Plants (Basel). 2023 Nov 10;12(22):3819. doi: 10.3390/plants12223819.

ABSTRACT

Paclitaxel is a chemotherapeutic drug reported to have excellent activity against tumors; however, various side effects, including peripheral neuropathy, limit its use in some cases. In this study, the effect of Phlomidis radix (P.Radix) extract was assessed on paclitaxel-induced cold and mechanical peripheral neuropathy in mice. Multiple paclitaxel injections (accumulative dose of 8 mg/kg, i.p.) induced increased behavioral responses to cold and mechanical stimuli in mice from D10 to D21 after the first paclitaxel injection. Cold and mechanical stimuli were performed by acetone drop and von Frey filament, respectively. Oral administrations of 25% ethanol extract of P.Radix (300 and 500 mg/kg) relieved cold and mechanical pain in a dose-dependent manner. Furthermore, among the various transient receptor potential (TRP) cation channel subfamilies, paclitaxel upregulated the spinal gene expression of transient receptor potential vanilloid 1 (TRPV1) and melastatin 4 (TRPM4), but not ankyrin 1 (TRPA1). However, 500 mg/kg but not 300 mg/kg of P.Radix extract significantly downregulated the gene expression of TRPV1 but not TRPM4. Among the components of P.Radix, sesamoside was identified and quantified by high-performance liquid chromatography (HPLC), and the administration of sesamoside (7.5 mg/kg, i.p.) showed a similar analgesic effect to 300 mg/kg P.Radix. These results suggest that P.Radix and sesamoside should be considered when treating paclitaxel-induced neuropathic pain.

PMID:38005716 | DOI:10.3390/plants12223819

Molecules. 2023 Nov 14;28(22):7594. doi: 10.3390/molecules28227594.

ABSTRACT

Cancer is a major global public health problem with high morbidity. Depression is known to be a high-frequency complication of cancer diseases that decreases patients' life quality and increases the mortality rate. Therefore, antidepressants are often used as a complementary treatment during cancer therapy. During recent decades, various studies have shown that the combination of antidepressants and anticancer drugs increases treatment efficiency. In recent years, further emerging evidence has suggested that the modulation of autophagy serves as one of the primary anticancer mechanisms for antidepressants to suppress tumor growth. In this review, we introduce the anticancer potential of antidepressants, including tricyclic antidepressants (TCAs), tetracyclic antidepressants (TeCAs), selective serotonin reuptake inhibitors (SSRIs), and serotonin-norepinephrine reuptake inhibitors (SNRIs). In particular, we focus on their autophagy-modulating mechanisms for regulating autophagosome formation and lysosomal degradation. We also discuss the prospect of repurposing antidepressants as anticancer agents. It is promising to repurpose antidepressants for cancer therapy in the future.

PMID:38005316 | DOI:10.3390/molecules28227594

Pharmaceuticals (Basel). 2023 Nov 14;16(11):1603. doi: 10.3390/ph16111603.

ABSTRACT

Entecavir (ETV) is a drug used as a first-line treatment for chronic hepatitis B (CHB) virus infection because it is a guanosine nucleoside analogue with activity against the hepatitis B virus polymerase. The ETV dosage can range from 0.5 mg to 1 mg once a day and the most common side effects include headache, insomnia, fatigue, dizziness, somnolence, vomiting, diarrhea, nausea, dyspepsia, and increased liver enzyme levels. In addition to its conventional use, ETV acts as an inhibitor of lysine-specific demethylase 5B (KDM5B), an enzyme that is overexpressed in breast, lung, skin, liver, and prostate tumors and is involved in the hormonal response, stem cell regeneration, genomic stability, cell proliferation, and differentiation. The KDM5B enzyme acts as a transcriptional repressor in tumor suppressor genes, silencing them, and its overexpression leads to drug resistance in certain tumor types. Furthermore, the literature suggests that KDM5B activates the PI3K/AKT signaling pathway, while reducing KDM5B expression decreases AKT signaling, resulting in decreased tumor cell proliferation. In silico studies have demonstrated that ETV can inhibit tumor cell proliferation and induce apoptosis by reducing KDM5B expression. ETV also appears to inhibit PARP-1, has a high genetic barrier, reducing the chance of resistance development, and can also prevent the reactivation of the hepatitis B virus in cancer patients, which have proven to be significant advantages regarding its use as a repurposed drug in oncology. Therefore, ETV holds promise beyond its original therapeutic indication.

PMID:38004468 | DOI:10.3390/ph16111603

Pharmaceuticals (Basel). 2023 Nov 12;16(11):1594. doi: 10.3390/ph16111594.

ABSTRACT

A series of rosmarinic acid-β-amino-α-ketoamide hybrids were synthesized and rationally repurposed towards the identification of new antileishmanial hit compounds. Two hybrids, 2g and 2h, showed promising activity (IC50 values of 9.5 and 8.8 μM against Leishmania donovani promastigotes, respectively). Their activities were comparable to erufosine. In addition, cytotoxicity evaluation employing human THP-1 cells revealed that the two hybrids 2g and 2h possess no cytotoxic effects up to 100 µM, while erufosine possessed cytotoxicity with CC50 value of 19.4 µM. In silico docking provided insights into structure-activity relationship emphasizing the importance of the aliphatic chain at the α-carbon of the cinnamoyl carbonyl group establishing favorable binding interactions with LdCALP and LARG in both hybrids 2g and 2h. In light of these findings, hybrids 2g and 2h are suggested as potential safe antileishmanial hit compounds for further development of anti-leishmanial agents.

PMID:38004459 | DOI:10.3390/ph16111594

Pharmaceuticals (Basel). 2023 Nov 6;16(11):1563. doi: 10.3390/ph16111563.

ABSTRACT

Osteoarthritis is characterized by progressive articular cartilage degradation, subchondral bone changes, and synovial inflammation, and affects various joints, causing pain and disability. Current osteoarthritis therapies, primarily focused on pain management, face limitations due to limited effectiveness and high risks of adverse effects. Safer and more effective treatments are urgently needed. Considering that the endocannabinoid 2-arachidonoyl glycerol is involved in pain processing, increasing its concentration through monoacylglycerol lipase (MAGL) inhibition reduces pain in various animal models. Furthermore, drug repurposing approaches leverage established drug safety profiles, presenting a cost-effective route to accelerate clinical application. To this end, cetirizine and levetiracetam were examined for their MAGL inhibitory effects. In vitro studies revealed that cetirizine and levetiracetam inhibited MAGL with IC50 values of 9.3931 µM and 3.0095 µM, respectively. In vivo experiments demonstrated that cetirizine, and to a lesser extent levetiracetam, reduced mechanical and thermal nociception in complete Freund adjuvant (CFA)-induced osteoarthritis in rats. Cetirizine exhibited a notable anti-inflammatory effect, reducing CFA-induced inflammation, as well as the inflammatory infiltrate and granuloma formation in the affected paw. These findings suggest that cetirizine may serve as a promising starting point for the development of novel compounds for osteoarthritis treatment, addressing both pain and inflammation.

PMID:38004429 | DOI:10.3390/ph16111563

J Pers Med. 2023 Nov 9;13(11):1586. doi: 10.3390/jpm13111586.

ABSTRACT

The discovery of therapeutic miRNAs is one of the most exciting challenges for pharmaceutical companies. Since the first miRNA was discovered in 1993, our knowledge of miRNA biology has grown considerably. Many studies have demonstrated that miRNA expression is dysregulated in many diseases, making them appealing tools for novel therapeutic approaches. This review aims to discuss miRNA biogenesis and function, as well as highlight strategies for delivering miRNA agents, presenting viral, non-viral, and exosomic delivery as therapeutic approaches for different cancer types. We also consider the therapeutic role of microRNA-mediated drug repurposing in cancer therapy.

PMID:38003902 | DOI:10.3390/jpm13111586

Int J Mol Sci. 2023 Nov 20;24(22):16511. doi: 10.3390/ijms242216511.

ABSTRACT

Drug repurposing is a widely used approach originally developed to aid in the identification of new uses of already existing drugs outside the scope of the original medical indication [...].

PMID:38003701 | DOI:10.3390/ijms242216511

Int J Mol Sci. 2023 Nov 16;24(22):16396. doi: 10.3390/ijms242216396.

ABSTRACT

Glioblastoma multiforme (GBM) is the most aggressive and lethal primary brain tumor whose median survival is less than 15 months. The current treatment regimen comprising surgical resectioning, chemotherapy with Temozolomide (TMZ), and adjuvant radiotherapy does not achieve total patient cure. Stem cells' presence and GBM tumor heterogeneity increase their resistance to TMZ, hence the poor overall survival of patients. A dysregulated cell cycle in glioblastoma enhances the rapid progression of GBM by evading senescence or apoptosis through an over-expression of cyclin-dependent kinases and other protein kinases that are the cell cycle's main regulatory proteins. Herein, we identified and validated the biomarker and predictive properties of a chemoradio-resistant oncogenic signature in GBM comprising CDK1, PBK, and CHEK1 through our comprehensive in silico analysis. We found that CDK1/PBK/CHEK1 overexpression drives the cell cycle, subsequently promoting GBM tumor progression. In addition, our Kaplan-Meier survival estimates validated the poor patient survival associated with an overexpression of these genes in GBM. We used in silico molecular docking to analyze and validate our objective to repurpose Dapagliflozin against CDK1/PBK/CHEK1. Our results showed that Dapagliflozin forms putative conventional hydrogen bonds with CDK1, PBK, and CHEK1 and arrests the cell cycle with the lowest energies as Abemaciclib.

PMID:38003585 | DOI:10.3390/ijms242216396

Int J Mol Sci. 2023 Nov 11;24(22):16209. doi: 10.3390/ijms242216209.

ABSTRACT

Dysregulated metal homeostasis is associated with many pathological conditions, including arthritic diseases. Osteoarthritis and rheumatoid arthritis are the two most prevalent disorders that damage the joints and lead to cartilage and bone destruction. Recent studies show that the levels of zinc (Zn) and copper (Cu) are generally altered in the serum of arthritis patients. Therefore, metal dyshomeostasis may reflect the contribution of these trace elements to the disease's pathogenesis and manifestations, suggesting their potential for prognosis and treatment. Carnosine (Car) also emerged as a biomarker in arthritis and exerts protective and osteogenic effects in arthritic joints. Notably, its zinc(II) complex, polaprezinc, has been recently proposed as a drug-repurposing candidate for bone fracture healing. On these bases, this review article aims to provide an overview of the beneficial roles of Cu and Zn in bone and cartilage health and their potential application in tissue engineering. The effects of Car and polaprezinc in promoting cartilage and bone regeneration are also discussed. We hypothesize that polaprezinc could exchange Zn for Cu, present in the culture media, due to its higher sequestering ability towards Cu. However, future studies should unveil the potential contribution of Cu in the beneficial effects of polaprezinc.

PMID:38003398 | DOI:10.3390/ijms242216209

Eur J Pharmacol. 2023 Nov 22:176176. doi: 10.1016/j.ejphar.2023.176176. Online ahead of print.

ABSTRACT

One of the cost-effective alternative methods to find new inhibitors has been the repositioning approach of existing drugs. The advantage of computational drug repositioning method is saving time and cost to remove the pre-clinical step and accelerate the drug discovery process. Hence, an ensemble computational-experimental approach, consisting of three different steps, a machine learning model, simulation of drug-target interaction and experimental characterization, was developed. The machine learning type used here was different tree classification method, which is one of the best randomize machine learning model to identify potential inhibitors from weak inhibitors. This model was trained more than one-hundred times, and forty top trained models were extracted for the drug repositioning step. The machine learning step aimed to discover the approved drugs with the highest possible success rate in the experimental step. Therefore, among all the identified molecules with more than 0.9 probability in more than 70% of the models, nine compounds, were selected. Besides, out of the nine chosen drugs, seven compounds have been confirmed to inhibit EGF in the published articles since 2019. Hence, two identified compounds, in addition to gefitinib, as a positive control, five weak-inhibitors and one neutral, were considered via molecular docking study. Finally, eight proposed drugs, including gefitinib, were investigated using MTT assay and In-Cell ELISA to characterize the drugs effect on A431 cell growth and EGF-signaling. From our experiments, we could conclude that salicylic acid and piperazine could play an EGF-inhibitor role like gefitinib.

PMID:38000720 | DOI:10.1016/j.ejphar.2023.176176

Exp Biol Med (Maywood). 2023 Nov 24:15353702231209413. doi: 10.1177/15353702231209413. Online ahead of print.

ABSTRACT

The coronavirus disease 2019 (COVID-19) global pandemic resulted in millions of people becoming infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and close to seven million deaths worldwide. It is essential to further explore and design effective COVID-19 treatment drugs that target the main protease of SARS-CoV-2, a major target for COVID-19 drugs. In this study, machine learning was applied for predicting the SARS-CoV-2 main protease binding of Food and Drug Administration (FDA)-approved drugs to assist in the identification of potential repurposing candidates for COVID-19 treatment. Ligands bound to the SARS-CoV-2 main protease in the Protein Data Bank and compounds experimentally tested in SARS-CoV-2 main protease binding assays in the literature were curated. These chemicals were divided into training (516 chemicals) and testing (360 chemicals) data sets. To identify SARS-CoV-2 main protease binders as potential candidates for repurposing to treat COVID-19, 1188 FDA-approved drugs from the Liver Toxicity Knowledge Base were obtained. A random forest algorithm was used for constructing predictive models based on molecular descriptors calculated using Mold2 software. Model performance was evaluated using 100 iterations of fivefold cross-validations which resulted in 78.8% balanced accuracy. The random forest model that was constructed from the whole training dataset was used to predict SARS-CoV-2 main protease binding on the testing set and the FDA-approved drugs. Model applicability domain and prediction confidence on drugs predicted as the main protease binders discovered 10 FDA-approved drugs as potential candidates for repurposing to treat COVID-19. Our results demonstrate that machine learning is an efficient method for drug repurposing and, thus, may accelerate drug development targeting SARS-CoV-2.

PMID:37997891 | DOI:10.1177/15353702231209413

BMC Chem. 2023 Nov 22;17(1):161. doi: 10.1186/s13065-023-01082-2.

ABSTRACT

Melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identified that simultaneous perturbation of Protein kinase B (AKT1) and oncoprotein murine double minute 2 (MDM2) drastically reduces EMT in melanoma. Using the structures of the two protein signatures, virtual screening strategies were performed with the FDA-approved drug library. Furthermore, by combining drug repurposing and computer-aided drug design techniques, followed by molecular dynamics simulation analysis, we identified two potent drugs (Tadalafil and Finasteride) that can efficiently inhibit AKT1 and MDM2 proteins. We propose that these two drugs could be considered for the development of therapeutic strategies for the management of aggressive melanoma.

PMID:37993971 | DOI:10.1186/s13065-023-01082-2

BMC Geriatr. 2023 Nov 22;23(1):767. doi: 10.1186/s12877-023-04457-1.

ABSTRACT

BACKGROUND: Delirium is a prevalent neuropsychiatric medical phenomenon that causes serious emergency outcomes, including mortality and morbidity. It also increases the suffering and the economic burden for families and carers. Unfortunately, the pathophysiology of delirium is still unknown, which is a major obstacle to therapeutic development. The modern network-based system biology and multi-omics analysis approach has been widely used to recover the key drug target biomolecules and signaling pathways associated with disease pathophysiology. This study aimed to identify the major drug target hub-proteins associated with delirium, their regulatory molecules with functional pathways, and repurposable drug candidates for delirium treatment.

METHODS: We used a comprehensive proteomic seed dataset derived from a systematic literature review and the Comparative Toxicogenomics Database (CTD). An integrated multi-omics network-based bioinformatics approach was utilized in this study. The STRING database was used to construct the protein-protein interaction (PPI) network. The gene set enrichment and signaling pathways analysis, the regulatory transcription factors and microRNAs were conducted using delirium-associated genes. Finally, hub-proteins associated repurposable drugs were retrieved from CMap database.

RESULTS: We have distinguished 11 drug targeted hub-proteins (MAPK1, MAPK3, TP53, JUN, STAT3, SRC, RELA, AKT1, MAPK14, HSP90AA1 and DLG4), 5 transcription factors (FOXC1, GATA2, YY1, TFAP2A and SREBF1) and 6 microRNA (miR-375, miR-17-5, miR-17-5p, miR-106a-5p, miR-125b-5p, and miR-125a-5p) associated with delirium. The functional enrichment and pathway analysis revealed the cytokines, inflammation, postoperative pain, oxidative stress-associated pathways, developmental biology, shigellosis and cellular senescence which are closely connected with delirium development and the hallmarks of aging. The hub-proteins associated computationally identified repurposable drugs were retrieved from database. The predicted drug molecules including aspirin, irbesartan, ephedrine-(racemic), nedocromil, and guanidine were characterized as anti-inflammatory, stimulating the central nervous system, neuroprotective medication based on the existing literatures. The drug molecules may play an important role for therapeutic development against delirium if they are investigated more extensively through clinical trials and various wet lab experiments.

CONCLUSION: This study could possibly help future research on investigating the delirium-associated therapeutic target biomarker hub-proteins and repurposed drug compounds. These results will also aid understanding of the molecular mechanisms that underlie the pathophysiology of delirium onset and molecular function.

PMID:37993790 | DOI:10.1186/s12877-023-04457-1

BMC Bioinformatics. 2023 Nov 22;24(1):442. doi: 10.1186/s12859-023-05572-x.

ABSTRACT

Drug repurposing is an exciting field of research toward recognizing a new FDA-approved drug target for the treatment of a specific disease. It has received extensive attention regarding the tedious, time-consuming, and highly expensive procedure with a high risk of failure of new drug discovery. Data-driven approaches are an important class of methods that have been introduced for identifying a candidate drug against a target disease. In the present study, a model is proposed illustrating the integration of drug-disease association data for drug repurposing using a deep neural network. The model, so-called IDDI-DNN, primarily constructs similarity matrices for drug-related properties (three matrices), disease-related properties (two matrices), and drug-disease associations (one matrix). Then, these matrices are integrated into a unique matrix through a two-step procedure benefiting from the similarity network fusion method. The model uses a constructed matrix for the prediction of novel and unknown drug-disease associations through a convolutional neural network. The proposed model was evaluated comparatively using two different datasets including the gold standard dataset and DNdataset. Comparing the results of evaluations indicates that IDDI-DNN outperforms other state-of-the-art methods concerning prediction accuracy.

PMID:37993777 | DOI:10.1186/s12859-023-05572-x

Transl Res. 2023 Oct 30:S1931-5244(23)00177-9. doi: 10.1016/j.trsl.2023.10.005. Online ahead of print.

ABSTRACT

ADPKD is characterized by progressive cyst formation and enlargement leading to kidney failure. Tolvaptan is currently the only FDA-approved treatment for ADPKD; however, it can cause serious adverse effects including hepatotoxicity. There remains an unmet clinical need for effective and safe treatments for ADPKD. The extracellular Ca2+-sensing receptor (CaSR) is a regulator of epithelial ion transport. FDA-approved CaSR activator cinacalcet can reduce cAMP-induced Cl- and fluid secretion in various epithelial cells by activating phosphodiesterases (PDE) that hydrolyze cAMP. Since elevated cAMP is a key mechanism of ADPKD progression by promoting cell proliferation, cyst formation and enlargement (via Cl- and fluid secretion), here we tested efficacy of cinacalcet in cell and animal models of ADPKD. Cinacalcet treatment reduced cAMP-induced Cl- secretion and CFTR activity in MDCK cells as suggested by ∼70 % lower short-circuit current (Isc) changes in response to forskolin and CFTRinh-172, respectively. Cinacalcet treatment inhibited forskolin-induced cAMP elevation by 60 % in MDCK cells, and its effect was completely reversed by IBMX (PDE inhibitor). In MDCK cells treated with forskolin, cinacalcet treatment concentration-dependently reduced cell proliferation, cyst formation and cyst enlargement by up to 50 % without affecting cell viability. Cinacalcet treatment (20 mg/kg/day for 7 days, subcutaneous) reduced renal cyst index in a mouse model of ADPKD (Pkd1flox/flox;Ksp-Cre) by 20 %. Lastly, cinacalcet treatment reduced cyst enlargement and cell proliferation in human ADPKD cells by 60 %. Considering its efficacy as shown here, and favorable safety profile including extensive post-approval data, cinacalcet can be repurposed as a novel ADPKD treatment.

PMID:37990828 | DOI:10.1016/j.trsl.2023.10.005

Int Arch Allergy Immunol. 2023 Nov 21:1-12. doi: 10.1159/000534827. Online ahead of print.

ABSTRACT

INTRODUCTION: Allergic disorders are common diseases marked by the abnormal immune response toward foreign antigens that are not pathogens. Often patients with food allergy also suffer from asthma and eczema. Given the similarities of these diseases and a shortage of effective treatments, developing novel therapeutics against common targets of multiple allergies would offer an efficient and cost-effective treatment for patients.

METHODS: We employed the artificial intelligence-driven target discovery platform, PandaOmics, to identify common targets for treating asthma, eczema, and food allergy. Thirty-two case-control comparisons were generated from 15, 11, and 6 transcriptomics datasets related to asthma (558 cases, 315 controls), eczema (441 cases, 371 controls), and food allergy (208 cases, 106 controls), respectively, and allocated into three meta-analyses for target identification. Top-100 high-confidence targets and Top-100 novel targets were prioritized by PandaOmics for each allergic disease.

RESULTS: Six common high-confidence targets (i.e., IL4R, IL5, JAK1, JAK2, JAK3, and NR3C1) across all three allergic diseases have approved drugs for treating asthma and eczema. Based on the targets' dysregulated expression profiles and their mechanism of action in allergic diseases, three potential therapeutic targets were proposed. IL5 was selected as a high-confidence target due to its strong involvement in allergies. PTAFR was identified for drug repurposing, while RNF19B was selected as a novel target for therapeutic innovation. Analysis of the dysregulated pathways commonly identified across asthma, eczema, and food allergy revealed the well-characterized disease signature and novel biological processes that may underlie the pathophysiology of allergies.

CONCLUSION: Altogether, our study dissects the shared pathophysiology of allergic disorders and reveals the power of artificial intelligence in the exploration of novel therapeutic targets.

PMID:37989115 | DOI:10.1159/000534827

IEEE J Biomed Health Inform. 2023 Nov 21;PP. doi: 10.1109/JBHI.2023.3335275. Online ahead of print.

ABSTRACT

Drug repositioning has emerged as a promising strategy for identifying new therapeutic applications for existing drugs. In this study, we present DRGBCN, a novel computational method that integrates heterogeneous information through a deep bilinear attention network to infer potential drugs for specific diseases. DRGBCN involves constructing a comprehensive drug-disease network by incorporating multiple similarity networks for drugs and diseases. Firstly, we introduce a layer attention mechanism to effectively learn the embeddings of graph convolutional layers from these networks. Subsequently, a bilinear attention network is constructed to capture pairwise local interactions between drugs and diseases. This combined approach enhances the accuracy and reliability of predictions. Finally, a multi-layer perceptron module is employed to evaluate potential drugs. Through extensive experiments on three publicly available datasets, DRGBCN demonstrates better performance over baseline methods in 10-fold cross-validation, achieving an average area under the receiver operating characteristic curve (AUROC) of 0.9399. Furthermore, case studies on bladder cancer and acute lymphoblastic leukemia confirm the practical application of DRGBCN in real-world drug repositioning scenarios. Importantly, our experimental results from the drug-disease network analysis reveal the successful clustering of similar drugs within the same community, providing valuable insights into drug-disease interactions. In conclusion, DRGBCN holds significant promise for uncovering new therapeutic applications of existing drugs, thereby contributing to the advancement of precision medicine.

PMID:37988217 | DOI:10.1109/JBHI.2023.3335275

Expert Opin Drug Discov. 2023 Nov 20:1-8. doi: 10.1080/17460441.2023.2284830. Online ahead of print.

ABSTRACT

INTRODUCTION: Selenium possesses numerous advantageous properties in the field of medicine, and a variety of selenium-containing compounds have been documented to exhibit anti-HIV activity. This paper aims to categorize these compounds and conduct SAR analysis to offer guidance for drug design and optimization.

AREAS COVERED: The authors present a comprehensive review of the reported SAR analysis conducted on selenium-based compounds against HIV, accompanied by a concise discussion regarding the pivotal role of selenium in drug development.

EXPERT OPINION: In addition to the conventional bioisosterism strategy, advanced strategies such as covalent inhibition, fragment-based growth and drug repositioning can also be incorporated into research on selenium-containing anti-HIV drugs. Ebselen, which acts as an HIV capsid inhibitor, serves as a valuable probe compound for the discovery of novel HIV integrase inhibitors. Furthermore, it is crucial not to underestimate the potential toxicity associated with organic selenium compounds despite no reported instances of severe toxicity.

PMID:37988053 | DOI:10.1080/17460441.2023.2284830

Naunyn Schmiedebergs Arch Pharmacol. 2023 Nov 21. doi: 10.1007/s00210-023-02849-z. Online ahead of print.

NO ABSTRACT

PMID:37987797 | DOI:10.1007/s00210-023-02849-z

medRxiv. 2023 Nov 7:2023.11.06.23297993. doi: 10.1101/2023.11.06.23297993. Preprint.

ABSTRACT

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease worldwide, with one in nine people over the age of 65 living with the disease in 2023. In this study, we used a phenome wide association study (PheWAS) approach to identify cross-phenotype associations between previously identified genetic AD and for electronic health record (EHR) diagnoses from the UK Biobank (UKBB) (n=361,194 of European ancestry) and the eMERGE Network (n=105,108 of diverse ancestry). Based on 497 previously identified AD-associated variants from the Alzheimer's Disease Variant Portal (ADVP), we found significant associations primarily in immune and cardiac related diseases in our PheWAS. Replicating variants have widespread impacts on immune genes in diverse tissue types. This study demonstrates the potential of using the PheWAS strategy to improve our understanding of AD progression as well as identify potential drug repurposing opportunities for new treatment and disease prevention strategies.

PMID:37986758 | PMC:PMC10659497 | DOI:10.1101/2023.11.06.23297993