Parasitol Res. 2021 Nov 6. doi: 10.1007/s00436-021-07354-x. Online ahead of print.

ABSTRACT

The infection caused by Entamoeba histolytica is still a serious public health problem, especially in developing countries. The goal of this study was to evaluate the effect of terfenadine against Entamoeba histolytica. The trophozoites were exposed to 1, 2, 3, and 4 μM of terfenadine, for 24 and 48 h. Consequently, the viability of cells was determined by trypan blue exclusion test. The effect of terfenadine on adhesion of Entamoeba histolytica was evaluated in Caco-2 cells. In addition, the effect of terfenadine on the erythrophagocytic capacity of the parasite was investigated. The results show that terfenadine affects the growth and cell viability in a time- and dose-dependent manner. The higher inhibitory effects were observed with 4 µM at 48 h; 91.6% of growth inhibition and only 22.5% of trophozoites were viable. Additionally, we demonstrate that terfenadine is highly selective for the parasite and has low toxicity on Caco-2 cells. Furthermore, adhesion to Caco-2 cells and erythrophagocytic capacity were significantly inhibited. These findings demonstrate that terfenadine exerts significant effects on the virulence of Entamoeba histolytica. This is the first study demonstrating the amoebicidal activity of terfenadine and the results suggest it may be effective in the treatment of amoebiasis.

PMID:34741218 | DOI:10.1007/s00436-021-07354-x

Metabolism. 2021 Nov 2:154925. doi: 10.1016/j.metabol.2021.154925. Online ahead of print.

ABSTRACT

Nonalcoholic fatty liver disease (NAFLD) is the most common form of chronic liver disease worldwide. With no Food and Drug Administration approved drugs, current treatment options include dietary restrictions and lifestyle modification. NAFLD is closely associated with metabolic disorders such as obesity, type 2 diabetes, and dyslipidemia. Hence, clinically various pharmacological approaches using existing drugs such as antidiabetic, anti-obesity, antioxidants, and cytoprotective agents have been considered in the management of NAFLD and nonalcoholic steatohepatitis (NASH). However, several pharmacological therapies aiming to alleviate NAFLD-NASH are currently being examined at various phases of clinical trials. Emerging data from these studies with drugs targeting diverse molecular mechanisms show promising outcomes. This review summarizes the current understanding of the pathogenic mechanisms of NAFLD and provides an insight into the pharmacological targets and emerging therapeutics with specific interventional mechanisms. In addition, we also discuss the importance and utility of new approach methodologies and regulatory perspectives for NAFLD-NASH drug development.

PMID:34740573 | DOI:10.1016/j.metabol.2021.154925

J Exp Clin Cancer Res. 2021 Nov 5;40(1):347. doi: 10.1186/s13046-021-02144-w.

ABSTRACT

BACKGROUND: Glioblastoma (GBM; grade IV glioma) is characterized by a very short overall survival time and extremely low 5-year survival rates. We intend to promote experimental and clinical research on rationale and scientifically driven drug repurposing. This may represent a safe and often inexpensive way to propose novel pharmacological approaches to GBM. Our precedent work describes the role of chlorpromazine (CPZ) in hindering malignant features of GBM. Here, we investigate in greater detail the molecular mechanisms at the basis of the effect of CPZ on GBM cells.

METHODS: We employed proteomics platforms, i.e., activity-based protein profiling plus mass spectrometry, to identify potential cellular targets of the drug. Then, by means of established molecular and cellular biology techniques, we assessed the effects of this drug on GBM cell metabolic and survival pathways.

RESULTS: The experimental output indicated as putative targets of CPZ several of factors implicated in endoplasmic reticulum (ER) stress, with consequent unfolded protein response (UPR). Such a perturbation culminated in a noticeable reactive oxygen species generation and intense autophagic response that resulted in cytotoxic and abortive effects for six GBM cell lines, three of which growing as neurospheres, while it appeared cytoprotective for the RPE-1 human non-cancer neuro-ectodermal cell line.

CONCLUSIONS: This discrepancy could be central in explaining the lethal effects of the drug on GBM cells and the relatively scarce cytotoxicity toward normal tissues attributed to this compound. The data presented here offer support to the multicenter phase II clinical trial we have undertaken, which consists of the addition of CPZ to first-line treatment of GBM patients carrying a hypo- or un-methylated MGMT gene, i.e. those characterized by intrinsic resistance to temozolomide.

PMID:34740374 | DOI:10.1186/s13046-021-02144-w

Cell Res. 2021 Nov 4. doi: 10.1038/s41422-021-00581-y. Online ahead of print.

ABSTRACT

In contrast to the extensive research about viral protein-host protein interactions that has revealed major insights about how RNA viruses engage with host cells during infection, few studies have examined interactions between host factors and viral RNAs (vRNAs). Here, we profiled vRNA-host protein interactomes for three RNA virus pathogens (SARS-CoV-2, Zika, and Ebola viruses) using ChIRP-MS. Comparative interactome analyses discovered both common and virus-specific host responses and vRNA-associated proteins that variously promote or restrict viral infection. In particular, SARS-CoV-2 binds and hijacks the host factor IGF2BP1 to stabilize vRNA and augment viral translation. Our interactome-informed drug repurposing efforts identified several FDA-approved drugs (e.g., Cepharanthine) as broad-spectrum antivirals in cells and hACE2 transgenic mice. A co-treatment comprising Cepharanthine and Trifluoperazine was highly potent against the newly emerged SARS-CoV-2 B.1.351 variant. Thus, our study illustrates the scientific and medical discovery utility of adopting a comparative vRNA-host protein interactome perspective.

PMID:34737357 | DOI:10.1038/s41422-021-00581-y

Methods. 2021 Nov 1:S1046-2023(21)00247-4. doi: 10.1016/j.ymeth.2021.10.007. Online ahead of print.

ABSTRACT

Computational prediction of drug-target interactions (DTIs) is of particular importance in the process of drug repositioning because of its efficiency in selecting potential candidates for DTIs. A variety of computational methods for predicting DTIs have been proposed oade. Our interest is which methods or techniques are the most advantageous for increasing prediction accuracy. This article provides a comprehensive overview of network-based, machine learning, and integrated DTI prediction methods. The network-based methods handle a DTI network along with drug and target similarities in a matrix form and apply graph-theoretic algorithms to identify new DTIs. Machine learning methods use known DTIs and the features of drugs and target proteins as training data to build a predictive model. Integrated methods combine these two techniques. We assessed the prediction performance of the selected state-of-the-art methods using two different benchmark datasets. Our experimental results demonstrate that the integrated methods outperform the others in general. Some previous methods showed low accuracy on predicting interactions of unknown drugs which do not exist in the training dataset. Combining similarity matrices from multiple features by data fusion was not beneficial in increasing prediction accuracy. Finally, we analyzed future directions for further improvements in DTI predictions.

PMID:34737033 | DOI:10.1016/j.ymeth.2021.10.007

BMC Med Inform Decis Mak. 2021 Nov 4;21(Suppl 1):308. doi: 10.1186/s12911-021-01648-x.

ABSTRACT

BACKGROUND: Disease-drug associations provide essential information for drug discovery and disease treatment. Many disease-drug associations remain unobserved or unknown, and trials to confirm these associations are time-consuming and expensive. To better understand and explore these valuable associations, it would be useful to develop computational methods for predicting unobserved disease-drug associations. With the advent of various datasets describing diseases and drugs, it has become more feasible to build a model describing the potential correlation between disease and drugs.

RESULTS: In this work, we propose a new prediction method, called LMFDA, which works in several stages. First, it studies the drug chemical structure, disease MeSH descriptors, disease-related phenotypic terms, and drug-drug interactions. On this basis, similarity networks of different sources are constructed to enrich the representation of drugs and diseases. Based on the fused disease similarity network and drug similarity network, LMFDA calculated the association score of each pair of diseases and drugs in the database. This method achieves good performance on Fdataset and Cdataset, AUROCs were 91.6% and 92.1% respectively, higher than many of the existing computational models.

CONCLUSIONS: The novelty of LMFDA lies in the introduction of multimodal fusion using low-rank tensors to fuse multiple similar networks and combine matrix complement technology to predict potential association. We have demonstrated that LMFDA can display excellent network integration ability for accurate disease-drug association inferring and achieve substantial improvement over the advanced approach. Overall, experimental results on two real-world networks dataset demonstrate that LMFDA able to delivers an excellent detecting performance. Results also suggest that perfecting similar networks with as much domain knowledge as possible is a promising direction for drug repositioning.

PMID:34736437 | DOI:10.1186/s12911-021-01648-x

Anticancer Res. 2021 Nov;41(11):5477-5480. doi: 10.21873/anticanres.15360.

ABSTRACT

BACKGROUND/AIM: Itraconazole shows anticancer activity in various types of cancer but its underlying mechanism is unclear. We investigated the effect of itraconazole on membrane-associated lipids.

MATERIALS AND METHODS: To investigate the influences of itraconazole on cholesterol trafficking, cervical cancer CaSki cells were cultured with itraconazole and analyzed by Filipin staining followed by confocal microscopy. Effect on the glycerophospholipid profiles was analyzed by liquid chromatography/mass spectrometry (LC/MS).

RESULTS: After itraconazole treatment, Filipin staining revealed cholesterol accumulation in the intracellular compartments, which was similar to the distribution after treatment of U18666A (cholesterol transport inhibitor). LC/MS analysis showed a significant decrease in phosphatidylserine levels and an increase in lysophosphatidylcholine levels in CaSki cells.

CONCLUSION: Itraconazole inhibited cholesterol trafficking and altered the phospholipid composition. Alterations in the cell membrane can potentiate the anticancer activity of itraconazole.

PMID:34732417 | DOI:10.21873/anticanres.15360

Methods Mol Biol. 2022;2390:383-407. doi: 10.1007/978-1-0716-1787-8_16.

ABSTRACT

The discovery and development of drugs is a long and expensive process with a high attrition rate. Computational drug discovery contributes to ligand discovery and optimization, by using models that describe the properties of ligands and their interactions with biological targets. In recent years, artificial intelligence (AI) has made remarkable modeling progress, driven by new algorithms and by the increase in computing power and storage capacities, which allow the processing of large amounts of data in a short time. This review provides the current state of the art of AI methods applied to drug discovery, with a focus on structure- and ligand-based virtual screening, library design and high-throughput analysis, drug repurposing and drug sensitivity, de novo design, chemical reactions and synthetic accessibility, ADMET, and quantum mechanics.

PMID:34731478 | DOI:10.1007/978-1-0716-1787-8_16

Methods Mol Biol. 2022;2390:103-112. doi: 10.1007/978-1-0716-1787-8_3.

ABSTRACT

The development of vaccines for the treatment of COVID-19 is paving the way for new hope. Despite this, the risk of the virus mutating into a vaccine-resistant variant still persists. As a result, the demand of efficacious drugs to treat COVID-19 is still pertinent. To this end, scientists continue to identify and repurpose marketed drugs for this new disease. Many of these drugs are currently undergoing clinical trials and, so far, only one has been officially approved by FDA. Drug repurposing is a much faster route to the clinic than standard drug development of novel molecules, nevertheless in a pandemic this process is still not fast enough to halt the spread of the virus. Artificial intelligence has already played a large part in hastening the drug discovery process, not only by facilitating the selection of potential drug candidates but also in monitoring the pandemic and enabling faster diagnosis of patients. In this chapter, we focus on the impact and challenges that artificial intelligence has demonstrated thus far with respect to drug repurposing of therapeutics for the treatment of COVID-19.

PMID:34731465 | DOI:10.1007/978-1-0716-1787-8_3

Interdiscip Sci. 2021 Nov 3. doi: 10.1007/s12539-021-00488-7. Online ahead of print.

ABSTRACT

Accurate prediction of drug-target interactions (DTIs), which is often used in the fields of drug discovery and drug repositioning, is regarded a key challenge in the study of drug science. In this paper, a new method called DeepStack-DTIs is proposed to predict DTIs. First, for the target protein, pseudo-position specific score matrix, pseudo amino acid composition and SPIDER3 are used to extract the different feature information of the target protein. Meanwhile, the path-based fingerprint features of each drug are extracted. Then, the synthetic minority oversampling technique (SMOTE) and light gradient boosting machine (LightGBM) are used for data balancing and feature selection, respectively. Finally, the processed features are input to the deep-stacked ensemble classifier composed of gated recurrent unit (GRU), deep neural network (DNN), support vector machine (SVM), eXtreme gradient boosting (XGBoost) and logistic regression (LR) to predict DTIs. Under the five-fold cross-validation and compared with existing methods, the proposed method achieves higher prediction accuracy on the gold standard dataset. To evaluate the predictive power of DeepStack-DTIs, we validate the method on another dataset and predict the drug-target interaction network. The results indicate that DeepStack-DTIs has excellent predictive ability than the other methods, and provides novel insights for the prediction of DTIs. A novel method DeepStack-DTIs for drug-target interactions prediction. PsePSSM, PseAAC, SPIDER3 and FP2 are fused to convert protein sequence and drug molecule information into digital information, respectively. The SMOTE algorithm is used to balance the dataset and LightGBM feature selection algorithm is employed to remove redundant and irrelevant features to select the optimal feature subset. This optimal feature subset is inputted into the deep-stacked ensemble classifier to predict drug-target interactions. The experimental results show DeepStack-DTIs method can significantly improve the prediction accuracy of drug-target interactions.

PMID:34731411 | DOI:10.1007/s12539-021-00488-7

Dig Dis Sci. 2021 Nov 2. doi: 10.1007/s10620-021-07273-y. Online ahead of print.

ABSTRACT

BACKGROUND: Beneficial response to first-line immunosuppressive azathioprine in patients with inflammatory bowel disease (IBD) is low due to high rates of adverse events. Co-administrating allopurinol has been shown to improve tolerability. However, data on this co-therapy as first-line treatment are scarce.

AIM: Retrospective comparison of long-term effectiveness and safety of first-line low-dose azathioprine-allopurinol co-therapy (LDAA) with first-line azathioprine monotherapy (AZAm) in patients with IBD without metabolite monitoring.

METHODS: Clinical benefit was defined as ongoing therapy without initiation of steroids, biologics or surgery. Secondary outcomes included CRP, HBI/SCCAI, steroid withdrawal and adverse events.

RESULTS: In total, 166 LDAA and 118 AZAm patients (median follow-up 25 and 27 months) were evaluated. Clinical benefit was more frequently observed in LDAA patients at 6 months (74% vs. 53%, p = 0.0003), 12 months (54% vs. 37%, p = 0.01) and in the long-term (median 36 months; 37% vs. 24%, p = 0.04). Throughout follow-up, AZAm patients were 60% more likely to fail therapy, due to a higher intolerance rate (45% vs. 26%, p = 0.001). Only 73% of the effective AZA dose was tolerated in AZAm patients, while LDAA could be initiated and maintained at its target dose. Incidence of myelotoxicity and elevated liver enzymes was similar in both cohorts, and both conditions led to LDAA withdrawal in only 2%. Increasing allopurinol from 100 to 200-300 mg/day significantly lowered liver enzymes in 5/6 LDAA patients with hepatotoxicity.

CONCLUSIONS: Our poor AZAm outcomes emphasize that optimization of azathioprine is needed. We demonstrated a long-term safe and more effective profile of first-line LDAA. This co-therapy may therefore be considered standard first-line immunosuppressive.

PMID:34729677 | DOI:10.1007/s10620-021-07273-y

Drug Discov Today. 2021 Oct 30:S1359-6446(21)00475-X. doi: 10.1016/j.drudis.2021.10.019. Online ahead of print.

ABSTRACT

Drug repurposing is an attractive strategy for identifying new indications for existing drugs. Three approved antidepressants have advanced into clinical trials for cancer therapy. In particular, further medicinal chemistry efforts with tranylcypromine (TCP) have led to the discovery of several TCP-based histone lysine specific demethylase 1 (LSD1) inhibitors that display therapeutic promise for treating cancer in the clinic. Thus repurposing antidepressants could be a promising strategy for cancer treatment. In this review, we illustrate the anticancer mechanisms of action of antidepressants and also discuss the challenges and future directions of repurposing antidepressants for anticancer drug discovery, to provide an overview of approved antidepressant cancer therapies.

PMID:34728374 | DOI:10.1016/j.drudis.2021.10.019

iScience. 2021 Oct 23:103331. doi: 10.1016/j.isci.2021.103331. Online ahead of print.

ABSTRACT

The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no approved effective antiviral drug. Flux balance analysis (FBA) is an efficient method to analyze metabolic networks. Here, FBA was applied on human lung cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to reposition metabolic drugs and drug combinations against the virus replication within the host tissue. Making use of expression data sets of infected lung tissue, genome-scale COVID-19-specific metabolic models were reconstructed. Then, host-specific essential genes and gene-pairs were determined through in silico knockouts that permit reducing the viral biomass production without affecting the host biomass. Key pathways that are associated with COVID-19 severity in lung tissue are related to oxidative stress, ferroptosis and pyrimidine metabolism. By in silico screening of FDA-approved drugs on the putative disease-specific essential genes and gene-pairs, 85 drugs and 52 drug combinations were predicted as promising candidates for COVID-19 (https://github.com/sysbiolux/DCcov).

PMID:34723158 | PMC:PMC8536485 | DOI:10.1016/j.isci.2021.103331

Front Genet. 2021 Oct 14;12:756784. doi: 10.3389/fgene.2021.756784. eCollection 2021.

ABSTRACT

Over 50% of diffuse large B-cell lymphoma (DLBCL) patients are diagnosed at an advanced stage. Although there are a few therapeutic strategies for DLBCL, most of them are more effective in limited-stage cancer patients. The prognosis of patients with advanced-stage DLBCL is usually poor with frequent recurrence and metastasis. In this study, we aimed to identify gene expression and network differences between limited- and advanced-stage DLBCL patients, with the goal of identifying potential agents that could be used to relieve the severity of DLBCL. Specifically, RNA sequencing data of DLBCL patients at different clinical stages were collected from the cancer genome atlas (TCGA). Differentially expressed genes were identified using DESeq2, and then, weighted gene correlation network analysis (WGCNA) and differential module analysis were performed to find variations between different stages. In addition, important genes were extracted by key driver analysis, and potential agents for DLBCL were identified according to gene-expression perturbations and the Crowd Extracted Expression of Differential Signatures (CREEDS) drug signature database. As a result, 20 up-regulated and 73 down-regulated genes were identified and 79 gene co-expression modules were found using WGCNA, among which, the thistle1 module was highly related to the clinical stage of DLBCL. KEGG pathway and GO enrichment analyses of genes in the thistle1 module indicated that DLBCL progression was mainly related to the NOD-like receptor signaling pathway, neutrophil activation, secretory granule membrane, and carboxylic acid binding. A total of 47 key drivers were identified through key driver analysis with 11 up-regulated key driver genes and 36 down-regulated key diver genes in advanced-stage DLBCL patients. Five genes (MMP1, RAB6C, ACCSL, RGS21 and MOCOS) appeared as hub genes, being closely related to the occurrence and development of DLBCL. Finally, both differentially expressed genes and key driver genes were subjected to CREEDS analysis, and 10 potential agents were predicted to have the potential for application in advanced-stage DLBCL patients. In conclusion, we propose a novel pipeline to utilize perturbed gene-expression signatures during DLBCL progression for identifying agents, and we successfully utilized this approach to generate a list of promising compounds.

PMID:34721544 | PMC:PMC8551569 | DOI:10.3389/fgene.2021.756784

Front Immunol. 2021 Oct 13;12:724277. doi: 10.3389/fimmu.2021.724277. eCollection 2021.

ABSTRACT

Atopic Dermatitis (AD) is a chronic and relapsing skin disease. The medications for treating AD are still limited, most of them are topical corticosteroid creams or antibiotics. The current study attempted to discover potential AD treatments by integrating a gene network and genomic analytic approaches. Herein, the Single Nucleotide Polymorphism (SNPs) associated with AD were extracted from the GWAS catalog. We identified 70 AD-associated loci, and then 94 AD risk genes were found by extending to proximal SNPs based on r2 > 0.8 in Asian populations using HaploReg v4.1. Next, we prioritized the AD risk genes using in silico pipelines of bioinformatic analysis based on six functional annotations to identify biological AD risk genes. Finally, we expanded them according to the molecular interactions using the STRING database to find the drug target genes. Our analysis showed 27 biological AD risk genes, and they were mapped to 76 drug target genes. According to DrugBank and Therapeutic Target Database, 25 drug target genes overlapping with 53 drugs were identified. Importantly, dupilumab, which is approved for AD, was successfully identified in this bioinformatic analysis. Furthermore, ten drugs were found to be potentially useful for AD with clinical or preclinical evidence. In particular, we identified filgotinub and fedratinib, targeting gene JAK1, as potential drugs for AD. Furthermore, four monoclonal antibody drugs (lebrikizumab, tralokinumab, tocilizumab, and canakinumab) were successfully identified as promising for AD repurposing. In sum, the results showed the feasibility of gene networking and genomic information as a potential drug discovery resource.

PMID:34721386 | PMC:PMC8548825 | DOI:10.3389/fimmu.2021.724277

Med Chem Res. 2021 Oct 27:1-15. doi: 10.1007/s00044-021-02808-4. Online ahead of print.

ABSTRACT

Methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Staphylococcus aureus (VRSA) are primary causes of skin and soft tissue infections worldwide. To address the emergency caused due to increasing multidrug-resistant (MDR) bacterial infections, a series of novel fluoro and trifluoromethyl-substituted salicylanilide derivatives were synthesized and their antimicrobial activity was investigated. MIC data reveal that the compounds inhibited S. aureus specifically (MIC 0.25-64 µg/mL). The in vitro cytotoxicity of compounds with MIC < 1 µg/mL against Vero cells led to identification of four compounds (20, 22, 24 and 25) with selectivity index above 10. These four compounds were tested against MDR S. aureus panel. Remarkably, 5-chloro-N-(4'-bromo-3'-trifluoromethylphenyl)-2-hydroxybenzamide (22) demonstrated excellent activity against nine MRSA and three VRSA strains with MIC 0.031-0.062 µg/mL, which is significantly better than the control drugs methicillin and vancomycin. The comparative time-kill kinetic experiment revealed that the effect of bacterial killing of 22 is comparable with vancomycin. Compound 22 did not synergize with or antagonize any FDA-approved antibiotic and reduced pre-formed S. aureus biofilm better than vancomycin. Overall, study suggested that 22 could be further developed as a potent anti-staphylococcal therapeutic.

PMID:34720564 | PMC:PMC8548355 | DOI:10.1007/s00044-021-02808-4

Biol Pharm Bull. 2021 Oct 30. doi: 10.1248/bpb.b21-00324. Online ahead of print.

ABSTRACT

With the development of structural biology and data mining, computer-aided drug design (CADD) has been playing an important role in all aspects of new drug development. Reverse docking, a method of virtual screening based on molecular docking in CADD, is widely used in drug repositioning, drug rescue, and traditional Chinese medicine (TCM) research, for it can search for macromolecular targets that can bind to a given ligand molecule. This review revealed the principle of reverse docking, summarized common target protein databases and docking procedures, and enumerated the applications of reverse docking in drug repositioning, adverse drug reactions, traditional Chinese medicine, and COVID-19 treatment. Hope our work can give some inspiration to researchers engaged in drug development.

PMID:34719576 | DOI:10.1248/bpb.b21-00324

J Parkinsons Dis. 2021 Oct 23. doi: 10.3233/JPD-212929. Online ahead of print.

ABSTRACT

Parkinson's disease is a debilitating neurodegenerative disorder whose etiology is still unclear, hampering the development of effective treatments. There is an urgent need to identify the etiology and provide further effective treatments. Recently, accumulating evidence has indicated that infection may play a role in the etiology of Parkinson's disease. The infective pathogens may act as a trigger for Parkinson's disease, the most common of which are hepatitis C virus, influenza virus, and Helicobacter pylori. In addition, gut microbiota is increasingly recognized to influence brain function through the gut-brain axis, showing an important role in the pathogenesis of Parkinson's disease. Furthermore, a series of anti-infective agents exhibit surprising neuroprotective effects via various mechanisms, such as interfering with α-synuclein aggregation, inhibiting neuroinflammation, attenuating oxidative stress, and preventing from cell death, independent of their antimicrobial effects. The pleiotropic agents affect important events in the pathogenesis of Parkinson's disease. Moreover, most of them are less toxic, clinically safe and have good blood-brain penetrability, making them hopeful candidates for the treatment of Parkinson's disease. However, the use of antibiotics and subsequent gut dysbiosis may also play a role in Parkinson's disease, making the long-term effects of anti-infective drugs worthy of further consideration and exploration. This review summarizes the current evidence for the association between infective pathogens and Parkinson's disease and subsequently explores the application prospects of anti-infective drugs in Parkinson's disease treatment, providing novel insights into the pathogenesis and treatment of Parkinson's disease.

PMID:34719435 | DOI:10.3233/JPD-212929

Drug Discov Today. 2021 Oct 27:S1359-6446(21)00448-7. doi: 10.1016/j.drudis.2021.10.010. Online ahead of print.

ABSTRACT

Drug repositioning is an attractive strategy for discovering new therapeutic uses for approved or investigational drugs, with potentially shorter development timelines and lower development costs. Various computational methods have been used in drug repositioning, promoting the efficiency and success rates of this approach. Recently, deep learning (DL) has attracted wide attention for its potential in target prediction and drug repositioning. Here, we provide an overview of the basic principles of commonly used DL architectures and their applications in target prediction and drug repositioning, and discuss possible ways of dealing with current challenges to help achieve its expected potential for drug repositioning.

PMID:34718208 | DOI:10.1016/j.drudis.2021.10.010

J Oral Biosci. 2021 Oct 27:S1349-0079(21)00141-9. doi: 10.1016/j.job.2021.10.005. Online ahead of print.

ABSTRACT

BACKGROUND: Several recent studies have focused on the utility of drug repurposing to expand clinical application of approved therapeutics. Here, we investigate the efficacy of midazolam (MDZ) and cytokines for regenerating calcified tissue, using immortalized porcine dental pulp (PPU7) and mouse skeletal muscle derived myoblast (C2C12) cells, with the goal of repurposing MDZ as a new treatment to facilitate calcified tissue regeneration.

HIGHLIGHTS: We noted that PPU7 and C2C12 cells cultured with various MDZ regimens displayed increased bone morphogenic protein (BMP-2), transforming growth factor beta (TGF-β), and alkaline phosphatase activity. These increases were highest in PPU7 cells cultured with MDZ alone, and in C2C12 cells cultured with MDZ and BMP-2. PPU7 cells cultured under these conditions demonstrated markedly elevated expression of odontoblastic gene markers, indicating their likely differentiation into odontoblasts. Expression levels of osteoblastic gene markers also increased in C2C12 cells, suggesting that MDZ potentiates the effect of BMP-2, inducing osteoblast differentiation in these cells. Newly formed calcified deposits in both PPU7 and C2C12 cells were identified as hydroxyapatite via crystallographic and crystal engineering analyses.

CONCLUSION: MDZ increases ALP activity, inducing expression of specific marker genes for both odontoblasts and osteoblasts while promoting hydroxyapatite production in both PPU7 and C2C12 cells. These responses were cell type specific. MDZ treatment alone could induce these changes in PPU7 cells, but C2C12 cell differentiation required BMP-2 addition.

PMID:34718143 | DOI:10.1016/j.job.2021.10.005