During the coronavirus disease 2019 (COVID-19) pandemic, a wave of rapid and collaborative drug discovery efforts took place in academia and industry, culminating in several therapeutics being discovered, approved and deployed in a 2-year time frame. This article summarizes the collective experience of several pharmaceutical companies and academic collaborations that were active in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antiviral discovery. We outline our opinions and experiences on key stages in the small-molecule drug discovery process: target selection, medicinal chemistry, antiviral assays, animal efficacy and attempts to pre-empt resistance. We propose strategies that could accelerate future efforts and argue that a key bottleneck is the lack of quality chemical probes around understudied viral targets, which would serve as a starting point for drug discovery. Considering the small size of the viral proteome, comprehensively building an arsenal of probes for proteins in viruses of pandemic concern is a worthwhile and tractable challenge for the community.
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http://dx.doi.org/10.1038/s41573-023-00692-8 | DOI Listing |
Org Lett
January 2025
Department of Pharmaceutical Sciences, Daniel K. Inouye College of Pharmacy, University of Hawai'i at Hilo, Hilo, Hawai'i 96720, United States.
A novel sesquiterpene lactone derivative, vernonolide A (), featuring an unprecedented carbon skeleton, along with its plausible biosynthetic precursor, vercinolide I (), and eight known sesquiterpene lactones (-) were isolated and characterized from the whole plants of (L.). The structures of and were elucidated using nuclear magnetic resonance spectroscopic analysis and calculated and experimental electronic circular dichroism spectra.
View Article and Find Full Text PDFMol Inform
January 2025
Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan.
Recent advances in machine learning have significantly impacted molecular design, notably the molecular generation method combining the chemical variational autoencoder (VAE) with Gaussian mixture regression (GMR). In this method, a mathematical model is constructed with X as the latent variable of the molecule and Y as the target properties and activities. Through direct inverse analysis of this model, it is possible to generate molecules with the desired target properties.
View Article and Find Full Text PDFBiomed Chromatogr
February 2025
School of Pharmaceutical Sciences, Jilin University, Changchun, People's Republic of China.
Previous studies have suggested that ginsenoside Rg glycine ester derivative (RG) exhibits therapeutic potential in mitigating hypoxia. This study aimed to elucidate the potential mechanism of RG in hypoxia injury through a combined approach of metabolomics and network pharmacology. Initially, a CoCl-induced cell hypoxia model was established, and the therapeutic impact of RG on biochemical indices was evaluated.
View Article and Find Full Text PDFExpert Opin Drug Deliv
January 2025
Advanced Drug Delivery, Pharmaceutical Sciences, R&D, AstraZeneca, Macclesfield, UK.
Introduction: mRNA therapeutics were a niche area in drug development before COVIDvaccines. Now they are used in vaccine development, for non-viral therapeuticgenome editing, chimericantigen receptor T (CAR T) celltherapies and protein replacement. mRNAis large, charged, and easily degraded by nucleases.
View Article and Find Full Text PDFJ Comput Chem
January 2025
Pharmaceutical Chemistry Research Laboratory 1, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.
Cyclooxygenase-2 (COX-2) is an enzyme that plays a crucial role in inflammation by converting arachidonic acid into prostaglandins. The overexpression of enzyme is associated with conditions such as cancer, arthritis, and Alzheimer's disease (AD), where it contributes to neuroinflammation. In silico virtual screening is pivotal in early-stage drug discovery; however, the absence of coding or machine learning expertise can impede the development of reliable computational models capable of accurately predicting inhibitor compounds based on their chemical structure.
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