Systematic Search for SARS-CoV-2 Main Protease Inhibitors for Drug Repurposing: Ethacrynic Acid as a Potential Drug

MMFA-DTA: Multimodal Feature Attention Fusion Network for Drug-Target Affinity Prediction for Drug Repurposing Against SARS-CoV-2

The continuous emergence of novel infectious diseases poses a significant threat to global public health security, necessitating the development of small-molecule inhibitors that directly target pathogens. The RNA-dependent RNA polymerase (RdRp) and main protease (Mpro) of SARS-CoV-2 have been validated as potential key antiviral drug targets for the treatment of COVID-19. However, the conventional new drug R&D cycle takes 10-15 years, failing to meet the urgent needs during epidemics. Here, we propose a general multimodal deep learning framework for drug repurposing, MMFA-DTA, to enable rapid virtual screening of known drugs and significantly improve discovery efficiency. By extracting graph topological and sequence features from both small molecules and proteins, we design attention mechanisms to achieve dynamic fusion across modalities. Results demonstrate the superior performance of MMFA-DTA in drug-target affinity prediction over several state-of-the-art baseline methods on Davis and KIBA data sets, validating the benefits of heterogeneous information integration for representation learning and interaction modeling. Further fine-tuning on COVID-19-relevant bioactivity data enhances model predictions for critical SARS-CoV-2 enzymes. Case studies screening the FDA-approved drug library successfully identify etacrynic acid as the potential lead compound against both RdRp and Mpro. Molecular dynamics simulations further confirm the stability and binding affinity of etacrynic acid to these targets. This study proves the great potential and advantages of deep learning and drug repurposing strategies in supporting antiviral drug discovery. The proposed general and rapid response computational framework holds significance for preparedness against future public health events.

Reconstruction of the unbinding pathways of new inhibitors of the SARS-CoV-2 papain-like protease using molecular dynamics simulation

Developing novel antiviral drugs against the SARS-CoV-2 virus and COVID-19 disease is imperative as the vaccines may not offer absolute protection. PLpro plays a crucial role in the viral life cycle, making it an attractive target for drug development. Several PLpro inhibitors have been developed, and their 3D structures in complex with PLpro are available. In this work, we employed Supervised Molecular Dynamics (SuMD), a specific Unbiased Molecular Dynamics (UMD) method, to investigate unbinding pathways of the novel inhibitors of PLpro (PDB IDs: 7LBR, 7RZC, 7SDR and 7E35) and GRL0617 (PDB ID: 7JRN) as a reference. We conducted three simulations for each ligand and achieved unbinding events in the nanosecond timescale in all simulations. We found that unbinding events are commonly affected by altering the conformation of the BL2 loop, which is caused by the natural fluctuations of the loop that are required to trap the substrate and throw out the product. BL2 loop is crucial for keeping the ligand and unbinding and acts as a double-edged sword. Any inhibitor designed to be effective must prevent the loop's natural fluctuations. We perceived that increasing ligands interactions with the binding pocket interior and the BL2 loop will help prevent natural fluctuation of the BL2 loop, Although the interactions with the binding pocket's inner side are more critical than the BL2 loop. These findings may be helpful in developing more potent inhibitors against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Development of a new experimental NMR strategy for covalent cysteine protease inhibitors screening: toward enhanced drug discovery

In the development of antiviral drugs, proteases and polymerases are among the most important targets. Cysteine proteases, also known as thiol proteases, catalyze the degradation of proteins by cleaving peptide bonds using the nucleophilic thiol group of cysteine. As part of our research, we are examining how cysteine, an essential amino acid found in the active site of the main protease (Mpro) enzyme in SARS-CoV-2, interacts with electrophilic groups present in ethacrynic acid (EA) and compounds 4, 6, and 8 to form sulfur-carbon bonds. Nuclear magnetic resonance (NMR) spectroscopy was used to monitor the reaction rate between cysteine and Michael acceptors. We found that the inhibitory activity of these compounds towards Mpro is correlated to their chemical reactivity toward cysteine. This approach may serve as a valuable tool in drug development for detecting potential covalent inhibitors of Mpro and other cysteine proteases.

The multifaceted role of proteases and modern analytical methods for investigation of their catalytic activity

We discuss the diverse functions of proteases in the context of their biotechnological and medical significance, as well as analytical approaches used to determine the functional activity of these enzymes. An insight into modern approaches to studying the kinetics and specificity of proteases, based on spectral (absorption, fluorescence), mass spectrometric, immunological, calorimetric, and electrochemical methods of analysis is given. We also examine in detail electrochemical systems for determining the activity and specificity of proteases. Particular attention is given to exploring innovative electrochemical systems based on the detection of the electrochemical oxidation signal of amino acid residues, thereby eliminating the need for extra redox labels in the process of peptide synthesis. In the review, we highlight the main prospects for the further development of electrochemical systems for the study of biotechnologically and medically significant proteases, which will enable the miniaturization of the analytical process for determining the catalytic activity of these enzymes.

Combination of Ethacrynic Acid and ATRA Triggers Differentiation and/or Apoptosis of Acute Myeloid Leukemia Cells through ROS

BACKGROUND AND OBJECTIVE

All-trans retinoic acid (ATRA), an effective differentiation inducer, has been applied clinically to treat acute promyelocytic leukemia (APL). Unfortunately, it is not as potent in other kinds of acute myeloid leukemia (AML). Ethacrynic acid (EA), a classical powerful diuretic, can increase reactive oxygen species (ROS) contents, which can assist ATRA in inducing differentiation in AML cells. Here, we investigated the effect of EA combined with ATRA (EA+RA) on some AML cells except APL.

METHODS

Apoptosis and differentiation were determined by morphology, cell viability, Annexin-V assay and CD11c expression. Western blot analysis and the detection of ROS and mitochondrial transmembrane potentials (MMP) were used to investigate the mechanisms.

RESULTS

AML cells exhibited differentiation and/or apoptosis after EA+RA treatment. EA+RA increased the intracellular ROS contents. EA+RA-induced apoptosis was accompanied by MMP attenuation and caspase-3/7 activation. EA+RA-induced differentiation was along with MEK/ERK and Akt activation and increased expression of PU.1, CCAAT/enhancer-binding protein β (C/EBPβ) and C/EBPε. N-acetyl-L-cysteine (NAC), an antioxidant, thoroughly reduced EA+RA-increased ROS, and also inhibited MMP attenuation, the activation of caspase- 3/7, MEK/ERK and Akt pathways, the elevation of PU.1 and C/EBPs, and apoptosis and differentiation. However, MEK or PI3K specific inhibitors only suppressed EA+RA-triggered differentiation and the elevation of PU.1 and C/EBPs, but not ROS levels.

CONCLUSION

EA+RA induced cell apoptosis through ROS dependent MMP attenuation and caspase 3/7 activation while inducing differentiation by ROS-MEK/ERK-PU.1/C/EBPs and ROS-Akt-PU.1/C/EBPs pathways. In summary, it may provide innovative ATRA-based combination therapy strategies for AML patients via ROS.

Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro

Introduction: The identification of chemical compounds that interfere with SARS-CoV-2 replication continues to be a priority in several academic and pharmaceutical laboratories. Computational tools and approaches have the power to integrate, process and analyze multiple data in a short time. However, these initiatives may yield unrealistic results if the applied models are not inferred from reliable data and the resulting predictions are not confirmed by experimental evidence. Methods: We undertook a drug discovery campaign against the essential major protease (MPro) from SARS-CoV-2, which relied on an in silico search strategy -performed in a large and diverse chemolibrary- complemented by experimental validation. The computational method comprises a recently reported ligand-based approach developed upon refinement/learning cycles, and structure-based approximations. Search models were applied to both retrospective (in silico) and prospective (experimentally confirmed) screening. Results: The first generation of ligand-based models were fed by data, which to a great extent, had not been published in peer-reviewed articles. The first screening campaign performed with 188 compounds (46 in silico hits and 100 analogues, and 40 unrelated compounds: flavonols and pyrazoles) yielded three hits against MPro (IC₅₀ ≤ 25 μM): two analogues of in silico hits (one glycoside and one benzo-thiazol) and one flavonol. A second generation of ligand-based models was developed based on this negative information and newly published peer-reviewed data for MPro inhibitors. This led to 43 new hit candidates belonging to different chemical families. From 45 compounds (28 in silico hits and 17 related analogues) tested in the second screening campaign, eight inhibited MPro with IC₅₀ = 0.12-20 μM and five of them also impaired the proliferation of SARS-CoV-2 in Vero cells (EC₅₀ 7-45 μM). Discussion: Our study provides an example of a virtuous loop between computational and experimental approaches applied to target-focused drug discovery against a major and global pathogen, reaffirming the well-known "garbage in, garbage out" machine learning principle.

AI-Aided Search for New HIV-1 Protease Ligands

The availability of drugs capable of blocking the replication of microorganisms has been one of the greatest triumphs in the history of medicine, but the emergence of an ever-increasing number of resistant strains poses a serious problem for the treatment of infectious diseases. The search for new potential ligands for proteins involved in the life cycle of pathogens is, therefore, an extremely important research field today. In this work, we have considered the HIV-1 protease, one of the main targets for AIDS therapy. Several drugs are used today in clinical practice whose mechanism of action is based on the inhibition of this enzyme, but after years of use, even these molecules are beginning to be interested by resistance phenomena. We used a simple artificial intelligence system for the initial screening of a data set of potential ligands. These results were validated by docking and molecular dynamics, leading to the identification of a potential new ligand of the enzyme which does not belong to any known class of HIV-1 protease inhibitors. The computational protocol used in this work is simple and does not require large computational power. Furthermore, the availability of a large number of structural information on viral proteins and the presence of numerous experimental data on their ligands, with which it is possible to compare the results obtained with computational methods, make this research field the ideal terrain for the application of these new computational techniques.

Mortality, Intensive Care Unit Admission, and Intubation among Hospitalized Patients with COVID-19: A One-Year Retrospective Study in Jordan

COVID-19 is a public health crisis that has caused numerous deaths, necessitated an increased number of hospital admissions, and led to extended inpatient stays. This study aimed to identify the factors associated with COVID-19 mortality, intensive care unit admission, intubation, and length of hospital stay among Jordanian patients. This was a one-year retrospective study of 745 COVID-19 patients admitted to Jordan University Hospital. Data regarding the patients’ demographics, clinical and co-morbid conditions, imaging, laboratory parameters, mortality, intensive care unit admission (ICU), and intubation were collected from their medical records using a coding manual. The data revealed that the overall rates of COVID-19-related mortality, ICU admission, and invasive intubation were 23.0%, 28.3%, and 10.8%, respectively. Chronic kidney disease (CKD), troponin, lactate dehydrogenase (LDH), and O2 saturation <90% were significantly associated with the mortality rate. The variables that were significantly associated with ICU admission were heart failure and the use of remdesivir. However, O2 saturation <90% and gastrointestinal (GI) symptoms were the only variables associated with invasive intubation. The findings of this study suggest that study-related health outcomes can be used to predict the severity of COVID-19, and they can inform future research aiming to identify specific populations who are at a higher risk of COVID-19 complications.

Therapeutic dilemmas in addressing SARS-CoV-2 infection: Favipiravir versus Remdesivir

Coronavirus disease 2019 (COVID-19) represents an unmet clinical need, due to a high mortality rate, rapid mutation rate in the virus, increased chances of reinfection, lack of effectiveness of repurposed drugs and economic damage. COVID-19 pandemic has created an urgent need for effective molecules. Clinically proven efficacy and safety profiles have made favipiravir (FVP) and remdesivir (RDV) promising therapeutic options for use against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Even though both are prodrug molecules with an antiviral role based on a similar mechanism of action, differences in pharmacological, pharmacokinetic and pharmacotoxicological mechanisms have been identified. The present study aims to provide a comprehensive comparative assessment of FVP and RDV against SARS-CoV-2 infections, by centralizing medical data provided by significant literature and authorized clinical trials, focusing on the importance of a better understanding of the interactions between drug molecules and infectious agents in order to improve the global management of COVID-19 patients and to reduce the risk of antiviral resistance.

Telaprevir is a potential drug for repurposing against SARS-CoV-2: computational and in vitro studies

Drug repurposing is an important approach to the assignment of already approved drugs for new indications. This technique bypasses some steps in the traditional drug approval system, which saves time and lives in the case of pandemics. Direct acting antivirals (DAAs) have repeatedly repurposed from treating one virus to another. In this study, 16 FDA-approved hepatitis C virus (HCV) DAA drugs were studied to explore their activities against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) human and viral targets. Among the 16 HCV DAA drugs, telaprevir has shown the best in silico evidence to work on both indirect human targets (cathepsin L [CTSL] and human angiotensin-converting enzyme 2 [hACE2] receptor) and direct viral targets (main protease [Mpro]). Moreover, the docked poses of telaprevir inside both hACE2 and Mpro were subjected to additional molecular dynamics simulations monitored by calculating the binding free energy using MM-GBSA. In vitro analysis of telaprevir showed inhibition of SARS-CoV-2 replication in cell culture (IC50 = 11.552 μM, CC50 = 60.865 μM, and selectivity index = 5.27). Accordingly, based on the in silico studies and supported by the presented in vitro analysis, we suggest that telaprevir may be considered for therapeutic development against SARS-CoV-2.

SARS-CoV-2 Main Protease Active Site Ligands in the Human Metabolome

In late 2019, a global pandemic occurred. The causative agent was identified as a member of the Coronaviridae family, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we present an analysis on the substances identified in the human metabolome capable of binding the active site of the SARS-CoV-2 main protease (Mpro). The substances present in the human metabolome have both endogenous and exogenous origins. The aim of this research was to find molecules whose biochemical and toxicological profile was known that could be the starting point for the development of antiviral therapies. Our analysis revealed numerous metabolites-including xenobiotics-that bind this protease, which are essential to the lifecycle of the virus. Among these substances, silybin, a flavolignan compound and the main active component of silymarin, is particularly noteworthy. Silymarin is a standardized extract of milk thistle, Silybum marianum, and has been shown to exhibit antioxidant, hepatoprotective, antineoplastic, and antiviral activities. Our results-obtained in silico and in vitro-prove that silybin and silymarin, respectively, are able to inhibit Mpro, representing a possible food-derived natural compound that is useful as a therapeutic strategy against COVID-19.