Bioinformation. 2022 Nov 30;18(11):1050-1061. doi: 10.6026/973206300181050. eCollection 2022.

ABSTRACT

Artificial Intelligence (AI) is expanding with colossal applications in various sectors. In the healthcare sector, it is booming to make life simpler with utmost accuracy by predicting, diagnosing and up to care with the help of Machine Learning (ML) and Deep Learning (DL) applications. Modern computer algorithms have attained accuracy levels comparable to those of human specialists in medical sciences, although computers often do jobs more quickly than people do. It is also expected that there will not be a mandate for humans to be present for the jobs that machines can do, and it is gaining the highest peak because of good trained artificial models in the medical field. ML enhances the therapeutic process and improves health by encouraging more patient participation. ML may get more accurate patient data when used with the Internet of Medical Things (IoMT) and automate message notifications that prompt patients to respond at certain times. The motivation behind this article is to make a comprehensive review of the on-going implementation of ML in medical science, what challenges it is facing now, and how it can be simplified for future researchers to contribute better to medical sciences while applying it to the practitioners' jobs easier. In this review, we have extensively mined the data and brought up systematised applications of AI in healthcare, what challenges have been faced by the experts, and what ethical responsibilities are liable to them while taking the data. We also tabulated which algorithms will be helpful for what kind of data and disease conditions will be useful for future researchers and developers. This article will provide a better insight into AI and ML for the beginner to the advanced developer and researcher to understand the concepts from the basics.

PMID:37693078 | PMC:PMC10484692 | DOI:10.6026/973206300181050

Am J Transl Res. 2023 Aug 15;15(8):4984-5006. eCollection 2023.

ABSTRACT

Drug repurposing, also known as drug repositioning, entails the application of pre-approved or formerly assessed drugs having potentially functional therapeutic amalgams for curing various disorders or disease conditions distinctive from their original remedial indication. It has surfaced as a substitute for the development of drugs for treating cancer, cardiovascular diseases, neurodegenerative disorders, and various infectious diseases like Covid-19. Although the earlier lines of findings in this area were serendipitous, recent advancements are based on patient centered approaches following systematic, translational, drug targeting practices that explore pathophysiological ailment mechanisms. The presence of definite information and numerous records with respect to beneficial properties, harmfulness, and pharmacologic characteristics of repurposed drugs increase the chances of approval in the clinical trial stages. The last few years have showcased the successful emergence of repurposed drug immunotherapy in treating various diseases. In this light, the present review emphasises on incorporation of drug repositioning with Immunotherapy targeted for several disorders.

PMID:37692967 | PMC:PMC10492070

J Adv Pharm Technol Res. 2023 Jul-Sep;14(3):196-201. doi: 10.4103/JAPTR.JAPTR_129_23. Epub 2023 Jul 28.

ABSTRACT

Fusobacterium nucleatum is a Gram-negative anaerobic bacteria that is commonly found in oral cavities and is associated with connective tissue destruction in periodontitis. UDP-N-acetylglucosamine 1-carboxyltransferase with enzyme commission number 2.5.1.7 is a transferases enzyme that plays a role in bacterial pathogenesis. Inhibiting binding sites of UDP-N-acetylglucosamine 1-carboxyltransferase is needed to find potential antibiotic candidates for periodontitis treatment. Hence, the research aimed to present potential UDP-N-acetylglucosamine 1-carboxyltransferase inhibiting compounds through molecular docking simulation by in silico analysis. DrugBank database was used to obtain the antibacterial candidates, which were further screened computationally using the AutoDock Vina program on Google Colab Pro. The top nine compounds yielded binding affinity ranging from -12.1 to -12.8 kcal/mol, with conivaptan as one of the three compounds having the highest binding affinity. Molecular dynamic study revealed that the ligand-protein complex for conivaptan had root-mean-square deviation values of 0.05-1.1 nm, indicating likeliness for stable interaction. Our findings suggest that conivaptan is the potent UDP-N-acetylglucosamine 1-carboxyltransferase inhibitor, hence its efficacy against periodontitis-causing bacteria.

PMID:37692019 | PMC:PMC10483916 | DOI:10.4103/JAPTR.JAPTR_129_23

J Biomol Struct Dyn. 2023 Sep 10:1-22. doi: 10.1080/07391102.2023.2256878. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) is a slowly progressive neurodegenerative disease and a leading cause of dementia. We aim to identify key genes for the development of therapeutic targets and biomarkers for potential treatments for AD. Meta-analysis was performed on six microarray datasets and identified the differentially expressed genes between healthy and Alzheimer's disease samples. Thereafter, we filtered out the common genes which were present in at least four microarray datasets for downstream analysis. We have constructed a gene-gene network for the common genes and identified six hub genes. Furthermore, we investigated the regulatory mechanisms of these hub genes by analysing their interaction with miRNAs and transcription factors. The gene ontology analysis results highlighted the enriched terms significantly associated with hub genes. Through an extensive literature survey, we found that three of the hub genes including GRIN1, SYN2, and SYT1 were critically involved in disease development. To leverage existing drugs for potential repurposing, we predicted drug-gene interaction using the drug-gene interaction database, and performed molecular docking studies. The docking results revealed that the drug compounds had strong interactions and favorable binding with selected hub genes. Lorazepam exhibits a binding energy of -7.3 kcal/mol with GRIN1, Lorediplon exhibits binding energies of -7.7 kcal/mol and -6.3 kcal/mol with the SYT1, and SYN2 respectively. In addition, 100 ns molecular dynamics simulations were carried out for the top complexes and apo protein as well. Furthermore, the MM-PBSA free energy calculations also revealed that these complexes are stable and had favorable energies. According to our study, the identified hub gene could serve as a biomarker as well as a therapeutic target for AD, and the proposed repurposed drug molecules appear to have promising efficacy in treating the disease.Communicated by Ramaswamy H. Sarma.

PMID:37691428 | DOI:10.1080/07391102.2023.2256878

NPJ Regen Med. 2023 Sep 9;8(1):48. doi: 10.1038/s41536-023-00325-z.

ABSTRACT

Alveolar type 2 (AT2) cells function as stem cells in the adult lung and aid in repair after injury. The current study aimed to understand the signaling events that control differentiation of this therapeutically relevant cell type during human development. Using lung explant and organoid models, we identified opposing effects of TGFβ- and BMP-signaling, where inhibition of TGFβ- and activation of BMP-signaling in the context of high WNT- and FGF-signaling efficiently differentiated early lung progenitors into AT2-like cells in vitro. AT2-like cells differentiated in this manner exhibit surfactant processing and secretion capabilities, and long-term commitment to a mature AT2 phenotype when expanded in media optimized for primary AT2 culture. Comparing AT2-like cells differentiated with TGFβ-inhibition and BMP-activation to alternative differentiation approaches revealed improved specificity to the AT2 lineage and reduced off-target cell types. These findings reveal opposing roles for TGFβ- and BMP-signaling in AT2 differentiation and provide a new strategy to generate a therapeutically relevant cell type in vitro.

PMID:37689780 | DOI:10.1038/s41536-023-00325-z

Physiol Rep. 2023 Sep;11(17):e15809. doi: 10.14814/phy2.15809.

ABSTRACT

OBJECTIVES: Myocardial infarction (MI) initiates a complex reparative response during which damaged cardiac muscle is replaced by connective tissue. While the initial repair is essential for survival, excessive fibrosis post-MI is a primary contributor to progressive cardiac dysfunction, and ultimately heart failure. Currently, there are no approved drugs for the prevention or the reversal of cardiac fibrosis. Therefore, we tested the therapeutic potential of repurposed mesalazine as a post-MI therapy, as distinct antifibrotic effects have recently been demonstrated.

METHODS: At 8 weeks of age, MI was induced in male C57BL/6J mice by LAD ligation. Mesalazine was administered orally at a dose of 100 μg/g body weight in drinking water. Fluid intake, weight development, and cardiac function were monitored for 28 days post intervention. Fibrosis parameters were assessed histologically and via qPCR.

RESULTS: Compared to controls, mesalazine treatment offered no survival benefit. However, no adverse effects on heart and kidney function and weight development were observed, either. While total cardiac fibrosis remained largely unaffected by mesalazine treatment, we found a distinct reduction of perivascular fibrosis alongside reduced cardiac collagen expression.

CONCLUSIONS: Our findings warrant further studies on mesalazine as a potential add-on therapy post-MI, as perivascular fibrosis development was successfully prevented.

PMID:37688424 | DOI:10.14814/phy2.15809

Molecules. 2023 Aug 30;28(17):6330. doi: 10.3390/molecules28176330.

ABSTRACT

The use of protease inhibitors in human immunodeficiency virus type 1 (HIV-1) treatment is limited by adverse effects, including metabolic complications. To address these challenges, efforts are underway in the pursuit of more potent and less toxic HIV-1 protease inhibitors. Repurposing existing drugs offers a promising avenue to expedite the drug discovery process, saving both time and costs compared to conventional de novo drug development. This study screened FDA-approved and investigational drugs in the DrugBank database for their potential as HIV-1 protease inhibitors. Molecular docking studies and cell-based assays, including anti-HIV-1 in vitro assays and XTT cell viability tests, were conducted to evaluate their efficacy. The study findings revealed that CBR003PS, an antibiotic currently in clinical use, and CBR013PS, an investigational drug for treating endometriosis and uterine fibroids, exhibited significant binding affinity to the HIV-1 protease with high stability. Their EC50 values, measured at 100% cell viability, were 9.4 nM and 36.6 nM, respectively. Furthermore, cell-based assays demonstrated that these two compounds showed promising results, with therapeutic indexes higher than 32. In summary, based on their favorable therapeutic indexes, CBR003PS and CBR013PS show potential for repurposing as HIV-1 protease inhibitors.

PMID:37687159 | DOI:10.3390/molecules28176330

Cancers (Basel). 2023 Sep 3;15(17):4406. doi: 10.3390/cancers15174406.

ABSTRACT

Cancer stem cells (CSCs) are relevant therapeutic targets for cancer treatment. Still, the molecular circuits behind CSC characteristics are not fully understood. The low number of CSCs can sometimes be an obstacle to carrying out assays that explore their properties. Thus, increasing CSC numbers via small molecule-mediated cellular reprogramming appears to be a valid alternative tool. Using the SORE6-GFP reporter system embedded in gastric non-CSCs (SORE6-), we performed a high-throughput image-based drug screen with 1200 small molecules to identify compounds capable of converting SORE6- to SORE6+ (CSCs). Here, we report that the antifungal agent ciclopirox olamine (CPX), a potential candidate for drug repurposing in cancer treatment, is able to reprogram gastric non-CSCs into cancer stem-like cells via activation of SOX2 expression and increased expression of C-MYC, HIF-1α, KLF4, and HMGA1. This reprogramming depends on the CPX concentration and treatment duration. CPX can also induce cellular senescence and the metabolic shift from oxidative phosphorylation (OXPHOS) to glycolysis. We also disclose that the mechanism underlying the cellular reprogramming is similar to that of cobalt chloride (CoCl2), a hypoxia-mimetic agent.

PMID:37686684 | DOI:10.3390/cancers15174406

Cancers (Basel). 2023 Sep 2;15(17):4402. doi: 10.3390/cancers15174402.

ABSTRACT

The etiology of bladder cancer remains unclear. This study investigates the impact of gene polymorphisms, particularly methylenetetrahydrofolate reductase gene (MTHFR), on bladder cancer susceptibility, focusing on the rs1801133 single-nucleotide polymorphism (SNP). A meta-analysis was conducted after systematically reviewing the MTHFR gene literature, adhering to PRISMA guidelines and registering in PROSPERO (CRD42023423064). Seven studies were included, showing a significant association between the MTHFR C677T (rs1801133) polymorphism and bladder cancer susceptibility. Individuals with the T-allele or TT genotype had a higher likelihood of bladder cancer. In the Asian population, the overall analysis revealed an odds ratio (OR) of 1.15 (95% CI 1.03-1.30; p-value = 0.03) for T-allele versus C-allele and an OR of 1.34 (95% CI 1.04-1.72; p-value = 0.02) for TT genotype versus TC+CC genotype. The CC genotype, however, showed no significant association with bladder cancer. Notably, epigenetic findings displayed low sensitivity but high specificity, indicating reliable identified associations while potentially overlooking some epigenetic factors related to bladder cancer. In conclusion, the MTHFR T-allele and TT genotype were associated with increased bladder cancer risk in the Asian population. These insights into genetic factors influencing bladder cancer susceptibility could inform targeted prevention and treatment strategies. Further research is warranted to validate and expand these findings.

PMID:37686678 | DOI:10.3390/cancers15174402

Cancers (Basel). 2023 Aug 30;15(17):4326. doi: 10.3390/cancers15174326.

ABSTRACT

The development of MDM4 inhibitors as an approach to reactivating p53 in human cancer is attracting increasing attention; however, whether they affect the function of MDM2 and how they interact with MDM2 inhibitors remain unknown. We addressed this question in the present study using CEP-1347, an inhibitor of MDM4 protein expression. The effects of CEP-1347, the genetic and/or pharmacological inhibition of MDM2, and their combination on the p53 pathway in malignant brain tumor cell lines expressing wild-type p53 were investigated by RT-PCR and Western blot analyses. The growth inhibitory effects of CEP-1347 alone or in combination with MDM2 on inhibition were examined by dye exclusion and/or colony formation assays. The treatment of malignant brain tumor cell lines with CEP-1347 markedly increased MDM2 protein expression, while blocking CEP-1347-induced MDM2 overexpression by genetic knockdown augmented the effects of CEP-1347 on the p53 pathway and cell growth. Blocking the MDM2-p53 interaction using the small molecule MDM2 inhibitor RG7112, but not MDM2 knockdown, reduced MDM4 expression. Consequently, RG7112 effectively cooperated with CEP-1347 to reduce MDM4 expression, activate the p53 pathway, and inhibit cell growth. The present results suggest the combination of CEP-1347-induced MDM2 overexpression with the selective inhibition of MDM2's interaction with p53, while preserving its ability to inhibit MDM4 expression, as a novel and rational strategy to effectively reactivate p53 in wild-type p53 cancer cells.

PMID:37686602 | DOI:10.3390/cancers15174326

Biochem Biophys Res Commun. 2023 Aug 26;679:116-121. doi: 10.1016/j.bbrc.2023.08.050. Online ahead of print.

ABSTRACT

Increased phosphoinositide signaling is commonly associated with cancers. While "one-drug one-target" has been a major drug discovery strategy for cancer therapy, a "one-drug multi-targets" approach for phosphoinositide enzymes has the potential to offer a new therapeutic approach. In this study, we sought a new way to target phosphoinositides metabolism. Using a high-throughput phosphatidylinositol 5-phosphate 4-kinase-alpha (PI5P4Kα) assay, we have identified that the immunosuppressor KRP203/Mocravimod induces a significant perturbation in phosphoinositide metabolism in U87MG glioblastoma cells. Despite high sequence similarity of PI5P4K and PI4K isozymes, in vitro kinase assays showed that KRP203 activates some (e.g., PI5P4Kα, PI4KIIβ) while inhibiting other phosphoinositide kinases (e.g., PI5P4Kβ, γ, PI4KIIα, class I PI3K-p110α, δ, γ). Furthermore, KRP203 enhances PI3P5K/PIKFYVE's substrate selectivity for phosphatidylinositol (PI) while preserving its selectivity for PI(3)P. At cellular levels, 3 h of KRP203 treatment induces a prominent increase of PI(3)P and moderate increase of PI(5)P, PI(3,5)P2, and PI(3,4,5)P3 levels in U87MG cells. Collectively, the finding of multimodal activity of KRP203 towards multi-phosphoinositide kinases may open a novel basis to modulate cellular processes, potentially leading to more effective treatments for diseases associated with phosphoinositide signaling pathways.

PMID:37683456 | DOI:10.1016/j.bbrc.2023.08.050

Medicine (Baltimore). 2023 Sep 8;102(36):e34671. doi: 10.1097/MD.0000000000034671.

ABSTRACT

Research has demonstrated that obesity is an important risk factor for cancer progression. Orlistat is a lipase inhibitor with promising therapeutic effects on obesity. In addition to being regarded as a slimming drug, a growing number of studies in recent years have suggested that orlistat has anti-tumor activities, while the underlying mechanism is still not well elucidated. This paper reviewed recent pharmacological effects and mechanisms of orlistat against tumors and found that orlistat can target cancer cells through activation or suppression of multiple signaling pathways. It can induce tumor cells apoptosis or death, interfere with tumor cells' cycles controlling, suppress fatty acid synthase activity, increase ferroptosis, inhibit tumor angiogenesis, and improve tumor cells glycolytic. Thus, this review may shed new light on anti-tumor mechanism and drug repurposing of orlistat, and anti-tumor drug development.

PMID:37682175 | DOI:10.1097/MD.0000000000034671

Cancer Invest. 2023 Sep 8:1-25. doi: 10.1080/07357907.2023.2255672. Online ahead of print.

ABSTRACT

This study aimed to reveal the drug repurposing candidates for colorectal cancer (CRC) via drug repurposing methods and network biology approaches. A novel, differentially co-expressed, highly interconnected, and co-regulated prognostic gene module was identified for CRC. Based on the gene module, polyethylene glycol, gallic acid, pyrazole, cordycepin, phenothiazine, pantoprazole, cysteamine, indisulam, valinomycin, trametinib, BRD-K81473043, AZD8055, dovitinib, BRD-A17065207, and tyrphostin AG1478 presented as drugs and small molecule candidates previously studied in the CRC. Lornoxicam, suxamethonium, oprelvekin, sirukumab, levetiracetam, sulpiride, NVP-TAE684, AS605240, 480743.cdx, HDAC6 inhibitor ISOX, BRD-K03829970, and L-6307 are proposed as novel drugs and small molecule candidates for CRC.

PMID:37682113 | DOI:10.1080/07357907.2023.2255672

Curr Drug Discov Technol. 2023 Sep 6. doi: 10.2174/1570163820666230901160043. Online ahead of print.

ABSTRACT

Drug-target interactions (DTIs) are an important part of the drug development process. When the drug (a chemical molecule) binds to a target (proteins or nucleic acids), it modulates the biological behavior/function of the target, returning it to its normal state. Predicting DTIs plays a vital role in the drug discovery (DD) process as it has the potential to enhance efficiency and reduce costs. However, DTI prediction poses significant challenges and expenses due to the time-consuming and costly nature of experimental assays. As a result, researchers have increased their efforts to identify the association between medications and targets in the hopes of speeding up drug development and shortening the time to market. This paper provides a detailed discussion of the initial stage in drug discovery, namely drug-target interactions. It focuses on exploring the application of machine learning methods within this step. Additionally, we aim to conduct a comprehensive review of relevant papers and databases utilized in this field. Drug target interaction prediction covers a wide range of applications: drug discovery, prediction of adverse effects and drug repositioning. The prediction of drugtarget interactions can be categorized into three main computational methods: docking simulation approaches, ligand-based methods, and machine-learning techniques.

PMID:37680152 | DOI:10.2174/1570163820666230901160043

Sci Rep. 2023 Sep 7;13(1):14733. doi: 10.1038/s41598-023-36796-9.

ABSTRACT

Clostridioides difficile infections (CDIs) are responsible for a significant number of antibiotic-associated diarrheal cases. The standard-of-care antibiotics for C. difficile are limited to fidaxomicin and vancomycin, with the recently obsolete metronidazole recommended if both are unavailable. No new antimicrobials have been approved for CDI since fidaxomicin in 2011, despite varying rates of treatment failure among all standard-of-care drugs. Drug repurposing is a rational strategy to generate new antimicrobials out of existing therapeutics approved for other indications. Auranofin is a gold-containing anti-rheumatic drug with antimicrobial activity against C. difficile and other microbes. In a previous report, our group hypothesized that inhibition of selenoprotein biosynthesis was auranofin's primary mechanism of action against C. difficile. However, in this study, we discovered that C. difficile mutants lacking selenoproteins are still just as sensitive to auranofin as their respective wild-type strains. Moreover, we found that selenite supplementation dampens the activity of auranofin against C. difficile regardless of the presence of selenoproteins, suggesting that selenite's neutralization of auranofin is not because of compensation for a chemically induced selenium deficiency. Our results clarify the findings of our original study and may aid drug repurposing efforts in discovering the compound's true mechanism of action against C. difficile.

PMID:37679389 | DOI:10.1038/s41598-023-36796-9

Math Biosci Eng. 2023 Jun 26;20(8):14136-14157. doi: 10.3934/mbe.2023632.

ABSTRACT

Drugs, which treat various diseases, are essential for human health. However, developing new drugs is quite laborious, time-consuming, and expensive. Although investments into drug development have greatly increased over the years, the number of drug approvals each year remain quite low. Drug repositioning is deemed an effective means to accelerate the procedures of drug development because it can discover novel effects of existing drugs. Numerous computational methods have been proposed in drug repositioning, some of which were designed as binary classifiers that can predict drug-disease associations (DDAs). The negative sample selection was a common defect of this method. In this study, a novel reliable negative sample selection scheme, named RNSS, is presented, which can screen out reliable pairs of drugs and diseases with low probabilities of being actual DDAs. This scheme considered information from k-neighbors of one drug in a drug network, including their associations to diseases and the drug. Then, a scoring system was set up to evaluate pairs of drugs and diseases. To test the utility of the RNSS, three classic classification algorithms (random forest, bayes network and nearest neighbor algorithm) were employed to build classifiers using negative samples selected by the RNSS. The cross-validation results suggested that such classifiers provided a nearly perfect performance and were significantly superior to those using some traditional and previous negative sample selection schemes.

PMID:37679129 | DOI:10.3934/mbe.2023632

Transl Res. 2023 Sep 5:S1931-5244(23)00141-X. doi: 10.1016/j.trsl.2023.09.001. Online ahead of print.

ABSTRACT

BACKGROUND & AIMS: Cyclic nucleotide elevation in intestinal epithelial cells is the key pathology causing intestinal fluid loss in secretory diarrheas such as cholera. Current secretory diarrhea treatment is primarily supportive, and oral rehydration solution is the mainstay of cholera treatment. There is an unmet need for safe, simple and effective diarrhea treatments. By promoting cAMP hydrolysis, extracellular calcium-sensing receptor (CaSR) is a regulator of intestinal fluid transport.

METHODS: We studied the antidiarrheal mechanisms of FDA-approved CaSR activator cinacalcet and tested its efficacy in clinically relevant human cell, mouse and intestinal organoid models of secretory diarrhea.

RESULTS: By using selective inhibitors, we found that cAMP agonists-induced secretory short-circuit currents (Isc) in human intestinal T84 cells are mediated by collective actions of apical membrane CFTR and Clc-2 Cl- channels, and basolateral membrane K+ channels. 30 μM cinacalcet pretreatment inhibited all three components of forskolin and cholera toxin-induced secretory Isc by ∼75%. In mouse jejunal mucosa, cinacalcet inhibited forskolin-induced secretory Isc by ∼60% in wild type mice, with no antisecretory effect in intestinal epithelia-specific Casr knockout mice (Casr-flox; Vil1-cre). In suckling mouse model of cholera induced by oral cholera toxin, single dose (30 mg/kg) oral cinacalcet treatment reduced intestinal fluid accumulation by ∼55% at 20 hours. Lastly, cinacalcet inhibited forskolin-induced secretory Isc by ∼75% in human colonic and ileal organoids.

CONCLUSIONS: Our findings suggest that CaSR activator cinacalcet has antidiarrheal efficacy in distinct human cell, organoid and mouse models of secretory diarrhea. Considering its excellent clinical safety profile, cinacalcet can be repurposed as a treatment for secretory diarrheas including cholera.

PMID:37678755 | DOI:10.1016/j.trsl.2023.09.001

Biol Psychiatry. 2023 Sep 5:S0006-3223(23)01554-8. doi: 10.1016/j.biopsych.2023.08.023. Online ahead of print.

ABSTRACT

BACKGROUND: Chronic pain is a common, poorly-understood condition. Genetic studies including genome wide association studies (GWAS) identify many relevant variants, which have yet to be translated into full understanding of chronic pain. Transcriptome wide association study using transcriptomic imputation (TI) methods such as S-PrediXcan can help bridge this genotype-phenotype gap.

METHODS: We carried out TI using S-PrediXcan to identify genetically regulated gene expression (GREX) associated with Multisite Chronic Pain (MCP), in thirteen brain tissues and whole blood. We then imputed GREX for over 31,000 Mount Sinai BioMe™ participants and performed phenome-wide association study (PheWAS) to investigate clinical relationships in chronic pain associated gene expression changes.

RESULTS: We identified 95 experiment-wide significant gene-tissue associations (p<7.97x10-7), including 35 unique genes, and an additional 134 gene-tissue associations reaching within-tissue significance, including 53 additional unique genes. Of 89 unique genes total, 59 were novel for MCP and 18 are established drug targets. Chronic pain GREX for 10 unique genes was significantly associated with cardiac dysrhythmia, metabolic syndrome, disc disorders/ dorsopathies, joint/ligament sprain, anemias, and neurological disorder phecodes. PheWAS analyses adjusting for mean painscore showed associations were not driven by mean painscore.

CONCLUSIONS: We carried out the largest TI study of any chronic pain trait to date. Results highlight potential causal genes in chronic pain development, and tissue and direction of effect. Several gene results were also drug targets. PheWAS results showed significant association for phecodes including cardiac dysrhythmia and metabolic syndrome, indicating potential shared mechanisms.

PMID:37678542 | DOI:10.1016/j.biopsych.2023.08.023

J Bioinform Comput Biol. 2023 Sep 6:2350018. doi: 10.1142/S021972002350018X. Online ahead of print.

ABSTRACT

Over the past decades, many existing drugs and clinical/preclinical compounds have been repositioned as new therapeutic indication from which they were originally intended and to treat off-target diseases by targeting their noncognate protein receptors, such as Sildenafil and Paxlovid, termed drug repurposing (DRP). Despite its significant attraction in the current medicinal community, the DRP is usually considered as a matter of accidents that cannot be fulfilled reliably by traditional drug discovery protocol. In this study, we proposed an integrated computational/experimental (iC/E) strategy to facilitate the DRP within a framework of rational drug design, which was practiced on the identification of new neuronal nitric oxide synthase (nNOS) inhibitors from a structurally diverse, functionally distinct drug pool. We demonstrated that the iC/E strategy is very efficient and readily feasible, which confirmed that the phosphodiesterase inhibitor DB06237 showed a high inhibitory potency against nNOS synthase domain, while other two general drugs, i.e. DB02302 and DB08258, can also inhibit the synthase at nanomolar level. Structural bioinformatics analysis revealed diverse noncovalent interactions such as hydrogen bonds, hydrophobic forces and van der Waals contacts across the complex interface of nNOS active site with these identified drugs, conferring both stability and specificity for the complex recognition and association.

PMID:37675491 | DOI:10.1142/S021972002350018X

Front Microbiol. 2023 Aug 22;14:1179414. doi: 10.3389/fmicb.2023.1179414. eCollection 2023.

ABSTRACT

INTRODUCTION: With the increasingly serious problem of antiviral drug resistance, drug repurposing offers a time-efficient and cost-effective way to find potential therapeutic agents for disease. Computational models have the ability to quickly predict potential reusable drug candidates to treat diseases.

METHODS: In this study, two matrix decomposition-based methods, i.e., Matrix Decomposition with Heterogeneous Graph Inference (MDHGI) and Bounded Nuclear Norm Regularization (BNNR), were integrated to predict anti-viral drugs. Moreover, global leave-one-out cross-validation (LOOCV), local LOOCV, and 5-fold cross-validation were implemented to evaluate the performance of the proposed model based on datasets of DrugVirus that consist of 933 known associations between 175 drugs and 95 viruses.

RESULTS: The results showed that the area under the receiver operating characteristics curve (AUC) of global LOOCV and local LOOCV are 0.9035 and 0.8786, respectively. The average AUC and the standard deviation of the 5-fold cross-validation for DrugVirus datasets are 0.8856 ± 0.0032. We further implemented cross-validation based on MDAD and aBiofilm, respectively, to evaluate the performance of the model. In particle, MDAD (aBiofilm) dataset contains 2,470 (2,884) known associations between 1,373 (1,470) drugs and 173 (140) microbes. In addition, two types of case studies were carried out further to verify the effectiveness of the model based on the DrugVirus and MDAD datasets. The results of the case studies supported the effectiveness of MHBVDA in identifying potential virus-drug associations as well as predicting potential drugs for new microbes.

PMID:37675432 | PMC:PMC10478006 | DOI:10.3389/fmicb.2023.1179414