Thousands Of Drugs Research Articles

CPMKG: a condition-based knowledge graph for precision medicine.

Personalized medicine tailors treatments and dosages based on a patient's unique characteristics, particularly its genetic profile. Over the decades, stratified research and clinical trials have uncovered crucial drug-related information-such as dosage, effectiveness, and side effects-affecting specific individuals with particular genetic backgrounds. This genetic-specific knowledge, characterized by complex multirelationships and conditions, cannot be adequately represented or stored in conventional knowledge systems. To address these challenges, we developed CPMKG, a condition-based platform that enables comprehensive knowledge representation. Through information extraction and meticulous curation, we compiled 307 614 knowledge entries, encompassing thousands of drugs, diseases, phenotypes (complications/side effects), genes, and genomic variations across four key categories: drug side effects, drug sensitivity, drug mechanisms, and drug indications. CPMKG facilitates drug-centric exploration and enables condition-based multiknowledge inference, accelerating knowledge discovery through three pivotal applications. To enhance user experience, we seamlessly integrated a sophisticated large language model that provides textual interpretations for each subgraph, bridging the gap between structured graphs and language expressions. With its comprehensive knowledge graph and user-centric applications, CPMKG serves as a valuable resource for clinical research, offering drug information tailored to personalized genetic profiles, syndromes, and phenotypes. Database URL: https://www.biosino.org/cpmkg/.

Assessing Animal Models to Study Impaired and Chronic Wounds.

Impaired healing wounds do not proceed through the normal healing processes in a timely and orderly manner, and while they do eventually heal, their healing is not optimal. Chronic wounds, on the other hand, remain unhealed for weeks or months. In the US alone, chronic wounds impact ~8.5 million people and cost ~USD 28-90 billion per year, not accounting for the psychological and physical pain and emotional suffering that patients endure. These numbers are only expected to rise in the future as the elderly populations and the incidence of comorbidities such as diabetes, hypertension, and obesity increase. Over the last few decades, scientists have used a variety of approaches to treat chronic wounds, but unfortunately, to date, there is no effective treatment. Indeed, while there are thousands of drugs to combat cancer, there is only one single drug approved for the treatment of chronic wounds. This is in part because wound healing is a very complex process involving many phases that must occur sequentially and in a timely manner. Furthermore, models that fully mimic human chronic wounds have not been developed. In this review, we assess various models currently being used to study the biology of impaired healing and chronic non-healing wounds. Among them, this paper also highlights one model which shows significant promise; this model uses aged and obese db/db-/- mice and the chronic wounds that develop show characteristics of human chronic wounds that include increased oxidative stress, chronic inflammation, damaged microvasculature, abnormal collagen matrix deposition, a lack of re-epithelialization, and the spontaneous development of multi-bacterial biofilm. We also discuss how important it is that we continue to develop chronic wound models that more closely mimic those of humans and that can be used to test potential treatments to heal chronic wounds.

Abstract 3556: A pan-cancer ex vivo drug screen database for next-generation pharmacogenomics and functional precision oncology

Abstract Large-scale drug screens across thousands of drugs and biological models enable pan-cancer analyses that can reveal novel insights into therapeutic response and resistance. Existing databases, such as the Cancer Dependency Map only screen established 2D cell lines, which are known to poorly translate to patients. Ex vivo models such as patient-derived cells, spheroids, and organoids, have consistently demonstrated superior translatability from drug screening responses as they better replicate the original tumor. However, the cost, complexity, and difficulty of developing and screening ex vivo models has thus far prevented the establishment of any large-scale database comparable to existing cell line databases. We present the pan-preclinical (PPC) project, a large pan-cancer database of ex vivo drug responses comprising 2.1M raw dose-response measurements with curve-level summary statistics and metadata. The PPC database is assembled from over 30 previously published ex vivo studies and contains 2,880 ex vivo models spanning 106 cancer subtypes tested against subsets of 3,023 drugs. Compounds are harmonized across studies and paired with detailed chemoinformatics derived from PubChem. Each ex vivo sample is matched to an underlying patient and paired, where available, with genomics, transcriptomics, and clinical information such as sex, treatment history, and overall survival. Two thirds of the underlying studies did not originally make the underlying drug screening data available at publication time, making the PPC database a novel resource beyond data integration. Results: To investigate the power of the PPC database, we developed a preliminary computational modeling and statistical analysis pipeline. A hierarchical Bayesian factor model was developed to estimate dose-response curves, remove batch effects across studies, and impute missing drugs. Low-dimensional projections of ex vivo drug responses show biologically meaningful clustering. Statistical analyses of both observed and imputed drug responses recapitulate standard of care drugs as significantly effective across several cancer types. Overall, we envision the PPC project laying the foundation for the next generation of large-scale pharmacogenomic analyses and aiding in the coming era of functional precision oncology. Citation Format: Anneliese Markus, Karl Pichotta, Jeffrey Quinn, Jessica White, Christopher Tosh, Jinrui Liu, Erin Coyne, Wesley Tansey. A pan-cancer ex vivo drug screen database for next-generation pharmacogenomics and functional precision oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3556.

ScDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases.

Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).

Inventory planning for self-serve pharmacy kiosks: A fill rate maximization approach

In recent years, self-serve kiosks have become increasingly popular due to their 24/7 availability and lower setup and operational costs compared to traditional brick-and-mortar stores. However, the inventory planning for pharmacy kiosks is a challenge due to its limited capacity to store thousands of drugs ordered in various quantities, each with low and sporadic drug demand. In this work, we model the pharmacy kiosk inventory planning problem as a capacitated multiproduct newsvendor problem under fill rate maximization objective. We present a data-driven robust optimization framework where product demand lies within an uncertainty set generated from product clustering. A column-and-constraint generation based solution approach is proposed to solve industry scale instances. The proposed robust framework is tested on actual pharmacy sales data and randomly generated instances with 2000 products. The robust solutions outperform scenario-based stochastic solutions with an increase in out-of-sample fill rate of 5.8%, on average, and of up to 17%. Comparative analysis with profit objective reveals that the fill rate objective results in 17% higher out-of-sample fill rate by compromising 20% in profits, on average.

Electrostatically Cross-Linked Bioinks for Jetting-Based Bioprinting of 3D Cell Cultures.

It has been acknowledged that thousands of drugs that passed two-dimensional (2D) cell culture models and animal studies often fail when entering human clinical trials. Despite the significant development of three-dimensional (3D) models, developing a high-throughput model that can be reproducible on a scale remains challenging. One of the main challenges is precise cell deposition and the formation of a controllable number of spheroids to achieve more reproducible results for drug discovery and treatment applications. Furthermore, when transitioning from manually generated structures to 3D bioprinted structures, the choice of material is limited due to restrictions on materials that are applicable with bioprinters. Herein, we have shown the capability of a fast-cross-linking bioink that can be used to create a single spheroid with varying diameters (660, 1100, and 1340 μm) in a high-throughput manner using a commercialized drop-on-demand bioprinter. Throughout this work, we evaluate the physical properties of printable ink with and without cells, printing optimization, cytocompatibility, cell sedimentation, and homogeneity in ink during the printing process. This work showcases the importance of ink characterization to determine printability and precise cell deposition. The knowledge gained from this work will accelerate the development of next-generation inks compatible with a drop-on-demand 3D bioprinter for various applications such as precision models to mimic diseases, toxicity tests, and the drug development process.

Digital Twins for Predictive, Preventive Personalized, and Participatory Treatment of Immune-Mediated Diseases.

Digital twins are computational models of complex systems, which aim to understand and optimize those systems more effectively than would be possible in real life. Ideally, digital twins can be translated to individual patients, to characterize and computationally treat their diseases with thousands of drugs, to select the drug or drugs that cure the patients. The background problem is that many patients do not respond adequately to drug treatment. This problem reflects a wide gap between the complexity of diseases and clinical practice. Each disease may involve altered interactions between thousands of genes that vary between different cell types in different organs. To our knowledge, these altered interactions have not been characterized on a genome-, cellulome-, and organ-wide scale in any disease. Thus, clinical translation of the digital twin ideal for predictive, preventive, personalized and participatory treatment involves formidable challenges, which are close to the limits of, or beyond today's technologies. Here, I discuss recent developments and challenges in relation to that ideal focusing on immune-mediated inflammatory diseases, as well as examples from other diseases.

ASSOCIATION OF ATTITUDE REGARDING EMERGENCY CONTRACEPTION WITH SOCIO-DEMOGRAPHIC VARIABLES

Thousands of drugs have been developed throughout the history of medicine, but only one hasbeen influential enough to earn the title simply, the pill. Introduced in May 1950, the oral contraceptive pill is a medical innovation that has dramatically changed generations. Women gained incredible freedom and reproductive autonomy. Birth control pill separated sexual practices from conception, forcing social, political, and religious considerations to be reconsidered and reconsidered. Quantitative Research approach was adopted in this study. A non-experimental research approach and an exploratory research design was used to assess the knowledge and attitude of staff nurses regarding emergency contraception and their relationship with selected variables i.e. age, religion, course, ward, type of family, duration of marriage, health agency facilities and source of information.

Reducing Medication Errors: Rethinking Prescribing Drugs in Standardized Units over Quantities of Ingredients

The prescribers need to memorize the dose and strength of thousands of drugs often with similar sounding names but differing actions, by their weight (strengths), which could lead to significant errors in medication. When prescribing, instead of using amounts in milligrams, a “standard unit” - a universal standard adult dose unit- concept can be employed to minimize such instances of erroneous recollection of dosages. Prescribers would not essentially need to know the composition and actual amount of active pharmaceutical ingredients in prescribed drugs if such a standardized unit prescribing system could be adopted. They could simply resort to prescribing the required number of units of medication instead. The standard adult dose of any drug could be referred to as one adult unit regardless of the amounts by weight. Only drug manufacturers and research scientists would need to know the actual strengths of the medications in milligrams of what would be referred to as a standard adult unit. Implementation of the unit system, along with the incorporation of electronic prescribing, could allow for simple data entry, and easier corrections for lean body weight/BMI, renal function, age, pregnancy, etc., leading to improvements in patient safety and optimal dosage achieved, eliminating difficult calculations. Moreover, a parallel paediatric unit could also be developed. The conceptual study to arouse an interest cannot address all issues and details relating to actual implementations.

Euro-BioImaging – Interdisciplinary research infrastructure bringing together communities and imaging facilities to support excellent research

Euro-BioImaging – Interdisciplinary research infrastructure bringing together communities and imaging facilities to support excellent research

High-Throughput Chemical Screening and Structure-Based Models to Predict hERG Inhibition.

Simple SummaryCardiovascular disease is the leading cause of death for people of most ethnicities in the United States. The human ether-a-go-go-related gene (hERG) potassium channel plays a pivotal role in cardiac rhythm regulation, and cardiotoxicity associated with hERG inhibition by drug molecules and environmental chemicals is a major public health concern. An evaluation of the effect of environmental chemicals on hERG channel function can help inform the potential public health risks of these compounds. To assess the cardiotoxic effect of diverse drugs and environmental compounds, the Tox21 federal research program has screened a collection of 9667 chemicals for inhibitory activity against the hERG channel. A set of molecular descriptors covering physicochemical and structural properties of chemicals, self-organizing maps, and hierarchical clustering were applied to characterize the chemicals inhibiting hERG. Machine learning approaches were applied to build robust statistical models that can predict the probability of any new chemical to cause cardiotoxicity via this mechanism.Chemical inhibition of the human ether-a -go-go-related gene (hERG) potassium channel leads to a prolonged QT interval that can contribute to severe cardiotoxicity. The adverse effects of hERG inhibition are one of the principal causes of drug attrition in clinical and pre-clinical development. Preliminary studies have demonstrated that a wide range of environmental chemicals and toxicants may also inhibit the hERG channel and contribute to the pathophysiology of cardiovascular (CV) diseases. As part of the US federal Tox21 program, the National Center for Advancing Translational Science (NCATS) applied a quantitative high throughput screening (qHTS) approach to screen the Tox21 library of 10,000 compounds (~7871 unique chemicals) at 14 concentrations in triplicate to identify chemicals perturbing hERG activity in the U2OS cell line thallium flux assay platform. The qHTS cell-based thallium influx assay provided a robust and reliable dataset to evaluate the ability of thousands of drugs and environmental chemicals to inhibit hERG channel protein, and the use of chemical structure-based clustering and chemotype enrichment analysis facilitated the identification of molecular features that are likely responsible for the observed hERG activity. We employed several machine-learning approaches to develop QSAR prediction models for the assessment of hERG liabilities for drug-like and environmental chemicals. The training set was compiled by integrating hERG bioactivity data from the ChEMBL database with the Tox21 qHTS thallium flux assay data. The best results were obtained with the random forest method (~92.6% balanced accuracy). The data and scripts used to generate hERG prediction models are provided in an open-access format as key in vitro and in silico tools that can be applied in a translational toxicology pipeline for drug development and environmental chemical screening.

The need for a multi-level drug targeting strategy to curb the COVID-19 pandemic.

Thousands of drugs, nutraceuticals and their combinations can be used to select candidate therapeutics for targeting SARS-CoV-2 and its symptoms in order to curb COVID-19. A comprehensive, multi-level strategy against COVID-19 should include drug targeting of biomolecules and biochemical pathways involved in the prevention and proliferation of the infection, and the fatal or serious symptoms following infection. Several drugs are routinely used in the treatment of different categories of seriously ill COVID-19 patients including tocilizumab, remdesivir and dexamethasone. The current risk/benefit assessment supports the emergency testing and approval of more drugs. The process for new drug selection could be based on the identification of one drug for one target, or of a multi-potent drug for many targets and drug combinations for one or more targets, that can cause a substantial reduction in the high mortality rate of COVID-19. Several drugs have been identified that can fit this potential role by targeting different stages of COVID-19 including baricitinib, molnupiravir and PF-07321332/ritonavir and also the combination of deferiprone with N-acetylcysteine for inhibiting the vicious circle of oxidative stress toxicity and endothelial cell damage. Most of these drugs are expected to be effective against all the SARS-CoV-2 variants including Omicron (B.1.1.529) and also the associated COVID-19 complications.

Expert Consensus Statement from the American Society of Echocardiography on Hypersensitivity Reactions to Ultrasound Enhancing Agents in Patients with Allergy to Polyethylene Glycol

Expert Consensus Statement from the American Society of Echocardiography on Hypersensitivity Reactions to Ultrasound Enhancing Agents in Patients with Allergy to Polyethylene Glycol

О возможной роли некоторых психотропных препаратов в терапии COVID-19 (краткий обзор)

Бушующая ныне в мире пандемия COVID-19 и связанный как с ней самой, так и с карантинными ограничениями и другими мерами борьбы с ней выраженный дистресс привели к резкой актуализации проблемы психических расстройств в человеческой популяции, прежде всего за счет повышения частоты встречаемости депрессивных, тревожных и связанных со стрессом расстройств. Это значительно увеличило количество тех людей в мире, которые нуждаются в приеме тех или иных психотропных препаратов. С другой же стороны, отсутствие в настоящее время эффективных специфических средств для лечения инфекции COVID-19 поставило перед исследователями и практическими врачами задачу найти среди уже зарегистрированных лекарств потенциальные кандидаты на перепрофилирование по новому показанию — для лечения этой инфекции. Среди подвергнутых скринингу на потенциальную эффективность в отношении нового вируса SARS-CoV-2 тысяч зарегистрированных в мире лекарств, разумеется, были и психотропные препараты. Некоторые из них действительно оказались перспективными кандидатами на такое перепрофилирование. В данном кратком обзоре мы показываем, что потенциальными кандидатами на перепрофилирование для лечения COVID-19 являются несколько классов психотропных лекарств: лиганды сигма-1 рецепторов (прежде всего — флувоксамин, но, возможно, и другие, в том числе инновационный российский анксиолитик фабомотизол (афобазол)), мелатонинергические агонисты (экзогенный мелатонин и, возможно, также агомелатин (вальдоксан) и фабомотизол (афобазол)), а также пептидные биорегуляторы с ноотропными, антидепрессивными, противотревожными и иммуномодулирующими свойствами, такие, как, например, ноопепт, селанк.

Digital twins will revolutionise healthcare

IN THE FUTURE, a digital twin of our genetic profile could be created for every one of us at birth. If we get ill, this 'virtual self' could be computationally treated with hundreds of thousands of drugs to ensure the most effective medication is chosen. It could also be used to discover which medical conditions we may or not be predisposed to, enabling us to prevent major diseases before they take effect.

Comprehensive UHPLC-HR-MSE screening workflow optimized for use in routine laboratory medicine: Four workflows in one analytical method

A comprehensive HR-MS screening can be used to identify thousands of drugs from a single analysis, which makes it a valuable tool for broad-scope component-resolved toxicological analysis. However, it is common practice in clinical toxicology to perform restricted data analysis to avoid examining and/or reporting data not requested for examination. In this study, a HR-MS screening workflow was developed to allow a comprehensive toxicological evaluation, but also restricted and levelled data analysis to fit in a clinical setting.Following precipitation and reconstitution, samples were injected on an UHPLC-HR-MS and data were analyzed with the data processing software UNIFI. Analytical validation of 38 selected drugs of abuse (DoA), included determination of matrix effect, recovery, process efficiency, and limit of identification (LOI). The method was tested on 49 authentic samples and matrix-matched ranges of calibrators for 95 drugs. The LOI ranged from 0.3 to 1426.7 ng mL−1 for most analytes which was within expected concentration range for authentic samples with THC-COOH (>1722.0 ng mL−1) and morphine (1426.7 ng mL−1) as notable exceptions. Four individual screening workflows were developed: 1) a targeted workflow to serve as orthogonal identification of the 38 selected DOAs from another in-house method, 2) a general toxicology workflow, 3) an extended toxicology workflow including new psychoactive substances (NPS), and 4) a workflow for NPS based on the online HighResNPS library.Our study presents a comprehensive LC-HR-MS toxicology screening method optimized for laboratory medicine. The workflow allows for levelled data reviewing when requested without compromising the ability to perform full toxicological analyses.

Drug Repurposing: A Quick and Easy Way of Finding New Medicines

Every year, we face infectious outbreaks produced by harmful microorganisms commonly called superbugs. Often, there is not enough time to find new treatments to cure infected patients. On average, it takes a decade to develop a promising new drug to the point where it can be used on patients! Also, many of the compounds that we identify in the laboratory as promising anti-infectives are not useful for treating patients, mainly because they have unexpected, unsafe side effects. However, researchers have already found thousands of drugs that can safely be used to treat specific diseases. These compounds are approved to be used on patients for particular illnesses, but many of them have not been tested to treat any other diseases. Some of these drugs could be repurposed to treat infections caused by new superbugs. In this article, we summarize some exciting strategies used to find new anti-infectives by drug repurposing.

Recent advances made in the synthesis of small drug molecules for clinical applications: An insight

Over decades dependency of humans on the drugs has become indispensable and irreplaceable. Thus, each year many new drugs are licensed. Nonetheless, drugs undergo rigorous testing and analysis to be available globally in economic price for the suitability of patients with different age and physiological conditions. The testing of drugs include phase I clinical trial using small group of 20–100 healthy volunteers for safety, pharmacology and efficacy; phase II clinical trial using 100–500 volunteer patients to optimize effective dose, dose interval, safety analysis and mode of delivery such as oral or intravenous; phase III clinical trial using 1000–5000 in a larger population of patients globally at different international places to collect sufficient safety and efficacy data for patenting and licencing. Moreover, thousands of drugs fail to achieve these objectives. Therefore, this mini-review intends to critically examine and assimilate the clinical applications of selected complex repurposed small drug molecules which are in different phase of trials for treating viral infection including complications due to COVID-19: (a) Remdesivir, (b) Galidesivir, (c) Favipiravir, (d) Baricitinib, and (e) Baloxavir.

Pharmacological Properties and Health Benefits of Eugenol: A Comprehensive Review.

The biologically active phytochemicals are sourced from edible and medicinally important plants and are important molecules being used for the formulation of thousands of drugs. These phytochemicals have great benefits against many ailments particularly the inflammatory diseases or oxidative stress-mediated chronic diseases. Eugenol (EUG) is a versatile naturally occurring molecule as phenolic monoterpenoid and frequently found in essential oils in a wide range of plant species. EUG bears huge industrial applications particularly in pharmaceutics, dentistry, flavoring of foods, agriculture, and cosmeceutics. It is being focused recently due to its great potential in preventing several chronic conditions. The World Health Organization (WHO) has declared EUG as a nonmutant and generally recognized as safe (GRAS) molecule. The available literature about pharmacological activities of EUG shows remarkable anti-inflammatory, antioxidant, analgesic, and antimicrobial properties and has a significant effect on human health. The current manuscript summarizes the pharmacological characteristics of EUG and its potential health benefits.

Triazavirin might be the new hope to fight Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)

Since the beginning of the outbreak, a large number of clinical trials have been registered worldwide, and thousands of drugs have been investigated to face new health emergency of highly contagious COVID-19 caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Drug repurposing, i.e., utilizing an approved drug for a different indication, offers a time- and cost-efficient alternative for making new (relevant) therapies available to physicians and patients. Considering given strategy, many approved and investigational antiviral compounds, alone or in various relevant combinations, used in the past to fight Severe Acute Respiratory Syndrome Coronavirus-1, Middle East Respiratory Syndrome Coronavirus, Human Immunodeficiency Virus type 1, or Influenza viruses are being evaluated against the SARS-CoV-2. Triazavirin (TZV), a non-toxic broad--spectrum antiviral compound, is efficient against various strains of the Influenza A virus (Influenza Virus A, Orthomyxoviridae), i.e., swine flu (H1N1, or H3N2), avian influenza (H5N1, H5N2, H9N2, or highly pathogenic H7N3 strain), Influenza B virus (Influenza Virus B, Orthomyxoviridae), Respiratory Syncytial Virus (Orthopneumovirus, Pneumoviridae), Tick-Borne Encephalitis Virus (known as Forest-Spring Encephalitis Virus; Flavivirus, Flaviviridae), West Nile Virus (Flavivirus, Flavaviridae), Rift Valley Fever Virus (Phlebovirus, Bunyaviridae), and Herpes viruses (Simplexviruses, Herpesviridae) as well. In regard to COVID-19, the molecule probably reduced inflammatory reactions, thus limiting the damage to vital organs and reducing the need for therapeutic support, respectively. In addition, in silico computational methods indicated relatively satisfactory binding affinities of the TZV ligand to both structural (E)- and (S)-proteins, non-structural 3-chymotrypsin-like protease (3-CLpro) of SARS-CoV-2 as well as human angiotensin-I converting enzyme-2 (ACE-2). The interactions between TZV and given viral structures or the ACE-2 receptor for SARS-CoV-2 might effectively block both the entry of the pathogen into a host cell and its replication. Promising treatment patterns of COVID-19 positive patients might be also based on a suitable combination of a membrane fusion inhibitor (umifenovir, for example) with viral RNA synthesis and replication inhibitor (TZV).