Drug repurposing for Alzheimer''s disease using knowledge graph embedding / 中国药科大学学报

@#Alzheimer''s disease (AD) has brought to us huge medical and economic burdens, and so discovery of its therapeutic drugs is of great significance.In this paper, we utilized knowledge graph embedding (KGE) models to explore drug repurposing for AD on the publicly available drug repurposing knowledge graph (DRKG).Specifically, we applied four KGE models, namely TransE, DistMult, ComplEx, and RotatE, to learn the embedding vectors of entities and relations on DRKG.By using three classical knowledge graph evaluation metrics, we then evaluated and compared the performance of these models as well as the quality of the learned embedded vectors.Based on our results, we selected the RotatE model for link prediction and identified 16 drugs that might be repurposed for the treatment of AD.Previous studies have confirmed the potential therapeutic effects of 12 drugs against AD, i.e., glutathione, haloperidol, capsaicin, quercetin, estradiol, glucose, disulfire, adenosine, paroxetine, paclitaxel, glybride and amitriptyline.Our study demonstrates that drug repurposing based on KGE may provide new ideas and methods for AD drug discovery.Moreover, the RotatE model effectively integrates multi-source information of DRKG, enabling promising AD drug repurposing.The source code of this paper is available at https://github.com/LuYF-Lemon-love/AD-KGE.

Research progress of feature-based deep learning for predicting compound-protein interaction / 中国药科大学学报

@#The prediction of compound-protein interaction (CPI) is a critical technological tool for discovering lead compounds and drug repurposing during the process of drug development.In recent years, deep learning has been widely used in CPI research, which has accelerated the development of CPI prediction in drug discovery.This review focuses on feature-based CPI prediction models.First, we described the datasets, as well as typical feature representation methods commonly used for compounds and proteins in CPI prediction.Based on the critical problems in modeling, we discussed models for CPI prediction from two perspectives: multimodal features and attention mechanisms.Then, the performance of 12 selected models was evaluated on 3 benchmark datasets for both classification and regression tasks.Finally, the review summarizes the existing challenges in this field and prospects for future directions.We believe that this investigation will provide some reference and insight for further research on CPI prediction.

ETCM v2.0: An update with comprehensive resource and rich annotations for traditional Chinese medicine

Existing traditional Chinese medicine (TCM)-related databases are still insufficient in data standardization, integrity and precision, and need to be updated urgently. Herein, an Encyclopedia of Traditional Chinese Medicine version 2.0 (ETCM v2.0, http://www.tcmip.cn/ETCM2/front/#/) was constructed as the latest curated database hosting 48,442 TCM formulas recorded by ancient Chinese medical books, 9872 Chinese patent drugs, 2079 Chinese medicinal materials and 38,298 ingredients. To facilitate the mechanistic research and new drug discovery, we improved the target identification method based on a two-dimensional ligand similarity search module, which provides the confirmed and/or potential targets of each ingredient, as well as their binding activities. Importantly, five TCM formulas/Chinese patent drugs/herbs/ingredients with the highest Jaccard similarity scores to the submitted drugs are offered in ETCM v2.0, which may be of significance to identify prescriptions/herbs/ingredients with similar clinical efficacy, to summarize the rules of prescription use, and to find alternative drugs for endangered Chinese medicinal materials. Moreover, ETCM v2.0 provides an enhanced JavaScript-based network visualization tool for creating, modifying and exploring multi-scale biological networks. ETCM v2.0 may be a major data warehouse for the quality marker identification of TCMs, the TCM-derived drug discovery and repurposing, and the pharmacological mechanism investigation of TCMs against various human diseases.

Network pharmacology approaches for research of Traditional Chinese Medicines / 中国天然药物

Pharmacodynamics material basis and effective mechanisms are the two main issues to decipher the mechnisms of action of Traditional Chinese medicines (TCMs) for the treatment of diseases. TCMs, in "multi-component, multi-target, multi-pathway" paradigm, show satisfactory clinical results in complex diseases. New ideas and methods are urgently needed to explain the complex interactions between TCMs and diseases. Network pharmacology (NP) provides a novel paradigm to uncover and visualize the underlying interaction networks of TCMs against multifactorial diseases. The development and application of NP has promoted the safety, efficacy, and mechanism investigations of TCMs, which then reinforces the credibility and popularity of TCMs. The current organ-centricity of medicine and the "one disease-one target-one drug" dogma obstruct the understanding of complex diseases and the development of effective drugs. Therefore, more attentions should be paid to shift from "phenotype and symptom" to "endotype and cause" in understanding and redefining current diseases. In the past two decades, with the advent of advanced and intelligent technologies (such as metabolomics, proteomics, transcriptomics, single-cell omics, and artificial intelligence), NP has been improved and deeply implemented, and presented its great value and potential as the next drug-discovery paradigm. NP is developed to cure causal mechanisms instead of treating symptoms. This review briefly summarizes the recent research progress on NP application in TCMs for efficacy research, mechanism elucidation, target prediction, safety evaluation, drug repurposing, and drug design.

Inhibitory effects of simeprevir on Staphylococcusepidermidis and itsbiofilm in vitro / 中南大学学报(医学版)

OBJECTIVES@#Staphylococcus epidermidis (S. epidermidis) is a Gram-positive opportunistic pathogen that often causes hospital infections. With the abuse of antibiotics, the resistance of S. epidermidis gradually increases, and drug repurposing has become a research hotspot in the treating of refractory drug-resistant bacterial infections. This study aims to study the antimicrobial and antibiofilm effects of simeprevir, an antiviral hepatitis drug, on S. epidermidis in vitro.@*METHODS@#The micro-dilution assay was used to determine the minimal inhibitory concentration (MIC) and minimal bactericidal concentration (MBC) of simeprevir against S. epidermidis. Crystal violet staining assay was used to detect the biofilm inhibitory effect of simeprevir. The antimicrobial activity of simeprevir against S. epidermidis and its biofilm were explored by SYTO9/PI fluorescent staining. The combined effect between simeprevir and gentamycin was assessed by checkerboard assay and was confirmed by time-inhibition assay.@*RESULTS@#Simeprevir showed significant antimicrobial effects against S. epidermidis type strains and clinical isolates with the MIC and MBC at 2-16 μg/mL and 4-32 μg/mL, respectively. The antimicrobial effects of simeprevir were confirmed by SYTO9/PI staining. Simeprevir at MIC could significantly inhibit and break the biofilm on cover slides. Similarly, simeprevir also significantly inhibit the biofilm formation on the surface of urine catheters either in TSB [from (0.700±0.020) to (0.050±0.004)] (t=54.03, P<0.001), or horse serum [from (1.00±0.02) to (0.13±0.01)] (t=82.78, P<0.001). Synergistic antimicrobial effect was found between simeprevir and gentamycin against S. epidermidis with the fractional inhibitory concentration index of 0.5.@*CONCLUSIONS@#Simeprevir shows antimicrobial effect and anti-biofilm activities against S. epidermidis.

Drug Repurposing of Clinically Approved Drugs to Target Epithelial-mesenchymal Transition Using Molecular Docking Approach

@#Introduction: Epithelial-mesenchymal transition (EMT) is a process of epithelial transformation into mesenchymal cells. It is also a process that contributes to the progression of fibrosis and cancer metastasis. Transforming growth factor-beta (TGF-β), as a potent inducer of EMT, has therefore became a potential therapeutic target. However, clinical developments of TGF-β inhibitors have been un-successful due to safety risks. Hence, drug repurposing of existing safe-to-use drugs could over-come this issue. Methods: In this study, the TGF-β receptor type 1 (ALK5) was selected as the target protein. Molecular docking was performed using known ALK5 inhibitors as positive controls. Clinical drugs with similar binding affinity and amino acid interaction were selected for in vitro experimental validation. Results: ALK5 inhibitor demonstrated binding affinities ranging from -11.2 to -9.5 kcal/mol. Analysis of amino acid interaction revealed that Val219, Ala230, Lys232, and Leu340 amino acid residues are crucial for binding. Subsequent screening of clinically approved drugs against ALK5 showed top five potential drugs (ergotamine, telmisartan, saquinavir, indinavir, and nelfinavir). The selected drugs were tested in TGF-β1-induced normal human bronchial epithelial cell line, BEAS-2B. Western blot analysis showed that the drugs did not exhibit inhibitory effects on the downregulation of epithelial proteins (E-cadherin) and upregulation of mesenchymal proteins (vimentin and α-smooth muscle actin). Conclusion: Based on these experimental outcome, it is postulated that the results from molecular docking were false positives. The tested drugs in this study could serve as negative controls in future screening against ALK5 protein.

Evaluation of predictive machine learning models for drug repurposing against delta variant of SARS-CoV-2 spike protein-Supplementary Data

Drug repurposing is a major approach used by researchers to tackle the COVID-19 pandemic which has been worsened by the current surge of delta variant in many countries. Though drugs like Remdesivir and Hydroxychloroquine have been repurposed, studies prove these drugs have insignificant effect in treatment. So, in this study, we use the already FDA approved database of 1615 drugs to apply semi-flexible and flexible molecular docking methods to calculate the docking scores and identify the best 20 potential inhibitors for our modelled delta variant spike protein RBD. Then, we calculate 2325 1-D and 2-D molecular descriptors and use machine-learning algorithms like K-Nearest Neighbor, Random Forest, Support Vector Machine and ensemble stacking method to build regression-based prediction models. We identify 15 best descriptors for the dataset all of which were found to be inversely correlated with ligand binding. With only these few descriptors, the models performed excellently with an area under curve (AUC) value of 0.952 in Regression Error Characteristic curve for ensemble stacking. Therefore, we comment that these 15 descriptors are the most important features for the binding of inhibitors to the spike protein and hence these should be studied properly in terms of drug repurposing and drug discovery.

Evaluation of predictive machine learning models for drug repurposing against delta variant of SARS-CoV-2 spike protein

Drug repurposing is a major approach used by researchers to tackle the COVID-19 pandemic which has been worsened by the current surge of delta variant in many countries. Though drugs like Remdesivir and Hydroxychloroquine have been repurposed, studies prove these drugs have insignificant effect in treatment. So, in this study, we use the already FDA approved database of 1615 drugs to apply semi-flexible and flexible molecular docking methods to calculate the docking scores and identify the best 20 potential inhibitors for our modelled delta variant spike protein RBD. Then, we calculate 2325 1-D and 2-D molecular descriptors and use machine-learning algorithms like K-Nearest Neighbor, Random Forest, Support Vector Machine and ensemble stacking method to build regression-based prediction models. We identify 15 best descriptors for the dataset all of which were found to be inversely correlated with ligand binding. With only these few descriptors, the models performed excellently with an area under curve (AUC) value of 0.952 in Regression Error Characteristic curve for ensemble stacking. Therefore, we comment that these 15 descriptors are the most important features for the binding of inhibitors to the spike protein and hence these should be studied properly in terms of drug repurposing and drug discovery.

A novel approach for drug discovery-drug repurposing

Drug repurposing refers to finding a new indication for a pre-existing drug. It is a novel way of drug discovery that greatly reduces the time and money spent in the drug discovery process. This approach is associated with a better chance of successful drug approval. Both previously approved drugs as well as drugs that have failed in the trials conducted for their original indication can be repurposed. Even drugs withdrawn from market for their original indication can be repurposed for a new indication. Starting with Sildenafil which is the oldest example of repurposed drug to the recently repurposed drug tocilizumab for COVID-19, the list of repurposed drugs is a big one. The regulatory pathway to be followed for a repurposed drug is different from that for a new chemical entity. Furthermore, the period of marketing exclusivity for repurposed drug is only 3 years as against the 20 years of patent protection period for new drug. The strategy for drug repurposing may be a serendipitous one or hypothesis driven one. The hypothesis driven strategy includes the experimental and computational approaches. Computational approaches for drug discovery, especially the Connectivity map approach, offer a lot of scope to understand the drug-disease-gene link, thereby acting as a kick-starter for drug repurposing. Drug repurposing has real potential to offer a cure for rare genetic conditions and cancers. This review covers the various drug repurposing approaches in detail, the regulatory pathway for repurposed drugs, salient examples of repurposed drugs and also the challenges associated with drug repurposing.

New possibilities for chromoblastomycosis and phaeohyphomycosis treatment: identification of two compounds from the MMV Pathogen Box® that present synergism with itraconazole

BACKGROUND Black fungi of the Herpotrichiellaceae family are agents of chromoblastomycosis and phaeohyphomycosis. There are few therapeutic options for these infections and it is common to associate antifungal drugs in their treatment. OBJECTIVES To investigate the Medicines for Malaria Venture (MMV) Pathogen Box® for possible compounds presenting synergism with antifungal drugs used to treat black fungal infections. METHODS An initial screening of the Pathogen Box® compounds was performed in combination with itraconazole or terbinafine at sub-inhibitory concentrations against Fonsecaea pedrosoi. Hits were further tested against eight Herpotrichiellaceae using the checkerboard method. FINDINGS No synergism was observed with terbinafine. MMV687273 (SQ109) and MMV688415 showed synergism with itraconazole against F. pedrosoi. Synergism of these compounds was confirmed with some black fungi by the checkerboard method. SQ109 and itraconazole presented synergism for Exophiala dermatitidis, F. pedrosoi, F. monophora and F. nubica, with fungicidal activity for F. pedrosoi and F. monophora. MMV688415 presented synergism with itraconazole only for F. pedrosoi, with fungicidal activity. The synergic compounds had high selectivity index values when combined with itraconazole. MAIN CONCLUSIONS These compounds in combination, particularly SQ109, are promising candidates to treat Fonsecaea spp. and E. dermatitidis infections, which account for most cases of chromoblastomycosis and phaeohyphomycosis.

Targeting PFKL with penfluridol inhibits glycolysis and suppresses esophageal cancer tumorigenesis in an AMPK/FOXO3a/BIM-dependent manner

As one of the hallmarks of cancer, metabolic reprogramming leads to cancer progression, and targeting glycolytic enzymes could be useful strategies for cancer therapy. By screening a small molecule library consisting of 1320 FDA-approved drugs, we found that penfluridol, an antipsychotic drug used to treat schizophrenia, could inhibit glycolysis and induce apoptosis in esophageal squamous cell carcinoma (ESCC). Gene profiling and Ingenuity Pathway Analysis suggested the important role of AMPK in action mechanism of penfluridol. By using drug affinity responsive target stability (DARTS) technology and proteomics, we identified phosphofructokinase, liver type (PFKL), a key enzyme in glycolysis, as a direct target of penfluridol. Penfluridol could not exhibit its anticancer property in PFKL-deficient cancer cells, illustrating that PFKL is essential for the bioactivity of penfluridol. High PFKL expression is correlated with advanced stages and poor survival of ESCC patients, and silencing of PFKL significantly suppressed tumor growth. Mechanistically, direct binding of penfluridol and PFKL inhibits glucose consumption, lactate and ATP production, leads to nuclear translocation of FOXO3a and subsequent transcriptional activation of BIM in an AMPK-dependent manner. Taken together, PFKL is a potential prognostic biomarker and therapeutic target in ESCC, and penfluridol may be a new therapeutic option for management of this lethal disease.

Repurposing non-oncology small-molecule drugs to improve cancer therapy: Current situation and future directions

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.

Discovery and repurposing of artemisinin / 医学前沿

Malaria is an ancient infectious disease that threatens millions of lives globally even today. The discovery of artemisinin, inspired by traditional Chinese medicine (TCM), has brought in a paradigm shift and been recognized as the "best hope for the treatment of malaria" by World Health Organization. With its high potency and low toxicity, the wide use of artemisinin effectively treats the otherwise drug-resistant parasites and helps many countries, including China, to eventually eradicate malaria. Here, we will first review the initial discovery of artemisinin, an extraordinary journey that was in stark contrast with many drugs in western medicine. We will then discuss how artemisinin and its derivatives could be repurposed to treat cancer, inflammation, immunoregulation-related diseases, and COVID-19. Finally, we will discuss the implications of the "artemisinin story" and how that can better guide the development of TCM today. We believe that artemisinin is just a starting point and TCM will play an even bigger role in healthcare in the 21st century.

Dexamethasone for treating SARS-CoV-2 infection: a systematic review and meta-analysis

ABSTRACT BACKGROUND: Considering the disruptions imposed by lockdowns and social distancing recommendations, coupled with overwhelmed healthcare systems, researchers worldwide have been exploring drug repositioning strategies for treating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). OBJECTIVE: To compile results from randomized clinical trials on the effect of dexamethasone, compared with standard treatment for management of SARS-CoV-2. DESIGN AND SETTING: We conducted a systematic review and meta-analysis in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines in a Brazilian public university. METHODS: We sought to compile data from 6724 hospitalized patients with confirmed or suspected SARS-CoV-2 infection. RESULTS: Treatment with dexamethasone significantly reduced mortality within 28 days (risk ratio, RR: 0.89; 95% confidence interval, CI: 0.82-0.97). Dexamethasone use was linked with being discharged alive within 28 days (odds ratio, OR: 1.20; 95% CI: 1.07-1.33). CONCLUSIONS: This study suggests that dexamethasone may significantly improve the outcome among hospitalized patients with SARS-CoV-2 infection and associated severe respiratory complications. ­Further studies need to consider both dose-dependent administration and outcomes in early and later stages of the disease. PROSPERO platform: CRD42021229825.

Medicines for Malaria Venture COVID Box: a source for repurposing drugs with antifungal activity against human pathogenic fungi

BACKGROUND Treatment of mycoses is often ineffective, usually prolonged, and has some side effects. These facts highlight the importance of discovering new molecules to treat fungal infections. OBJECTIVES To search the Medicines for Malaria Venture COVID Box for drugs with antifungal activity. METHODS Fourteen human pathogenic fungi were tested against the 160 drugs of this collection at 1.0 µM concentration. We evaluated the ability of the drugs to impair fungal growth, their fungicidal nature, and morphological changes caused to cells. FINDINGS Thirty-four molecules (21.25%) presented antifungal activity. Seven are antifungal drugs and one is the agricultural fungicide cycloheximide. The other drugs with antifungal activity included antibiotics (n = 3), antimalarials (n = 4), antivirals (n = 2), antiparasitcs (n = 3), antitumor agents (n = 5), nervous system agents (n = 3), immunosuppressants (n = 3), antivomiting (n = 1), antiasthmatic (n = 1), and a genetic disorder agent (n = 1). Several of these drugs inhibited Histoplasma capsulatum and Paracoccidioides brasiliensis growth (15 and 20, respectively), while Fusarium solani was not affected by the drugs tested. Most drugs were fungistatic, but niclosamide presented fungicidal activity against the three dimorphic fungi tested. Cyclosporine affected morphology of Cryptococcus neoformans. MAIN CONCLUSIONS These drugs represent new alternatives to the development of more accessible and effective therapies to treat human fungal infections.

Drug repurposing against coronavirus disease 2019 (COVID-19):A review / 药物分析学报

Since December 2019,severe acute respiratory syndrome coronavirus 2 has been found to be the culprit in the coronavirus disease 2019 (COVID-19),causing a global pandemic.Despite the existence of many vaccine programs,the number of confirmed cases and fatalities due to COVID-19 is still increasing.Furthermore,a number of variants have been reported.Because of the absence of approved anti-coronavirus drugs,the treatment and management of COVID-19 has become a global challenge.Under these circumstances,drug repurposing is an effective method to identify candidate drugs with a shorter cycle of clinical trials.Here,we summarize the current status of the application of drug repurposing in COVID-19,including drug repurposing based on virtual computer screening,network pharmacology,and bioactivity,which may be a beneficial COVID-19 treatment.

High-throughput screening identifies established drugs as SARS-CoV-2 PLpro inhibitors

A new coronavirus (SARS-CoV-2) has been identified as the etiologic agent for the COVID-19 outbreak. Currently, effective treatment options remain very limited for this disease; therefore, there is an urgent need to identify new anti-COVID-19 agents. In this study, we screened over 6,000 compounds that included approved drugs, drug candidates in clinical trials, and pharmacologically active compounds to identify leads that target the SARS-CoV-2 papain-like protease (PLpro). Together with main protease (M

Atorvastatin ameliorates viral burden and neural stem/ progenitor cell (NSPC) death in an experimental model of Japanese encephalitis

Japanese encephalitis virus, a neurotropic flavivirus, causes sporadic encephalitis with nearly 25% fatal casereports. JEV infects neural stem/progenitor cells (NSPCs) and decreases their proliferation. Statin, a commonlyused class of cholesterol lowering drug, has been shown to possess potent anti-inflammatory and neuroprotective effects in acute brain injury and chronic neurodegenerative conditions. Here, we aimed to check theefficacy of atorvastatin in alleviating the symptoms of Japanese encephalitis (JE). Using BALB/c mouse modelof JEV infection, we observed that atorvastatin effectively reduces viral load in the subventricular zone (SVZ)of infected pups and decreases the resultant cell death. Furthermore, atorvastatin abrogates microglial activation and production of proinflammatory cyto/chemokine production post JEV infection in vivo. It alsoreduced interferon-b response in the neurogenic environs. The neuroprotective role of atorvastatin is againevident from the rescued neurosphere size and decreased cell death in vitro. It has also been observed that uponatorvastatin administration, cell cycle regulatory proteins and cell survival proteins are also restored to theirrespective expression level as observed in uninfected animals. Thus the antiviral, immunomodulatory andneuroprotective roles of atorvastatin reflect in our experimental observations. Therefore, this drug broadens apath for future therapeutic measures against JEV infection.

Drug targets for COVID-19 therapeutics: Ongoing global efforts

The current global pandemic COVID-19 caused by the SARS-CoV-2 virus has already inflicted insurmountable damage both to the human lives and global economy. There is an immediate need for identificationof effective drugs to contain the disastrous virus outbreak. Global efforts are already underway at a war footingto identify the best drug combination to address the disease. In this review, an attempt has been made tounderstand the SARS-CoV-2 life cycle, and based on this information potential druggable targets againstSARS-CoV-2 are summarized. Also, the strategies for ongoing and future drug discovery against the SARSCoV-2 virus are outlined. Given the urgency to find a definitive cure, ongoing drug repurposing efforts beingcarried out by various organizations are also described. The unprecedented crisis requires extraordinary effortsfrom the scientific community to effectively address the issue and prevent further loss of human lives andhealth.

COVID-19: molecular targets, drug repurposing and new avenues for drug discovery

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly contagious infection that may break the healthcare system of several countries. Here, we aimed at presenting a critical view of ongoing drug repurposing efforts for COVID-19 as well as discussing opportunities for development of new treatments based on current knowledge of the mechanism of infection and potential targets within. Finally, we also discuss patent protection issues, cost effectiveness and scalability of synthetic routes for some of the most studied repurposing candidates since these are key aspects to meet global demand for COVID-19 treatment.