3D‐quantitative structure‐activity relationship study for the design of novel enterovirus A71 3C protease inhibitors

Nie, Quandeng; Xu, Xiaoyi; Zhang, Qi; Ma, Yuying; Yin, Zheng; Shang, Luqing

doi:10.1111/cbdd.13344

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…As expected, the model identified several well-known HIV-1 protease inhibitors (ritonavir and lopinavir) 54 and identified substances (RNs 2243743-58-8, 1934276-50-2, and 2229818-46-4) that target 3C protease/3CLpro and was shown to inhibit Enterovirus, MERS-CoV, and SARS-CoV-1 when tested in bioassays. 55 − 57 These could represent new lead candidates as therapeutic agents for COVID-19 or other viral infections. The model further identified substances against host proteins involved in cellular processes, including diltiazem hydrochloride and leflunomide.…”

Section: Resultsmentioning

confidence: 99%

Quantitative Structure–Activity Relationship Machine Learning Models and their Applications for Identifying Viral 3CLpro- and RdRp-Targeting Compounds as Potential Therapeutics for COVID-19 and Related Viral Infections

et al. 2020

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In response to the ongoing COVID-19 pandemic, there is a worldwide effort being made to identify potential anti-SARS-CoV-2 therapeutics. Here, we contribute to these efforts by building machine-learning predictive models to identify novel drug candidates for the viral targets 3 chymotrypsin-like protease (3CLpro) and RNA-dependent RNA polymerase (RdRp). Chemist-curated training sets of substances were assembled from CAS data collections and integrated with curated bioassay data. The best-performing classification models were applied to screen a set of FDA-approved drugs and CAS REGISTRY substances that are similar to, or associated with, antiviral agents. Numerous substances with potential activity against 3CLpro or RdRp were found, and some were validated by published bioassay studies and/or by their inclusion in upcoming or ongoing COVID-19 clinical trials. This study further supports that machine learning-based predictive models may be used to assist the drug discovery process for COVID-19 and other diseases.

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Section: Resultsmentioning

confidence: 99%

Quantitative Structure–Activity Relationship Machine Learning Models and their Applications for Identifying Viral 3CLpro- and RdRp-Targeting Compounds as Potential Therapeutics for COVID-19 and Related Viral Infections

et al. 2020

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Non-Polio Enterovirus Inhibitors: Scaffolds, Targets, and Potency─What’s New?

Roux,

Touret,

Rathelot

et al. 2024

ACS Infect. Dis.

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Development of an allosteric inhibitor class blocking RNA elongation by the respiratory syncytial virus polymerase complex

Cox

Toots

Yoon

et al. 2018

Journal of Biological Chemistry

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Respiratory syncytial virus (RSV) represents a significant health threat to infants and to elderly or immunocompromised individuals. There are currently no vaccines available to prevent RSV infections, and disease management is largely limited to supportive care, making the identification and development of effective antiviral therapeutics against RSV a priority. To identify effective chemical scaffolds for managing RSV disease, we conducted a high-throughput anti-RSV screen of a 57,000-compound library. We identified a hit compound that specifically blocked activity of the RSV RNA-dependent RNA polymerase (RdRp) complex, initially with moderate low-micromolar potency. Mechanistic characterization in an RSV RdRp assay indicated that representatives of this compound class block elongation of RSV RNA products after initial extension by up to three nucleotides. Synthetic hit-to-lead exploration yielded an informative 3D quantitative structure-activity relationship (3D-QSAR) model and resulted in analogs with more than 20-fold improved potency and selectivity indices (SIs) of>1,000. However, first-generation leads exhibited limited water solubility and poor metabolic stability. A second optimization strategy informed by the 3D-QSAR model combined with pharmacokinetics (PK) predictions yielded an advanced lead, AVG-233, that demonstrated nanomolar activity against both laboratory-adapted RSV strains and clinical RSV isolates. This anti-RSV activity extended to infection of established cell lines and primary human airway cells. PK profiling in mice revealed 34% oral bioavailability of AVG-233 and sustained high drug levels in the circulation after a single oral dose of 20 mg/kg. This promising first-in-class lead warrants further development as an anti-RSV drug.

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3D‐quantitative structure‐activity relationship study for the design of novel enterovirus A71 3C protease inhibitors

Cited by 3 publications

References 25 publications

Quantitative Structure–Activity Relationship Machine Learning Models and their Applications for Identifying Viral 3CLpro- and RdRp-Targeting Compounds as Potential Therapeutics for COVID-19 and Related Viral Infections

Quantitative Structure–Activity Relationship Machine Learning Models and their Applications for Identifying Viral 3CLpro- and RdRp-Targeting Compounds as Potential Therapeutics for COVID-19 and Related Viral Infections

Non-Polio Enterovirus Inhibitors: Scaffolds, Targets, and Potency─What’s New?

Development of an allosteric inhibitor class blocking RNA elongation by the respiratory syncytial virus polymerase complex

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