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

Res Sq. 2023 Aug 25:rs.3.rs-3269607. doi: 10.21203/rs.3.rs-3269607/v1. Preprint.

ABSTRACT

Background Parkinson disease (PD) is the fastest growing neurodegenerative disease. The molecular pathology of PD in the prodromal phase is poorly understood; as such, there are no specific prognostic or diagnostic tests. A validated Pink1 genetic knockout rat was used to model early-onset and progressive PD. Male Pink1 -/- rats exhibit progressive declines in ultrasonic vocalizations as well as hindlimb and forelimb motor deficits by mid-to-late adulthood. Previous RNA-sequencing work identified upregulation of genes involved in disease pathways and inflammation within the brainstem and vocal fold muscle. The purpose of this study was to identify gene pathways within the whole blood of young Pink1 -/- rats (3 months of age) and to link gene expression to early acoustical changes. To accomplish this, limb motor testing (open field and cylinder tests) and ultrasonic vocalization data were collected, immediately followed by the collection of whole blood and RNA extraction. Illumina® Total RNA-Seq TruSeq platform was used to profile differential expression of genes. Statistically significant genes were identified and Weighted Gene Co-expression Network Analysis was used to construct co-expression networks and modules from the whole blood gene expression dataset as well as the open field, cylinder, and USV acoustical dataset. ENRICHR was used to identify the top up-regulated biological pathways. Results The data suggest that inflammation and interferon signaling upregulation in the whole blood is present during early PD. We also identified genes involved in the dysregulation of ribosomal protein and RNA processing gene expression as well as prion protein gene expression. Conclusions These data identified several potential blood biomarkers and pathways that may be linked to anxiety and vocalization acoustic parameters and are key candidates for future drug-repurposing work and comparison to human datasets.

PMID:37674708 | PMC:PMC10479403 | DOI:10.21203/rs.3.rs-3269607/v1

IEEE J Biomed Health Inform. 2023 Sep 6;PP. doi: 10.1109/JBHI.2023.3312374. Online ahead of print.

ABSTRACT

Many powerful computational methods based on graph neural networks (GNNs) have been proposed to predict drug-protein interactions (DPIs). It can effectively reduce laboratory workload and the cost of drug discovery and drug repurposing. However, many clinical functions of drugs and proteins are unknown due to their unobserved indications. Therefore, it is difficult to establish a reliable drug-protein heterogeneous network that can describe the relationships between drugs and proteins based on the available information. To solve this problem, we propose a DPI prediction method that can self-adaptively adjust the topological structure of the heterogeneous networks, and name it SATS. SATS establishes a representation learning module based on graph attention network to carry out the drug-protein heterogeneous network. It can self-adaptively learn the relationships among the nodes based on their attributes and adjust the topological structure of the network according to the training loss of the model. Finally, SATS predicts the interaction propensity between drugs and proteins based on their embeddings. The experimental results show that SATS can effectively improve the topological structure of the network. The performance of SATS outperforms several state-of-the-art DPI prediction methods under various evaluation metrics. These prove that SATS is useful to deal with incomplete data and unreliable networks. The case studies on the top section of the prediction results further demonstrate that SATS is powerful for discovering novel DPIs.

PMID:37672364 | DOI:10.1109/JBHI.2023.3312374

iScience. 2023 Aug 18;26(9):107663. doi: 10.1016/j.isci.2023.107663. eCollection 2023 Sep 15.

ABSTRACT

The medicinal effect of a drug acts through a series of genes, and the pathological mechanism of a disease is also related to genes with certain biological functions. However, the complex information between drug or disease and a series of genes is neglected by traditional message passing methods. In this study, we proposed a new framework using two different strategies for gene-drug/disease and drug-disease networks, respectively. We employ long short-term memory (LSTM) network to extract the flow of message from series of genes (gene path) to drug/disease. Incorporating the resulting information of gene paths into drug-disease network, we utilize graph convolutional network (GCN) to predict drug-disease associations. Experimental results showed that our method GeneDR (gene-based drug repurposing) makes better use of the information in gene paths, and performs better in predicting drug-disease associations.

PMID:37670781 | PMC:PMC10475505 | DOI:10.1016/j.isci.2023.107663

Biol Direct. 2023 Sep 5;18(1):55. doi: 10.1186/s13062-023-00410-9.

ABSTRACT

BACKGROUND: The active pursuit of network medicine for drug repurposing, particularly for combating Covid-19, has stimulated interest in the concept of structural controllability in cellular networks. We sought to extend this theory, focusing on the defense rather than control of the cell against viral infections. Accordingly, we extended structural controllability to total structural controllability and introduced the concept of control hubs. Perturbing any control hub may render the cell uncontrollable by exogenous stimuli like viral infections, so control hubs are ideal drug targets.

RESULTS: We developed an efficient algorithm to identify all control hubs, applying it to a largest homogeneous network of human protein interactions, including interactions between human and SARS-CoV-2 proteins. Our method recognized 65 druggable control hubs with enriched antiviral functions. Utilizing these hubs, we categorized potential drugs into four groups: antiviral and anti-inflammatory agents, drugs acting on the central nervous system, dietary supplements, and compounds enhancing immunity. An exemplification of our approach's effectiveness, Fostamatinib, a drug initially developed for chronic immune thrombocytopenia, is now in clinical trials for treating Covid-19. Preclinical trial data demonstrated that Fostamatinib could reduce mortality rates, ICU stay length, and disease severity in Covid-19 patients.

CONCLUSIONS: Our findings confirm the efficacy of our novel strategy that leverages control hubs as drug targets. This approach provides insights into the molecular mechanisms of potential therapeutics for Covid-19, making it a valuable tool for interpretable drug discovery. Our new approach is general and applicable to repurposing drugs for other diseases.

PMID:37670359 | DOI:10.1186/s13062-023-00410-9

PLoS Negl Trop Dis. 2023 Sep 5;17(9):e0011205. doi: 10.1371/journal.pntd.0011205. Online ahead of print.

ABSTRACT

Hundreds of millions of people worldwide are infected with the whipworm Trichuris trichiura. Novel treatments are urgently needed as current drugs, such as albendazole, have relatively low efficacy. We have investigated whether drugs approved for other human diseases could be repurposed as novel anti-whipworm drugs. In a previous comparative genomics analysis, we identified 409 drugs approved for human use that we predicted to target parasitic worm proteins. Here we tested these ex vivo by assessing motility of adult worms of Trichuris muris, the murine whipworm, an established model for human whipworm research. We identified 14 compounds with EC50 values of ≤50 μM against T. muris ex vivo, and selected nine for testing in vivo. However, the best worm burden reduction seen in mice was just 19%. The high number of ex vivo hits against T. muris shows that we were successful at predicting parasite proteins that could be targeted by approved drugs. In contrast, the low efficacy of these compounds in mice suggest challenges due to their chemical properties (e.g. lipophilicity, polarity, molecular weight) and pharmacokinetics (e.g. absorption, distribution, metabolism, and excretion) that may (i) promote absorption by the host gastrointestinal tract, thereby reducing availability to the worms embedded in the large intestine, and/or (ii) restrict drug uptake by the worms. This indicates that identifying structural analogues that have reduced absorption by the host, and increased uptake by worms, may be necessary for successful drug development against whipworms.

PMID:37669291 | DOI:10.1371/journal.pntd.0011205

Assay Drug Dev Technol. 2023 Sep 1. doi: 10.1089/adt.2023.081. Online ahead of print.

NO ABSTRACT

PMID:37668595 | DOI:10.1089/adt.2023.081

J Mycol Med. 2023 Aug 30;33(4):101431. doi: 10.1016/j.mycmed.2023.101431. Online ahead of print.

ABSTRACT

Fungal infections caused by Cryptococcus spp. pose a threat to health, especially in immunocompromised individuals. The available arsenal of drugs against cryptococcosis is limited, due to their toxicity and/or lack of accessibility in low-income countries, requiring more therapeutic alternatives. Selective serotonin reuptake inhibitors (SSRIs), through drug repositioning, are a promising alternative to broaden the range of new antifungals against Cryptococcus spp. This study evaluates the antifungal activity of three SSRIs, sertraline, paroxetine, and fluoxetine, against Cryptococcus spp. strains, as well as assesses their possible mechanism of action. Seven strains of Cryptococcus spp. were used. Sensitivity to SSRIs, fluconazole, and itraconazole was evaluated using the broth microdilution assay. The interactions resulting from combinations of SSRIs and azoles were investigated using the checkerboard assay. The possible action mechanism of SSRIs against Cryptococcus spp. was evaluated through flow cytometry assays. The SSRIs exhibited in vitro antifungal activity against Cryptococcus spp. strains, with minimum inhibitory concentrations ranging from 2 to 32 μg/mL, and had synergistic and additive interactions with azoles. The mechanism of action of SSRIs against Cryptococcus spp. involved damage to the mitochondrial membrane and increasing the production of reactive oxygen species, resulting in loss of cellular viability and apoptotic cell death. Fluoxetine also was able to cause significant damage to yeast DNA. These findings demonstrate the in vitro antifungal potential of SSRIs against Cryptococcus spp. strains.

PMID:37666030 | DOI:10.1016/j.mycmed.2023.101431

Sci Rep. 2023 Sep 4;13(1):14570. doi: 10.1038/s41598-023-41820-z.

ABSTRACT

Monkeypox viral infection is an emerging threat and a major concern for the human population. The lack of drug molecules to treat this disease may worsen the problem. Identifying potential drug targets can significantly improve the process of developing potent drug molecules for treating monkeypox. The proteins responsible for viral replication are attractive drug targets. Identifying potential inhibitors from known drug molecules that target these proteins can be key to finding a cure for monkeypox. In this work, two viral proteins, DNA-dependent RNA polymerase (DdRp) and viral core cysteine proteinase, were considered as potential drug targets. Sixteen antibiotic drugs from the tetracycline class were screened against both viral proteins through high-throughput virtual screening. These tetracycline class of antibiotic drugs have the ability to inhibit bacterial protein synthesis, which makes these antibiotics drugs a prominent candidate for drug repurposing. Based on the screening result obtained against DdRp, top two compounds, namely Tigecycline and Eravacycline with docking scores of - 8.88 and - 7.87 kcal/mol, respectively, were selected for further analysis. Omadacycline and minocycline, with docking scores of - 10.60 and - 7.51 kcal/mol, are the top two compounds obtained after screening proteinase with the drug library. These compounds, along with reference compounds GTP for DdRp and tecovirimat for proteinase, were used to form protein-ligand complexes, followed by their evaluation through a 300 ns molecular dynamic simulation. The MM/GBSA binding free energy calculation and principal components analysis of these selected complexes were also conducted for understanding the dynamic stability and binding affinity of these compounds with respective target proteins. Overall, this study demonstrates the repurposing of tetracycline-derived drugs as a therapeutic solution for monkeypox viral infection.

PMID:37666979 | DOI:10.1038/s41598-023-41820-z

Sci Rep. 2023 Sep 4;13(1):14586. doi: 10.1038/s41598-023-40033-8.

ABSTRACT

By utilizing Optum Life Sciences Claims Data, we constructed Real World Data (RWD) cohorts comprising over 3 million patients and simulated a clinical trial observational study design to evaluate over 200 FDA-approved drugs with COVID-19 repurposing potential, and identified a dozen candidates exhibiting significant reduction in the odds of severe COVID-19 outcomes such as death, intensive care unit (ICU) admission, hospitalization and pneumonia. Notably, certain drug combinations demonstrated effects comparable to those of COVID-19 vaccines. Furthermore, our study revealed a novel finding: a quantitative linear relationship between COVID-19 outcomes and overall patient health risks. This discovery enabled a more precise estimation of drug efficacy using the risk adjustment. The top performing drugs identified include emtricitabine, tenofovir, folic acid, progesterone, estradiol, epinephrine, disulfiram, nitazoxanide and some drug combinations including aspirin-celecoxib.

PMID:37666866 | DOI:10.1038/s41598-023-40033-8

Front Cell Dev Biol. 2023 Aug 17;11:1226707. doi: 10.3389/fcell.2023.1226707. eCollection 2023.

ABSTRACT

Duchenne Muscular Dystrophy (DMD)'s complex multi-system pathophysiology, coupled with the cost-prohibitive logistics of multi-year drug screening and follow-up, has hampered the pursuit of new therapeutic approaches. Here we conducted a systematic historical and text mining-based pilot feasibility study to explore the potential of established or previously tested drugs as prospective DMD therapeutic agents. Our approach utilized a Swanson linking-inspired method to uncover meaningful yet largely hidden deep semantic connections between pharmacologically significant DMD targets and drugs developed for unrelated diseases. Specifically, we focused on molecular target-based MeSH terms and categories as high-yield bioinformatic proxies, effectively tagging relevant literature with categorical metadata. To identify promising leads, we comprehensively assembled published reports from 2011 and sampling from subsequent years. We then determined the earliest year when distinct MeSH terms or category labels of the relevant cellular target were referenced in conjunction with the drug, as well as when the pertinent target itself was first conclusively identified as holding therapeutic value for DMD. By comparing the earliest year when the drug was identifiable as a DMD treatment candidate with that of the first actual report confirming this, we computed an Index of Delayed Discovery (IDD), which serves as a metric of Swanson-linked latent knowledge. Using these findings, we identified data from previously unlinked articles subsetted via MeSH-derived Swanson linking or from target classes within the DrugBank repository. This enabled us to identify new but untested high-prospect small-molecule candidates that are of particular interest in repurposing for DMD and warrant further investigations.

PMID:37664462 | PMC:PMC10469615 | DOI:10.3389/fcell.2023.1226707

Sci Rep. 2023 Sep 2;13(1):14468. doi: 10.1038/s41598-023-41442-5.

ABSTRACT

Drug-resistant Neisseria gonorrhoeae represents a major threat to public health; without new effective antibiotics, untreatable gonococcal infections loom as a real possibility. In a previous drug-repurposing study, we reported that salicylic acid had good potency against azithromycin-resistant N. gonorrhoeae. We now report that the anti-gonococcal activity in this scaffold is easily lost by inopportune substitution, but that select substituted naphthyl analogs (3b, 3o and 3p) have superior activity to salicylic acid itself. Furthermore, these compounds retained potency against multiple ceftriaxone- and azithromycin-resistant strains, exhibited rapid bactericidal activity against N. gonorrhoeae, and showed high tolerability to mammalian cells (CC50 > 128 µg/mL). Promisingly, these compounds also show very weak growth inhibition of commensal vaginal bacteria.

PMID:37660222 | DOI:10.1038/s41598-023-41442-5

Sci Rep. 2023 Sep 2;13(1):14429. doi: 10.1038/s41598-023-39647-9.

ABSTRACT

Treatment of infections caused by multi-drug resistant (MDR) enterobacteria remains challenging due to the limited therapeutic options available. Drug repurposing could accelerate the development of new urgently needed successful interventions. This work aimed to identify and characterise novel drug combinations against Klebsiella pneumoniae based on the concepts of synergy and drug repurposing. We first performed a semi-qualitative high-throughput synergy screen (sHTSS) with tigecycline, colistin and fosfomycin (last-line antibiotics against MDR Enterobacteriaceae) against a FDA-library containing 1430 clinically approved drugs; a total of 109 compounds potentiated any of the last-line antibiotics. Selected hits were further validated by secondary checkerboard (CBA) and time-kill (TKA) assays, obtaining 15.09% and 65.85% confirmation rates, respectively. Accordingly, TKA were used for synergy classification based on determination of bactericidal activities at 8, 24 and 48 h, selecting 27 combinations against K. pneumoniae. Among them, zidovudine or azithromycin combinations with last-line antibiotics were further evaluated by TKA against a panel of 12 MDR/XDR K. pneumoniae strains, and their activities confronted with those clinical combinations currently used for MDR enterobacteria treatment; these combinations showed better bactericidal activities than usual treatments without added cytotoxicity. Our studies show that sHTSS paired to TKA are powerful tools for the identification and characterisation of novel synergistic drug combinations against K. pneumoniae. Further pre-clinical studies might support the translational potential of zidovudine- and azithromycin-based combinations for the treatment of these infections.

PMID:37660210 | DOI:10.1038/s41598-023-39647-9

Acta Pharm Sin B. 2023 Aug;13(8):3238-3251. doi: 10.1016/j.apsb.2023.05.021. Epub 2023 May 23.

ABSTRACT

Emerging evidence has demonstrated the vital role of metabolism in various diseases or disorders. Metabolomics provides a comprehensive understanding of metabolism in biological systems. With advanced analytical techniques, metabolomics exhibits unprecedented significant value in basic drug research, including understanding disease mechanisms, identifying drug targets, and elucidating the mode of action of drugs. More importantly, metabolomics greatly accelerates the drug development process by predicting pharmacokinetics, pharmacodynamics, and drug response. In addition, metabolomics facilitates the exploration of drug repurposing and drug-drug interactions, as well as the development of personalized treatment strategies. Here, we briefly review the recent advances in technologies in metabolomics and update our knowledge of the applications of metabolomics in drug research and development.

PMID:37655318 | PMC:PMC10465962 | DOI:10.1016/j.apsb.2023.05.021

Bioinformation. 2022 Oct 31;18(10):845-852. doi: 10.6026/97320630018845. eCollection 2022.

ABSTRACT

Epilepsy is one of the most common neurological disorders, affecting millions of patients with a substantial economic and human burden. About 30-40% of epileptic patients remain un-treated after the therapeutic option. Genetic or idiopathic epilepsy count about 40% of total epilepsy patients, showing a maximum percentage for drug-resistant epilepsy. Since the last century basic approach to understanding disease progression and drug discovery has been through the prism, exploring all possible causes and treatment options. Here we report about the gene expression-based drug repositioning study for epilepsy. Epilepsy gene expression data was retrieved from the Gene Expression Omnibus database, while drugs-associated gene expression data was retrieved from the Connectivity map (CMAP). The study predicted309 drug compounds which can alter genetic epilepsy-mediated gene signature using an in-house developed R-script. These compounds were docked against identified epilepsy targets- Voltage-gated sodium channel subunit α2 (Nav1.2); GABA receptor α1-β1; and Voltage-gated calcium channel α1G (Cav3.1)using Carbamazepine, Clonazepam, and Pregabalin as standard drugs, respectively. Twenty-one predicted drug compounds showed better binding affinity than respective standards against the selected epileptic receptors. Among these drug compounds, Ergocalciferol, Oxaprozin, Flunarizine, Triprolidine and Cyproheptadine have been previously reported for anti-epileptic activities and can be potential hits to target idiopathic epilepsy.

PMID:37654844 | PMC:PMC10465761 | DOI:10.6026/97320630018845

J Immunother Cancer. 2023 Aug;11(8):e007253. doi: 10.1136/jitc-2023-007253.

ABSTRACT

BACKGROUND: Massive tumor-associated macrophage (TAM) infiltration is observed in many tumors, which usually display the immune-suppressive M2-like phenotype but can also be converted to an M1-like antitumor phenotype due to their high degree of plasticity. The macrophage polarization state is associated with changes in cell shape, macrophage morphology is associated with activation status. M1 macrophages appeared large and rounded, while M2 macrophages were stretched and elongated cells. Manipulating cell morphology has been shown to affect the polarization state of macrophages. The shape of the cell is largely dependent on cytoskeletal proteins, especially, microtubules. As a microtubule-targetting drug, vinblastine (VBL) has been used in chemotherapy. However, no study to date has explored the effect of VBL on TAM shape changes and its role in tumor immune response.

METHOD: We used fluorescent staining of the cytoskeleton and quantitative analysis to reveal the morphological differences between M0, M1, M2, TAM and VBL-treated TAM. Flow cytometry was used to confirm the polarization states of these macrophages using a cell surface marker-based classification. In vivo antibody depletion experiments in tumor mouse models were performed to test whether macrophages and CD8+ T cell populations were required for the antitumor effect of VBL. VBL and anti-PD-1 combination therapy was then investigated in comparison with monotherapy. RNA-seq of TAM of treated and untreated with VBL was performed to explore the changes in pathway activities. siRNA mediated knockdown experiments were performed to verify the target pathway that was affected by VBL treatment.

RESULTS: Here, we showed that VBL, an antineoplastic agent that destabilizes microtubule, drove macrophage polarization into the M1-like phenotype both in vitro and in tumor models. The antitumor effect of VBL was attenuated in the absence of macrophages or CD8+ T cells. Mechanistically, VBL induces the activation of NF-κB and Cyba-dependent reactive oxygen species generation, thus polarizing TAMs to the M1 phenotype. In parallel, VBL promotes the nuclear translocation of transcription factor EB, inducing lysosome biogenesis and a dramatic increase in phagocytic activity in macrophages.

CONCLUSIONS: This study explored whether manipulating cellular morphology affects macrophage polarization and consequently induces an antitumor response. Our data reveal a previously unrecognized antitumor mechanism of VBL and suggest a drug repurposing strategy combining VBL with immune checkpoint inhibitors to improve malignant tumor immunotherapy.

PMID:37652576 | DOI:10.1136/jitc-2023-007253