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The 2020 SARS-CoV-2 coronavirus pandemic highlighted the urgent need for novel small molecule antiviral drugs. (S)- DNDI-6510 is a non-covalent SARS-CoV-2 main protease inhibitor developed by the open science collaboration COVID Moonshot. Here, we report on the metabolic and toxicologic optimization of the lead series previously disclosed by the COVID Moonshot Initiative, leading up to the selection of (S)- DNDI-6510 as the preclinical candidate. We describe the thorough profiling of the series, identifying key risks such as formation of genotoxic metabolites and high clearance, which were successfully addressed during lead optimization. In addition, we disclose the and evaluation of (S)- DNDI-6510 in pharmacokinetic and pharmacodynamic models, exploring multiple approaches to ameliorate rodent-specific metabolic clearance, and show that both co-dosing of (S)- DNDI-6510 with an ABT inhibitor and utilizing a metabolically humanized mouse model (8HUM) achieve significant improvements in exposure. Through comparisons of ABT co-dosing and humanized mouse models in efficacy experiments, we demonstrate that continuous exposure over cellular EC is required for SARS-CoV-2 antiviral efficacy in an antiviral model using a mouse-adapted SARS-CoV-2 strain. Finally, (S)- DNDI-6510 was assessed in maximum tolerated dose experiments in two species, demonstrating significant in vivo PXR-linked auto-induction of metabolism, leading to the discontinuation of this compound. In summary, we report the successful effort to overcome series-specific AMES liabilities in a lead development program. Downstream optimization of existing series will require in-depth optimization of rodent-specific liabilities and metabolic induction profile.
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http://dx.doi.org/10.1101/2025.06.16.660018 | DOI Listing |
bioRxiv
June 2025
Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
The 2020 SARS-CoV-2 coronavirus pandemic highlighted the urgent need for novel small molecule antiviral drugs. (S)- DNDI-6510 is a non-covalent SARS-CoV-2 main protease inhibitor developed by the open science collaboration COVID Moonshot. Here, we report on the metabolic and toxicologic optimization of the lead series previously disclosed by the COVID Moonshot Initiative, leading up to the selection of (S)- DNDI-6510 as the preclinical candidate.
View Article and Find Full Text PDFBMC Genomics
May 2025
Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Bunkyo, Tokyo, Japan.
We present the Japan Omics Browser (JOB), which enables integrative analysis of human omics at different layers. JOB offers visualization of per-variant regulatory effects in the human blood at mRNA and protein level distinctively, quantified from statistical fine-mapping of mRNA-expression quantitative loci (eQTL) and protein QTLs (pQTLs) in 1,405 Japanese, together with fine-mapping results of 94 complex traits in UK Biobank. In addition, JOB shows per-tissue regulatory effect prediction score (EMS), trained via multi-task learning.
View Article and Find Full Text PDFPLoS One
May 2025
Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Kafrelsheikh University, Kafrelsheikh, Egypt.
COVID-19 still poses a worldwide health threat due to continuous viral mutations and potential resistance to vaccination. SARS-CoV-2 viral multiplication hindrance by inhibiting the viral main protease (Mpro) deemed propitious. Structure-based virtual screening (SBVS) is a conventional strategy for discovering new inhibitors.
View Article and Find Full Text PDFDigit Discov
February 2025
School of Natural and Environmental Sciences, Newcastle University Newcastle Upon Tyne NE1 7RU UK
FEgrow is an open-source software package for building congeneric series of compounds in protein binding pockets. For a given ligand core and receptor structure, it employs hybrid machine learning/molecular mechanics potential energy functions to optimise the bioactive conformers of supplied linkers and functional groups. Here, we introduce significant new functionality to automate, parallelise and accelerate the building and scoring of compound suggestions, such that it can be used for automated design.
View Article and Find Full Text PDFJ Cheminform
January 2025
Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, UK.
Current strategies centred on either merging or linking initial hits from fragment-based drug design (FBDD) crystallographic screens generally do not fully leaverage 3D structural information. We show that an algorithmic approach (Fragmenstein) that 'stitches' the ligand atoms from this structural information together can provide more accurate and reliable predictions for protein-ligand complex conformation than general methods such as pharmacophore-constrained docking. This approach works under the assumption of conserved binding: when a larger molecule is designed containing the initial fragment hit, the common substructure between the two will adopt the same binding mode.
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