10.12688/f1000research.109017.2

Pharmaceutics. 2023 Aug 24;15(9):2190. doi: 10.3390/pharmaceutics15092190.

ABSTRACT

Conventional drug discovery involves significant steps, time, and expenses; therefore, novel methods for drug discovery remain unmet, particularly for patients with intractable diseases. For this purpose, the drug repurposing method has been recently used to search for new therapeutic agents. Repurposed drugs are mostly previously approved drugs, which were carefully tested for their efficacy for other diseases and had their safety for the human body confirmed following careful pre-clinical trials, clinical trials, and post-marketing surveillance. Therefore, using these approved drugs for other diseases that cannot be treated using conventional therapeutic methods could save time and economic costs for testing their clinical applicability. In this review, we have summarized the methods for identifying repurposable drugs focusing on immunotherapy.

PMID:37765160 | DOI:10.3390/pharmaceutics15092190

Pharmaceuticals (Basel). 2023 Sep 21;16(9):1332. doi: 10.3390/ph16091332.

ABSTRACT

Giardia lamblia is a highly infectious protozoan that causes giardiasis, a gastrointestinal disease with short-term and long-lasting symptoms. The currently available drugs for giardiasis treatment have limitations such as side effects and drug resistance, requiring the search for new antigiardial compounds. Drug repurposing has emerged as a promising strategy to expedite the drug development process. In this study, we evaluated the cytotoxic effect of terfenadine on Giardia lamblia trophozoites. Our results showed that terfenadine inhibited the growth and cell viability of Giardia trophozoites in a time-dose-dependent manner. In addition, using scanning electron microscopy, we identified morphological damage; interestingly, an increased number of protrusions on membranes and tubulin dysregulation with concomitant dysregulation of Giardia GiK were observed. Importantly, terfenadine showed low toxicity for Caco-2 cells, a human intestinal cell line. These findings highlight the potential of terfenadine as a repurposed drug for the treatment of giardiasis and warrant further investigation to elucidate its precise mechanism of action and evaluate its efficacy in future research.

PMID:37765140 | DOI:10.3390/ph16091332

Nutrients. 2023 Sep 6;15(18):3886. doi: 10.3390/nu15183886.

ABSTRACT

Metabolic dysfunction, which includes intra-abdominal adiposity, glucose intolerance, insulin resistance, dyslipidemia, and hypertension, manifests into metabolic syndrome and related diseases. Therefore, the discovery of new therapies in the fight against metabolic syndrome is very challenging. This study aims to reveal the existence of an edible bird nest (EBN) as a functional food candidate that may be a new alternative in fighting metabolic syndrome. The study included three approaches: in silico molecular docking simulation, in vitro, and in vivo in rats fed on cholesterol- and fat-enriched diets. Four terpenoids of Bakuchiol, Curculigosaponin A, Dehydrolindestrenolide, and 1-methyl-3-(1-methyl-ethyl)-benzene in EBN have been identified through LCMS/MS-QTOF. In molecular docking simulations, Bakuchiol and Dehydrolindestrenolide are considered very potent because they have higher inhibitory power on the four receptors (iNOS, ROS1 kinase, FTO, and lipase) than standard drugs. In vitro tests also provide insight into the antioxidant, antidiabetic, and antiobesity activities of EBN, which is quite feasible due to the smaller EC50 value of EBN compared to standard drugs. Interestingly, in vivo studies also showed significant improvements (p < 0.05) in the lipid profile, blood glucose, enzymatic levels, and inflammatory biomarkers in rats given high-dose dietary supplementation of EBN. More interestingly, high-dose dietary supplementation of EBN upregulates PGC-1α and downregulates HMG-CoA reductase. Comprehensively, it has been revealed that EBN can be novel functional foods for combating metabolic syndrome.

PMID:37764670 | DOI:10.3390/nu15183886

Molecules. 2023 Sep 13;28(18):6604. doi: 10.3390/molecules28186604.

ABSTRACT

Obesity is a global issue faced by many individuals worldwide. However, no drug has a pronounced effect with few side effects. Green tea, a well-known natural product, shows preventive effects against obesity by decreasing lipogenesis and increasing fat oxidation and antioxidant capacity. In contrast, other natural products are known to contribute to obesity. Relevant articles published on the therapeutic effect of natural products on obesity were retrieved from PubMed, Web of Science, and Scopus. The search was conducted by entering keywords such as "obesity", "natural product", and "clinical trial". The natural products were classified as single compounds, foods, teas, fruits, herbal medicines-single extract, herbal medicines-decoction, and herbal medicines-external preparation. Then, the mechanisms of these medicines were organized into lipid metabolism, anti-inflammation, antioxidation, appetite loss, and thermogenesis. This review aimed to assess the efficacy and mechanisms of effective natural products in managing obesity. Several clinical studies reported that natural products showed antiobesity effects, including Coffea arabica (coffee), Camellia sinensis (green tea), Caulerpa racemosa (green algae), Allium sativum (garlic), combined Ephedra intermedia Schrenk, Thea sinensis L., and Atractylodes lancea DC extract (known as Gambisan), Ephedra sinica Stapf, Angelica Gigantis Radix, Atractylodis Rhizoma Alba, Coicis semen, Cinnamomi cortex, Paeoniae radix alba, and Glycyrrhiza uralensis (known as Euiiyin-tang formula). Further studies are expected to refine the pharmacological effects of natural products for clinical use.

PMID:37764380 | DOI:10.3390/molecules28186604

Molecules. 2023 Sep 9;28(18):6546. doi: 10.3390/molecules28186546.

ABSTRACT

The prediction of drug-target interaction (DTI) is crucial to drug discovery. Although the interactions between the drug and target can be accurately verified by traditional biochemical experiments, the determination of DTI through biochemical experiments is a time-consuming, laborious, and expensive process. Therefore, we propose a learning-based framework named BG-DTI for drug-target interaction prediction. Our model combines two main approaches based on biological features and heterogeneous networks to identify interactions between drugs and targets. First, we extract original features from the sequence to encode each drug and target. Later, we further consider the relationships among various biological entities by constructing drug-drug similarity networks and target-target similarity networks. Furthermore, a graph convolutional network and a graph attention network in the graph representation learning module help us learn the features representation of drugs and targets. After obtaining the features from graph representation learning modules, these features are combined into fusion descriptors for drug-target pairs. Finally, we send the fusion descriptors and labels to a random forest classifier for predicting DTI. The evaluation results show that BG-DTI achieves an average AUC of 0.938 and an average AUPR of 0.930, which is better than those of five existing state-of-the-art methods. We believe that BG-DTI can facilitate the development of drug discovery or drug repurposing.

PMID:37764321 | DOI:10.3390/molecules28186546

Microorganisms. 2023 Sep 6;11(9):2242. doi: 10.3390/microorganisms11092242.

ABSTRACT

The emergence of multidrug-resistant strains requires the urgent discovery of new antibacterial drugs. In this context, an antibacterial screening of a subset of anthelmintic avermectins against gram-positive and gram-negative strains was performed. Selamectin completely inhibited bacterial growth at 6.3 μg/mL concentrations against reference gram-positive strains, while no antibacterial activity was found against gram-negative strains up to the highest concentration tested of 50 μg/mL. Given its relevance as a community and hospital pathogen, further studies have been performed on selamectin activity against Staphylococcus aureus (S. aureus), using clinical isolates with different antibiotic resistance profiles and a reference biofilm-producing strain. Antibacterial studies have been extensive on clinical S. aureus isolates with different antibiotic resistance profiles. Mean MIC90 values of 6.2 μg/mL were reported for all tested S. aureus strains, except for the macrolide-resistant isolate with constitutive macrolide-lincosamide-streptogramin B resistance phenotype (MIC90 9.9 μg/mL). Scanning Electron Microscopy (SEM) showed that selamectin exposure caused relevant cell surface alterations. A synergistic effect was observed between ampicillin and selamectin, dictated by an FIC value of 0.5 against methicillin-resistant strain. Drug administration at MIC concentration reduced the intracellular bacterial load by 81.3%. The effect on preformed biofilm was investigated via crystal violet and confocal laser scanning microscopy. Selamectin reduced the biofilm biomass in a dose-dependent manner with minimal biofilm eradication concentrations inducing a 50% eradication (MBEC50) at 5.89 μg/mL. The cytotoxic tests indicated that selamectin exhibited no relevant hemolytic and cytotoxic activity at active concentrations. These data suggest that selamectin may represent a timely and promising macrocyclic lactone for the treatment of S. aureus infections.

PMID:37764086 | DOI:10.3390/microorganisms11092242