COVID-19 Drug Research Articles

Graph theoretic approach for calculation of new Banhatti indices VIA recent algebraic polynomials with a chemical application

In this article, we design new distance-based topological indices which are computed by a recent polynomial approach. Also, we present a chemical application on the suitability of these indices with some drugs used for the treatment of COVID-19 via QSPR analysis. Curvilinear regression models are obtained and analysed for the physico-chemical properties of the COVID-19 drugs. Our models and findings could aid in the development of new drugs for the treatment of COVID-19.

A novel robust inhibitor of papain-like protease (PLpro) as a COVID-19 drug

Regarding the significance of SARS-CoV-2, scientists have shown considerable interest in developing effective drugs. Inhibitors for PLpro are the primary strategies for locating suitable COVID-19 drugs. Natural compounds comprise the majority of COVID-19 drugs. Due to limitations on the safety of clinical trials in cases of COVID, computational methods are typically utilized for inhibition studies. Whereas papain is highly similar to PLpro and is entirely safe, the current study aimed to examine several plant secondary metabolites to identify the most effective papain inhibitor and validate the results using molecular dynamics and docking. This simulation was conducted identically for PLpro and the optimal inhibitor. The results indicated that the experimental results are comparable to those obtained In-Silico, and the inhibition effects of Chlorogenic acid (CGA) on papain attained in the experiment were validated (IC50=0.54 mM). CGA as an inhibitor was located in the active site of PLpro and papain (total energy −2009410 and −456069 kJ/mol, respectively) at the desired location and distance. The study revealed that CGA and its derivatives are effective PLpro inhibitors against SARS-CoV-2. Communicated by Ramaswamy H. Sarma

Potentially Highly Effective Drugs for COVID-19: Virtual Screening and Molecular Docking Study Through PyRx-Vina Approach

Introduction: The World Health Organization (WHO) has declared the novel coronavirus (COVID-2019) infection outbreak a global health emergency. Drug repurposing, which concerns the investigation of existing drugs for new therapeutic target indications, has emerged as a successful strategy for drug discovery due to the reduced costs and expedited approval procedures.Material and Methods: The crystal structure of a protein essential for virus replication has been filed in the Protein Data Bank recently. Based on this structure and existing experimental datasets for SARS-CoV2(COVID-19) we present results deriving from the virtual screening of a database of more than 1000 drugs in the DrugBank that have been approved by Food and Drug Administration (FDA).Results: Results showed that some of the known protease inhibitors currently used in HIV and Cancer infections might be helpful for the therapy of COVID-19 also. Results also showed that Levomefolic acid, or vitamin B9, is recommended therapy because of its oral sources and no side effects.Conclusion: Between all studied FDA-approved drug, VitaminB9 and Etoposide which used for HIV protease inhibitor, revealed strong interaction with protease binding pocket and placed well into the pocket even better than the lopinavir-ritonavir, and since this compound is FDA-approved and successfully passed various testing steps, therefor there is a hope that this drug, could be a potential drug to treating the COVID-19.

Molecular Docking-based Screening of Natural Heterocyclic Compounds as a Potential Drug for COVID-19

Introduction: The coronavirus pandemic poses significant challenges for the pharmaceutical industry. Methods: Coronavirus enters host cells via the angiotensin-converting enzyme 2 receptors (ACE2). The SARS-CoV-2 spike glycoprotein is a potential target for medicinal chemists in the development of specific drugs. The current study investigates molecular modeling studies to identify potential drug candidates. Molecular docking simulations were run on 11 natural heterocyclic compounds/flavonoids. Results: When tested against the viral spike protein receptor, isoquercetin had a docking binding energy of -6.74kcal/mol (PDBID:6LU7). Conclusion: A docking study revealed the interaction of the receptor-binding domain with various flavonoid compounds.

Quality of reporting of adverse events in clinical trials of covid-19 drugs: systematic review

ObjectiveTo assess the quality of reporting of adverse events in clinical trials of covid-19 drugs based on the CONSORT (Consolidated Standards of Reporting Trials) harms extension and according to clinical...

Repurposed drug studies on the primary prevention of SARS-CoV-2 infection during the pandemic: systematic review and meta-analysis

ObjectiveCurrent evidence on the effectiveness of SARS-CoV-2 prophylaxis is inconclusive. We aimed to systematically evaluate published studies on repurposed drugs for the prevention of laboratory-confirmed SARS-CoV-2 infection and/or COVID-19 among...

Australian National Clinical Evidence Taskforce COVID-19 drug treatment guidelines: challenges of producing a living guideline.

Australian National Clinical Evidence Taskforce COVID-19 drug treatment guidelines: challenges of producing a living guideline.

Drug–drug interaction of Nirmatrelvir/ritonavir and tacrolimus: A potential risk disproportionality analysis of nephrotoxicity from COVID-19 reports in FAERS

ABSTRACT Background Nirmatrelvir/ritonavir is a new oral antiviral agent for COVID-19, and tacrolimus is a widely used immunosuppressant. Drug–drug interaction between Nirmatrelvir/ritonavir and tacrolimus is expected. However, information regarding the drug–drug interaction in a real-world setting is limited. We aim to evaluate drug–drug interaction between tacrolimus and Nirmatrelvir/ritonavir and perform a disproportionality analysis to assess the potential risk of nephrotoxicity due to their combination for COVID-19 treatment based on the FAERS database. Research design and methods Disproportionality analysis was performed using the reporting odds ratio (ROR) method, and subset analysis was conducted based on the background of COVID-19 drugs combined with tacrolimus more than 10 times. Results In disproportionality analysis, combination of Nirmatrelvir/ritonavir and tacrolimus was significantly associated with acute kidney injury (41.13%), serum creatinine increased (14.18%), renal failure (2.84%), and renal impairment (2.84%). These positive signals of acute kidney injury and serum creatinine increased still strongly retained in subset analysis. No similar positive signals were detected in Nirmatrelvir/ritonavir-single group. Only in Cilgavimab/Tixagevimab-tacrolimus group and Remdesivir-tacrolimus group, acute kidney injury was recognized as weakly positive signals and disappeared in subset analysis. Conclusions The study results show significant drug–drug interaction between Nirmatrelvir/ritonavir and tacrolimus and confirm that their combination for COVID-19 treatment greatly increases risk of acute kidney injury.

Drug-CoV: a drug-origin knowledge graph discovering drug repurposing targeting COVID-19

Drug repurposing is a technique for probing new usages of existing medicines, but its traditional methods, such as computational approaches, can be time-consuming and laborious. Recently, knowledge graphs (KGs) have emerged as a powerful approach for graph-based representation in drug repurposing, encoding entities and relations to predict new connections and facilitate drug discovery. As COVID-19 has become a major public health concern, it is critical to establish an appropriate COVID-19 KG for drug repurposing to combat the spread of the virus. However, most publicly available COVID-19 KGs lack support for multi-relations and comprehensive entity types. Moreover, none of them originates from COVID-19-related drugs, making it challenging to identify effective treatments. To tackle these issues, we developed Drug-CoV, a drug-origin and multi-relational COVID-19 KG. We evaluated the quality of Drug-CoV by performing link prediction and comparing the results to another publicly available COVID-19 KG. Our results showed that Drug-CoV outperformed the comparing KG in predicting new links between entities. Overall, Drug-CoV represents a valuable resource for COVID-19 drug repurposing efforts and demonstrates the potential of KGs for facilitating drug discovery.

Interferons as a Potential Therapeutic Drug for COVID-19: A Literature Review of Mechanisms, Current Clinical Trials, and Challenges

The 2019 COVID-19 pandemic caused by SARS-CoV-2 has resulted in many fatalities worldwide. Despite various types of supportive care, mortality rates for patients with comorbidities remain high. To explore alternative treatment options, interferons (IFNs) have emerged as promising therapeutic drugs for SARS-CoV-2. This review aims to investigate the potential of IFNs as a drug with details on their mechanisms of action, and available data on their use with ongoing clinical trials, results, potential limitations, and challenges. Recently published research articles, which are systematically searched through online databases, have been selected and found that IFNs have colossal potential in treating SARS-CoV-2 infection by modulating the host’s immune response and inhibiting viral replication and decreasing the severity of disease and hospitalization (p = 0.03, ± 0.05) and (p = 0.04, ± 0.05) respectively. However, due to less available data, more controlled and randomized trials are needed to confirm the efficacy and safety of IFN therapy. The optimal dosing and duration of IFN therapy also remain to be determined. Although further research is needed the wait for ongoing clinical trial results under investigation is also important for a better understanding of IFN therapy.

Potency of Xanthone Derivatives from Garcinia mangostana L. for COVID-19 Treatment through Angiotensin-Converting Enzyme 2 and Main Protease Blockade: A Computational Study.

ACE2 and Mpro in the pathology of SARS-CoV-2 show great potential in developing COVID-19 drugs as therapeutic targets, due to their roles as the "gate" of viral entry and viral reproduction. Of the many potential compounds for ACE2 and Mpro inhibition, α-mangostin is a promising candidate. Unfortunately, the potential of α-mangostin as a secondary metabolite with the anti-SARS-CoV-2 activity is hindered due to its low solubility in water. Other xanthone isolates, which also possess the xanthone core structure like α-mangostin, are predicted to be potential alternatives to α-mangostin in COVID-19 treatment, addressing the low drug-likeness of α-mangostin. This study aims to assess the potential of xanthone derivative compounds in the pericarp of mangosteen (Garcinia mangostana L.) through computational study. The study was conducted through screening activity using molecular docking study, drug-likeness prediction using Lipinski's rule of five filtration, pharmacokinetic and toxicity prediction to evaluate the safety profile, and molecular dynamic study to evaluate the stability of formed interactions. The research results showed that there were 11 compounds with high potential to inhibit ACE2 and 12 compounds to inhibit Mpro. However, only garcinone B, in addition to being indicated as active, also possesses a drug-likeness, pharmacokinetic, and toxicity profile that was suitable. The molecular dynamic study exhibited proper stability interaction between garcinone B with ACE2 and Mpro. Therefore, garcinone B, as a xanthone derivative isolate compound, has promising potential for further study as a COVID-19 treatment as an ACE2 and Mpro inhibitor.

A Green, Validated and Stability Indicating UV Spectrophotometric Determination And HPLC Comparison of Covid-19 Drug Favipiravir in Tablets

A Green, Validated and Stability Indicating UV Spectrophotometric Determination And HPLC Comparison of Covid-19 Drug Favipiravir in Tablets

KG-Hub-building and exchanging biological knowledge graphs.

Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification. https://kghub.org.

Alveolar epithelial-like cell differentiation in a dynamic bioreactor: a promising 3D-approach for the high-throughput generation of lung cell types from human induced pluripotent stem cells

PurposeHuman induced pluripotent stem cell (hiPSC)-derived lung cell types such as alveolar epithelial cells are promising for toxicological and pharmaceutical in vitro screenings. Reproducible differentiation processes are highly demanded, but protocols which are suitable for the high-throughput generation of lung cell types from hiPSCs are lacking.MethodsIn this study, a new approach for the hiPSC-differentiation in alveolar epithelial-like cells type 2 under dynamic 3D-conditions in a suspension bioreactor is presented. Gene and protein expression analyses of key markers during the embryonal lung development have been performed in comparison to cells differentiated under static 2D-conditions to evaluate the differentiation efficacy of the new bioreactor-based approach. Finally, the resulting cells were infected by SARS-CoV-2 pseudotypes to demonstrate their functionality and suitability for e.g. COVID-19 drug development.ResultsThe dynamic bioreactor is suitable to differentiate hiPSCs in spheroids, which express relevant lung markers in each developmental stage on gene and protein level. The 3D method is able to significantly increase the expression of some markers in comparison to conventional 2D differentiation. 3D-differentiated alveolar epithelial-like cells express functional SARS-CoV-2 receptors and can display the viral infection.ConclusionThe presented dynamic 3D-differentiation is a promising, new approach to generate alveolar epithelial-like cells from hiPSCs as cell source for in vitro lung models.

Development of complemented comprehensive networks for rapid screening of repurposable drugs applicable to new emerging disease outbreaks

BackgroundComputational drug repurposing is crucial for identifying candidate therapeutic medications to address the urgent need for developing treatments for newly emerging infectious diseases. The recent COVID-19 pandemic has taught us the importance of rapidly discovering candidate drugs and providing them to medical and pharmaceutical experts for further investigation. Network-based approaches can provide repurposable drugs quickly by leveraging comprehensive relationships among biological components. However, in a case of newly emerging disease, applying a repurposing methods with only pre-existing knowledge networks may prove inadequate due to the insufficiency of information flow caused by the novel nature of the disease.MethodsWe proposed a network-based complementary linkage method for drug repurposing to solve the lack of incoming new disease-specific information in knowledge networks. We simulate our method under the controlled repurposing scenario that we faced in the early stage of the COVID-19 pandemic. First, the disease-gene-drug multi-layered network was constructed as the backbone network by fusing comprehensive knowledge database. Then, complementary information for COVID-19, containing data on 18 comorbid diseases and 17 relevant proteins, was collected from publications or preprint servers as of May 2020. We estimated connections between the novel COVID-19 node and the backbone network to construct a complemented network. Network-based drug scoring for COVID-19 was performed by applying graph-based semi-supervised learning, and the resulting scores were used to validate prioritized drugs for population-scale electronic health records-based medication analyses.ResultsThe backbone networks consisted of 591 diseases, 26,681 proteins, and 2,173 drug nodes based on pre-pandemic knowledge. After incorporating the 35 entities comprised of complemented information into the backbone network, drug scoring screened top 30 potential repurposable drugs for COVID-19. The prioritized drugs were subsequently analyzed in electronic health records obtained from patients in the Penn Medicine COVID-19 Registry as of October 2021 and 8 of these were found to be statistically associated with a COVID-19 phenotype.ConclusionWe found that 8 of the 30 drugs identified by graph-based scoring on complemented networks as potential candidates for COVID-19 repurposing were additionally supported by real-world patient data in follow-up analyses. These results show that our network-based complementary linkage method and drug scoring algorithm are promising strategies for identifying candidate repurposable drugs when new emerging disease outbreaks.

COVID-19 and non-steroid anti-inflammatory drugs

The literature review deals with the analysis of the results of studies on the effectiveness and safety of nonsteroidal anti-inflammatory drugs (NSAIDs) in the intensive care programs for patients with COVID-19 in 2020–2021. NSAIDs used for other indications or low-dose aspirin do not increase the body’s sensitivity to SARS-CoV-2 and are not additional risk factors for the severity of the course and mortality from COVID-19. Therefore, in patients receiving NSAIDs or low-dose aspirin for comorbid conditions, these drugs should not be withdrawn and may be used during the treatment of COVID-19. It has now been clarified that the pro-inflammatory effect in SARS-CoV-2 is caused by prostaglandins. Suppression of their production with the help of NSAIDs can have a positive effect during the initial phase of a viral infection, before the development of acute respiratory distress syndrome. To date, the clinical benefits of low-dose aspirin for the primary prevention of thrombotic complications in hospitalized patients with COVID-19 have not been proven, and therefore it is not recommended to use aspirin for this purpose.

Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms.

COVID-19, a serious respiratory disease that has the potential to affect numerous organs, is a serious threat to the health of people around the world. The objective of this article is to investigate the potential biological targets and mechanisms by which SARS-CoV-2 affects benign prostatic hyperplasia (BPH) and related symptoms. We downloaded the COVID-19 datasets (GSE157103 and GSE166253) and the BPH datasets (GSE7307 and GSE132714) from the Gene Expression Omnibus (GEO) database. In GSE157103 and GSE7307, differentially expressed genes (DEGs) were found using the "Limma" package, and the intersection was utilized to obtain common DEGs. Further analyses followed, including those using Protein-Protein Interaction (PPI), Gene Ontology (GO) function enrichment analysis, and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Potential hub genes were screened using three machine learning methods, and they were later verified using GSE132714 and GSE166253. The CIBERSORT analysis and the identification of transcription factors, miRNAs, and drugs as candidates were among the subsequent analyses. We identified 97 common DEGs from GSE157103 and GSE7307. According to the GO and KEGG analyses, the primary gene enrichment pathways were immune-related pathways. Machine learning methods were used to identify five hub genes (BIRC5, DNAJC4, DTL, LILRB2, and NDC80). They had good diagnostic properties in the training sets and were validated in the validation sets. According to CIBERSORT analysis, hub genes were closely related to CD4 memory activated of T cells, T cells regulatory and NK cells activated. The top 10 drug candidates (lucanthone, phytoestrogens, etoposide, dasatinib, piroxicam, pyrvinium, rapamycin, niclosamide, genistein, and testosterone) will also be evaluated by the P value, which is expected to be helpful for the treatment of COVID-19-infected patients with BPH. Our findings reveal common signaling pathways, possible biological targets, and promising small molecule drugs for BPH and COVID-19. This is crucial to understand the potential common pathogenic and susceptibility pathways between them.

Insights into the Adsorption Properties of Mixed Matrix Membranes (Pebax 1657-g-Chitosan-PVDF-Bovine Serum Albumin@ZIF-CO3-1) for the Antiviral COVID-19 Treatment Drugs Remdesivir and Nirmatrelvir: An In Silico Study.

The effect of the COVID-19 pandemic on the accumulation of environmental pollutants has been significant. In that way, waste management systems have faced problems, and the amount of hazardous and medical wastes has increased. As pharmaceuticals associated with the treatment of COVID-19 enter the environment, aquatic and terrestrial ecosystems have been negatively impacted, potentially disrupting natural processes and harming aquatic life. This analysis seeks to appraise the potential of mixed matrix membranes (MMMs) composed of Pebax 1657-g-chitosan-polyvinylidene fluoride (PEX-g-CHS-PVDF)-bovine serum albumin (BSA)@ZIF-CO3-1 as adsorbents for removing remdesivir (REMD) and nirmatrelvir (NIRM) from aqueous environments. An in silico study was conducted to explore the adsorption characteristics, physicochemical properties, and structural features of these MMMs, employing quantum mechanical (QM) calculations, molecular dynamics (MD) simulations, and Monte Carlo (MC) simulations as research methodologies. Incorporating BSA@ZIF-CO3-1 into the PEX-g-CHS-PVDF polymer matrix improved the physicochemical properties of MMMs by promoting the compatibility and interfacial adhesion between the two materials, facilitated by electrostatic interactions, van der Waals forces, and hydrogen bonding. Investigation of the interaction mechanism between the title pharmaceutical pollutants and the surfaces of MMMs, along with the description of their adsorption behavior, was also conducted by applying MD and MC approaches. Our observations indicate that the adsorption behavior of REMD and NIRM is influenced by molecular size, shape, and the presence of functional groups. Molecular simulation analysis demonstrated that the MMM membrane is a highly suitable adsorbent for the adsorption of REMD and NIRM drugs, with a higher affinity toward REMD adsorption. Our study emphasizes the significance of computational modeling in developing practical strategies for eliminating COVID-19 drug contaminants from wastewater. The knowledge obtained through our molecular simulations and QM calculations can assist in creating more efficient adsorption materials, resulting in a cleaner and healthier environment.

Vaccination approach in patients with an allergic reaction to COVID-19 vaccines or at risk of developing allergic reactions

There is consensus that patients at risk of developing an allergic reaction to COVID-19 vaccines should be evaluated by an immunologist-allergist to determine whether vaccination should be recommended. We wanted to share our experiences in the management of these high-risk patients, from diagnostic tests in allergological evaluation to the vaccination process. Our retrospective cross-sectional study included patients who had previously developed an allergic reaction to COVID-19 vaccines or drugs and therefore were referred to our allergy and immunology clinic. Prick and intradermal tests were performed on all patients with methylprednisolone acetate (Depo-Medrol®, Pfizer) 40 mg/mL containing polyethylene Glycol (PEG) and triamcinolone acetonide (Kenacort®, Deva) 40 mg/mL containing polysorbate 80. While vaccination with desensitization was recommended for all patients with positive skin tests, split-dose vaccination was recommended for patients with negative skin tests. After explaining the risks and benefits, the choice of the vaccine (Pfizer/BioNTech or Sinovac/ CoronoVac) was left to the patients' discretion. A total of 41 patients, 10 males, and 31 females, with a mean age of 42.37 ± 14.177 years were included. Eighteen patients with a history of allergy after COVID-19 vaccines were analyzed according to the type of reaction and type of vaccine administered (Pfizer/BioNTech/Coronovac; Anaphylaxis: 4/1, Urticaria: 11/2). Moreover, there was a history of drug allergy in 23 patients who had not been vaccinated before. Skin tests with PEG were positive in a total of seven patients while skin tests with polysorbate 80 were negative in all patients. No allergic reaction developed in seven patients who underwent desensitization and in 34 patients who received a split dose. Considering the potentially life-saving benefits of vaccination in a global pandemic environment, it is a safe and effective method to administer vaccines to at-risk patients using desensitization or split dosing techniques, based on their sensitivity status determined through a PEG skin test. This approach allows for the avoidance of preventing access to vaccines, while still ensuring the safety of patients.

Disposable and Sensitive Electrochemical Determination of Molnupiravir at a Pencil Graphite Electrode (PGE) by Differential Pulse Voltammetry (DPV)

This study describes the sensitive voltammetric determination of the COVID-19 drug, molnupiravir, at a pencil graphite electrode (PGE) which is easily available, inexpensive, disposable, and highly electrochemically active. For comparison purposes, cyclic and differential pulse voltammetric responses of molnupiravir were compared at the carbon paste electrode (CPE) and glassy carbon electrode (GCE). The results show that the oxidation potential of molnupiravir shifts to a more negative direction and the peak current of molnupiravir increases significantly with the PGE compared to the CPE and GCE. The PGE demonstrates remarkable electrocatalysis upon the oxidation of molnupiravir. The detection limit and linear range for molnupiravir were determined to be 7.2 nM and 25 to 1000 nM by differential pulse voltammetry. In addition, the selectivity of PGE is high for molnupiravir. The applicability of the sensor was examined by the analysis of pharmaceutical and artificial blood samples. This low-cost and practical sensor was demonstrated to determine molnupiravir with good accuracy and precision.