Computationally repurposed drugs and natural products against RNA dependent RNA polymerase as potential COVID-19 therapies

Drug-disease association prediction with literature based multi-feature fusion

Introduction: Exploring the potential efficacy of a drug is a valid approach for drug development with shorter development times and lower costs. Recently, several computational drug repositioning methods have been introduced to learn multi-features for potential association prediction. However, fully leveraging the vast amount of information in the scientific literature to enhance drug-disease association prediction is a great challenge. Methods: We constructed a drug-disease association prediction method called Literature Based Multi-Feature Fusion (LBMFF), which effectively integrated known drugs, diseases, side effects and target associations from public databases as well as literature semantic features. Specifically, a pre-training and fine-tuning BERT model was introduced to extract literature semantic information for similarity assessment. Then, we revealed drug and disease embeddings from the constructed fusion similarity matrix by a graph convolutional network with an attention mechanism. Results: LBMFF achieved superior performance in drug-disease association prediction with an AUC value of 0.8818 and an AUPR value of 0.5916. Discussion: LBMFF achieved relative improvements of 31.67% and 16.09%, respectively, over the second-best results, compared to single feature methods and seven existing state-of-the-art prediction methods on the same test datasets. Meanwhile, case studies have verified that LBMFF can discover new associations to accelerate drug development. The proposed benchmark dataset and source code are available at: https://github.com/kang-hongyu/LBMFF.

Identifying SARS-CoV-2 Drugs Binding to the Spike Fatty Acid Binding Pocket Using In Silico Docking and Molecular Dynamics

Drugs against novel targets are needed to treat COVID-19 patients, especially as SARS-CoV-2 is capable of rapid mutation. Structure-based de novo drug design and repurposing of drugs and natural products is a rational approach to discovering potentially effective therapies. These in silico simulations can quickly identify existing drugs with known safety profiles that can be repurposed for COVID-19 treatment. Here, we employ the newly identified spike protein free fatty acid binding pocket structure to identify repurposing candidates as potential SARS-CoV-2 therapies. Using a validated docking and molecular dynamics protocol effective at identifying repurposing candidates inhibiting other SARS-CoV-2 molecular targets, this study provides novel insights into the SARS-CoV-2 spike protein and its potential regulation by endogenous hormones and drugs. Some of the predicted repurposing candidates have already been demonstrated experimentally to inhibit SARS-CoV-2 activity, but most of the candidate drugs have yet to be tested for activity against the virus. We also elucidated a rationale for the effects of steroid and sex hormones and some vitamins on SARS-CoV-2 infection and COVID-19 recovery.

A hypothesis on designing strategy of effective RdRp inhibitors for the treatment of SARS-CoV-2

Vaccines are used as one of the major weapons for the eradication of pandemic. However, the rise of different variants of the SARS-CoV-2 virus is creating doubts regarding the end of the pandemic. Hence, there is an urgent need to develop more drug candidates which can be useful for the treatment of COVID-19. In the present research for the scientific hypothesis, emphasis was given on the direct antiviral therapy available for the treatment of COVID-19. In lieu of this, the available molecular targets which include Severe Acute Respiratory Syndrome Chymotrypsin-like Protease (SARS-3CLpro), Papain-Like Cysteine Protease (PLpro), and RNA-Dependent RNA Polymerase (RdRp) were explored. As per the current scientific reports and literature, among all the available molecular targets, RNA-Dependent RNA Polymerase (RdRp) was found to be a crucial molecular target for the treatment of COVID-19. Most of the inhibitors which are reported against this target consisted of the free amine group and carbonyl group which might be playing an important role in the binding interaction with the RdRp protein. Among all the reported RdRp inhibitors, remdesivir, favipiravir, and molnupiravir were found to be the most promising drugs against COVID-19. Overall, the structural features of this RNA-Dependent RNA Polymerase (RdRp) inhibitors proved the importance of pyrrolo-triazine and pyrimidine scaffolds. Previous computational models of these drug molecules indicated that substitution with the polar functional group, hydrogen bond donor, and electronegative atoms on these scaffolds may increase the activity against the RdRp protein. Hence, in line with the proposed hypothesis, in the present research work for the evaluation of the hypothesis, new molecules were designed from the pyrrolo-triazine and pyrimidine scaffolds. Further, molecular docking and MD simulation studies were performed with these designed molecules. All these designed molecules (DM-1, DM-2, and DM-3) showed the results as per the proposed hypothesis. Among all the designed molecules, DM-1 showed promising results against the RdRp protein of SARS-CoV-2. In the future, these structural features can be used for the development of new RdRp inhibitors with improved activity. Also, in the future lead compound DM-1 can be explored against the RdRp protein for the treatment of COVID-19.

Potential COVID-19 Therapies from Computational Repurposing of Drugs and Natural Products against the SARS-CoV-2 Helicase

Repurposing of existing drugs is a rapid way to find potential new treatments for SARS-CoV-2. Here, we applied a virtual screening approach using Autodock Vina and molecular dynamic simulation in tandem to screen and calculate binding energies of repurposed drugs against the SARS-CoV-2 helicase protein (non-structural protein nsp13). Amongst the top hits from our study were antivirals, antihistamines, and antipsychotics, plus a range of other drugs. Approximately 30% of our top 87 hits had published evidence indicating in vivo or in vitro SARS-CoV-2 activity. Top hits not previously reported to have SARS-CoV-2 activity included the antiviral agents, cabotegravir and RSV-604; the NK1 antagonist, aprepitant; the trypanocidal drug, aminoquinuride; the analgesic, antrafenine; the anticancer intercalator, epirubicin; the antihistamine, fexofenadine; and the anticoagulant, dicoumarol. These hits from our in silico SARS-CoV-2 helicase screen warrant further testing as potential COVID-19 treatments.

Non-nucleoside structured compounds with antiviral activity—past 10 years (2010–2020)

Nucleosides and their derivatives are a well-known and well-described class of compounds with antiviral activity. Currently, in the era of the COVID-19 pandemic, scientists are also looking for compounds not related to nucleosides with antiviral properties. This review aims to provide an overview of selected synthetic antiviral agents not associated to nucleosides developed against human viruses and introduced to preclinical and clinical trials as well as drugs approved for antiviral therapy over the last 10 years. The article describes for the first time the wide classification of such antiviral drugs and drug candidates and briefly summarizes the biological target and clinical applications of the compounds. The described compounds are arranged according to the antiviral mechanism of action. Knowledge of the drug's activity toward specific molecular targets may be the key to researching new antiviral compounds and repositioning drugs already approved for clinical use. The paper also briefly discusses the future directions of antiviral therapy. The described examples of antiviral compounds can be helpful for further drug development.