Targeting SARS-CoV-2 Main Protease: A Successful Story Guided by an <i>In Silico</i> Drug Repurposing Approach

Ambrosio, Francesca Alessandra; Costa, Giosuè; Romeo, Isabella; Esposito, Francesca; Alkhatib, Mohammad; Salpini, Romina; Svicher, Valentina; Corona, Angela; Malune, Paolo; Tramontano, Enzo; Ceccherini‐Silberstein, Francesca; Alcaro, Stefano; Artese, Anna

doi:10.1021/acs.jcim.3c00282

Cited by 6 publications

(6 citation statements)

References 56 publications

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“…Furthermore, considering that this protein has no human homolog [8] and a high interspecific similarity among coronaviruses [9], it is evident that M pro is one of the most promising drug targets. Moreover, the extreme degree of conservation of the M pro observed in analyzing over 13.7 million SARS-CoV-2 variants' sequences reinforces the role of M pro as an appealing target for antiviral drugs, exhibiting broad-spectrum efficacy against all variants of SARS-CoV-2 [10].…”

Section: Introductionmentioning

confidence: 76%

New Thiazolidine-4-One Derivatives as SARS-CoV-2 Main Protease Inhibitors

Messore,

Malune,

Patacchini

et al. 2024

Pharmaceuticals

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It has been more than four years since the first report of SARS-CoV-2, and humankind has experienced a pandemic with an unprecedented impact. Moreover, the new variants have made the situation even worse. Among viral enzymes, the SARS-CoV-2 main protease (Mpro) has been deemed a promising drug target vs. COVID-19. Indeed, Mpro is a pivotal enzyme for viral replication, and it is highly conserved within coronaviruses. It showed a high extent of conservation of the protease residues essential to the enzymatic activity, emphasizing its potential as a drug target to develop wide-spectrum antiviral agents effective not only vs. SARS-CoV-2 variants but also against other coronaviruses. Even though the FDA-approved drug nirmatrelvir, a Mpro inhibitor, has boosted the antiviral therapy for the treatment of COVID-19, the drug shows several drawbacks that hinder its clinical application. Herein, we report the synthesis of new thiazolidine-4-one derivatives endowed with inhibitory potencies in the micromolar range against SARS-CoV-2 Mpro. In silico studies shed light on the key structural requirements responsible for binding to highly conserved enzymatic residues, showing that the thiazolidinone core acts as a mimetic of the Gln amino acid of the natural substrate and the central role of the nitro-substituted aromatic portion in establishing π-π stacking interactions with the catalytic His-41 residue.

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Section: Introductionmentioning

confidence: 76%

New Thiazolidine-4-One Derivatives as SARS-CoV-2 Main Protease Inhibitors

Messore,

Malune,

Patacchini

et al. 2024

Pharmaceuticals

Self Cite

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“…The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the coronavirus disease 2019 (COVID- 19) pandemic and continues to pose a threat to global public health. 1 According to the World Health Organization (WHO), as of August 14, 2023, there have been over 769 million confirmed cases of COVID-19 worldwide, including more than 287,000 on 14 August 2023, and more than 6.9 million deaths (https://covid19.who.int).…”

Section: Introductionmentioning

confidence: 99%

“…[14][15][16] Different from de novo drug design, drug repurposing uses existing drugs that were originally developed for different indications to screen drugs for new diseases. Because of the knowledge and understanding of an existing drug's safety, mechanisms of action, and pharmacokinetics, drug repurposing reduces the risks associated with drug development, and has shown promising results for the discovery of the main protease inhibitors, [17][18][19] especially when combined with the power of artificial intelligence approaches. 20 Machine learning is the approach to learn from the provided data, identify the patterns within the provided data, and make predictions on new data using what it has learned.…”

Section: Introductionmentioning

confidence: 99%

Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment

Liu,

Xu,

Guo

et al. 2023

Exp Biol Med (Maywood)

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The coronavirus disease 2019 (COVID-19) global pandemic resulted in millions of people becoming infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and close to seven million deaths worldwide. It is essential to further explore and design effective COVID-19 treatment drugs that target the main protease of SARS-CoV-2, a major target for COVID-19 drugs. In this study, machine learning was applied for predicting the SARS-CoV-2 main protease binding of Food and Drug Administration (FDA)-approved drugs to assist in the identification of potential repurposing candidates for COVID-19 treatment. Ligands bound to the SARS-CoV-2 main protease in the Protein Data Bank and compounds experimentally tested in SARS-CoV-2 main protease binding assays in the literature were curated. These chemicals were divided into training (516 chemicals) and testing (360 chemicals) data sets. To identify SARS-CoV-2 main protease binders as potential candidates for repurposing to treat COVID-19, 1188 FDA-approved drugs from the Liver Toxicity Knowledge Base were obtained. A random forest algorithm was used for constructing predictive models based on molecular descriptors calculated using Mold2 software. Model performance was evaluated using 100 iterations of fivefold cross-validations which resulted in 78.8% balanced accuracy. The random forest model that was constructed from the whole training dataset was used to predict SARS-CoV-2 main protease binding on the testing set and the FDA-approved drugs. Model applicability domain and prediction confidence on drugs predicted as the main protease binders discovered 10 FDA-approved drugs as potential candidates for repurposing to treat COVID-19. Our results demonstrate that machine learning is an efficient method for drug repurposing and, thus, may accelerate drug development targeting SARS-CoV-2.

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“…Due to the extensive accessibility of public biomedical databases, the virtual screening of DTIs can assist in identifying new associations between known drugs and their unknown off targets. Successful applications of the in silico methodology can be exemplified by the reuse of astemizole [ 16 ] and betrixaban [ 17 ]. Especially during the global spread of the highly infectious coronavirus disease 2019 (COVID-19) pandemic, the application of computational predictions of DTIs in drug repositioning could offer a promising solution [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…Successful applications of the in silico methodology can be exemplified by the reuse of astemizole [ 16 ] and betrixaban [ 17 ]. Especially during the global spread of the highly infectious coronavirus disease 2019 (COVID-19) pandemic, the application of computational predictions of DTIs in drug repositioning could offer a promising solution [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

AMMVF-DTI: A Novel Model Predicting Drug–Target Interactions Based on Attention Mechanism and Multi-View Fusion

Wang,

Zhou,

Chen

2023

IJMS

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Accurate identification of potential drug–target interactions (DTIs) is a crucial task in drug development and repositioning. Despite the remarkable progress achieved in recent years, improving the performance of DTI prediction still presents significant challenges. In this study, we propose a novel end-to-end deep learning model called AMMVF-DTI (attention mechanism and multi-view fusion), which leverages a multi-head self-attention mechanism to explore varying degrees of interaction between drugs and target proteins. More importantly, AMMVF-DTI extracts interactive features between drugs and proteins from both node-level and graph-level embeddings, enabling a more effective modeling of DTIs. This advantage is generally lacking in existing DTI prediction models. Consequently, when compared to many of the start-of-the-art methods, AMMVF-DTI demonstrated excellent performance on the human, C. elegans, and DrugBank baseline datasets, which can be attributed to its ability to incorporate interactive information and mine features from both local and global structures. The results from additional ablation experiments also confirmed the importance of each module in our AMMVF-DTI model. Finally, a case study is presented utilizing our model for COVID-19-related DTI prediction. We believe the AMMVF-DTI model can not only achieve reasonable accuracy in DTI prediction, but also provide insights into the understanding of potential interactions between drugs and targets.

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Targeting SARS-CoV-2 Main Protease: A Successful Story Guided by an In Silico Drug Repurposing Approach

Cited by 6 publications

References 56 publications

New Thiazolidine-4-One Derivatives as SARS-CoV-2 Main Protease Inhibitors

New Thiazolidine-4-One Derivatives as SARS-CoV-2 Main Protease Inhibitors

Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment

AMMVF-DTI: A Novel Model Predicting Drug–Target Interactions Based on Attention Mechanism and Multi-View Fusion

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