J Biomol Struct Dyn. 2022 Mar 10:1-11. doi: 10.1080/07391102.2022.2044907. Online ahead of print.

ABSTRACT

Before the rise of SARS-CoV-2, emergence of different coronaviruses such as SARS-CoV and MERS-CoV has been reported that indicates possibility of the future novel pathogen from the coronavirus family at a pandemic level. In this context, explicit studies on identifying inhibitors focused on the coronavirus life cycle, are immensely important. The main protease is critical for the life cycle of coronaviruses. Majority of the work done on the inhibitor studies on the catalytically active dimeric SARS-CoV-2 main protease (Mpro), primarily focussed on the catalytic site of a single protomer, with a few targeting the dimeric site. In this study, we have exploited the FDA-approved drugs, for a computational drug repurposing study against the Mpro. A virtual screening approach was employed with docking and molecular dynamics (MD) methods. Out of 1576, FDA-approved compounds, our study suggests three compounds: netupitant, paliperidone and vilazodone as possible inhibitors with a potential to inhibit both sites (monomeric and dimeric) of the Mpro. These compounds were found to be stable during the MD simulations and their post simulation binding energies were also correlated for both the targeted sites, suggesting equal binding capacity. This unique efficiency of the reported compounds might support further experimental studies on developing inhibitors against SARS-CoV-2 main protease.Communicated by Ramaswamy H. Sarma.

PMID:35266856 | DOI:10.1080/07391102.2022.2044907

Drug Dev Res. 2022 Mar 9. doi: 10.1002/ddr.21927. Online ahead of print.

ABSTRACT

Microtubules are dynamic cytoskeletal filaments composed of alpha- (α) and beta (β)-tubulin proteins. α-tubulin proteins are posttranslationally acetylated, and loss of acetylation is associated with axonal transport defects, a common alteration contributing to the pathomechanisms of several neurodegenerative diseases. Restoring α-tubulin acetylation by pharmacological inhibition of HDAC6, a primary α-tubulin deacetylase, can rescue impaired transport. Therefore, HDAC6 is considered a promising therapeutic target for neurodegenerative diseases, but currently, there is no clinically approved inhibitor for this purpose. In this study, using drug repurposing strategy, we aimed to identify compounds possessing HDAC6 inhibition activity and inducing α-tubulin acetylation. We systematically analyzed the FDA-approved library by utilizing virtual screening and consensus scoring approaches. Inhibition activities of promising compounds were tested using in vitro assays. Motor neuron-like NSC34 cells were treated with the candidate compounds, and α-tubulin acetylation levels were determined by Western blot. Our results demonstrated that rutin, a natural flavonoid, inhibits cellular HDAC6 activity without inducing any toxicity, and it significantly increases α-tubulin acetylation level in motor neuron-like cells.

PMID:35266183 | DOI:10.1002/ddr.21927

Inflammopharmacology. 2022 Mar 10. doi: 10.1007/s10787-022-00945-9. Online ahead of print.

ABSTRACT

Monoamine oxidase inhibitors (MAOI) are presently used to treat depression, parkinsonian, and other psychiatric disorders. The present study was aimed to repurpose the use of MOAI in Rheumatoid Arthritis (RA). The animal model of RA was developed using collagen type II (CII) in Freund's complete adjuvant (FCA) followed by lipopolysaccharide (LPS) and a booster dose of CII in FCA. The effect of MAOI, Selegiline was evaluated whereas the indicators like paw thickness, arthritic score, and the splenic index were measured and compared with the standard drug Methotrexate. Further to explore the molecular mechanism, the expression of serum inflammatory cytokines (IL-6 and TNF-α), radiographical and histopathological study of hind paw were also checked and analyzed. Treatment with MAOI, Selegiline not only reduced the paw thickness, arthritic score, and the splenic index, but also greatly improved the inflammatory biochemical and hematologic parameters and improved the arthritis score. The serum level of IL-6 and TNF-α are considerably decreased dose dependently, however, the notable significant effect (**p < 0.01) observed at concentration of 30 mg/kg b.w. when the RA animals treated by Selegiline. Collectively, Selegiline improved the progression of RA possibly via decreased catecholamine breakdown at synovial fluid resulting decrease hydrogen peroxide (H2O2) generation and inhibition of pro-inflammatory cytokines in situ. Thus, the finding support and indicate the repurposing of MAOI for the treatment of RA meriting further studies on synovial monoamine oxidase as a new therapeutic target to design a new drug for RA.

PMID:35266068 | DOI:10.1007/s10787-022-00945-9

Brief Bioinform. 2022 Mar 9:bbac059. doi: 10.1093/bib/bbac059. Online ahead of print.

ABSTRACT

Accurate prediction of drug-target interactions (DTIs) can reduce the cost and time of drug repositioning and drug discovery. Many current methods integrate information from multiple data sources of drug and target to improve DTIs prediction accuracy. However, these methods do not consider the complex relationship between different data sources. In this study, we propose a novel computational framework, called MccDTI, to predict the potential DTIs by multiview network embedding, which can integrate the heterogenous information of drug and target. MccDTI learns high-quality low-dimensional representations of drug and target by preserving the consistent and complementary information between multiview networks. Then MccDTI adopts matrix completion scheme for DTIs prediction based on drug and target representations. Experimental results on two datasets show that the prediction accuracy of MccDTI outperforms four state-of-the-art methods for DTIs prediction. Moreover, literature verification for DTIs prediction shows that MccDTI can predict the reliable potential DTIs. These results indicate that MccDTI can provide a powerful tool to predict new DTIs and accelerate drug discovery. The code and data are available at: https://github.com/ShangCS/MccDTI.

PMID:35262678 | DOI:10.1093/bib/bbac059

Br J Clin Pharmacol. 2022 Mar 8. doi: 10.1111/bcp.15262. Online ahead of print.

ABSTRACT

There is international interest for consensus advice for prescribers working in the field of drug resistant epilepsy intending to trial potential therapies that are nonregistered or off-label. Cannabinoids are one such therapy. In 2017, the New South Wales State Government (Australia) set up a cannabinoid prescribing guidance service for a wide variety of indications, based on known pharmacology together with the relevant new literature as it became available. Increasing interest in cannabis medicines use outside this State over the following 5 years together with a paucity of registration-standard clinical trials, lack of information around dosing issues, drug interactions and biological plausibility meant there remained a large unmet need for such advice. To address the unmet need in epilepsy, and until medicines were registered or regulator quality data were available, it was agreed to bring together a working group comprising paediatric and adult epilepsy specialists, clinical pharmacists., clinical pharmacologists and cannabis researchers from across Australia to develop interim consensus advice for prescribers. Although interim, this consensus advice addresses much of the current practice gap by providing an informed overview of the different cannabis medicines currently available for use in the treatment of epilepsy in paediatric and adult settings, with information on dose, drug interactions, toxicity, type of seizure and frequency of symptom relief. As such it supplements the limited evidence currently available from clinical trials with experience from front-line practice. It is expected that this consensus advice will be updated as new evidence emerges and will provide guidance for a subsequent Guideline.

PMID:35261078 | DOI:10.1111/bcp.15262

Acad Pediatr. 2022 Mar 4:S1876-2859(22)00089-4. doi: 10.1016/j.acap.2022.02.021. Online ahead of print.

ABSTRACT

To examine dispensed opioid prescription patterns for limb fractures across racial/ethnic groups in a pediatric population METHOD: : We used South Carolina's Medicaid claims data 2000-2018 for pediatric limb fracture cases (under age 19) discharged from the emergency department. The key independent variable was the child's race/ethnicity. The outcomes were: 1) whether the patient had a dispensed opioid prescription; and 2) whether dispensed opioid supply was longer than 5 days among cases with any dispensed opioid prescriptions. Logistic regression models were used to test the association between race/ethnicity and the outcomes. Covariates included age-at-service, gender, service year, and having multiple fracture injuries RESULTS: : Compared with non-Hispanic White cases (NHW), the odds of receiving dispensed opioid prescriptions were lower for cases of non-Hispanic Black (NHB) (OR=0.73; 95% confidence interval [CI]:0.71, 0.75), Asian (OR=0.69; CI:0.53, 0.90), Other/Unknown (OR=0.86; CI:0.80, 0.92), and Hispanic (OR=0.84; CI:0.79, 0.90) race/ethnicity. The odds of receiving >5 days of dispensed opioid prescription supply did not differ significantly among race/ethnic categories CONCLUSION: : Our study confirms previous findings that as compared to NHW, the NHB children were less likely to receive dispensed opioid prescriptions. Also, it reveals that the different minority race/ethnic groups are not homogenous in their likelihoods of receiving dispensed opioid prescriptions after a limb fracture compared to NHW-findings underreported in previous studies.

PMID:35257927 | DOI:10.1016/j.acap.2022.02.021

Drug Discov Today. 2022 Mar 4:S1359-6446(22)00093-9. doi: 10.1016/j.drudis.2022.03.003. Online ahead of print.

ABSTRACT

Despite the availability of advanced interventions, stroke remains one of the most significant causes of mortality and morbidity worldwide. US Food and Drug Administration (FDA)-approved treatment options for stroke include tissue plasminogen activators (tPAs) and mechanical thrombectomy (MT). However, these are limited by a narrow therapeutic time window. Additionally, poststroke rehabilitation therapies can provide functional recovery but take a long time to show benefits. Drug repurposing could be a novel approach to broaden treatment options in this scenario. In this review, we summarize marketed drugs that could be repurposed based on their safety and efficacy data. We also briefly discuss their mechanisms of action and provide a list of repurposed drugs under trials for ischemic stroke therapy.

PMID:35257857 | DOI:10.1016/j.drudis.2022.03.003

Acta Pharm Sin B. 2022 Feb;12(2):532-557. doi: 10.1016/j.apsb.2021.09.006. Epub 2021 Sep 10.

ABSTRACT

Drug repurposing or repositioning has been well-known to refer to the therapeutic applications of a drug for another indication other than it was originally approved for. Repurposing non-oncology small-molecule drugs has been increasingly becoming an attractive approach to improve cancer therapy, with potentially lower overall costs and shorter timelines. Several non-oncology drugs approved by FDA have been recently reported to treat different types of human cancers, with the aid of some new emerging technologies, such as omics sequencing and artificial intelligence to overcome the bottleneck of drug repurposing. Therefore, in this review, we focus on summarizing the therapeutic potential of non-oncology drugs, including cardiovascular drugs, microbiological drugs, small-molecule antibiotics, anti-viral drugs, anti-inflammatory drugs, anti-neurodegenerative drugs, antipsychotic drugs, antidepressants, and other drugs in human cancers. We also discuss their novel potential targets and relevant signaling pathways of these old non-oncology drugs in cancer therapies. Taken together, these inspiring findings will shed new light on repurposing more non-oncology small-molecule drugs with their intricate molecular mechanisms for future cancer drug discovery.

PMID:35256933 | PMC:PMC8897051 | DOI:10.1016/j.apsb.2021.09.006

J Cheminform. 2022 Mar 7;14(1):10. doi: 10.1186/s13321-022-00590-y.

ABSTRACT

Deep learning's automatic feature extraction has been a revolutionary addition to computational drug discovery, infusing both the capabilities of learning abstract features and discovering complex molecular patterns via learning from molecular data. Since biological and chemical knowledge are necessary for overcoming the challenges of data curation, balancing, training, and evaluation, it is important for databases to contain information regarding the exact target and disease of each bioassay. The existing depositories such as PubChem or ChEMBL offer the screening data for millions of molecules against a variety of cells and targets, however, their bioassays contain complex biological descriptions which can hinder their usage by the machine learning community. In this work, a comprehensive disease and target-based dataset is collected from PubChem in order to facilitate and accelerate molecular machine learning for better drug discovery. MolData is one the largest efforts to date for democratizing the molecular machine learning, with roughly 170 million drug screening results from 1.4 million unique molecules assigned to specific diseases and targets. It also provides 30 unique categories of targets and diseases. Correlation analysis of the MolData bioassays unveils valuable information for drug repurposing for multiple diseases including cancer, metabolic disorders, and infectious diseases. Finally, we provide a benchmark of more than 30 models trained on each category using multitask learning. MolData aims to pave the way for computational drug discovery and accelerate the advancement of molecular artificial intelligence in a practical manner. The MolData benchmark data is available at https://GitHub.com/Transilico/MolData as well as within the additional files.

PMID:35255958 | DOI:10.1186/s13321-022-00590-y

Endocrinol Metab (Seoul). 2022 Feb;37(1):62-64. doi: 10.3803/EnM.2022.1403. Epub 2022 Feb 28.

NO ABSTRACT

PMID:35255602 | DOI:10.3803/EnM.2022.1403

Curr Opin Pharmacol. 2022 Mar 4;63:102201. doi: 10.1016/j.coph.2022.102201. Online ahead of print.

ABSTRACT

The greatest challenge of current biomedicine is to identify curative therapies for every disease in a personalized way so that every individual gets benefit. To that end, however, we need fully understand mechanisms of disease that will drive the design of novel therapies and innovative approaches. For rare diseases (RDs) which individually affect low numbers of people (< 1:2000), but together, affect 300 million (∼10% of the world population) the constraints are greater. This is because: 1) there is limited knowledge on RD physiopathology; 2) the low number of patients strongly limits clinical trials; 3) there is low commercial interest by pharma; 4) when specific drugs reach the market, their high cost precludes their reaching all those who need them. Several possibilities that can help mitigate these barriers are discussed here, including orphan drug designation, drug repurposing, break-down into theratypes (as currently in place for Cystic Fibrosis), or novel precision-medicine-based approaches.

PMID:35255452 | DOI:10.1016/j.coph.2022.102201

Drugs. 2022 Mar 7. doi: 10.1007/s40265-022-01689-0. Online ahead of print.

ABSTRACT

Around 20% of the American population have chronic pain and estimates in other Western countries report similar numbers. This represents a major challenge for global health care systems. Additional problems for the treatment of chronic and persistent pain are the comparably low efficacy of existing therapies, the failure to translate effects observed in preclinical pain models to human patients and related setbacks in clinical trials from previous attempts to develop novel analgesics. Drug repurposing offers an alternative approach to identify novel analgesics as it can bypass various steps of classical drug development. In recent years, several approved drugs were attributed analgesic properties. Here, we review available data and discuss recent findings suggesting that the approved drugs minocycline, fingolimod, pioglitazone, nilotinib, telmisartan, and others, which were originally developed for the treatment of different pathologies, can have analgesic, antihyperalgesic, or neuroprotective effects in preclinical and clinical models of inflammatory or neuropathic pain. For our analysis, we subdivide the drugs into substances that can target neuroinflammation or substances that can act on peripheral sensory neurons, and highlight the proposed mechanisms. Finally, we discuss the merits and challenges of drug repurposing for the development of novel analgesics.

PMID:35254645 | DOI:10.1007/s40265-022-01689-0

iScience. 2022 Feb 9;25(3):103890. doi: 10.1016/j.isci.2022.103890. eCollection 2022 Mar 18.

ABSTRACT

Non-alcoholic fatty liver disease (NAFLD) is among the most common liver pathologies, however, none approved condition-specific therapy yet exists. The present study introduces a drug repositioning (DR) approach that combines in vitro steatosis models with a network-based computational platform, constructed upon genomic data from diseased liver biopsies and compound-treated cell lines, to propose effectively repositioned therapeutic compounds. The introduced in silico approach screened 20'000 compounds, while complementary in vitro and proteomic assays were developed to test the efficacy of the 46 in silico predictions. This approach successfully identified six compounds, including the known anti-steatogenic drugs resveratrol and sirolimus. In short, gallamine triethiotide, diflorasone, fenoterol, and pralidoxime ameliorate steatosis similarly to resveratrol/sirolimus. The implementation holds great potential in reducing screening time in the early drug discovery stages and in delivering promising compounds for in vivo testing.

PMID:35252807 | PMC:PMC8889147 | DOI:10.1016/j.isci.2022.103890

BMC Med Genomics. 2022 Mar 6;15(1):48. doi: 10.1186/s12920-022-01203-1.

ABSTRACT

BACKGROUND: Besides binding to proteins, the most recent advances in pharmacogenomics indicate drugs can regulate the expression of non-coding RNAs (ncRNAs). The polypharmacological feature in drugs enables us to find new uses for existing drugs (namely drug repositioning). However, current computational methods for drug repositioning mainly consider proteins as drug targets. Meanwhile, these methods identify only statistical relationships between drugs and diseases. They provide little information about how drug-disease associations are formed at the molecular target level.

METHODS: Herein, we first comprehensively collect proteins and two categories of ncRNAs as drug targets from public databases to construct drug-target interactions. Experimentally confirmed drug-disease associations are downloaded from an established database. A canonical correlation analysis (CCA) based method is then applied to the two datasets to extract correlated sets of targets and diseases. The correlated sets are regarded as canonical components, and they are used to investigate drug's mechanism of actions. We finally develop a strategy to predict novel drug-disease associations for drug repositioning by combining all the extracted correlated sets.

RESULTS: We receive 400 canonical components which correlate targets with diseases in our study. We select 4 components for analysis and find some top-ranking diseases in an extracted set might be treated by drugs interfacing with the top-ranking targets in the same set. Experimental results from 10-fold cross-validations show integrating different categories of target information results in better prediction performance than only using proteins or ncRNAs as targets. When compared with 3 state-of-the-art approaches, our method receives the highest AUC value 0.8576. We use our method to predict new indications for 789 drugs and confirm 24 predictions in the top 1 predictions.

CONCLUSIONS: To the best of our knowledge, this is the first computational effort which combines both proteins and ncRNAs as drug targets for drug repositioning. Our study provides a biologically relevant interpretation regarding the forming of drug-disease associations, which is useful for guiding future biomedical tests.

PMID:35249529 | DOI:10.1186/s12920-022-01203-1

Cell Death Dis. 2022 Mar 4;13(3):203. doi: 10.1038/s41419-022-04651-w.

ABSTRACT

Despite several approved therapeutic modalities, multiple myeloma (MM) remains an incurable blood malignancy and only a small fraction of patients achieves prolonged disease control. The common anti-MM treatment targets proteasome with specific inhibitors (PI). The resulting interference with protein degradation is particularly toxic to MM cells as they typically accumulate large amounts of toxic proteins. However, MM cells often acquire resistance to PIs through aberrant expression or mutations of proteasome subunits such as PSMB5, resulting in disease recurrence and further treatment failure. Here we propose CuET-a proteasome-like inhibitor agent that is spontaneously formed in-vivo and in-vitro from the approved alcohol-abuse drug disulfiram (DSF), as a readily available treatment effective against diverse resistant forms of MM. We show that CuET efficiently kills also resistant MM cells adapted to proliferate under exposure to common anti-myeloma drugs such as bortezomib and carfilzomib used as the first-line therapy, as well as to other experimental drugs targeting protein degradation upstream of the proteasome. Furthermore, CuET can overcome also the adaptation mechanism based on reduced proteasome load, another clinically relevant form of treatment resistance. Data obtained from experimental treatment-resistant cellular models of human MM are further corroborated using rather unique advanced cytotoxicity experiments on myeloma and normal blood cells obtained from fresh patient biopsies including newly diagnosed as well as relapsed and treatment-resistant MM. Overall our findings suggest that disulfiram repurposing particularly if combined with copper supplementation may offer a promising and readily available treatment option for patients suffering from relapsed and/or therapy-resistant multiple myeloma.

PMID:35246527 | DOI:10.1038/s41419-022-04651-w

BMC Bioinformatics. 2022 Mar 4;23(1):84. doi: 10.1186/s12859-022-04608-y.

ABSTRACT

BACKGROUND: Drug repurposing aims at finding new targets for already developed drugs. It becomes more relevant as the cost of discovering new drugs steadily increases. To find new potential targets for a drug, an abundance of methods and existing biomedical knowledge from different domains can be leveraged. Recently, knowledge graphs have emerged in the biomedical domain that integrate information about genes, drugs, diseases and other biological domains. Knowledge graphs can be used to predict new connections between compounds and diseases, leveraging the interconnected biomedical data around them. While real world use cases such as drug repurposing are only interested in one specific relation type, widely used knowledge graph embedding models simultaneously optimize over all relation types in the graph. This can lead the models to underfit the data that is most relevant for the desired relation type. For example, if we want to learn embeddings to predict links between compounds and diseases but almost the entirety of relations in the graph is incident to other pairs of entity types, then the resulting embeddings are likely not optimised to predict links between compounds and diseases. We propose a method that leverages domain knowledge in the form of metapaths and use them to filter two biomedical knowledge graphs (Hetionet and DRKG) for the purpose of improving performance on the prediction task of drug repurposing while simultaneously increasing computational efficiency.

RESULTS: We find that our method reduces the number of entities by 60% on Hetionet and 26% on DRKG, while leading to an improvement in prediction performance of up to 40.8% on Hetionet and 14.2% on DRKG, with an average improvement of 20.6% on Hetionet and 8.9% on DRKG. Additionally, prioritization of antiviral compounds for SARS CoV-2 improves after task-driven filtering is applied.

CONCLUSION: Knowledge graphs contain facts that are counter productive for specific tasks, in our case drug repurposing. We also demonstrate that these facts can be removed, resulting in an improved performance in that task and a more efficient learning process.

PMID:35246025 | DOI:10.1186/s12859-022-04608-y

Biomed Pharmacother. 2022 Feb 28;148:112744. doi: 10.1016/j.biopha.2022.112744. Online ahead of print.

ABSTRACT

BACKGROUND: Drug repositioning is a cost-effective method to identify novel disease indications for approved drugs; it requires a shorter developmental period than conventional drug discovery methods. We aimed to identify prophylactic drugs for oxaliplatin-induced peripheral neuropathy by drug repositioning using data from large-scale medical information and life science information databases.

METHODS: Herein, we analyzed the reported data between 2007 and 2017 retrieved from the FDA's database of spontaneous adverse event reports (FAERS) and the LINCS database provided by the National Institute of Health. The efficacy of the drug candidates for oxaliplatin-induced peripheral neuropathy obtained from the database analysis was examined using a rat model of peripheral neuropathy. Additionally, we compared the incidence of peripheral neuropathy in patients who received oxaliplatin at the Tokushima University Hospital, Japan. The effects of statins on the animal model were examined in six-week-old male Sprague-Dawley rats and seven or eight-week-old male BALB/C mice. Retrospective medical chart review included clinical data from Tokushima University Hospital from April 2009 to March 2018.

RESULTS: Simvastatin, indicated for dyslipidemia, significantly reduced the severity of peripheral neuropathy and oxaliplatin-induced hyperalgesia. In the nerve tissue of model rats, the mRNA expression of Gstm1 increased with statin administration. A retrospective medical chart review using clinical data revealed that the incidence of peripheral neuropathy decreased with statin use.

CONCLUSION AND RELEVANCE: Thus, drug repositioning using data from large-scale basic and clinical databases enables the discovery of new indications for approved drugs with a high probability of success.

PMID:35240525 | DOI:10.1016/j.biopha.2022.112744

Brief Bioinform. 2022 Mar 3:bbac047. doi: 10.1093/bib/bbac047. Online ahead of print.

ABSTRACT

Inhibition of host protein functions using established drugs produces a promising antiviral effect with excellent safety profiles, decreased incidence of resistant variants and favorable balance of costs and risks. Genomic methods have produced a large number of robust host factors, providing candidates for identification of antiviral drug targets. However, there is a lack of global perspectives and systematic prioritization of known virus-targeted host proteins (VTHPs) and drug targets. There is also a need for host-directed repositioned antivirals. Here, we integrated 6140 VTHPs and grouped viral infection modes from a new perspective of enriched pathways of VTHPs. Clarifying the superiority of nonessential membrane and hub VTHPs as potential ideal targets for repositioned antivirals, we proposed 543 candidate VTHPs. We then presented a large-scale drug-virus network (DVN) based on matching these VTHPs and drug targets. We predicted possible indications for 703 approved drugs against 35 viruses and explored their potential as broad-spectrum antivirals. In vitro and in vivo tests validated the efficacy of bosutinib, maraviroc and dextromethorphan against human herpesvirus 1 (HHV-1), hepatitis B virus (HBV) and influenza A virus (IAV). Their drug synergy with clinically used antivirals was evaluated and confirmed. The results proved that low-dose dextromethorphan is better than high-dose in both single and combined treatments. This study provides a comprehensive landscape and optimization strategy for druggable VTHPs, constructing an innovative and potent pipeline to discover novel antiviral host proteins and repositioned drugs, which may facilitate their delivery to clinical application in translational medicine to combat fatal and spreading viral infections.

PMID:35238349 | DOI:10.1093/bib/bbac047

Am J Sports Med. 2022 Mar 2:3635465221077116. doi: 10.1177/03635465221077116. Online ahead of print.

ABSTRACT

BACKGROUND: Wnt/β-catenin signaling suppresses the differentiation of cultured tenocytes, but its roles in tendon repair remain mostly elusive. No chemical compounds are currently available to treat tendon injury.

HYPOTHESIS: We hypothesized that the inhibition of Wnt/β-catenin signaling would accelerate tendon healing.

STUDY DESIGN: Controlled laboratory study.

METHODS: Tendon-derived cells (TDCs) were isolated from rat Achilles tendons. The right Achilles tendon was injured via a dermal punch, while the left tendon was sham operated. A Wnt/β-catenin inhibitor, IWR-1, and an antihistamine agent, promethazine (PH), were locally and intramuscularly injected, respectively, for 2 weeks after surgery. The healing tendons were histologically and biomechanically evaluated.

RESULTS: The amount of β-catenin protein was increased in the injured tendons from postoperative weeks 0.5 to 2. Inhibition of Wnt/β-catenin signaling by IWR-1 in healing tendons improved the histological abnormalities and decreased β-catenin, but it compromised the biomechanical properties. As we previously reported that antihistamine agents suppressed Wnt/β-catenin signaling in human chondrosarcoma cells, we examined the effects of antihistamines on TDCs. We found that a first-generation antihistamine agent, PH, increased the expression of the tendon marker genes Mkx and Tnmd in TDCs. Intramuscular injection of PH did not improve histological abnormalities, but it decreased β-catenin in healing tendons and increased the peak force and stiffness of the healing tendons on postoperative week 2. On postoperative week 8, however, the biomechanical properties of vehicle-treated tendons became similar to those of PH-treated tendons.

CONCLUSION: IWR-1 and PH suppressed Wnt/β-catenin signaling and improved the histological abnormalities of healing tendons. IWR-1, however, compromised the biomechanical properties of healing tendons, whereas PH improved them.

CLINICAL RELEVANCE: PH is a candidate repositioned drug that potentially accelerates tendon repair.

PMID:35234523 | DOI:10.1177/03635465221077116

Acta Crystallogr D Struct Biol. 2022 Mar 1;78(Pt 3):321-327. doi: 10.1107/S2059798322000079. Epub 2022 Feb 18.

ABSTRACT

Clorsulon is an anthelmintic drug that is clinically used against Fasciola hepatica. Due to the presence of two sulfonamide moieties in its core nucleus, which are well recognized as zinc-binding groups, it was proposed that it may be efficacious in the inhibition of parasite carbonic anhydrases (CAs). Proteomic analyses revealed the presence of CA in the tegument of Schistosoma mansoni, and recently the druggability of this target was explored by testing the inhibitory activities of several sulfonamide-based derivatives. According to the principles of drug repurposing, the aim was to demonstrate a putative new mechanism of action of clorsulon and thus widen its antiparasitic spectrum. For this purpose, the inhibitory activity and isoform selectivity of clorsulon was studied using human CA I and S. mansoni CA, revealing different modes of binding of clorsulon that explain its inhibitory potency against the two enzymes. The information obtained in this study could be crucial in the design of more active and selective derivatives.

PMID:35234146 | DOI:10.1107/S2059798322000079