Bioorg Chem. 2022 Jul 6;127:106009. doi: 10.1016/j.bioorg.2022.106009. Online ahead of print.

ABSTRACT

Leishmaniases have a broad spectrum of clinical manifestations, ranging from a cutaneous to a progressive and fatal visceral disease. Chemotherapy is nowadays the almost exclusive way to fight the disease but limited by its scarce therapeutic arsenal, on its own compromised by adverse side effects and clinical resistance. Cyclobenzaprine (CBP), an FDA-approved oral muscle relaxant drug has previously demonstrated in vitro and in vivo activity against Leishmania sp., but its targets were not fully unveiled. This study aimed to define the role of energy metabolism as a target for the leishmanicidal mechanisms of CBP. Methodology to assess CBP leishmanicidal mechanism variation of intracellular ATP levels using living Leishmania transfected with a cytoplasmic luciferase. Induction of plasma membrane permeability by assessing depolarization with DiSBAC(2)3 and entrance of the vital dye SYTOX® Green. Mitochondrial depolarization by rhodamine 123 accumulation. Mapping target site within the respiratory chain by oxygen consumption rate. Reactive oxygen species (ROS) production using MitoSOX. Morphological changes by transmission electron microscopy. CBP caused on L. infantum promastigotes a decrease of intracellular ATP levels, with irreversible depolarization of plasma membrane, the collapse of the mitochondrial electrochemical potential, mild uncoupling of the respiratory chain, and ROS production, with ensuing intracellular Ca2+ imbalance and DNA fragmentation. Electron microscopy supported autophagic features but not a massive plasma membrane disruption. The severe and irreversible mitochondrial damage induced by CBP endorsed the bioenergetics metabolism as a relevant target within the lethal programme induced by CBP in Leishmania. This, together with the mild-side effects of this oral drug, endorses CBP as an appealing novel candidate as a leishmanicidal drug under a drug repurposing strategy.

PMID:35841672 | DOI:10.1016/j.bioorg.2022.106009

Stem Cell Res Ther. 2022 Jul 15;13(1):303. doi: 10.1186/s13287-022-02987-w.

ABSTRACT

BACKGROUND: The rarity of systemic sclerosis (SSc) has hampered the development of therapies for this intractable autoimmune disease. Induced pluripotent stem cell (iPSC) can be differentiated into the key disease-affected cells in vitro. The generation of patient-derived iPSCs has opened up possibilities for rare disease modeling. Since these cells can recapitulate the disease phenotypes of the cell in question, they are useful high-throughput platforms for screening for drugs that can reverse these abnormal phenotypes.

METHODS: SSc iPSC was generated from PBMC by Sendai virus. Human iPSC lines from SSc patients were differentiated into dermal fibroblasts and keratinocytes. The iPSC-derived differentiated cells from the SSc patients were used on high-throughput platforms to screen for FDA-approved drugs that could be effective treatments for SSc.

RESULTS: Skin organoids were generated from these cells exhibited fibrosis that resembled SSc skin. Screening of the 770-FDA-approved drug library showed that the anti-osteoporotic drug raloxifene reduced SSc iPSC-derived fibroblast proliferation and extracellular matrix production and skin fibrosis in organoids and bleomycin-induced SSc-model mice.

CONCLUSIONS: This study reveals that a disease model of systemic sclerosis generated using iPSCs-derived skin organoid is a novel tool for in vitro and in vivo dermatologic research. Since raloxifene and bazedoxifene are well-tolerated anti-osteoporotic drugs, our findings suggest that selective estrogen receptor modulator (SERM)-class drugs could treat SSc fibrosis.

PMID:35841004 | DOI:10.1186/s13287-022-02987-w

J Biomed Inform. 2022 Jul 12:104133. doi: 10.1016/j.jbi.2022.104133. Online ahead of print.

ABSTRACT

The emergence of large-scale phenotypic, genetic, and other multi-model biochemical data has offered unprecedented opportunities for drug discovery including drug repurposing. Various knowledge graph-based methods have been developed to integrate and analyze complex and heterogeneous data sources to find new therapeutic applications for existing drugs. However, existing methods have limitations in modeling and capturing context-sensitive inter-relationships among tens of thousands of biomedical entities. In this paper, we developed KG-Predict: a knowledge graph computational framework for drug repurposing. We first integrated multiple types of entities and relations from various genotypic and phenotypic databases to construct a knowledge graph termed GP-KG. GP-KG was composed of 1,246,726 associations between 61,146 entities. KG-Predict then aggregated the heterogeneous topological and semantic information from GP-KG to learn low-dimensional representations of entities and relations, and further utilized these representations to infer new drug-disease interactions. In cross-validation experiments, KG-Predict achieved high performances [AUROC (the area under receiver operating characteristic) = 0.981, AUPR (the area under precision-recall) = 0.409 and MRR (the mean reciprocal rank) = 0.261], outperforming other state-of-art graph embedding methods. We applied KG-Predict in identifying novel repositioned candidate drugs for Alzheimer's disease (AD) and showed that KG-Predict prioritized both FDA-approved and active clinical trial anti-AD drugs among the top (AUROC = 0.868 and AUPR = 0.364).

PMID:35840060 | DOI:10.1016/j.jbi.2022.104133

IEEE/ACM Trans Comput Biol Bioinform. 2022 Jul 15;PP. doi: 10.1109/TCBB.2022.3191392. Online ahead of print.

ABSTRACT

Drug repurposing is a highly active research area, aiming at finding novel uses for drugs that have been previously developed for other therapeutic purposes. Despite the flourishing of methodologies, success is still partial, and different approaches offer, each, peculiar advantages. In this composite landscape, we present a novel methodology focusing on an efficient mathematical procedure based on gene similarity scores and biased random walks which rely on robust drug-gene-disease association data sets. The recommendation mechanism is further unveiled by means of the Markov chain underlying the random walk process, hence providing explainability about how findings are suggested. Performances evaluation and the analysis of a case study on rheumatoid arthritis show that our approach is accurate in providing useful recommendations and is computationally efficient, compared to the state of the art of drug repurposing approaches.

PMID:35839194 | DOI:10.1109/TCBB.2022.3191392

Front Med (Lausanne). 2022 Jun 28;9:881322. doi: 10.3389/fmed.2022.881322. eCollection 2022.

ABSTRACT

BACKGROUND: IgA nephropathy (IgAN) is the most common primary glomerular disease and the leading cause of the end-stage renal disease in the world. The pathogenesis of IgAN has not been well elucidated, and yet treatment is limited. High-throughput microarray has been applied for elucidating molecular biomarkers and potential mechanisms involved in IgAN. This study aimed to identify the potential key genes and therapeutics associated with IgAN using integrative bioinformatics and transcriptome-based computational drug repurposing approach.

METHODS: Three datasets of mRNA expression profile were obtained from the gene expression omnibus database and differentially expressed genes (DEGs) between IgAN glomeruli and normal tissue were identified by integrated analysis. Gene ontology and pathway enrichment analyses of the DEGs were performed by R software, and protein-protein interaction networks were constructed using the STRING online search tool. External dataset and immunohistochemical assessment of kidney biopsy specimens were used for hub gene validation. Potential compounds for IgAN therapy were obtained by Connectivity Map (CMap) analysis and preliminarily verified in vitro. Stimulated human mesangial cells were collected for cell proliferation and cell cycle analysis using cell counting kit 8 and flow cytometry, respectively.

RESULTS: 134 DEGs genes were differentially expressed across kidney transcriptomic data from IgAN patients and healthy living donors. Enrichment analysis showed that the glomerular compartments underwent a wide range of interesting pathological changes during kidney injury, focused on anion transmembrane transporter activity and protein digestion and absorption mostly. Hub genes (ITGB2, FCER1G, CSF1R) were identified and verified to be significantly upregulated in IgAN patients, and associated with severity of renal lesions. Computational drug repurposing with the CMap identified tetrandrine as a candidate treatment to reverse IgAN hub gene expression. Tetrandrine administration significantly reversed mesangial cell proliferation and cell cycle transition.

CONCLUSION: The identification of DEGs and related therapeutic strategies of IgAN through this integrated bioinformatics analysis provides a valuable resource of therapeutic targets and agents of IgAN. Especially, our findings suggest that tetrandrine might be beneficial for IgAN, which deserves future research.

PMID:35836957 | PMC:PMC9273898 | DOI:10.3389/fmed.2022.881322

J Clin Transl Sci. 2022 May 16;6(1):e74. doi: 10.1017/cts.2022.401. eCollection 2022.

ABSTRACT

INTRODUCTION: COVID-19 is a major health threat around the world causing hundreds of millions of infections and millions of deaths. There is a pressing global need for effective therapies. We hypothesized that leukotriene inhibitors (LTIs), that have been shown to lower IL6 and IL8 levels, may have a protective effect in patients with COVID-19.

METHODS: In this retrospective controlled cohort study, we compared death rates in COVID-19 patients who were taking a LTI with those who were not taking an LTI. We used the Department of Veterans Affairs (VA) Corporate Data Warehouse (CDW) to create a cohort of COVID-19-positive patients and tracked their use of LTIs between November 1, 2019 and November 11, 2021.

RESULTS: Of the 1,677,595 cohort of patients tested for COVID-19, 189,195 patients tested positive for COVID-19. Forty thousand seven hundred one were admitted. 38,184 had an oxygen requirement and 1214 were taking an LTI. The use of dexamethasone plus a LTI in hospital showed a survival advantage of 13.5% (CI: 0.23%-26.7%; p < 0.01) in patients presenting with a minimal O2Sat of 50% or less. For patients with an O2Sat of <60 and <50% if they were on LTIs as outpatients, continuing the LTI led to a 14.4% and 22.25 survival advantage if they were continued on the medication as inpatients.

CONCLUSIONS: When combined dexamethasone and LTIs provided a mortality benefit in COVID-19 patients presenting with an O2 saturations <50%. The LTI cohort had lower markers of inflammation and cytokine storm.

PMID:35836784 | PMC:PMC9274389 | DOI:10.1017/cts.2022.401

BMC Bioinformatics. 2022 Jul 14;23(1):278. doi: 10.1186/s12859-022-04831-7.

ABSTRACT

Predicting drug-target interactions (DTIs) has become an important bioinformatics issue because it is one of the critical and preliminary stages of drug repositioning. Therefore, scientists are trying to develop more accurate computational methods for predicting drug-target interactions. These methods are usually based on machine learning or recommender systems and use biological and chemical information to improve the accuracy of predictions. In the background of these methods, there is a hypothesis that drugs with similar chemical structures have similar targets. So, the similarity between drugs as chemical information is added to the computational methods to improve the prediction results. The question that arises here is whether this claim is actually true? If so, what method should be used to calculate drug-drug chemical structure similarities? Will we obtain the same improvement from any DTI prediction method we use? Here, we investigated the amount of improvement that can be achieved by adding the drug-drug chemical structure similarities to the problem. For this purpose, we considered different types of real chemical similarities, random drug-drug similarities, four gold standard datasets and four state-of-the-art methods. Our results show that the type and size of data, the method which is used to predict the interactions, and the algorithm used to calculate the chemical similarities between drugs are all important, and it cannot be easily stated that adding drug-drug similarities can significantly improve the results. Therefore, our results could suggest a checklist for scientists who want to improve their machine learning methods.

PMID:35836119 | DOI:10.1186/s12859-022-04831-7

Biochem Biophys Rep. 2022 Jul 8;31:101307. doi: 10.1016/j.bbrep.2022.101307. eCollection 2022 Sep.

ABSTRACT

BACKGROUND: One of the main challenges in personalized medicine is to establish and apply a large number of variants from genomic databases into clinical diagnostics and further facilitate genome-driven drug repurposing. By utilizing biological chronic hepatitis B infection (CHB) risk genes, our study proposed a systematic approach to use genomic variants to drive drug repurposing for CHB.

METHOD: The genomic variants were retrieved from the Genome-Wide Association Study (GWAS) and Phenome-Wide Association Study (PheWAS) databases. Then, the biological CHB risk genes crucial for CHB progression were prioritized based on the scoring system devised with five strict functional annotation criteria. A score of ≥ 2 were categorized as the biological CHB risk genes and further shed light on drug target genes for CHB treatments. Overlapping druggable targets were identified using two drug databases (DrugBank and Drug-Gene Interaction Database (DGIdb)).

RESULTS: A total of 44 biological CHB risk genes were screened based on the scoring system from five functional annotation criteria. Interestingly, we found 6 druggable targets that overlapped with 18 drugs with status of undergoing clinical trials for CHB, and 9 druggable targets that overlapped with 20 drugs undergoing preclinical investigations for CHB. Eight druggable targets were identified, overlapping with 25 drugs that can potentially be repurposed for CHB. Notably, CD40 and HLA-DPB1 were identified as promising targets for CHB drug repurposing based on the target scores.

CONCLUSION: Through the integration of genomic variants and a bioinformatic approach, our findings suggested the plausibility of CHB genomic variant-driven drug repurposing for CHB.

PMID:35832745 | PMC:PMC9271961 | DOI:10.1016/j.bbrep.2022.101307

Comput Struct Biotechnol J. 2022 Apr 28;20:2134-2142. doi: 10.1016/j.csbj.2022.04.033. eCollection 2022.

ABSTRACT

Tetrodotoxin (TTX) is a lethal neurotoxin produced by the endosymbiotic bacteria in the gut of puffer fish. Currently, there is no effective and economical method to detect TTX, so it is very interesting to develop low-cost and high-sensitivity detection methods by using nucleic-acid aptamers as the recognition molecules. However, traditional SELEX screening of aptamers for targeting small molecules such as TTX is labor-intensive, and usually the success rate is low. Here, we employed a strategy of "repurposing old aptamers for new uses" to develop high-affinity aptamers for TTX. To this end, we first collected thermally stable DNA aptamers and predicted their affinities for TTX by molecular docking; then, we identified high-affinity candidates and verified them by microscale thermophoresis (MST) experiments. In this way, two thermally stable aptamers (Tv-51 and AI-57) were found to possess nanomolar affinities for TTX. Moreover, we performed spontaneous binding simulations to reveal their binding mechanisms to TTX and thereby identified the key bases for the binding. Guided by these, two variants (Tv-46 and AI-52) with higher affinities and specificities were subsequently engineered and confirmed by the MST experiments. So, this study not only provides potential recognition molecules for the technology developments of TTX detection, but also demonstrates an effective repurposing approach to the discovery of high-affinity aptamers for new target molecules.

PMID:35832627 | PMC:PMC9092388 | DOI:10.1016/j.csbj.2022.04.033

Comput Struct Biotechnol J. 2022 Jun 30;20:3422-3438. doi: 10.1016/j.csbj.2022.06.060. eCollection 2022.

ABSTRACT

Hepatitis C virus (HCV) infection causes viral hepatitis leading to hepatocellular carcinoma. Despite the clinical use of direct-acting antivirals (DAAs) still there is treatment failure in 5-10% cases. Therefore, it is crucial to develop new antivirals against HCV. In this endeavor, we developed the "Anti-HCV" platform using machine learning and quantitative structure-activity relationship (QSAR) approaches to predict repurposed drugs targeting HCV non-structural (NS) proteins. We retrieved experimentally validated small molecules from the ChEMBL database with bioactivity (IC50/EC50) against HCV NS3 (454), NS3/4A (495), NS5A (494) and NS5B (1671) proteins. These unique compounds were divided into training/testing and independent validation datasets. Relevant molecular descriptors and fingerprints were selected using a recursive feature elimination algorithm. Different machine learning techniques viz. support vector machine, k-nearest neighbour, artificial neural network, and random forest were used to develop the predictive models. We achieved Pearson's correlation coefficients from 0.80 to 0.92 during 10-fold cross validation and similar performance on independent datasets using the best developed models. The robustness and reliability of developed predictive models were also supported by applicability domain, chemical diversity and decoy datasets analyses. The "Anti-HCV" predictive models were used to identify potential repurposing drugs. Representative candidates were further validated by molecular docking which displayed high binding affinities. Hence, this study identified promising repurposed drugs viz. naftifine, butalbital (NS3), vinorelbine, epicriptine (NS3/4A), pipecuronium, trimethaphan (NS5A), olodaterol and vemurafenib (NS5B) etc. targeting HCV NS proteins. These potential repurposed drugs may prove useful in antiviral drug development against HCV.

PMID:35832613 | PMC:PMC9271984 | DOI:10.1016/j.csbj.2022.06.060

Sci Rep. 2022 Jul 13;12(1):11934. doi: 10.1038/s41598-022-15898-w.

ABSTRACT

COVID-19 is a global pandemic impacting the daily living of millions. As variants of the virus evolve, a complete comprehension of the disease and drug targets becomes a decisive duty. The Omicron variant, for example, has a notably high transmission rate verified in 155 countries. We performed integrative transcriptomic and network analyses to identify drug targets and diagnostic biomarkers and repurpose FDA-approved drugs for SARS-CoV-2. Upon the enrichment of 464 differentially expressed genes, pathways regulating the host cell cycle were significant. Regulatory and interaction networks featured hsa-mir-93-5p and hsa-mir-17-5p as blood biomarkers while hsa-mir-15b-5p as an antiviral agent. MYB, RRM2, ERG, CENPF, CIT, and TOP2A are potential drug targets for treatment. HMOX1 is suggested as a prognostic biomarker. Enhancing HMOX1 expression by neem plant extract might be a therapeutic alternative. We constructed a drug-gene network for FDA-approved drugs to be repurposed against the infection. The key drugs retrieved were members of anthracyclines, mitotic inhibitors, anti-tumor antibiotics, and CDK1 inhibitors. Additionally, hydroxyquinone and digitoxin are potent TOP2A inhibitors. Hydroxyurea, cytarabine, gemcitabine, sotalol, and amiodarone can also be redirected against COVID-19. The analysis enforced the repositioning of fluorouracil and doxorubicin, especially that they have multiple drug targets, hence less probability of resistance.

PMID:35831333 | DOI:10.1038/s41598-022-15898-w

Sci Data. 2022 Jul 13;9(1):405. doi: 10.1038/s41597-022-01532-x.

ABSTRACT

Worldwide, there are intensive efforts to identify repurposed drugs as potential therapies against SARS-CoV-2 infection and the associated COVID-19 disease. To date, the anti-inflammatory drug dexamethasone and (to a lesser extent) the RNA-polymerase inhibitor remdesivir have been shown to be effective in reducing mortality and patient time to recovery, respectively, in patients. Here, we report the results of a phenotypic screening campaign within an EU-funded project (H2020-EXSCALATE4COV) aimed at extending the repertoire of anti-COVID therapeutics through repurposing of available compounds and highlighting compounds with new mechanisms of action against viral infection. We screened 8702 molecules from different repurposing libraries, to reveal 110 compounds with an anti-cytopathic IC50 < 20 µM. From this group, 18 with a safety index greater than 2 are also marketed drugs, making them suitable for further study as potential therapies against COVID-19. Our result supports the idea that a systematic approach to repurposing is a valid strategy to accelerate the necessary drug discovery process.

PMID:35831315 | DOI:10.1038/s41597-022-01532-x

Sci Rep. 2022 Jul 13;12(1):11918. doi: 10.1038/s41598-022-15756-9.

ABSTRACT

Anticancer drugs and molecular targeted therapies are used for refractory desmoid-type fibromatosis (DF), but occasionally cause severe side effects. The purpose of this study was to identify an effective drug with fewer side effects against DF by drug repositioning, and evaluate its efficacy. FDA-approved drugs that inhibit the proliferation of DF cells harboring S45F mutations of CTNNB1 were screened. An identified drug was subjected to the investigation of apoptotic effects on DF cells with analysis of Caspase 3/7 activity. Expression of β-catenin was evaluated with western blot analysis, and immunofluorescence staining. Effects of the identified drug on in vivo DF were analyzed using Apc1638N mice. Auranofin was identified as a drug that effectively inhibits the proliferation of DF cells. Auranofin did not affect Caspase 3/7 activity compared to control. The expression level of β-catenin protein was not changed regardless of auranofin concentration. Auranofin effectively inhibited the development of tumorous tissues by both oral and intraperitoneal administration, particularly in male mice. Auranofin, an anti-rheumatic drug, was identified to have repositioning effects on DF. Since auranofin has been used for many years as an FDA-approved drug, it could be a promising drug with fewer side effects for DF.

PMID:35831372 | DOI:10.1038/s41598-022-15756-9

Front Aging. 2022 Apr 27;3:846977. doi: 10.3389/fragi.2022.846977. eCollection 2022.

ABSTRACT

Activation of a cytoprotective cellular pathway known as the heat shock response (HSR) is a promising strategy for the treatment of Alzheimer's disease and other neurodegenerative diseases. Geranylgeranylacetone (GGA) is a commonly used anti-ulcer drug in Japan that has been shown to activate the HSR. Here, we establish C. elegans as a model system to investigate the effects of GGA. First, we show that GGA-mediated activation of the HSR is conserved in worms. Then, we show that GGA can ameliorate beta-amyloid toxicity in both muscle and neuronal worm Alzheimer's disease models. Finally, we find that exposure to GGA is sufficient to extend the lifespan of wild-type worms. Significantly, the beneficial effects of GGA on both beta-amyloid toxicity and lifespan are dependent on HSR activation. Taken together, this research supports further development of GGA as a therapeutic for Alzheimer's disease, provides evidence that HSR activation is a relevant therapeutic mechanism, and indicates that the beneficial effects of GGA are not limited to disease.

PMID:35821801 | PMC:PMC9261441 | DOI:10.3389/fragi.2022.846977

Cell Mol Biol (Noisy-le-grand). 2022 Feb 4;67(5):387-398. doi: 10.14715/cmb/2021.67.5.51.

ABSTRACT

Despite the accelerated emerging of vaccines, development against the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) drugs discovery is still in demand. Repurposing the existing drugs is an ideal time/cost-effective strategy to tackle the clinical impact of SARS CoV-2. Thereby, the present study is a promising strategy that proposes the repurposing of approved drugs against pivotal proteins that are responsible for the viral propagation of SARS-CoV-2 virus Angiotensin-converting enzyme-2 (ACE2; 2AJF), 3CL-protease: main protease (6LU7), Papain-like protease (6W9C), Receptor Binding Domain of Spike protein (6VW1), Transmembrane protease serine 2 (TMPRSS-2; 5AFW) and Furin (5MIM) by in silico methods. Molecular docking results were analyzed based on the binding energy and active site interactions accomplished with pharmacokinetic analysis. It was observed that both anisomycin and oleandomycin bind to all selected target proteins with good binding energy, achieving the most favorable interactions. Considering the results of binding affinity, pharmacokinetics and toxicity of anisomycin and oleandomycin, it is proposed that they can act as potential drugs against the SARS CoV-2 infection. Further clinical testing of the reported drugs is essential for their use in the treatment of SARS CoV-2 infection.

PMID:35818229 | DOI:10.14715/cmb/2021.67.5.51

Mol Divers. 2022 Jul 12. doi: 10.1007/s11030-022-10491-9. Online ahead of print.

ABSTRACT

Hepatitis C virus (HCV) infection is a major public health concern, and almost two million people are infected per year globally. This is occurred by the diverse spectrum of viral genotypes, which are directly associated with chronic liver disease (fibrosis, and cirrhosis). Indeed, the viral genome encodes three principal proteins as sequentially core, E1, and E2. Both E1 and E2 proteins play a crucial role in the attachment of the host system, but E2 plays a more fundamental role in attachment. The researchers have found the "E2-CD81 complex" at the entry site, and therefore, CD81 is the key receptor for HCV entrance in both humans, and chimpanzees. So, the researchers are trying to block the host CD81 receptor and halt the virus entry within the cellular system via plant-derived compounds. Perhaps that is why the current research protocol is designed to perform an in silico analysis of the flavonoid compounds for targeting the tetraspanin CD81 receptor of hepatocytes. To find out the best flavonoid compounds from our library, web-based tools (Swiss ADME, pKCSM), as well as computerized tools like the PyRx, PyMOL, BIOVIA Discovery Studio Visualizer, Ligplot+ V2.2, and YASARA were employed. For molecular docking studies, the flavonoid compounds docked with the targeted CD81 protein, and herein, the best-outperformed compounds are Taxifolin, Myricetin, Puerarin, Quercetin, and (-)-Epicatechin, and outstanding binding affinities are sequentially - 7.5, - 7.9, - 8.2, - 8.4, and - 8.5 kcal/mol, respectively. These compounds have possessed more interactions with the targeted protein. To validate the post docking data, we analyzed both 100 ns molecular dynamic simulation, and MM-PBSA via the YASARA simulator, and finally finds the more significant outcomes. It is concluded that in the future, these compounds may become one of the most important alternative antiviral agents in the fight against HCV infection. It is suggested that further in vivo, and in vitro research studies should be done to support the conclusions of this in silico research workflow.

PMID:35821161 | DOI:10.1007/s11030-022-10491-9

Pharmacol Res. 2022 Jul 9:106347. doi: 10.1016/j.phrs.2022.106347. Online ahead of print.

ABSTRACT

Complex Regional Pain Syndrome (CRPS) represents severe chronic pain, hypersensitivity, and inflammation induced by sensory-immune-vascular interactions after a small injury. Since the therapy is unsatisfactory, there is a great need to identify novel drug targets. Unbiased transcriptomic analysis of the dorsal root ganglia (DRG) was performed in a passive transfer-trauma mouse model, and the predicted pathways were confirmed by pharmacological interventions. In the unilateral L3-5 DRGs 125 genes were differentially expressed in response to plantar incision and injecting IgG of CRPS patients. These are related to inflammatory and immune responses, cytokines, chemokines and neuropeptides. Pathway analysis revealed the involvement of Tumor Necrosis Factor (TNF) and Janus kinase (JAK-STAT) signaling. The relevance of these pathways was proven by abolished CRPS IgG-induced hyperalgesia and reduced microglia and astrocyte markers in pain-associated central nervous system regions after treatment with the soluble TNF alpha receptor etanercept or JAK inhibitor tofacitinib. These results provide the first evidence for CRPS-related neuroinflammation and abnormal cytokine signaling at the level of the primary sensory neurons in a translational mouse model and suggest that etanercept and tofacitinib might have drug repositioning potentials for CRPS-related pain.

PMID:35820612 | DOI:10.1016/j.phrs.2022.106347

J Biomol Struct Dyn. 2022 Jul 12:1-15. doi: 10.1080/07391102.2022.2097312. Online ahead of print.

ABSTRACT

HIV-protease inhibitor Ritonavir (RTV) is a clinical-stage drug. We exhibit here the synergistic effect of RTV coupled with cisplatin as potential combination therapy for treatment of cervical cancer. Knowledge about the interaction of RTV with the high-expression signatures in cancer is limited. Therefore, we utilized computational techniques to understand and assess the drug-binding affinity and drug-target interaction of RTV with these altered protein signatures. Computational studies revealed the potential interaction ability of RTV along with few other HIV protease inhibitors against these altered cancer targets. All targets exhibited good affinity towards RTV and the highest affinity was exhibited by CYP450 3A4, PDGFR and ALK. RTV established stable interaction with PDGFR and molecular dynamics simulation confirms their frequent interaction for 300 ns. Control docking of PDGFR with standard PDGFR inhibitor exhibited lower binding affinity when compared with RTV-PDGFR complex. In search of drugs as a part of combination therapy to reduce side effects of Cisplatin, this paper further evaluated the effect of combination of RTV and Cisplatin in cervical cancer cells. We propose several combination models that combines anti-viral drug RTV and standard chemotherapeutic agent, Cisplatin to be synergistic with CI value ranging from of 0.01 to 1.14. These observations suggest that anti-viral compound (RTV) could act synergistically with Cisplatin for cervical cancer therapy. However, further studies are warranted to investigate the combinatorial mode of action of RTV and Cisplatin on different molecular pathways to have a translational outcome in cervical cancer.Communicated by Ramaswamy H. Sarma.

PMID:35818867 | DOI:10.1080/07391102.2022.2097312

Inform Med Unlocked. 2022 Jul 6:101003. doi: 10.1016/j.imu.2022.101003. Online ahead of print.

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been circulating since 2019, and its global dominance is rising. Evidences suggest the respiratory illness SARS-CoV-2 has a sensitive affect on causing organ damage and other complications to the patients with autoimmune diseases (AD), posing a significant risk factor. The genetic interrelationships and molecular appearances between SARS-CoV-2 and AD are yet unknown. We carried out the transcriptomic analytical framework to delve into the SARS-CoV-2 impacts on AD progression. We analyzed both gene expression microarray and RNA-Seq datasets from SARS-CoV-2 and AD affected tissues. With neighborhood-based benchmarks and multilevel network topology, we obtained dysfunctional signaling and ontological pathways, gene disease (diseasesome) association network and protein-protein interaction network (PPIN), uncovered essential shared infection recurrence connectivities with biological insights underlying between SARS-CoV-2 and AD. We found a total of 77, 21, 9, 54 common DEGs for SARS-CoV-2 and inflammatory bowel disorder (IBD), SARS-CoV-2 and rheumatoid arthritis (RA), SARS-CoV-2 and systemic lupus erythematosus (SLE) and SARS-CoV-2 and type 1 diabetes (T1D). The enclosure of these common DEGs with bimolecular networks revealed 10 hub proteins (FYN, VEGFA, CTNNB1, KDR, STAT1, B2M, CD3G, ITGAV, TGFB3). Drugs such as amlodipine besylate, vorinostat, methylprednisolone, and disulfiram have been identified as a common ground between SARS-CoV-2 and AD from drug repurposing investigation which will stimulate the optimal selection of medications in the battle against this ongoing pandemic triggered by COVID-19.

PMID:35818398 | PMC:PMC9259025 | DOI:10.1016/j.imu.2022.101003

Int J Biol Sci. 2022 May 21;18(9):3747-3761. doi: 10.7150/ijbs.70679. eCollection 2022.

ABSTRACT

Colorectal cancer (CRC) is one of the most common malignancies worldwide, yet successful treatment still remains a challenge. In this study, we found that oxiconazole (OXI), a broad-spectrum antifungal agent, exhibits certain anti-tumor effect against CRC. Autophagy arrest and subsequent apoptosis are characterized as pivotal events involving OXI-induced growth suppression of CRC cells. Mechanistically, OXI downregulates the protein levels of peroxiredoxin-2 (PRDX2), an antioxidant enzyme, for reactive oxygen species (ROS) detoxication, to initiate autophagy by inactivating the Akt/mTOR pathway and inhibiting RAB7A-mediated fusion of autophagosome and lysosome, which lead to extreme accumulation of autophagosomes and subsequent growth suppression of CRC cells. Consistently, interfering with autophagy or overexpressing PRDX2 significantly impedes OXI-induced growth suppression of CRC cells. Moreover, OXI plus oxaliplatin, a mainstay drug for CRC treatment, achieves an improved anti-tumor effect. Taken together, our findings bring novel mechanistic insights into OXI-induced autophagy arrest and the growth inhibitory effect on CRC cells, and suggest a promisingly therapeutic role of OXI for CRC treatment.

PMID:35813474 | PMC:PMC9254464 | DOI:10.7150/ijbs.70679