Cancers (Basel). 2022 Sep 23;14(19):4621. doi: 10.3390/cancers14194621.

ABSTRACT

The advent of molecular targeted therapies has made a significant impact on survival of women with ovarian cancer who have defects in homologous recombination repair (HRR). High-grade serous ovarian cancer (HGSOC) is the most common histological subtype of ovarian cancer, with over 50% displaying defective HRR. Poly ADP ribose polymerases (PARPs) are a family of enzymes that catalyse the transfer of ADP-ribose to target proteins, functioning in fundamental cellular processes including transcription, chromatin remodelling and DNA repair. In cells with deficient HRR, PARP inhibitors (PARPis) cause synthetic lethality leading to cell death. Despite the major advances that PARPis have heralded for women with ovarian cancer, questions and challenges remain, including: can the benefits of PARPis be brought to a wider range of women with ovarian cancer; can other drugs in clinical use function in a similar way or with greater efficacy than currently clinically approved PARPis; what can we learn from long-term responders to PARPis; can PARPis sensitise ovarian cancer cells to immunotherapy; and can synthetic lethal strategies be employed more broadly to develop new therapies for women with ovarian cancer. We examine these, and other, questions with focus on improving outcomes for women with ovarian cancer.

PMID:36230543 | DOI:10.3390/cancers14194621

Sci Rep. 2022 Oct 13;12(1):17185. doi: 10.1038/s41598-022-21947-1.

NO ABSTRACT

PMID:36229506 | DOI:10.1038/s41598-022-21947-1

Comput Biol Med. 2022 Aug 31;150:105992. doi: 10.1016/j.compbiomed.2022.105992. Online ahead of print.

ABSTRACT

Drug repurposing is an approach to identify new medical indications of approved drugs. This work presents a graph neural network drug repurposing model, which we refer to as GDRnet, to efficiently screen a large database of approved drugs and predict the possible treatment for novel diseases. We pose drug repurposing as a link prediction problem in a multi-layered heterogeneous network with about 1.4 million edges capturing complex interactions between nearly 42,000 nodes representing drugs, diseases, genes, and human anatomies. GDRnet has an encoder-decoder architecture, which is trained in an end-to-end manner to generate scores for drug-disease pairs under test. We demonstrate the efficacy of the proposed model on real datasets as compared to other state-of-the-art baseline methods. For a majority of the diseases, GDRnet ranks the actual treatment drug in the top 15. Furthermore, we apply GDRnet on a coronavirus disease (COVID-19) dataset and show that many drugs from the predicted list are being studied for their efficacy against the disease.

PMID:36228466 | DOI:10.1016/j.compbiomed.2022.105992

Biomed Pharmacother. 2022 Oct 10;156:113846. doi: 10.1016/j.biopha.2022.113846. Online ahead of print.

ABSTRACT

Diabetic neuropathy is a chronic complication to metabolic disorder, diabetes mellitus. Till date, diagnosis and treatment of diabetic neuropathy remain elusive with challenges associated with the efficacy and safety of the current therapeutics. Considering, the hurdles associated with discovery of de novo drugs, repurposing of old drugs for new therapeutic modalities sounds promising. This review, focuses on a molecular pathways involved in the progression of diabetic neuropathy, and the current pharmacological and non-pharmacological therapies implemented. Furthermore, a holistic and mechanism centric drug repurposing approach is pursued for identification of existing drugs as novel therapy in the treatment of diabetic neuropathy. The global status of ongoing clinical research on diabetic neuropathy is also highlighted. In conclusion, the barriers associated with drug repurposing is identified to stimulate the curiosity of the researchers to overcome them and rapidly translate the drugs to the patients suffering from diabetic neuropathy.

PMID:36228378 | DOI:10.1016/j.biopha.2022.113846

Front Pharmacol. 2022 Sep 26;13:974849. doi: 10.3389/fphar.2022.974849. eCollection 2022.

ABSTRACT

Drug repurposing has become an effective approach to drug discovery, as it offers a new way to explore drugs. Based on the Science Citation Index Expanded (SCI-E) and Social Sciences Citation Index (SSCI) databases of the Web of Science core collection, this study presents a bibliometric analysis of drug repurposing publications from 2010 to 2020. Data were cleaned, mined, and visualized using Derwent Data Analyzer (DDA) software. An overview of the history and development trend of the number of publications, major journals, major countries, major institutions, author keywords, major contributors, and major research fields is provided. There were 2,978 publications included in the study. The findings show that the United States leads in this area of research, followed by China, the United Kingdom, and India. The Chinese Academy of Science published the most research studies, and NIH ranked first on the h-index. The Icahn School of Medicine at Mt Sinai leads in the average number of citations per study. Sci Rep, Drug Discov. Today, and Brief. Bioinform. are the three most productive journals evaluated from three separate perspectives, and pharmacology and pharmacy are unquestionably the most commonly used subject categories. Cheng, FX; Mucke, HAM; and Butte, AJ are the top 20 most prolific and influential authors. Keyword analysis shows that in recent years, most research has focused on drug discovery/drug development, COVID-19/SARS-CoV-2/coronavirus, molecular docking, virtual screening, cancer, and other research areas. The hotspots have changed in recent years, with COVID-19/SARS-CoV-2/coronavirus being the most popular topic for current drug repurposing research.

PMID:36225586 | PMC:PMC9549161 | DOI:10.3389/fphar.2022.974849

Front Pharmacol. 2022 Sep 23;13:1003480. doi: 10.3389/fphar.2022.1003480. eCollection 2022.

ABSTRACT

Most drug molecules modulate multiple target proteins, leading either to therapeutic effects or unwanted side effects. Such target promiscuity partly contributes to high attrition rates and leads to wasted costs and time in the current drug discovery process, and makes the assessment of compound selectivity an important factor in drug development and repurposing efforts. Traditionally, selectivity of a compound is characterized in terms of its target activity profile (wide or narrow), which can be quantified using various statistical and information theoretic metrics. Even though the existing selectivity metrics are widely used for characterizing the overall selectivity of a compound, they fall short in quantifying how selective the compound is against a particular target protein (e.g., disease target of interest). We therefore extended the concept of compound selectivity towards target-specific selectivity, defined as the potency of a compound to bind to the particular protein in comparison to the other potential targets. We decompose the target-specific selectivity into two components: 1) the compound's potency against the target of interest (absolute potency), and 2) the compound's potency against the other targets (relative potency). The maximally selective compound-target pairs are then identified as a solution of a bi-objective optimization problem that simultaneously optimizes these two potency metrics. In computational experiments carried out using large-scale kinase inhibitor dataset, which represents a wide range of polypharmacological activities, we show how the optimization-based selectivity scoring offers a systematic approach to finding both potent and selective compounds against given kinase targets. Compared to the existing selectivity metrics, we show how the target-specific selectivity provides additional insights into the target selectivity and promiscuity of multi-targeting kinase inhibitors. Even though the selectivity score is shown to be relatively robust against both missing bioactivity values and the dataset size, we further developed a permutation-based procedure to calculate empirical p-values to assess the statistical significance of the observed selectivity of a compound-target pair in the given bioactivity dataset. We present several case studies that show how the target-specific selectivity can distinguish between highly selective and broadly-active kinase inhibitors, hence facilitating the discovery or repurposing of multi-targeting drugs.

PMID:36225560 | PMC:PMC9549418 | DOI:10.3389/fphar.2022.1003480

Cell Rep Med. 2022 Oct 6:100749. doi: 10.1016/j.xcrm.2022.100749. Online ahead of print.

ABSTRACT

Effective drugs for atrial fibrillation (AF) are lacking, resulting in significant morbidity and mortality. This study demonstrates that network proximity analysis of differentially expressed genes from atrial tissue to drug targets can help prioritize repurposed drugs for AF. Using enrichment analysis of drug-gene signatures and functional testing in human inducible pluripotent stem cell (iPSC)-derived atrial-like cardiomyocytes, we identify metformin as a top repurposed drug candidate for AF. Using the active compactor, a new design analysis of large-scale longitudinal electronic health record (EHR) data, we determine that metformin use is significantly associated with a reduced risk of AF (odds ratio = 0.48, 95%, confidence interval [CI] 0.36-0.64, p < 0.001) compared with standard treatments for diabetes. This study utilizes network medicine methodologies to identify repurposed drugs for AF treatment and identifies metformin as a candidate drug.

PMID:36223777 | DOI:10.1016/j.xcrm.2022.100749

IEEE Trans Vis Comput Graph. 2022 Oct 12;PP. doi: 10.1109/TVCG.2022.3209435. Online ahead of print.

ABSTRACT

Whether AI explanations can help users achieve specific tasks efficiently (i.e., usable explanations) is significantly influenced by their visual presentation. While many techniques exist to generate explanations, it remains unclear how to select and visually present AI explanations based on the characteristics of domain users. This paper aims to understand this question through a multidisciplinary design study for a specific problem: explaining graph neural network (GNN) predictions to domain experts in drug repurposing, i.e., reuse of existing drugs for new diseases. Building on the nested design model of visualization, we incorporate XAI design considerations from a literature review and from our collaborators' feedback into the design process. Specifically, we discuss XAI-related design considerations for usable visual explanations at each design layer: target user, usage context, domain explanation, and XAI goal at the domain layer; format, granularity, and operation of explanations at the abstraction layer; encodings and interactions at the visualization layer; and XAI and rendering algorithm at the algorithm layer. We present how the extended nested model motivates and informs the design of DrugExplorer, an XAI tool for drug repurposing. Based on our domain characterization, DrugExplorer provides path-based explanations and presents them both as individual paths and meta-paths for two key XAI operations, why and what else. DrugExplorer offers a novel visualization design called MetaMatrix with a set of interactions to help domain users organize and compare explanation paths at different levels of granularity to generate domain-meaningful insights. We demonstrate the effectiveness of the selected visual presentation and DrugExplorer as a whole via a usage scenario, a user study, and expert interviews. From these evaluations, we derive insightful observations and reflections that can inform the design of XAI visualizations for other scientific applications.

PMID:36223348 | DOI:10.1109/TVCG.2022.3209435

Curr Drug Discov Technol. 2022 Oct 10. doi: 10.2174/1570163819666221010115118. Online ahead of print.

ABSTRACT

BACKGROUND: SARS-CoV-2 main protease (Mpro or 3CLpro) and papain-like protease (PLpro) are common viral targets for repurposed drugs to combat COVID-19 disease. Recently, several anti-depressants (such as fluoxetine, venlafaxine and citalopram) belonging to the Selective Serotonin Reuptake Inhibitors (SSRIs) and the Serotonin-Norepinephrine Reuptake Inhibitors (SNRI) classes have been shown to in vitro inhibit viral replication.

AIM: Investigate a possible action of fluoxetine and derivatives on SARS-CoV-2 protease sites.

METHODS: molecular docking was performed using AutoDock Vina. Both proteases structures and different drugs conformations were used to explore the possibility of SARS-CoV-2 inhibition on a Mpro or PLpro related pathway. Drug structures were obtained by optimization with the Avogadro software and MOPAC using PM6 method. Results were analysed on Discovery Studio Visualizer.

RESULTS: The results indicated that Mpro interacted in a thermodynamically favorable way with fluoxetine, venlafaxine, citalopram, atomoxetine, nisoxetine and norfluoxetine in the region of the active site, whether PLpro conformers did not come close to active site.

CONCLUSION: In an in silico perspective, it is likely that the SSRIs and other anti-depressants could interact with Mpro and cause the enzyme to malfunction. Unfortunately, the same drugs did not present similar results on PLpro crystal, therefore no inhibition is expected on an in vitro trial. Anyway, in vitro test are necessary for the better understanding the links between SARS-CoV-2 proteases and anti-depressants.

PMID:36221883 | DOI:10.2174/1570163819666221010115118

J Cachexia Sarcopenia Muscle. 2022 Oct 11. doi: 10.1002/jcsm.13092. Online ahead of print.

ABSTRACT

BACKGROUND: Skeletal muscle atrophy can occur in response to numerous factors, such as ageing and certain medications, and produces a major socio-economic burden. At present, there are no approved drugs for treating skeletal muscle atrophy. Arachidonate 5-lipoxygenase (Alox5) is a drug target for a number of diseases. However, pharmacological targeting of Alox5, and its role in skeletal muscle atrophy, is unclear.

METHODS: The potential effects of gene knockdown and pharmacological targeting of Alox5 on skeletal muscle atrophy were investigated using cell-based models, animal models and human skeletal muscle primary cells. Malotilate, a clinically safe drug developed for enhancing liver regeneration and Alox5 inhibitor, was investigated as a repurposing candidate. Mechanism(s) of action in skeletal muscle atrophy was assessed by measuring the expression level or activation status of key regulatory pathways and validated using gene knockdown and RNA sequencing.

RESULTS: Myotubes treated with the atrophy-inducing glucocorticoid, dexamethasone, were protected from catabolic responses by treatment with malotilate (+41.29%, P < 0.01). Similar anti-atrophy effects were achieved by gene knockdown of Alox5 (+30.4%, P < 0.05). Malotilate produced anti-atrophy effects without affecting the myogenic differentiation programme. In an in vivo model of skeletal muscle atrophy, malotilate treatment preserved muscle force/strength (grip strength: +35.72%, latency to fall: +553.1%, P < 0.05), increased mass and fibre cross-sectional area (quadriceps: +23.72%, soleus: +33.3%, P < 0.01) and down-regulated atrogene expression (Atrogin-1: -61.58%, Murf-1: -66.06%, P < 0.01). Similar, beneficial effects of malotilate treatment were observed in an ageing muscle model, which also showed the preservation of fast-twitch fibres (Type 2a: +56.48%, Type 2b: +37.32%, P < 0.01). Leukotriene B4, a product of Alox5 activity with inflammatory and catabolic functions, was found to be elevated in skeletal muscle undergoing atrophy (quadriceps: +224.4%, P < 0.001). Cellular transcriptome analysis showed that targeting Alox5 up-regulated biological processes regulating organogenesis and increased the expression of insulin-like growth factor-1, a key anti-atrophy hormone (+226.5%, P < 0.05). Interestingly, these effects were restricted to the atrophy condition and not observed in normal skeletal muscle cultures with Alox5 inhibition. Human myotubes were also protected from atrophy by pharmacological targeting of Alox5 (+23.68%, P < 0.05).

CONCLUSIONS: These results shed new light on novel drug targets and mechanisms underpinning skeletal muscle atrophy. Alox5 is a regulator and drug target for muscle atrophy, and malotilate is an attractive compound for repurposing studies to treat this disease.

PMID:36221153 | DOI:10.1002/jcsm.13092

Psychopharmacology (Berl). 2022 Oct 12. doi: 10.1007/s00213-022-06232-3. Online ahead of print.

ABSTRACT

RATIONALE: Tau hyperphosphorylation and aggregation is considered as a main pathological mechanism underlying Alzheimer's disease (AD). Rose Bengal (RB) is a synthetic dye used for disease diagnosis, which was reported to inhibit tau toxicity via inhibiting tau aggregation in Drosophila. However, it was unknown if RB could produce anti-AD effects in rodents.

OBJECTIVES: The research aimed to investigate if and how RB could prevent β-amyloid (Aβ) oligomers-induced tau hyperphosphorylation in rodents.

METHODS AND RESULTS: RB was tested in vitro (0.3-1 μM) and prevented Aβ oligomers-induced tau hyperphosphorylation in PC12 cells. Moreover, RB (10-30 mg/kg, i.p.) effectively attenuated cognitive impairments induced by Aβ oligomers in mice. Western blotting analysis demonstrated that RB significantly increased the expression of pSer473-Akt, pSer9-glycogen synthase kinase-3β (GSK3β) and reduced the expression of cyclin-dependent kinase 5 (CDK5) both in vitro and in vivo. Molecular docking analysis suggested that RB might directly interact with GSK3β and CDK5 by acting on ATP binding sites. Gene Ontology enrichment analysis indicated that RB might act on protein phosphorylation pathways to inhibit tau hyperphosphorylation.

CONCLUSIONS: RB was shown to inhibit tau neurotoxicity at least partially via inhibiting the activity of GSK3β and CDK5, which is a novel neuroprotective mechanism besides the inhibition of tau aggregation. As tau hyperphosphorylation is an important target for AD therapy, this study also provided support for investigating the drug repurposing of RB as an anti-AD drug candidate.

PMID:36221038 | DOI:10.1007/s00213-022-06232-3

Acta Pharmacol Sin. 2022 Oct 10. doi: 10.1038/s41401-022-00996-2. Online ahead of print.

ABSTRACT

Computationally identifying new targets for existing drugs has drawn much attention in drug repurposing due to its advantages over de novo drugs, including low risk, low costs, and rapid pace. To facilitate the drug repurposing computation, we constructed an automated and parameter-free virtual screening server, namely DrugRep, which performed molecular 3D structure construction, binding pocket prediction, docking, similarity comparison and binding affinity screening in a fully automatic manner. DrugRep repurposed drugs not only by receptor-based screening but also by ligand-based screening. The former automatically detected possible binding pockets of the receptor with our cavity detection approach, and then performed batch docking over drugs with a widespread docking program, AutoDock Vina. The latter explored drugs using seven well-established similarity measuring tools, including our recently developed ligand-similarity-based methods LigMate and FitDock. DrugRep utilized easy-to-use graphic interfaces for the user operation, and offered interactive predictions with state-of-the-art accuracy. We expect that this freely available online drug repurposing tool could be beneficial to the drug discovery community. The web site is http://cao.labshare.cn/drugrep/ .

PMID:36216900 | DOI:10.1038/s41401-022-00996-2

Acta Pharmacol Sin. 2022 Oct 10. doi: 10.1038/s41401-022-00993-5. Online ahead of print.

ABSTRACT

Necroptosis is a form of regulated necrosis involved in various pathological diseases. The process of necroptosis is controlled by receptor-interacting kinase 1 (RIPK1), RIPK3, and pseudokinase mixed lineage kinase domain-like protein (MLKL), and pharmacological inhibition of these kinases has been shown to have therapeutic potentials in a variety of diseases. In this study, using drug repurposing strategy combined with high-throughput screening (HTS), we discovered that AZD4547, a previously reported FGFR inhibitor, is able to interfere with necroptosis through direct targeting of RIPK1 kinase. In both human and mouse cell models, AZD4547 blocked RIPK1-dependent necroptosis. In addition, AZD4547 rescued animals from TNF-induced lethal shock and inflammatory responses. Together, our study demonstrates that AZD4547 is a potent and selective inhibitor of RIPK1 with therapeutic potential for the treatment of inflammatory disorders that involve necroptosis.

PMID:36216899 | DOI:10.1038/s41401-022-00993-5

J Gen Virol. 2022 Oct;103(10). doi: 10.1099/jgv.0.001795.

ABSTRACT

Antiviral therapy for human cytomegalovirus (HCMV) currently relies upon direct-acting antiviral drugs. However, it is now well known that these drugs have shortcomings, which limit their use. Here I review the identification and investigation of compounds targeting cellular proteins that have anti-HCMV activity and could supersede those anti-HCMV drugs currently in use. This includes discussion of drug repurposing, for example the use of artemisinin compounds, and discussion of new directions to identify compounds that target cellular factors in HCMV-infected cells, for example screening of kinase inhibitors. In addition, I highlight developing areas such as the use of machine learning and emphasize how interaction with fields outside virology will be critical for development of anti-HCMV compounds.

PMID:36215160 | DOI:10.1099/jgv.0.001795

J Biomol Struct Dyn. 2022 Oct 10:1-21. doi: 10.1080/07391102.2022.2127906. Online ahead of print.

ABSTRACT

Significant metabolic pathways have been linked to AKR1B1 and AKR1B10. These enzymes are crucial biological targets in the therapy of colon cancer. In the past several decades, drug repurposing has gained appeal as a time and cost-efficient strategy for providing new indications for existing drugs. The structural properties of the plant-based alkaloidal drugs theobromine and theophylline were examined using density functional theory (DFT) computations, where the B3LYP/SVP method was used to quantify the dipole moment, polarizability, and optimization energy. Optimized structures obtained through DFT studies were docked inside the active pocket of target proteins to evaluate their inhibitory potential. Moreover, molecular dynamic simulation provides significant insight into a dynamic view of molecular interactions. The findings of current revealed theobromine and theophylline as strong AKR1B1 and AKR1B10 inhibitors, respectively. In addition, the anti-cancer potential of theophylline and theobromine was validated by targeting various tumor proteins, i.e. NF-κB, cellular tumor antigen P53 and caspase-3 using a molecular docking approach. Theobromine was found to be strongly interacted with NF-κB and caspase-3, whereas theophylline potentially inhibited cellular tumor antigen P53. In addition, the ADMET characteristics of theobromine and theophylline were identified, confirming their drug-like capabilities. These results should open the way for further experimental validation and structure-based drug design/repurposing of AKR1B1/AKR1B10 inhibitors for the treatment of colon cancer and associated malignancies.Communicated by Ramaswamy H. Sarma.

PMID:36214620 | DOI:10.1080/07391102.2022.2127906

Int J Nanomedicine. 2022 Oct 3;17:4677-4696. doi: 10.2147/IJN.S376216. eCollection 2022.

ABSTRACT

Cancer immunotherapy, a major breakthrough in cancer treatment, has been successfully applied to treat a number of tumors. However, given the presence of factors in the tumor microenvironment (TME) that impede immunotherapy, only a small proportion of patients achieve a good clinical response. With the ability to increase permeability and cross biological barriers, nanomaterials have been successfully applied to deliver immunotherapeutic agents, thus realizing the anti-cancer therapeutic potential of therapeutic agents. This has driven a wave of research into systems for the delivery of immunotherapeutic agents, which has resulted in widespread interest in nanomaterial-based drug delivery systems. Nanomaterial-based drug delivery systems are able to overcome the challenges from TME and thus achieve good results in cancer immunotherapy. If it can make a breakthrough in improving biocompatibility and reducing cytotoxicity, it will be more widely used in clinical practice. Different types of nanomaterials may also have some subtle differences in enhancing cancer immunotherapy. Moreover, delivery systems made of nanomaterials loaded with drugs, such as cytotoxic drugs, cytokines, and adjuvants, could be used for cancer immunotherapy because they avoid the toxicity and side effects associated with these drugs, thereby enabling their reuse. Therefore, further insights into nanomaterial-based drug delivery systems will provide more effective treatment options for cancer patients.

PMID:36211025 | PMC:PMC9541303 | DOI:10.2147/IJN.S376216

Lung Cancer. 2022 Oct 1;174:1-13. doi: 10.1016/j.lungcan.2022.09.013. Online ahead of print.

ABSTRACT

Leptomeningeal metastasis (LM) is a rare complication of non-small cell lung cancer (NSCLC) with highly mortality. LM will occur once tumor cells spread to the cerebrospinal fluid (CSF) space. Patients may suffer blindness, paralysis, and mental disorders that seriously affect their quality of life. There is a clear unmet need to improve the efficacy of diagnosis and treatment of LM. To better solve this problem, it is helpful to clarify the potential mechanisms of LM. Clinical manifestations, magnetic resonance imaging, and CSF biopsy are the key components in the diagnosis of NSCLC with LM. CSF cytology is insufficient and should be combined with liquid biology. The application of radiotherapy, intrathecal treatment, targeted therapy and immunotherapy provides more options for LM patients. Each treatment has a particular level of efficacy and can be used alone or in combination for individual patients. New technologies in radiotherapy, drug repositioning in intrathecal treatment, and the higher CSF permeability in TKIs have brought new breakthroughs in the treatment of LM. This review focused on clarifying the potential mechanisms, discussing the major clinical challenges, and summarizing recent advances in the diagnosis and treatment of LM from NSCLC. Future research is essential to improve the efficiency of diagnosis, to optimize therapy and to enhance patient prognosis.

PMID:36206679 | DOI:10.1016/j.lungcan.2022.09.013

J Biomol Struct Dyn. 2022 Oct 7:1-15. doi: 10.1080/07391102.2022.2130987. Online ahead of print.

ABSTRACT

The large (L) protein of Mononegavirales is a multi-domain protein that performs transcription and genome replication. One of the important domains in L is the RNA-dependent RNA polymerase (RdRp), a promising target for antiviral drugs. In this work, we employed rigorous computational comparative modeling to predict the structure of L protein of Nipah virus (NiV). The RdRp domain was targeted by a panel of nucleotide analogs, previously reported to inhibit different viral RNA polymerases, using molecular docking. Best binder compounds were subjected to molecular dynamics simulation to validate their binding. Molecular mechanics/generalized-born surface area (MM/GBSA) calculations estimated the binding free energy. The predicted model of NiV L has an excellent quality as judged by physics- and knowledge-based validation tests. Galidesivir, AT-9010 and Norov-29 scored the top nucleotide analogs to bind to the RdRp. Their binding free energies obtained by MM/GBSA (-31.01 ± 3.9 to -38.37 ± 4.8 kcal/mol) ranked Norov-29 as the best potential inhibitor. Purine nucleotide analogs are expected to harbor the scaffold for an effective drug against NiV. Finally, this study is expected to provide a start point for medicinal chemistry and drug discovery campaigns toward identification of effective chemotherapeutic agent(s) against NiV.Communicated by Ramaswamy H. Sarma.

PMID:36205638 | DOI:10.1080/07391102.2022.2130987

Front Cardiovasc Med. 2022 Sep 20;9:959457. doi: 10.3389/fcvm.2022.959457. eCollection 2022.

ABSTRACT

INTRODUCTION: Vascular calcification (VC) is a major risk factor for cardiovascular morbidity and mortality. Depending on the location of mineral deposition within the arterial wall, VC is classified as intimal and medial calcification. Using in vitro mineralization assays, we developed protocols triggering both types of calcification in vascular smooth muscle cells (SMCs) following diverging molecular pathways.

MATERIALS AND METHODS AND RESULTS: Human coronary artery SMCs were cultured in osteogenic medium (OM) or high calcium phosphate medium (CaP) to induce a mineralized extracellular matrix. OM induces osteoblast-like differentiation of SMCs-a key process in intimal calcification during atherosclerotic plaque remodeling. CaP mimics hyperphosphatemia, associated with chronic kidney disease-a risk factor for medial calcification. Transcriptomic analysis revealed distinct gene expression profiles of OM and CaP-calcifying SMCs. OM and CaP-treated SMCs shared 107 differentially regulated genes related to SMC contraction and metabolism. Real-time extracellular efflux analysis demonstrated decreased mitochondrial respiration and glycolysis in CaP-treated SMCs compared to increased mitochondrial respiration without altered glycolysis in OM-treated SMCs. Subsequent kinome and in silico drug repurposing analysis (Connectivity Map) suggested a distinct role of protein kinase C (PKC). In vitro validation experiments demonstrated that the PKC activators prostratin and ingenol reduced calcification triggered by OM and promoted calcification triggered by CaP.

CONCLUSION: Our direct comparison results of two in vitro calcification models strengthen previous observations of distinct intracellular mechanisms that trigger OM and CaP-induced SMC calcification in vitro. We found a differential role of PKC in OM and CaP-calcified SMCs providing new potential cellular and molecular targets for pharmacological intervention in VC. Our data suggest that the field should limit the generalization of results found in in vitro studies using different calcification protocols.

PMID:36204585 | PMC:PMC9530266 | DOI:10.3389/fcvm.2022.959457

Front Pharmacol. 2022 Sep 20;13:942126. doi: 10.3389/fphar.2022.942126. eCollection 2022.

ABSTRACT

Accurate identification of molecular targets of disease plays an important role in diagnosis, prognosis, and therapies. Breast cancer (BC) is one of the most common malignant cancers in women worldwide. Thus, the objective of this study was to accurately identify a set of molecular targets and small molecular drugs that might be effective for BC diagnosis, prognosis, and therapies, by using existing bioinformatics and network-based approaches. Nine gene expression profiles (GSE54002, GSE29431, GSE124646, GSE42568, GSE45827, GSE10810, GSE65216, GSE36295, and GSE109169) collected from the Gene Expression Omnibus (GEO) database were used for bioinformatics analysis in this study. Two packages, LIMMA and clusterProfiler, in R were used to identify overlapping differential expressed genes (oDEGs) and significant GO and KEGG enrichment terms. We constructed a PPI (protein-protein interaction) network through the STRING database and identified eight key genes (KGs) EGFR, FN1, EZH2, MET, CDK1, AURKA, TOP2A, and BIRC5 by using six topological measures, betweenness, closeness, eccentricity, degree, MCC, and MNC, in the Analyze Network tool in Cytoscape. Three online databases GSCALite, Network Analyst, and GEPIA were used to analyze drug enrichment, regulatory interaction networks, and gene expression levels of KGs. We checked the prognostic power of KGs through the prediction model using the popular machine learning algorithm support vector machine (SVM). We suggested four TFs (TP63, MYC, SOX2, and KDM5B) and four miRNAs (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, and hsa-mir-23b-3p) as key transcriptional and posttranscriptional regulators of KGs. Finally, we proposed 16 candidate repurposing drugs YM201636, masitinib, SB590885, GSK1070916, GSK2126458, ZSTK474, dasatinib, fedratinib, dabrafenib, methotrexate, trametinib, tubastatin A, BIX02189, CP466722, afatinib, and belinostat for BC through molecular docking analysis. Using BC cell lines, we validated that masitinib inhibits the mTOR signaling pathway and induces apoptotic cell death. Therefore, the proposed results might play an effective role in the treatment of BC patients.

PMID:36204232 | PMC:PMC9531711 | DOI:10.3389/fphar.2022.942126