J Int Med Res. 2022 Apr;50(4):3000605221090363. doi: 10.1177/03000605221090363.

ABSTRACT

OBJECTIVE: Hydroxychloroquine (HCQ) has been used during the coronavirus disease 2019 (COVID-19) pandemic because of its reported anti-viral activity. This study examined the association of chronic HCQ use with the incidence and complications of COVID-19.

METHODS: This retrospective cohort study included adults with rheumatoid arthritis and/or systemic lupus erythematosus who visited rheumatology clinics in three tertiary hospitals in Riyadh, Saudi Arabia between January 2019 and December 2020. Patients were categorized into two groups based on HCQ use. Data were obtained from the electronic health record and by interviews with patients. The primary study objective was the incidence of COVID-19 and its complications from March 2020 to February 2021.

RESULTS: Almost 11% of the study cohort was positive for COVID-19, and the incidence of COVID-19 was similar between HCQ users (11.11%) and nonusers (10.86%). Disease complication rates were similar in the study arms, and they mainly included fever, dry cough, fatigue, and breathing difficulty.

CONCLUSIONS: This study revealed no significant association between chronic HCQ use and the incidence of COVID-19, and disease complications were similar in the study arms.

PMID:35387504 | DOI:10.1177/03000605221090363

Front Pharmacol. 2022 Mar 21;13:854506. doi: 10.3389/fphar.2022.854506. eCollection 2022.

ABSTRACT

Statins, a class of lipid-lowering drugs, are used in drug repositioning for treatment of human cancer. However, the molecular mechanisms underlying statin-induced cancer cell death and autophagy are not clearly defined. In the present study, we showed that pitavastatin could increase apoptosis in a FOXO3a-dependent manner in the oral cancer cell line, SCC15, and the colon cancer cell line, SW480, along with the blockade of autophagy flux. The inhibition of autophagy by silencing the LC3B gene reduced apoptosis, while blockade of autophagy flux using its inhibitor, Bafilomycin A1, further induced apoptosis upon pitavastatin treatment, which suggested that autophagy flux blockage was the cause of apoptosis by pitavastatin. Further, the FOXO3a protein accumulated due to the blockade of autophagy flux which in turn was associated with the induction of ER stress by transcriptional upregulation of PERK-CHOP pathway, subsequently causing apoptosis due to pitavastatin treatment. Taken together, pitavastatin-mediated blockade of autophagy flux caused an accumulation of FOXO3a protein, thereby leading to the induction of PERK, ultimately causing CHOP-mediated apoptosis in cancer cells. Thus, the present study highlighted the additional molecular mechanism underlying the role of autophagy flux blockade in inducing ER stress, eventually leading to apoptosis by pitavastatin.

PMID:35387352 | PMC:PMC8977529 | DOI:10.3389/fphar.2022.854506

Front Pharmacol. 2022 Mar 21;13:879611. doi: 10.3389/fphar.2022.879611. eCollection 2022.

NO ABSTRACT

PMID:35387337 | PMC:PMC8978335 | DOI:10.3389/fphar.2022.879611

Comput Struct Biotechnol J. 2022 Mar 16;20:1427-1438. doi: 10.1016/j.csbj.2022.03.013. eCollection 2022.

ABSTRACT

Alzheimer's disease (AD) is a progressive neurodegenerative disease and the most common type of dementia. With no disease-curing drugs available and an ever-growing AD-related healthcare burden, novel approaches for identifying therapies are needed. In this work, we propose stage-specific candidate repurposed drugs against AD by using a novel network-based method for drug repurposing against different stages of AD severity. For each AD stage, this approach a) ranks the candidate repurposed drugs based on a novel network-based score emerging from the weighted sum of connections in a network resembling the structural similarity with failed, approved or currently ongoing drugs b) re-ranks the candidate drugs based on functional, structural and a priori information according to a recently developed method by our group and c) checks and re-ranks for permeability through the Blood Brain Barrier (BBB). Overall, we propose for further experimental validation 10 candidate repurposed drugs for each AD stage comprising a set of 26 elite candidate repurposed drugs due to overlaps between the three AD stages. We applied our methodology in a retrospective way on the known clinical trial drugs till 2016 and we show that we were able to highly rank a drug that did enter clinical trials in the following year. We expect that our proposed network-based drug-repurposing methodology will serve as a paradigm for application for ranking candidate repurposed drugs in other brain diseases beyond AD.

PMID:35386099 | PMC:PMC8957022 | DOI:10.1016/j.csbj.2022.03.013

Life Sci. 2022 Apr 3:120536. doi: 10.1016/j.lfs.2022.120536. Online ahead of print.

ABSTRACT

AIMS: Metastatic colorectal cancer (mCRC) predominantly contributes to cancer-related mortalities secondary to distant metastasis. This study aimed at investigating anti-tumor activity and safety of mebendazole in patients with mCRC.

MATERIALS AND METHODS: This prospective, randomized double blind placebo-controlled study enrolled 40 mCRC patients who were randomized into two groups; the control group (n = 20) which received 6 cycles of bevacizumab with FOLFOX4 plus placebo tablets BID and mebendazole group (n = 20) which received 6 cycles of bevacizumab with FOLFOX4 plus mebendazole 500 mg orally BID for 12 weeks. Computed tomography scanning and serum levels of carcinoembryonic antigen (CEA), vascular endothelial growth factor (VEGF), liver and renal parameters were assessed at baseline and after 12 weeks. One-year overall survival and progression free survival (PFS) were also determined. Data were analyzed using paired, independent sample-t-tests, Mann-Whitney U, Chi-Square and Kaplan-Meier tests and p < 0.05 was considered statistically significant.

KEY FINDINGS: Mebendazole was well tolerated and its addition to bevacizumab and FOLFOX4 enhanced tumor response to treatment which was translated by significant improvement of overall response rate 12 weeks after intervention [(10% (2) versus 65% (13) for control and mebendazole groups, respectively; p = 0.000] and significant elevation of PFS (median: 3 and 9.25 months for control and mebendazole groups, respectively; p = 0.000). Furthermore, mebendazole produced significant decline in VEGF level (p = 0.006) with non-significant variation in CEA level (p = 0.063).

SIGNIFICANCE: Mebendazole may represent an attractive candidate for drug repositioning against mCRC secondary to its safety and efficacy in enhancing tumor response to chemotherapy.

GOV ID: NCT03925662, retrospectively.

PMID:35385794 | DOI:10.1016/j.lfs.2022.120536

Bioinformatics. 2022 Apr 6:btac205. doi: 10.1093/bioinformatics/btac205. Online ahead of print.

ABSTRACT

MOTIVATION: Drug repositioning is an attractive alternative to de novo drug discovery due to reduced time and costs to bring drugs to market. Computational repositioning methods, particularly non-black-box methods that can account for and predict a drug's mechanism, may provide great benefit for directing future development. By tuning both data and algorithm to utilize relationships important to drug mechanisms, a computational repositioning algorithm can be trained to both predict and explain mechanistically novel indications.

RESULTS: In this work, we examined the 123 curated drug mechanism paths found in the drug mechanism database (DrugMechDB) and after identifying the most important relationships, we integrated 18 data sources to produce a heterogeneous knowledge graph, MechRepoNet, capable of capturing the information in these paths. We applied the Rephetio repurposing algorithm to MechRepoNet using only a subset of relationships known to be mechanistic in natureand found adequate predictive ability on an evaluation set with AUROC value of 0.83. The resulting repurposing model allowed us to prioritize paths in our knowledge graph to produce a predicted treatment mechanism. We found that DrugMechDB paths, when present in the network were rated highly among predicted mechanisms. We then demonstrated MechRepoNet's ability to use mechanistic insight to identify a drug's mechanistic target, with a mean reciprocal rank of 0.525 on a test set of known drug-target interactions. Finally, we walked through repurposing examples of the anti-cancer drug imatinib for use in the treatment of asthma, and metolazone for use in the treatment of osteoporosis, to demonstrate this method's utility in providing mechanistic insight into repurposing predictions it provides.

AVAILABILITY: The Python code to reproduce the entirety of this analysis is available at: https://github.com/SuLab/MechRepoNet.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:35385070 | DOI:10.1093/bioinformatics/btac205

Sci Rep. 2022 Apr 5;12(1):5706. doi: 10.1038/s41598-022-09592-0.

ABSTRACT

Although an increasing number of patients benefit from immunotherapy and targeted therapies, melanoma remains incurable with increasing incidence. Drug repositioning and repurposing is an alternative strategy to discover and develop novel anticancer drugs or combined therapeutic regimens. In this study, we demonstrated that albendazole (ABZ), an Food and Drug Administration (FDA)-approved broad-spectrum antiparasitic agent, significantly inhibits the proliferation of melanoma cells in vitro and in vivo. RNA sequencing and flow cytometry analysis revealed that ABZ arrests melanoma cells at the G2/M phase of the cell cycle and induces cell apoptosis. More importantly, the CDK4/6 inhibitor palbociclib, as a member of the first and only class of highly specific CDK inhibitors approved for cancer treatment to date, showed significant synergistic effects with ABZ treatment in melanoma cells and mouse models. Taken together, we revealed a previously unappreciated function of ABZ in antimelanoma proliferation by inducing cell cycle arrest and apoptosis and provided a novel combined therapeutic regimen of ABZ plus CDK4/6 inhibitor treatment in melanoma.

PMID:35383224 | DOI:10.1038/s41598-022-09592-0

Curr Pharmacol Rep. 2022 Apr 1:1-15. doi: 10.1007/s40495-022-00285-w. Online ahead of print.

ABSTRACT

PURPOSE OF REVIEW: This article provides a review of the recent literature related to the FDA-approved drugs that had been repurposed as potential drug candidates against COVID-19. Moreover, we performed a quality pharmacophore study for frequently studied targets, namely, the main protease, RNA-dependent RNA polymerase, and spike protein.

RECENT FINDINGS: Ever since the COVID-19 pandemic, the whole spectrum of scientific community is still unable to invent an absolute therapeutic agent for COVID-19. Considering such a fact, drug repurposing strategies seem a truly viable approach to develop novel therapeutic interventions.

SUMMERY: Drug repurposing explores previously approved drugs of known safety and pharmacokinetics profile for possible new effects, reducing the cost, time, and predicting prospective side effects and drug interactions. COVID-19 virulent machinery appeared similar to other viruses, making antiviral agents widely repurposed in pursuit for curative candidates. Our main protease pharmacophoric study revealed multiple features and could be a probable starting point for upcoming research.

PMID:35381996 | PMC:PMC8970976 | DOI:10.1007/s40495-022-00285-w

Brief Bioinform. 2022 Apr 4:bbac109. doi: 10.1093/bib/bbac109. Online ahead of print.

ABSTRACT

Drug-target interaction (DTI) prediction plays an important role in drug repositioning, drug discovery and drug design. However, due to the large size of the chemical and genomic spaces and the complex interactions between drugs and targets, experimental identification of DTIs is costly and time-consuming. In recent years, the emerging graph neural network (GNN) has been applied to DTI prediction because DTIs can be represented effectively using graphs. However, some of these methods are only based on homogeneous graphs, and some consist of two decoupled steps that cannot be trained jointly. To further explore GNN-based DTI prediction by integrating heterogeneous graph information, this study regards DTI prediction as a link prediction problem and proposes an end-to-end model based on HETerogeneous graph with Attention mechanism (DTI-HETA). In this model, a heterogeneous graph is first constructed based on the drug-drug and target-target similarity matrices and the DTI matrix. Then, the graph convolutional neural network is utilized to obtain the embedded representation of the drugs and targets. To highlight the contribution of different neighborhood nodes to the central node in aggregating the graph convolution information, a graph attention mechanism is introduced into the node embedding process. Afterward, an inner product decoder is applied to predict DTIs. To evaluate the performance of DTI-HETA, experiments are conducted on two datasets. The experimental results show that our model is superior to the state-of-the-art methods. Also, the identification of novel DTIs indicates that DTI-HETA can serve as a powerful tool for integrating heterogeneous graph information to predict DTIs.

PMID:35380622 | DOI:10.1093/bib/bbac109

BMC Bioinformatics. 2022 Apr 4;23(1):121. doi: 10.1186/s12859-022-04650-w.

ABSTRACT

BACKGROUND: As many interactions between the chemical and genomic space remain undiscovered, computational methods able to identify potential drug-target interactions (DTIs) are employed to accelerate drug discovery and reduce the required cost. Predicting new DTIs can leverage drug repurposing by identifying new targets for approved drugs. However, developing an accurate computational framework that can efficiently incorporate chemical and genomic spaces remains extremely demanding. A key issue is that most DTI predictions suffer from the lack of experimentally validated negative interactions or limited availability of target 3D structures.

RESULTS: We report DT2Vec, a pipeline for DTI prediction based on graph embedding and gradient boosted tree classification. It maps drug-drug and protein-protein similarity networks to low-dimensional features and the DTI prediction is formulated as binary classification based on a strategy of concatenating the drug and target embedding vectors as input features. DT2Vec was compared with three top-performing graph similarity-based algorithms on a standard benchmark dataset and achieved competitive results. In order to explore credible novel DTIs, the model was applied to data from the ChEMBL repository that contain experimentally validated positive and negative interactions which yield a strong predictive model. Then, the developed model was applied to all possible unknown DTIs to predict new interactions. The applicability of DT2Vec as an effective method for drug repurposing is discussed through case studies and evaluation of some novel DTI predictions is undertaken using molecular docking.

CONCLUSIONS: The proposed method was able to integrate and map chemical and genomic space into low-dimensional dense vectors and showed promising results in predicting novel DTIs.

PMID:35379165 | DOI:10.1186/s12859-022-04650-w

Mini Rev Med Chem. 2022 Apr 4. doi: 10.2174/1389557522666220404085858. Online ahead of print.

ABSTRACT

Ebola virus (EBOV) is an infectious disease that mainly affects the cardiovascular system. It belongs to the Filoviridae family, consisting of filamentous envelopes, non-segmented negative RNA genome. EBOV was initially identified in Sudan and Zaire (now named the Democratic Republic of Congo), around 1967. It is transmitted mainly by contact with secretions (blood, sweat, saliva, and tears) from infected wild animals, such as non-human primates and bats. It has gained more prominence in recent years, due to the recent EBOV outbreaks that occurred from 2013 to 2016, resulting in approximately 28,000 infected individuals, with a mortality rate of 40-70%, affecting mainly Liberia, Guinea, and Sierra Leone. Despite these alarming levels, there is no FDA-approved drug for the effective treatment of these diseases until now. The most advanced drug to treat EBOV is remdesivir. However, it is a high-cost drug and is available only for intravenous use. In sense, more investments are needed in the research focused on the development of new antiviral drugs. In this context, medicinal chemistry strategies have been improving and increasingly providing the discovery of new hits that can be used in the future as a treatment against these diseases. Thus, this review will address the main advances in medicinal chemistry, such as drug discovery through computational techniques (virtual screening and virtual high throughput screening), in addition drug repurposing, phenotypic screening assays, and employing classical medicinal chemistry, such as bioisosterism, metabolism-based drug design and the discovery of new inhibitors through natural products, presenting several compounds promising that may contain the advance of these pathogens.

PMID:35379146 | DOI:10.2174/1389557522666220404085858

Res Sq. 2022 Mar 30:rs.3.rs-1490282. doi: 10.21203/rs.3.rs-1490282/v1. Preprint.

ABSTRACT

The COVID-19 pandemic spurred a broad interest in antiviral drug discovery. The SARS-CoV-2 main protease (M pro ) and papain-like protease (PL pro ) are attractive antiviral drug targets given their vital roles in viral replication and modulation of host immune response. Structurally disparate compounds were reported as M pro and PL pro inhibitors from either drug repurposing or rational design. Two polyphenols dieckol and 1,2,3,4,6-pentagalloylglucose (PGG) were recently reported as SARS-CoV-2 main protease (M pro ) inhibitors. With our continuous interest in studying the mechanism of inhibition and resistance of M pro inhibitors, we report herein our independent validation/invalidation of these two natural products. Our FRET-based enzymatic assay showed that neither dieckol nor PGG inhibited SARS-CoV-2 M pro (IC 50 > 20 µM), which is in contrary to previous reports. Serendipitously, PGG was found to inhibit the SARS-CoV-2 papain-like protease (PL pro ) with an IC 50 of 3.90 µM. The binding of PGG to PL pro was further confirmed in the thermal shift assay. However, PGG was cytotoxic in 293T-ACE2 cells (CC 50 = 7.7 µM), so its intracellular PL pro inhibitory activity could not be quantified by the cell-based Flip-GFP PL pro assay. In addition, we also invalidated ebselen, disulfiram, carmofur, PX12, and tideglusib as SARS-CoV-2 PL pro inhibitors using the Flip-GFP assay. Overall, our results call for stringent hit validation, and the serendipitous discovery of PGG as a putative PL pro inhibitor might worth further pursuing.

PMID:35378761 | PMC:PMC8978949 | DOI:10.21203/rs.3.rs-1490282/v1

Int J Antimicrob Agents. 2022 Apr 1:106582. doi: 10.1016/j.ijantimicag.2022.106582. Online ahead of print.

ABSTRACT

Infections caused by multidrug-resistant (MDR) bacteria, especially MDR Gram-negative bacteria, have posed a great challenge to healthcare systems globally. To address the shortage of effective antibiotics against MDR Gram-negative bacterial infections, two non-antibiotic drugs auranofin (rheumatoid arthritis drug) and pentamidine (antiprotozoal drug) are, for the first time, repurposed to treat MDR Gram-negative bacteria by a combination approach. Herein, chequerboard microdilution assay is used to determine the interaction of auranofin and pentamidine against drug-susceptible and MDR Gram-negative bacteria (E. coli, A. baumannii, K. pneumonia), and the results show that these two non-antibiotic drugs display a strong synergistic antibacterial effect with the fraction inhibitory concentration (FIC) index ranging from 0.094 to 0.506. Minimum inhibitory concentration (MIC) of auranofin reduces by as high as ≥1024-fold when it is combined with pentamidine at sub-MIC. Fluorescence and inductively coupled plasma mass spectrometry (ICP-MS) analyses reveal that bacterial membrane disruption caused by pentamidine treatment at sub-MIC leads to an increased intracellular auranofin content upon the combination treatment. The enhanced auranofin uptake in bacteria results in efficient bacteria killing. More importantly, the auranofin/pentamidine combination slowed down auranofin resistance development in clinically isolated MDR bacteria (K. pneumonia) than the combination of auranofin and colistin that is a last-line antibiotic with a membrane-lytic antibacterial mechanism. The combination of non-antibiotic drugs with complementary antibacterial mechanisms provides a potentially promising approach to discover new antibacterial drugs and delay drug resistance development.

PMID:35378227 | DOI:10.1016/j.ijantimicag.2022.106582

ACS Synth Biol. 2022 Apr 4. doi: 10.1021/acssynbio.1c00381. Online ahead of print.

ABSTRACT

We present a cell-free assay for rapid screening of candidate inhibitors of protein binding, focusing on inhibition of the interaction between the SARS-CoV-2 Spike receptor binding domain (RBD) and human angiotensin-converting enzyme 2 (hACE2). The assay has two components: fluorescent polystyrene particles covalently coated with RBD, termed virion-particles (v-particles), and fluorescently labeled hACE2 (hACE2F) that binds the v-particles. When incubated with an inhibitor, v-particle-hACE2F binding is diminished, resulting in a reduction in the fluorescent signal of bound hACE2F relative to the noninhibitor control, which can be measured via flow cytometry or fluorescence microscopy. We determine the amount of RBD needed for v-particle preparation, v-particle incubation time with hACE2F, hACE2F detection limit, and specificity of v-particle binding to hACE2F. We measure the dose response of the v-particles to known inhibitors. Finally, utilizing an RNA-binding protein tdPP7 incorporated into hACE2F, we demonstrate that RNA-hACE2F granules trap v-particles effectively, providing a basis for potential RNA-hACE2F therapeutics.

PMID:35377616 | DOI:10.1021/acssynbio.1c00381

Front Genet. 2022 Mar 17;13:870795. doi: 10.3389/fgene.2022.870795. eCollection 2022.

NO ABSTRACT

PMID:35368698 | PMC:PMC8969764 | DOI:10.3389/fgene.2022.870795

Int J Antimicrob Agents. 2022 Mar 31:106578. doi: 10.1016/j.ijantimicag.2022.106578. Online ahead of print.

ABSTRACT

OBJECTIVE: to retrieve and examine published studies related to in vitro and in vivo evaluation of disulfiram for the treatment of bacterial pathogens.

MATERIAL AND METHODS: Five scientific databases (PubMed, Embase, Scopus, Web of Science, and Latin American and Caribbean Health Sciences Literature) were searched to retrieve the maximum literature about the aim of this study.

RESULTS: The search strategy retrieved a total of 870 studies, of which 31 were included and 19 approached the disulfiram as the primary aim, and 12 included it as a secondary finding from other investigational objectives. The evidence pointed out five main aspects of pre-clinical testing about disulfiram antibacterial activity: spectrum of antimicrobial action, drug combination, intracellular studies, animal studies, and bacterial targets.

CONCLUSION: Findings to emerge from this study are the observed potential of disulfiram as a non-antibiotic drug being proposed as a potential drug to contribute to the treatment of bacterial diseases usually with few treatment alternatives in the context of drug resistance. We evaluated the potency and selectivity of disulfiram, which indeed until now, shows a potential to be explored in using it as an adjunctive chemical to antimicrobial ones. Even with the level of evidence being reserved, the potential of combining disulfiram with other drugs, already used or new to be used for the treatment of mycobacterial diseases, as well as its likely immunomodulatory effect, deserve to be further investigated. Furthermore, the copper dependency mode of action in Gram-positive bacteria is an alternative to be explored in drug design or repurposing of chemicals.

PMID:35367599 | DOI:10.1016/j.ijantimicag.2022.106578

Epilepsia. 2022 Apr 1. doi: 10.1111/epi.17247. Online ahead of print.

ABSTRACT

OBJECTIVE: Rett Syndrome (RTT), commonly caused by methyl-CpG-binding protein 2 (MECP2) pathogenic variants, has many co-morbidities. 50-90% of children with RTT have epilepsy which is often drug resistant. Cannabidivarin (CBDV), a non-hallucinogenic phytocannabinoid has shown benefit in MECP2 animal models. This Phase I trial assessed the safety and tolerability of CBDV in female children with RTT and drug resistant epilepsy, as well as the effect on mean monthly seizure frequency (MMSF), the electroencephalogram (EEG), and non-epilepsy co-morbid symptoms.

METHODS: Five female children with drug resistant epilepsy and a pathogenic MECP2 variant were enrolled. Baseline clinical and laboratory assessments, including monthly seizure frequency, were recorded. CBDV oral solution (50mg/mL) was prescribed and titrated to 10mg/kg/day. Data collected over 15 months included pharmacokinetics, seizure type and frequency, adverse events, EEG, and responses to Rett syndrome behaviour questionnaire (RSBQ) and Rett syndrome symptom severity index, and was compared to baseline data.

RESULTS: All five children reached the maximum CBDV dose of 10mg/kg/day and had a reduction in MMSF (median 79% reduction). Three children had MMSF reduction >75%. This corresponded to an overall reduction in seizure frequency from 32 to 7.2 seizures per month. 91% of adverse events were mild or moderate and none required drug withdrawal. 62% were judged unrelated to CBDV. 31% of adverse events were identified as possibly related, of which nearly all were mild, and the remainder were later assessed as RTT symptoms. Hypersomnolence and drooling were identified as related to CBDV. No serious adverse events reported were related to CBDV. No significant change was noted in EEG or non-epilepsy related symptoms of RTT.

SIGNIFICANCE: 10mg/kg/day of CBDV is safe and well tolerated in a paediatric Rett syndrome cohort and suggests improved seizure control in children with MECP2-related Rett syndrome.

PMID:35364618 | DOI:10.1111/epi.17247

Methods. 2022 Mar 29:S1046-2023(22)00083-4. doi: 10.1016/j.ymeth.2022.03.016. Online ahead of print.

ABSTRACT

The ongoing global pandemic of COVID-19, caused by SARS-CoV-2 has killed more than 5.9 million individuals out of ∼43 million confirmed infections. At present, several parts of the world are encountering the 3rd wave. Mass vaccination has been started in several countries but they are less likely to be broadly available for the current pandemic, repurposing of the existing drugs has drawn highest attention for an immediate solution. A recent publication has mapped the physical interactions of SARS-CoV-2 and human proteins by affinity-purification mass spectrometry (AP-MS) and identified 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Here, we taken a network biology approach and constructed a human protein-protein interaction network (PPIN) with the above SARS-CoV-2 targeted proteins. We utilized a combination of essential network centrality measures and functional properties of the human proteins to identify the critical human targets of SARS-CoV-2. Four human proteins, namely PRKACA, RHOA, CDK5RAP2, and CEP250 have emerged as the best therapeutic targets, of which PRKACA and CEP250 were also found by another group as potential candidates for drug targets in COVID-19. We further found candidate drugs/compounds, such as guanosine triphosphate, remdesivir, adenosine monophosphate, MgATP, and H-89 dihydrochloride that bind the target human proteins.The urgency to prevent the spread of infection and the death of diseased individuals has prompted the search for agents from the pool of approved drugs to repurpose them for COVID-19. Our results indicate that host targeting therapy with the repurposed drugs may be a useful strategy for the treatment of SARS-CoV-2 infection.

PMID:35364279 | PMC:PMC8960288 | DOI:10.1016/j.ymeth.2022.03.016

Front Pharmacol. 2022 Mar 10;13:755745. doi: 10.3389/fphar.2022.755745. eCollection 2022.

ABSTRACT

Background: COVID-19 treatment remains a challenge for medicine because of the extremely short time for clinical studies of drug candidates, so the drug repurposing strategy, which implies the use of well-known and safe substances, is a promising approach. Objective: We present the results of an observational clinical study that focused on the influence of riboflavin (vitamin B2) supplementation on the immune markers of COVID-19 severity in patients with mental health disorders. Results: We have found that 10 mg of flavin mononucleotide (a soluble form of riboflavin) intramuscularly twice a day within 7 days correlated with the normalization of clinically relevant immune markers (neutrophils and lymphocytes counts, as well as their ratio) in COVID-19 patients. Additionally, we demonstrated that total leucocytes, neutrophils, and lymphocytes counts, as well as the neutrophils to leucocytes ratio (NLR), correlated with the severity of the disease. We also found that patients with organic disorders (F0 in ICD-10) demonstrated higher inflammation then patients with schizophrenia (F2 in ICD-10). Conclusion: We suggest that riboflavin supplementation could be promising for decreasing inflammation in COVID-19, and further evaluation is required. This observational clinical trial has been registered by the Sverzhevsky Research Institute of Clinical Otorhinolaryngology (Moscow, Russia), Protocol No. 4 dated 05/27/2020.

PMID:35359854 | PMC:PMC8960625 | DOI:10.3389/fphar.2022.755745

Future Virol. 2022 Mar. doi: 10.2217/fvl-2021-0160. Epub 2022 Mar 18.

ABSTRACT

Novel COVID-19 is a public health emergency that poses a serious threat to people worldwide. Given the virus spreading so quickly, novel antiviral medications are desperately needed. Repurposing existing drugs is the first strategy. Anti-parasitic drugs were among the first to be considered as a potential treatment option for this disease. Even though many papers have discussed the efficacy of various anti-parasitic drugs in treating COVID-19 separately, so far, no single study comprehensively discussed these drugs. This study reviews some anti-parasitic recommended drugs to treat COVID-19, in terms of function and in vitro as well as clinical results. Finally, we briefly review the advanced techniques, such as artificial intelligence, that have been used to find effective drugs for the treatment of COVID-19.

PMID:35359702 | PMC:PMC8940209 | DOI:10.2217/fvl-2021-0160