Pharmaceutics. 2021 Dec 28;14(1):59. doi: 10.3390/pharmaceutics14010059.

ABSTRACT

The RAS-RAF-MEK-ERK pathway plays a key role in malevolent cell progression in many tumors. The high structural complexity in the upstream kinases limits the treatment progress. Thus, MEK inhibition is a promising strategy since it is easy to inhibit and is a gatekeeper for the many malignant effects of its downstream effector. Even though MEK inhibitors are under investigation in many cancers, drug resistance continues to be the principal limiting factor to achieving cures in patients with cancer. Hence, we accomplished a high-throughput virtual screening to overcome this bottleneck by the discovery of dual-targeting therapy in cancer treatment. Here, a total of 11,808 DrugBank molecules were assessed through high-throughput virtual screening for their activity against MEK. Further, the Glide docking, MLSF and prime-MM/GBSA methods were implemented to extract the potential lead compounds from the database. Two compounds, DB012661 and DB07642, were outperformed in all the screening analyses. Further, the study results reveal that the lead compounds also have a significant binding capability with the co-target PIM1. Finally, the SIE-based free energy calculation reveals that the binding of compounds was majorly affected by the van der Waals interactions with MEK receptor. Overall, the in silico binding efficacy of these lead compounds against both MEK and PIM1 could be of significant therapeutic interest to overcome drug resistance in the near future.

PMID:35056955 | DOI:10.3390/pharmaceutics14010059

Pharmaceutics. 2021 Dec 26;14(1):44. doi: 10.3390/pharmaceutics14010044.

ABSTRACT

Chronic inflammation and dysregulated epithelial differentiation, especially of hair follicle keratinocytes, have been suggested as the major pathogenetic pathways of hidradenitis suppurativa/acne inversa (HS). On the other hand, obesity and metabolic syndrome have additionally been considered as an important risk factor. With adalimumab, a drug has already been approved and numerous other compounds are in advanced-stage clinical studies. A systematic review was conducted to detect and corroborate HS pathogenetic mechanisms at the molecular level and identify HS molecular markers. The obtained data were used to confirm studied and off-label administered drugs and to identify additional compounds for drug repurposing. A robust, strongly associated group of HS biomarkers was detected. The triad of HS pathogenesis, namely upregulated inflammation, altered epithelial differentiation and dysregulated metabolism/hormone signaling was confirmed, the molecular association of HS with certain comorbid disorders, such as inflammatory bowel disease, arthritis, type I diabetes mellitus and lipids/atherosclerosis/adipogenesis was verified and common biomarkers were identified. The molecular suitability of compounds in clinical studies was confirmed and 31 potential HS repurposing drugs, among them 10 drugs already launched for other disorders, were detected. This systematic review provides evidence for the importance of molecular studies to advance the knowledge regarding pathogenesis, future treatment and biomarker-supported clinical course follow-up in HS.

PMID:35056940 | DOI:10.3390/pharmaceutics14010044

Pharmaceutics. 2021 Dec 21;14(1):3. doi: 10.3390/pharmaceutics14010003.

ABSTRACT

MicroRNAs (miRNAs) are short non-coding RNAs that play important roles in the body and affect various diseases, including cancers. Controlling miRNAs with small molecules is studied herein to provide new drug repurposing perspectives for miRNA-related diseases. Experimental methods are time- and effort-consuming, so computational techniques have been applied, relying mostly on biological feature similarities and a network-based scheme to infer new miRNA-small molecule associations. Collecting such features is time-consuming and may be impractical. Here we suggest an alternative method of similarity calculation, representing miRNAs and small molecules through continuous feature representation. This representation is learned by the proposed deep learning auto-encoder architecture. Our suggested representation was compared to previous works and achieved comparable results using 5-fold cross validation (92% identified within top 25% predictions), and better predictions for most of the case studies (avg. of 31% vs. 25% identified within the top 25% of predictions). The results proved the effectiveness of our proposed method to replace previous time- and effort-consuming methods.

PMID:35056899 | DOI:10.3390/pharmaceutics14010003

Molecules. 2022 Jan 14;27(2):505. doi: 10.3390/molecules27020505.

ABSTRACT

Amburana cearensis (Allemão) ACSm. belongs to the Fabaceae family and occurs in the Brazilian semiarid, Argentina, Paraguay, Bolivia, and Peru. Numerous studies that portray its ethnobotany, use in popular medicine, chemical composition, and biological activities exist in the literature. This review aimed to provide an overview of the chemical composition, ethnopharmacology, and biological activities associated with A. cearensis and its isolated constituents. Information was collected from internet searches in the Scopus, Medline, PubMed, Google Scholar, and ScienceDirect databases were performed covering publications from 1997-2020. An ethnopharmacological literature analysis revealed that A. cearensis is used to treat a wide range of respiratory disorders in addition to intestinal, circulatory, and inflammatory problems. Coumarins, flavonoids, phenolic glycosides, phenolic acids, phenylpropanoid derivatives, and triterpenoids, among others, have been reported as active compounds, with High-Performance Liquid Chromatography (HPLC) being the main analytical technique used. The A. cearensis extracts and compounds presented several biological activities, including antimicrobial, antinociceptive, anti-inflammatory, antioxidant, neuroprotective, and myorelaxant activities, among others. This review provides a useful bibliography for future investigations and A. cearensis applications; however, future studies should focus on its toxic effects and the mechanisms of action of its extracts and isolated constituents to guide clinical applications.

PMID:35056820 | DOI:10.3390/molecules27020505

Pharmaceuticals (Basel). 2022 Jan 14;15(1):94. doi: 10.3390/ph15010094.

ABSTRACT

Even if amyotrophic lateral sclerosis is still considered an orphan disease to date, its prevalence among the population is growing fast. Despite the efforts made by researchers and pharmaceutical companies, the cryptic information related to the biological and physiological onset mechanisms, as well as the complexity in identifying specific pharmacological targets, make it almost impossible to find effective treatments. Furthermore, because of complex ethical and economic aspects, it is usually hard to find all the necessary resources when searching for drugs for new orphan diseases. In this context, computational methods, based either on receptors or ligands, share the capability to improve the success rate when searching and selecting potential candidates for further experimentation and, consequently, reduce the number of resources and time taken when delivering a new drug to the market. In the present work, a computational strategy based on Molecular Topology, a mathematical paradigm capable of relating the chemical structure of a molecule to a specific biological or pharmacological property by means of numbers, is presented. The result was the creation of a reliable and accessible tool to help during the early in silico stages in the identification and repositioning of potential hits for ALS treatment, which can also apply to other orphan diseases. Considering that further computational and experimental results will be required for the final identification of viable hits, three linear discriminant equations combined with molecular docking simulations on specific proteins involved in ALS are reported, along with virtual screening of the Drugbank database as a practical example. In this particular case, as reported, a clinical trial has been already started for one of the drugs proposed in the present study.

PMID:35056151 | DOI:10.3390/ph15010094

Pharmaceuticals (Basel). 2021 Dec 28;15(1):38. doi: 10.3390/ph15010038.

ABSTRACT

Chronic pain determines a substantial burden on individuals, employers, healthcare systems, and society. Most of the affected patients report dissatisfaction with currently available treatments. There are only a few and poor therapeutic options-some therapeutic agents are an outgrowth of drugs targeting acute pain, while others have several serious side effects. One of the primary degradative enzymes for endocannabinoids, fatty acid amide hydrolase (FAAH) attracted attention as a significant molecular target for developing new therapies for neuropsychiatric and neurological diseases, including chronic pain. Using chemical graph mining, quantitative structure-activity relationship (QSAR) modeling, and molecular docking techniques we developed a multi-step screening protocol to identify repurposable drugs as FAAH inhibitors. After screening the DrugBank database using our protocol, 273 structures were selected, with five already approved drugs, montelukast, repaglinide, revefenacin, raloxifene, and buclizine emerging as the most promising repurposable agents for treating chronic pain. Molecular docking studies indicated that the selected compounds interact with the enzyme mostly non-covalently (except for revefenacin) through shape complementarity to the large substrate-binding pocket in the active site. A molecular dynamics simulation was employed for montelukast and revealed stable interactions with the enzyme. The biological activity of the selected compounds should be further confirmed by employing in vitro and in vivo studies.

PMID:35056095 | DOI:10.3390/ph15010038

Pharmaceuticals (Basel). 2021 Dec 25;15(1):31. doi: 10.3390/ph15010031.

ABSTRACT

Ischemic heart disease is a significant public health problem with high mortality and morbidity. Extensive scientific investigations from basic sciences to clinics revealed multilevel alterations from metabolic imbalance, altered electrophysiology, and defective Ca2+/Na+ homeostasis leading to lethal arrhythmias. Despite the recent identification of numerous molecular targets with potential therapeutic interest, a pragmatic observation on the current pharmacological R&D output confirms the lack of new therapeutic offers to patients. By contrast, from recent trials, molecules initially developed for other fields of application have shown cardiovascular benefits, as illustrated with some anti-diabetic agents, regardless of the presence or absence of diabetes, emphasizing the clear advantage of "old" drug repositioning. Ranolazine is approved as an antianginal agent and has a favorable overall safety profile. This drug, developed initially as a metabolic modulator, was also identified as an inhibitor of the cardiac late Na+ current, although it also blocks other ionic currents, including the hERG/Ikr K+ current. The latter actions have been involved in this drug's antiarrhythmic effects, both on supraventricular and ventricular arrhythmias (VA). However, despite initial enthusiasm and promising development in the cardiovascular field, ranolazine is only authorized as a second-line treatment in patients with chronic angina pectoris, notwithstanding its antiarrhythmic properties. A plausible reason for this is the apparent difficulty in linking the clinical benefits to the multiple molecular actions of this drug. Here, we review ranolazine's experimental and clinical knowledge on cardiac metabolism and arrhythmias. We also highlight advances in understanding novel effects on neurons, the vascular system, skeletal muscles, blood sugar control, and cancer, which may open the way to reposition this "old" drug alone or in combination with other medications.

PMID:35056088 | DOI:10.3390/ph15010031

ACS Biomater Sci Eng. 2022 Jan 20. doi: 10.1021/acsbiomaterials.1c01481. Online ahead of print.

ABSTRACT

Drug repurposing has been gaining increasing interest recently due to the reduction in development cost and reduced development timelines. Here, we report the antibacterial activity of the anticancer drug etoposide investigated in combination with the eggshell-derived hydroxyapatite (EHA). Hydroxyapatite (HA) is a well-known bioactive material with enhanced osteoconductivity and possesses superior drug delivery properties. In the present work, we have synthesized etoposide-loaded EHA by the wet precipitation method. The physicochemical characterization of the samples confirmed the composition and amount of drug encapsulation. Screening for antibacterial activity confirmed the antibacterial effect of etoposide against Staphylococcus aureus. Biofilm formation test on pristine and etoposide-loaded samples showed the inhibition of biofilm formation on etoposide loading, which was further studied by confocal laser scanning microscopy (CLSM) and colony forming units (CFUs). It has been found that etoposide-loaded HA exhibited a sustained release of the drug upto 168 h. Analysis of the inhibition mechanism of etoposide against S. aureus revealed damage to the cell membrane and has been quantified using flow cytometry by the uptake of propidium iodide. Etoposide-loaded eggshell-derived HA (EHA-ET) exhibited excellent bioactivity and cytocompatibility against mouse fibroblast cells (L929) and supressed the growth of osteosarcoma cells (MG-63). Our studies reveal that the EHA-ET has a great potential for treating osteosarcoma and osteomyelitis.

PMID:35050575 | DOI:10.1021/acsbiomaterials.1c01481

Autoimmunity. 2022 Jan 20:1-10. doi: 10.1080/08916934.2022.2027922. Online ahead of print.

ABSTRACT

Rheumatoid arthritis (RA) is an autoimmune disease that results in the destruction of tissue by attacks on the patient by his or her own immune system. Current treatment strategies are not sufficient to overcome RA. In the present study, various transcriptomic data from synovial fluids, synovial fluid-derived macrophages, and blood samples from patients with RA were analysed using bioinformatics approaches to identify tissue-specific repurposing drug candidates for RA. Differentially expressed genes (DEGs) were identified by integrating datasets for each tissue and comparing diseased to healthy samples. Tissue-specific protein-protein interaction (PPI) networks were generated and topologically prominent proteins were selected. Transcription-regulating biomolecules for each tissue type were determined from protein-DNA interaction data. Common DEGs and reporter biomolecules were used to identify drug candidates for repurposing using the hypergeometric test. As a result of bioinformatic analyses, 19 drugs were identified as repurposing candidates for RA, and text mining analyses supported our findings. We hypothesize that the FDA-approved drugs momelotinib, ibrutinib, and sodium butyrate may be promising candidates for RA. In addition, CHEMBL306380, Compound 19a (CHEMBL3116050), ME-344, XL-019, TG100801, JNJ-26483327, and NV-128 were identified as novel repurposing candidates for the treatment of RA. Preclinical and further validation of these drugs may provide new treatment options for RA.

PMID:35048767 | DOI:10.1080/08916934.2022.2027922

Front Pharmacol. 2022 Jan 3;12:790913. doi: 10.3389/fphar.2021.790913. eCollection 2021.

ABSTRACT

Cisplatin-induced acute kidney injury (CI-AKI) is a significant co-morbidity of chemotherapeutic regimens. While this condition is associated with substantially lower survival and increased economic burden, there is no pharmacological agent to effectively treat CI-AKI. The disease is hallmarked by acute tubular necrosis of the proximal tubular epithelial cells primarily due to increased oxidative stress. We investigated a drug delivery strategy to improve the pharmacokinetics of an approved therapy that does not normally demonstrate appreciable efficacy in CI-AKI, as a preventive intervention. In prior work, we developed a kidney-selective mesoscale nanoparticle (MNP) that targets the renal proximal tubular epithelium. Here, we found that the nanoparticles target the kidneys in a mouse model of CI-AKI with significant damage. We evaluated MNPs loaded with the reactive oxygen species scavenger edaravone, currently used to treat stroke and ALS. We found a marked and significant therapeutic benefit with edaravone-loaded MNPs, including improved renal function, which we demonstrated was likely due to a decrease in tubular epithelial cell damage and death imparted by the specific delivery of edaravone. The results suggest that renal-selective edaravone delivery holds potential for the prevention of acute kidney injury among patients undergoing cisplatin-based chemotherapy.

PMID:35046813 | PMC:PMC8762298 | DOI:10.3389/fphar.2021.790913

Brief Bioinform. 2022 Jan 18:bbab581. doi: 10.1093/bib/bbab581. Online ahead of print.

ABSTRACT

Drug repositioning is an efficient and promising strategy for traditional drug discovery and development. Many research efforts are focused on utilizing deep-learning approaches based on a heterogeneous network for modeling complex drug-disease associations. Similar to traditional latent factor models, which directly factorize drug-disease associations, they assume the neighbors are independent of each other in the network and thus tend to be ineffective to capture localized information. In this study, we propose a novel neighborhood and neighborhood interaction-based neural collaborative filtering approach (called DRWBNCF) to infer novel potential drugs for diseases. Specifically, we first construct three networks, including the known drug-disease association network, the drug-drug similarity and disease-disease similarity networks (using the nearest neighbors). To take the advantage of localized information in the three networks, we then design an integration component by proposing a new weighted bilinear graph convolution operation to integrate the information of the known drug-disease association, the drug's and disease's neighborhood and neighborhood interactions into a unified representation. Lastly, we introduce a prediction component, which utilizes the multi-layer perceptron optimized by the α-balanced focal loss function and graph regularization to model the complex drug-disease associations. Benchmarking comparisons on three datasets verified the effectiveness of DRWBNCF for drug repositioning. Importantly, the unknown drug-disease associations predicted by DRWBNCF were validated against clinical trials and three authoritative databases and we listed several new DRWBNCF-predicted potential drugs for breast cancer (e.g. valrubicin and teniposide) and small cell lung cancer (e.g. valrubicin and cytarabine).

PMID:35039838 | DOI:10.1093/bib/bbab581

Elife. 2022 Jan 17;11:e68832. doi: 10.7554/eLife.68832.

ABSTRACT

Insulin resistance (IR) contributes to the pathophysiology of diabetes, dementia, viral infection, and cardiovascular disease. Drug repurposing (DR) may identify treatments for IR; however, barriers include uncertainty whether in vitro transcriptomic assays yield quantitative pharmacological data, or how to optimise assay design to best reflect in vivo human disease. We developed a clinical-based human tissue IR signature by combining lifestyle-mediated treatment responses (>500 human adipose and muscle biopsies) with biomarkers of disease status (fasting IR from >1200 biopsies). The assay identified a chemically diverse set of >130 positively acting compounds, highly enriched in true positives, that targeted 73 proteins regulating IR pathways. Our multi-gene RNA assay score reflected the quantitative pharmacological properties of a set of epidermal growth factor receptor-related tyrosine kinase inhibitors, providing insight into drug target specificity; an observation supported by deep learning-based genome-wide predicted pharmacology. Several drugs identified are suitable for evaluation in patients, particularly those with either acute or severe chronic IR.

PMID:35037854 | DOI:10.7554/eLife.68832

ACS Pharmacol Transl Sci. 2022 Jan 3;5(1):8-19. doi: 10.1021/acsptsci.1c00182. eCollection 2022 Jan 14.

ABSTRACT

Drug development for specific antiviral agents against coronavirus disease 2019 (COVID-19) is still an unmet medical need as the pandemic continues to spread globally. Although huge efforts for drug repurposing and compound screens have been put forth, only a few compounds are in late-stage clinical trials. New approaches and assays are needed to accelerate COVID-19 drug discovery and development. Here, we report a time-resolved fluorescence resonance energy transfer-based assay that detects the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid protein (NP) produced in infected cells. It uses two specific anti-NP monoclonal antibodies conjugated to donor and acceptor fluorophores that produce a robust ratiometric signal for high throughput screening of large compound collections. Using this assay, we measured a half maximal inhibitory concentration (IC50) for remdesivir of 9.3 μM against infection with SARS-CoV-2 USA/WA1/2020 (WA-1). The assay also detected SARS-CoV-2 South African (Beta, β), Brazilian/Japanese P.1 (Gamma, γ), and Californian (Epsilon, ε) variants of concern (VoC). Therefore, this homogeneous SARS-CoV-2 NP detection assay can be used for accelerating lead compound discovery for drug development and for evaluating drug efficacy against emerging SARS-CoV-2 VoC.

PMID:35036857 | PMC:PMC8751018 | DOI:10.1021/acsptsci.1c00182

Front Pharmacol. 2021 Dec 31;12:828856. doi: 10.3389/fphar.2021.828856. eCollection 2021.

ABSTRACT

Cancer has become a global health problem, accounting for one out of six deaths. Despite the recent advances in cancer therapy, there is still an ever-growing need for readily accessible new therapies. The process of drug discovery and development is arduous and takes many years, and while it is ongoing, the time for the current lead compounds to reach clinical trial phase is very long. Drug repurposing has recently gained significant attention as it expedites the process of discovering new entities for anticancer therapy. One such potential candidate is the antimalarial drug, artemisinin that has shown anticancer activities in vitro and in vivo. In this review, major molecular and cellular mechanisms underlying the anticancer effect of artemisinin and its derivatives are summarised. Furthermore, major mechanisms of action and some key signaling pathways of this group of compounds have been reviewed to explore potential targets that contribute to the proliferation and metastasis of tumor cells. Despite its established profile in malaria treatment, pharmacokinetic properties, anticancer potency, and current formulations that hinder the clinical translation of artemisinin as an anticancer agent, have been discussed. Finally, potential solutions or new strategies are identified to overcome the bottlenecks in repurposing artemisinin-type compounds as anticancer drugs.

PMID:35035355 | PMC:PMC8758560 | DOI:10.3389/fphar.2021.828856

Biochem Biophys Res Commun. 2022 Jan 7;592:87-92. doi: 10.1016/j.bbrc.2022.01.003. Online ahead of print.

ABSTRACT

We screened pre-approved drugs for the survival of the Hu5/KD3 human myogenic progenitors. We found that meclozine, an anti-histamine drug that has long been used for motion sickness, promoted the proliferation and survival of Hu5/KD3 cells. Meclozine increased expression of MyoD, but reduced expression of myosin heavy chain and suppressed myotube formation. Withdrawal of meclozine, however, resumed the ability of Hu5/KD3 cells to differentiate into myotubes. We examined the effects of meclozine on mdx mouse carrying a nonsense mutation in the dystrophin gene and modeling for Duchenne muscular dystrophy. Intragastric administration of meclozine in mdx mouse increased the body weight, the muscle mass in the lower limbs, the cross-sectional area of the paravertebral muscle, and improved exercise performances. Previous reports show that inhibition of phosphorylation of ERK1/2 improves muscle functions in mouse models for Emery-Dreifuss muscular dystrophy and cancer cachexia, as well as in mdx mice. We and others previously showed that meclozine blocks the phosphorylation of ERK1/2 in cultured cells. We currently showed that meclozine decreased phosphorylation of ERK1/2 in muscles in mdx mice but not in wild-type mice. This was likely to be one of the underlying mechanisms of the effects of meclozine on mdx mice.

PMID:35033871 | DOI:10.1016/j.bbrc.2022.01.003

Microb Pathog. 2022 Jan 12:105401. doi: 10.1016/j.micpath.2022.105401. Online ahead of print.

ABSTRACT

The nosocomial pathogen Enterococcus faecalis critically implicated in the hospital environment. Its major virulence attributes biofilm formation and antibiotic resistance. The novel therapeutics are required to inhibit E. faecalis biofilm formation and virulence. Thus combinatorial and drug repurposing has been promising approaches to tackling biofilm-associated infections. Here, we have used a bacterium that produced indole terpenoid Rhodethrin (Rdn) with a combination of known antibiotic chloramphenicol (Chpl) against E. faecalis (ATCC 19433). The fractional inhibitory concentration index (FICI) values showed between 0.25 and 0.33 synergistic activities. The exopolysaccharides (EPSs) production significant decrease with Rdn (34.6 ± 4.6%), Chpl (31.0 ± 5.2%), and combination (Rdn-Chpl) (76.0 ± 4.5%) (p > 0.05). However, the biofilm interruption can attenuate of total biofilm was shown with Rdn (39.7 ± 5.1%), Chpl (32.6 ± 4.7%), and Rdn-Chpl (69.0 ± 5.3%), (p > 0.05). The microscopic observations reveal that the gradually unstructured biofilm architecture in E. faecalis. Furthermore, in silico, studies on biofilm-associated proteins (GelE, LuxS), virulence regulating (SprE), and cell division (FtsZ) have resulted in high and reasonable binding affinity, respectively. Thus, our results suggested that the synergism of Rdn-Chpl has the potential to function as a combinatorial antibiotic accelerates in treating vancomycin-resistant Enterococcus faecalis infections.

PMID:35032606 | DOI:10.1016/j.micpath.2022.105401

Comput Biol Med. 2022 Jan 5;142:105214. doi: 10.1016/j.compbiomed.2022.105214. Online ahead of print.

ABSTRACT

Drug-target interaction (DTI) prediction reduces the cost and time of drug development, and plays a vital role in drug discovery. However, most of research does not fully explore the molecular structures of drug compounds in DTI prediction. To this end, we propose a deep learning model to capture the molecular structure information of drug compounds for DTI prediction. This model utilizes a transformer network incorporating multilayer graph information, which captures the features of a drug's molecular structure so that the interactions between atoms of drug compounds can be explored more deeply. At the same time, a convolutional neural network is employed to capture the local residue information in the target sequence, and effectively extract the feature information of the target. The experiments on the DrugBank dataset showed that the proposed model outperformed previous models based on the structure of target sequences. The results indicate that the improved transformer network fuses the feature information between layers in the graph convolutional neural network and extracts the interaction data for the molecular structure. The drug repositioning experiment on COVID-19 and Alzheimer's disease demonstrated the proposed model's ability to find therapeutic drugs in drug discovery. The code of our model is available at https://github.com/zhangpl109/DeepMGT-DTI.

PMID:35030496 | DOI:10.1016/j.compbiomed.2022.105214

Pharmacol Res Perspect. 2022 Feb;10(1):e00911. doi: 10.1002/prp2.911.

ABSTRACT

Infection of humans with SARS-CoV-2 virus causes a disease known colloquially as "COVID-19" with symptoms ranging from asymptomatic to severe pneumonia. Initial pathology is due to the virus binding to the ACE-2 protein on endothelial cells lining blood vessels and entering these cells in order to replicate. Viral replication causes oxidative stress due to elevated levels of reactive oxygen species. Many (~60%) of the infected people appear to have eliminated the virus from their body after 28 days and resume normal activity. However, a significant proportion (~40%) experience a variety of symptoms (loss of smell and/or taste, fatigue, cough, aching pain, "brain fog," insomnia, shortness of breath, and tachycardia) after 12 weeks and are diagnosed with a syndrome named "LONG COVID." Longitudinal clinical studies in a group of subjects who were infected with SARS-CoV-2 have been compared to a non-infected matched group of subjects. A cohort of infected subjects can be identified by a battery of cytokine markers to have persistent, low level grade of inflammation and often self-report two or more troubling symptoms. There is no drug that will relieve their symptoms effectively. It is hypothesized that drugs that activate the intracellular transcription factor, nuclear factor erythroid-derived 2-like 2 (NRF2) may increase the expression of enzymes to synthesize the intracellular antioxidant, glutathione that will quench free radicals causing oxidative stress. The hormone melatonin has been identified as an activator of NRF2 and a relatively safe chemical for most people to ingest chronically. Thus, it is an option for consideration of re-purposing studies in "LONG COVID" subjects experiencing insomnia, depression, fatigue, and "brain fog" but not tachycardia. Appropriately designed clinical trials are required to evaluate melatonin.

PMID:35029046 | DOI:10.1002/prp2.911

Front Chem. 2021 Dec 28;9:757826. doi: 10.3389/fchem.2021.757826. eCollection 2021.

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 is a virus that belongs to the Coronaviridae family. This group of viruses commonly causes colds but possesses a tremendous pathogenic potential. In humans, an outbreak of SARS caused by the SARS-CoV virus was first reported in 2003, followed by 2012 when the Middle East respiratory syndrome coronavirus (MERS-CoV) led to an outbreak of Middle East respiratory syndrome (MERS). Moreover, COVID-19 represents a serious socioeconomic and global health problem that has already claimed more than four million lives. To date, there are only a handful of therapeutic options to combat this disease, and only a single direct-acting antiviral, the conditionally approved remdesivir. Since there is an urgent need for active drugs against SARS-CoV-2, the strategy of drug repurposing represents one of the fastest ways to achieve this goal. An in silico drug repurposing study using two methods was conducted. A structure-based virtual screening of the FDA-approved drug database on SARS-CoV-2 main protease was performed, and the 11 highest-scoring compounds with known 3CLpro activity were identified while the methodology was used to report further 11 potential and completely novel 3CLpro inhibitors. Then, inverse molecular docking was performed on the entire viral protein database as well as on the Coronaviridae family protein subset to examine the hit compounds in detail. Instead of target fishing, inverse docking fingerprints were generated for each hit compound as well as for the five most frequently reported and direct-acting repurposed drugs that served as controls. In this way, the target-hitting space was examined and compared and we can support the further biological evaluation of all 11 newly reported hits on SARS-CoV-2 3CLpro as well as recommend further in-depth studies on antihelminthic class member compounds. The authors acknowledge the general usefulness of this approach for a full-fledged inverse docking fingerprint screening in the future.

PMID:35028304 | PMC:PMC8748264 | DOI:10.3389/fchem.2021.757826

Comput Biol Med. 2021 Dec 29;142:105177. doi: 10.1016/j.compbiomed.2021.105177. Online ahead of print.

ABSTRACT

Cancer Stem Cells (CSCs) contribute to cancer aggressiveness, metastasis, chemo/radio-therapy resistance, and tumor recurrence. Recent studies emphasized the importance of metabolic reprogramming of CSCs for the maintenance and progression of the cancer phenotype through both the fulfillment of the energetic requirements and the supply of substrates fundamental for fast-cell growth, as well as through metabolite-induced epigenetic regulation. Therefore, it is of paramount importance to develop therapeutic strategies tailored to target the metabolism of CSCs. In this work, we built computational Genome-Scale Metabolic Models (GSMMs) for CSCs of different tissues. Flux simulations were then used to predict metabolic phenotypes, identify potential therapeutic targets, and spot already-known Transcription Factors (TFs), miRNAs and antimetabolites that could be used as part of drug repurposing strategies against cancer. Results were in accordance with experimental evidence, provided insights of new metabolic mechanisms for already known agents, and allowed for the identification of potential new targets and compounds that could be interesting for further in vitro and in vivo validation.

PMID:35026576 | DOI:10.1016/j.compbiomed.2021.105177