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Schuller M, Zarganes-Tzitzikas T, Bennett J, De Cesco S, Fearon D, von Delft F, Fedorov O, Brennan PE, Ahel I. Discovery and Development Strategies for SARS-CoV-2 NSP3 Macrodomain Inhibitors. Pathogens 2023; 12:324. [PMID: 36839595 PMCID: PMC9965906 DOI: 10.3390/pathogens12020324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/03/2023] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
The worldwide public health and socioeconomic consequences caused by the COVID-19 pandemic highlight the importance of increasing preparedness for viral disease outbreaks by providing rapid disease prevention and treatment strategies. The NSP3 macrodomain of coronaviruses including SARS-CoV-2 is among the viral protein repertoire that was identified as a potential target for the development of antiviral agents, due to its critical role in viral replication and consequent pathogenicity in the host. By combining virtual and biophysical screening efforts, we discovered several experimental small molecules and FDA-approved drugs as inhibitors of the NSP3 macrodomain. Analogue characterisation of the hit matter and crystallographic studies confirming binding modes, including that of the antibiotic compound aztreonam, to the active site of the macrodomain provide valuable structure-activity relationship information that support current approaches and open up new avenues for NSP3 macrodomain inhibitor development.
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Affiliation(s)
- Marion Schuller
- Sir William Dunn School of Pathology, University of Oxford, Oxford OX1 3RE, UK
| | | | - James Bennett
- Centre for Medicines Discovery, University of Oxford, Headington OX3 7DQ, UK
| | - Stephane De Cesco
- Centre for Medicines Discovery, University of Oxford, Headington OX3 7DQ, UK
| | - Daren Fearon
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Frank von Delft
- Centre for Medicines Discovery, University of Oxford, Headington OX3 7DQ, UK
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
- Structural Genomics Consortium, University of Oxford, Headington OX3 7DQ, UK
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
| | - Oleg Fedorov
- Centre for Medicines Discovery, University of Oxford, Headington OX3 7DQ, UK
| | - Paul E. Brennan
- Centre for Medicines Discovery, University of Oxford, Headington OX3 7DQ, UK
| | - Ivan Ahel
- Sir William Dunn School of Pathology, University of Oxford, Oxford OX1 3RE, UK
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Laeremans T, Sands ZA, Claes P, De Blieck A, De Cesco S, Triest S, Busch A, Felix D, Kumar A, Jaakola VP, Menet C. Accelerating GPCR Drug Discovery With Conformation-Stabilizing VHHs. Front Mol Biosci 2022; 9:863099. [PMID: 35677880 PMCID: PMC9170359 DOI: 10.3389/fmolb.2022.863099] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 03/22/2022] [Indexed: 01/19/2023] Open
Abstract
The human genome encodes 850 G protein-coupled receptors (GPCRs), half of which are considered potential drug targets. GPCRs transduce extracellular stimuli into a plethora of vital physiological processes. Consequently, GPCRs are an attractive drug target class. This is underlined by the fact that approximately 40% of marketed drugs modulate GPCRs. Intriguingly 60% of non-olfactory GPCRs have no drugs or candidates in clinical development, highlighting the continued potential of GPCRs as drug targets. The discovery of small molecules targeting these GPCRs by conventional high throughput screening (HTS) campaigns is challenging. Although the definition of success varies per company, the success rate of HTS for GPCRs is low compared to other target families (Fujioka and Omori, 2012; Dragovich et al., 2022). Beyond this, GPCR structure determination can be difficult, which often precludes the application of structure-based drug design approaches to arising HTS hits. GPCR structural studies entail the resource-demanding purification of native receptors, which can be challenging as they are inherently unstable when extracted from the lipid matrix. Moreover, GPCRs are flexible molecules that adopt distinct conformations, some of which need to be stabilized if they are to be structurally resolved. The complexity of targeting distinct therapeutically relevant GPCR conformations during the early discovery stages contributes to the high attrition rates for GPCR drug discovery programs. Multiple strategies have been explored in an attempt to stabilize GPCRs in distinct conformations to better understand their pharmacology. This review will focus on the use of camelid-derived immunoglobulin single variable domains (VHHs) that stabilize disease-relevant pharmacological states (termed ConfoBodies by the authors) of GPCRs, as well as GPCR:signal transducer complexes, to accelerate drug discovery. These VHHs are powerful tools for supporting in vitro screening, deconvolution of complex GPCR pharmacology, and structural biology purposes. In order to demonstrate the potential impact of ConfoBodies on translational research, examples are presented of their role in active state screening campaigns and structure-informed rational design to identify de novo chemical space and, subsequently, how such matter can be elaborated into more potent and selective drug candidates with intended pharmacology.
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Abstract
When trying to identify new potential therapeutic protein targets, access to data and knowledge is increasingly important. In a field where new resources and data sources become available every day, it is crucial to be able to take a step back and look at the wider picture in order to identify potential drug targets. While this task is routinely performed by bespoke literature searches, it is often time-consuming and lacks uniformity when comparing multiple targets at one time. To address this challenge, we developed TargetDB, a tool that aggregates public information available on given target(s) (links to disease, safety, 3D structures, ligandability, novelty, etc.) and assembles it in an easy to read output ready for the researcher to analyze. In addition, we developed a target scoring system based on the desirable attributes of good therapeutic targets and machine learning classification system to categorize novel targets as having promising or challenging tractrability. In this manuscript, we present the methodology used to develop TargetDB as well as test cases.
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Affiliation(s)
- Stephane De Cesco
- Nuffield Department of Medicine, ARUK Oxford Drug Discovery Institute, Target Discovery Institute, University of Oxford, Oxford, United-Kingdom
- * E-mail: (PEB); (SDC)
| | - John B. Davis
- Nuffield Department of Medicine, ARUK Oxford Drug Discovery Institute, Target Discovery Institute, University of Oxford, Oxford, United-Kingdom
| | - Paul E. Brennan
- Nuffield Department of Medicine, ARUK Oxford Drug Discovery Institute, Target Discovery Institute, University of Oxford, Oxford, United-Kingdom
- * E-mail: (PEB); (SDC)
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Affiliation(s)
- Henriëtte Willems
- The ALBORADA Drug Discovery Institute, University of Cambridge, Island Research Building, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0AH, U.K
| | - Stephane De Cesco
- Alzheimer’s Research UK Oxford Drug Discovery Institute, University of Oxford, NDM Research Building, Old Road Campus, Roosevelt Drive, Oxford OX3 7FZ, U.K
| | - Fredrik Svensson
- Alzheimer’s Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, London WC1E 6BT, U.K
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Fang H, De Wolf H, Knezevic B, Burnham KL, Osgood J, Sanniti A, Lledó Lara A, Kasela S, De Cesco S, Wegner JK, Handunnetthi L, McCann FE, Chen L, Sekine T, Brennan PE, Marsden BD, Damerell D, O'Callaghan CA, Bountra C, Bowness P, Sundström Y, Milani L, Berg L, Göhlmann HW, Peeters PJ, Fairfax BP, Sundström M, Knight JC. A genetics-led approach defines the drug target landscape of 30 immune-related traits. Nat Genet 2019; 51:1082-1091. [PMID: 31253980 PMCID: PMC7124888 DOI: 10.1038/s41588-019-0456-1] [Citation(s) in RCA: 117] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 05/24/2019] [Indexed: 12/22/2022]
Abstract
Most candidate drugs currently fail later-stage clinical trials, largely due to poor prediction of efficacy on early target selection1. Drug targets with genetic support are more likely to be therapeutically valid2,3, but the translational use of genome-scale data such as from genome-wide association studies for drug target discovery in complex diseases remains challenging4-6. Here, we show that integration of functional genomic and immune-related annotations, together with knowledge of network connectivity, maximizes the informativeness of genetics for target validation, defining the target prioritization landscape for 30 immune traits at the gene and pathway level. We demonstrate how our genetics-led drug target prioritization approach (the priority index) successfully identifies current therapeutics, predicts activity in high-throughput cellular screens (including L1000, CRISPR, mutagenesis and patient-derived cell assays), enables prioritization of under-explored targets and allows for determination of target-level trait relationships. The priority index is an open-access, scalable system accelerating early-stage drug target selection for immune-mediated disease.
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Affiliation(s)
- Hai Fang
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | | | - Bogdan Knezevic
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Katie L Burnham
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Julie Osgood
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Anna Sanniti
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Alicia Lledó Lara
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Silva Kasela
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Stephane De Cesco
- Alzheimer's Research UK Oxford Drug Discovery Institute, Target Discovery Institute, University of Oxford, Oxford, UK
| | | | | | - Fiona E McCann
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Liye Chen
- Botnar Research Centre, University of Oxford, Oxford, UK
| | - Takuya Sekine
- Botnar Research Centre, University of Oxford, Oxford, UK
| | - Paul E Brennan
- Alzheimer's Research UK Oxford Drug Discovery Institute, Target Discovery Institute, University of Oxford, Oxford, UK
- Structural Genomics Consortium, University of Oxford, Oxford, UK
| | - Brian D Marsden
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
- Structural Genomics Consortium, University of Oxford, Oxford, UK
| | - David Damerell
- Structural Genomics Consortium, University of Oxford, Oxford, UK
| | - Chris A O'Callaghan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Chas Bountra
- Structural Genomics Consortium, University of Oxford, Oxford, UK
| | - Paul Bowness
- Botnar Research Centre, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Yvonne Sundström
- Structural Genomics Consortium, Department of Medicine, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden
| | - Lili Milani
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Louise Berg
- Structural Genomics Consortium, Department of Medicine, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden
| | | | | | - Benjamin P Fairfax
- Department of Oncology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Michael Sundström
- Structural Genomics Consortium, Department of Medicine, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden
| | - Julian C Knight
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.
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Di Trani JM, De Cesco S, O'Leary R, Plescia J, do Nascimento CJ, Moitessier N, Mittermaier AK. Rapid measurement of inhibitor binding kinetics by isothermal titration calorimetry. Nat Commun 2018; 9:893. [PMID: 29497037 PMCID: PMC5832847 DOI: 10.1038/s41467-018-03263-3] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Accepted: 01/31/2018] [Indexed: 11/23/2022] Open
Abstract
Although drug development typically focuses on binding thermodynamics, recent studies suggest that kinetic properties can strongly impact a drug candidate’s efficacy. Robust techniques for measuring inhibitor association and dissociation rates are therefore essential. To address this need, we have developed a pair of complementary isothermal titration calorimetry (ITC) techniques for measuring the kinetics of enzyme inhibition. The advantages of ITC over standard techniques include speed, generality, and versatility; ITC also measures the rate of catalysis directly, making it ideal for quantifying rapid, inhibitor-dependent changes in enzyme activity. We used our methods to study the reversible covalent and non-covalent inhibitors of prolyl oligopeptidase (POP). We extracted kinetics spanning three orders of magnitude, including those too rapid for standard methods, and measured sub-nM binding affinities below the typical ITC limit. These results shed light on the inhibition of POP and demonstrate the general utility of ITC-based enzyme inhibition kinetic measurements. There is growing evidence that the kinetics of interactions between inhibitors and their targets can strongly impact therapeutic efficacy. Here the authors describe an isothermal titration calorimetry-based method that can rapidly quantify inhibition kinetics and measure sub-nM binding affinities.
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Affiliation(s)
- Justin M Di Trani
- Department of Chemistry, McGill University, Montreal, QC, H3A 0B8, Canada
| | - Stephane De Cesco
- Department of Chemistry, McGill University, Montreal, QC, H3A 0B8, Canada
| | - Rebecca O'Leary
- Department of Chemistry, McGill University, Montreal, QC, H3A 0B8, Canada
| | - Jessica Plescia
- Department of Chemistry, McGill University, Montreal, QC, H3A 0B8, Canada
| | - Claudia Jorge do Nascimento
- Department of Chemistry, McGill University, Montreal, QC, H3A 0B8, Canada.,Institute of Biosciences, Federal University of the State of Rio de Janeiro, Urca, 22290-240, Rio de Janeiro, Brazil
| | - Nicolas Moitessier
- Department of Chemistry, McGill University, Montreal, QC, H3A 0B8, Canada
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Abstract
In the history of therapeutics, covalent drugs occupy a very distinct category. While representing a significant fraction of the drugs on the market, very few have been deliberately designed to interact covalently with their biological target. In this review, the prevalence of covalent drugs will first be briefly covered, followed by an introduction to their mechanisms of action and more detailed discussions of their discovery and the development of safe and efficient covalent enzyme inhibitors. All stages of a drug discovery program will be covered, from target considerations to lead optimization, strategies to tune reactivity and computational methods. The goal of this article is to provide an overview of the field and to outline good practices that are needed for the proper assessment and development of covalent inhibitors as well as a good understanding of the potential and limitations of current computational methods for the design of covalent drugs.
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Affiliation(s)
- Stephane De Cesco
- Department of Chemistry, McGill University, 801 Sherbrooke St. W., Montréal, Québec H3A 0B8, Canada
| | - Jerry Kurian
- Department of Chemistry, McGill University, 801 Sherbrooke St. W., Montréal, Québec H3A 0B8, Canada
| | - Caroline Dufresne
- Department of Chemistry, McGill University, 801 Sherbrooke St. W., Montréal, Québec H3A 0B8, Canada
| | - Anthony K Mittermaier
- Department of Chemistry, McGill University, 801 Sherbrooke St. W., Montréal, Québec H3A 0B8, Canada
| | - Nicolas Moitessier
- Department of Chemistry, McGill University, 801 Sherbrooke St. W., Montréal, Québec H3A 0B8, Canada.
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