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Leveridge M, Chung CW, Gross JW, Phelps CB, Green D. Integration of Lead Discovery Tactics and the Evolution of the Lead Discovery Toolbox. SLAS DISCOVERY 2018; 23:881-897. [PMID: 29874524 DOI: 10.1177/2472555218778503] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
There has been much debate around the success rates of various screening strategies to identify starting points for drug discovery. Although high-throughput target-based and phenotypic screening has been the focus of this debate, techniques such as fragment screening, virtual screening, and DNA-encoded library screening are also increasingly reported as a source of new chemical equity. Here, we provide examples in which integration of more than one screening approach has improved the campaign outcome and discuss how strengths and weaknesses of various methods can be used to build a complementary toolbox of approaches, giving researchers the greatest probability of successfully identifying leads. Among others, we highlight case studies for receptor-interacting serine/threonine-protein kinase 1 and the bromo- and extra-terminal domain family of bromodomains. In each example, the unique insight or chemistries individual approaches provided are described, emphasizing the synergy of information obtained from the various tactics employed and the particular question each tactic was employed to answer. We conclude with a short prospective discussing how screening strategies are evolving, what this screening toolbox might look like in the future, how to maximize success through integration of multiple tactics, and scenarios that drive selection of one combination of tactics over another.
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Affiliation(s)
- Melanie Leveridge
- 1 GlaxoSmithKline Drug Design and Selection, Platform Technology and Science, Stevenage, Hertfordshire, UK
| | - Chun-Wa Chung
- 1 GlaxoSmithKline Drug Design and Selection, Platform Technology and Science, Stevenage, Hertfordshire, UK
| | - Jeffrey W Gross
- 2 GlaxoSmithKline Drug Design and Selection, Platform Technology and Science, Collegeville, PA, USA
| | - Christopher B Phelps
- 3 GlaxoSmithKline Drug Design and Selection, Platform Technology and Science, Cambridge, MA, USA
| | - Darren Green
- 1 GlaxoSmithKline Drug Design and Selection, Platform Technology and Science, Stevenage, Hertfordshire, UK
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Karnachi PS, Brown FK. Practical Approaches to Efficient Screening: Information-Rich Screening Protocol. ACTA ACUST UNITED AC 2016; 9:678-86. [PMID: 15634794 DOI: 10.1177/1087057104269570] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The past approach of high-throughput screening of everything in the corporate collection has been shown to be very expensive in terms of reagents cost, disposal cost, and compound collection depletion. It is well known that screening campaigns produce several hits, ofwhich only 50% confirmon average. More efficientways of screening can provide an informative structure-activity relationship (SAR), which in turn can be used to buildmathematical models for further probing the activity space and directing chemical synthesis. The authors report new methods and insights to extract themaximum possible information from a screening experiment and findmost of the possible hits in the corporate collection while screening as few compounds as possible.
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Affiliation(s)
- Prabha S Karnachi
- Johnson & Johnson Pharmaceutical Research and Development, LLC, 1000 Route 202, P.O. Box 300, Raritan, NJ 08869.
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3
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Baringhaus KH, Hessler G. Fast similarity searching and screening hit analysis. DRUG DISCOVERY TODAY. TECHNOLOGIES 2014; 1:197-202. [PMID: 24981485 DOI: 10.1016/j.ddtec.2004.11.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Similarity searching allows a fast identification of analogues to biologically active molecules. Depending on the applied similarity metrics, either structurally close analogues or more diverse compounds can be identified. This is of particular interest for the analysis of high-throughput screening (HTS) hits. A combination of similarity searching and data mining applied to HTS data derives early structure-activity relationships to guide a subsequent optimization of hits.:
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Affiliation(s)
- Karl-Heinz Baringhaus
- Aventis Pharma Deutschland GmbH (A company of the sanofi-aventis group), Chemistry/Computational Chemistry, Industriepark Hoechst, Building G 878, 65926 Frankfurt am Main, Germany.
| | - Gerhard Hessler
- Aventis Pharma Deutschland GmbH (A company of the sanofi-aventis group), Chemistry/Computational Chemistry, Industriepark Hoechst, Building G 878, 65926 Frankfurt am Main, Germany
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4
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GSA: a GPU-accelerated structure similarity algorithm and its application in progressive virtual screening. Mol Divers 2012; 16:759-69. [DOI: 10.1007/s11030-012-9403-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2012] [Accepted: 10/08/2012] [Indexed: 12/21/2022]
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Yang Y, Carta G, Peters MB, Price T, O'Boyle N, Knox AJ, Fayne D, Williams DC, Meegan MJ, Lloyd DG. ‘tieredScreen’ - Layered Virtual Screening Tool for the Identification of Novel Estrogen Receptor Alpha Modulators. Mol Inform 2010; 29:421-30. [DOI: 10.1002/minf.201000034] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2009] [Accepted: 04/10/2010] [Indexed: 11/06/2022]
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Chen X, Wilson LJ, Malaviya R, Argentieri RL, Yang SM. Virtual screening to successfully identify novel janus kinase 3 inhibitors: a sequential focused screening approach. J Med Chem 2008; 51:7015-9. [PMID: 18844338 DOI: 10.1021/jm800662z] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
In an effort to identify novel Janus kinase 3 inhibitors, a sequential focused screening approach was adopted to search our in-house chemical database. By biologically testing only 79 selected compounds, we successfully identified 19 compounds showing IC 50 < 20 microM, with four of them in the nanomolar range. Particularly, a 3,5-disubstituted pyrazolo[4,3- d]pyrimidine scaffold emerged as a promising candidate for further lead optimization. With the advantages of efficiency and flexibility, this approach may be utilized to identify leads for other therapeutic targets.
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Affiliation(s)
- Xin Chen
- Computer Assisted Drug Discovery, High-throughput Chemistry, and Inflammation Therapeutics, Research and Early Development, Johnson & Johnson Pharmaceutical Research and Development, LLC, Raritan, New Jersey 08869, USA.
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Abstract
Novel starting points for drug discovery projects are generally found either by screening large collections of compounds or smaller more-focused libraries. Ideally, hundreds or even thousands of actives are initially found, and these need to be reduced to a handful of promising lead series. In several sequential steps, many actives are dropped and only some are followed up. Computational chemistry tools are used in this context to predict properties, cluster hits, design focused libraries and search for close analogues to explore the potential of hit series. At the end of hit-to-lead, the project must commit to one, or preferably a few, lead series that will be refined during lead optimization and hopefully produce a drug candidate. Striving for the best possible decision is crucial because choosing the wrong series is a costly one-way street.
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Affiliation(s)
- Volker Schnecke
- Computational Lead Discovery, Department of Medicinal Chemistry, AstraZeneca R&D Mölndal, S-43183 Mölndal, Sweden.
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8
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Abstract
The widespread use of HTS and combinatorial chemistry techniques has led to the generation of large amounts of pharmacological data, which, in turn, has catalyzed the development of computational methods designed to reduce the time and cost in identifying molecules suitable for pharmaceutical development. This review focuses on the use of substructure-based in silico techniques for lead discovery, an effective and increasingly popular approach for augmenting the chance of selecting drug-like compounds for preclinical and clinical development.
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Affiliation(s)
- Cédric Merlot
- Serono Pharmaceutical Research Institute, 14, ch. des Aulx, 1228-Plan-les-Ouates, Geneva, Switzerland
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9
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Engels MFM. Creating knowledge from high-throughput screening data. ERNST SCHERING RESEARCH FOUNDATION WORKSHOP 2003:87-101. [PMID: 12664537 DOI: 10.1007/978-3-662-05314-0_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Affiliation(s)
- M F M Engels
- Janssen Research Foundation, Turmhouteseweg 30, 2340 Beerse, Belgium.
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Abstract
High-throughput and virtual screening are important components of modern drug discovery research. Typically, these screening technologies are considered distinct approaches, as one is experimental and the other is theoretical in nature. However, given their similar tasks and goals, these approaches are much more complementary to each other than often thought. Various statistical, informatics and filtering methods have recently been introduced to foster the integration of experimental and in silico screening and maximize their output in drug discovery. Although many of these ideas and efforts have not yet proceeded much beyond the conceptual level, there are several success stories and good indications that early-stage drug discovery will benefit greatly from a more unified and knowledge-based approach to biological screening, despite the many technical advances towards even higher throughput that are made in the screening arena.
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Affiliation(s)
- Jürgen Bajorath
- Department of Computer-Aided Drug Discovery, Albany Molecular Research, Inc., Bothell Research Center, 18804 North Creek Parkway, Bothell, Washington 98011, USA.
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Blower P, Fligner M, Verducci J, Bjoraker J. On combining recursive partitioning and simulated annealing to detect groups of biologically active compounds. JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES 2002; 42:393-404. [PMID: 11911709 DOI: 10.1021/ci0101049] [Citation(s) in RCA: 40] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Statistical data mining methods have proven to be powerful tools for investigating correlations between molecular structure and biological activity. Recursive partitioning (RP), in particular, offers several advantages in mining large, diverse data sets resulting from high throughput screening. When used with binary molecular descriptors, the standard implementation of RP splits on single descriptors. We use simulated annealing (SA) to find combinations of molecular descriptors whose simultaneous presence best separates off the most active, chemically similar group of compounds. The search is incorporated into a recursive partitioning design to produce a regression tree for biological activity on the space of structural fingerprints. Each node is characterized by a specific combination of structural features, and the terminal nodes with high average activities correspond, roughly, to different classes of compounds. Using LeadScope structural features as descriptors to mine a database from the National Cancer Institute, the merging of RP and SA consistently identifies structurally homogeneous classes of highly potent anticancer agents.
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Affiliation(s)
- Paul Blower
- Leadscope, Inc., 1245 Kinnear Road, Columbus, Ohio 43212, USA.
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Gedeck P, Willett P. Visual and computational analysis of structure--activity relationships in high-throughput screening data. Curr Opin Chem Biol 2001; 5:389-95. [PMID: 11470601 DOI: 10.1016/s1367-5931(00)00219-2] [Citation(s) in RCA: 34] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Novel analytic methods are required to assimilate the large volumes of structural and bioassay data generated by combinatorial chemistry and high-throughput screening programmes in the pharmaceutical and agrochemical industries. Recent work in visualisation and data mining has been used to develop structure--activity relationships from such chemical-biological datasets.
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Affiliation(s)
- P Gedeck
- Novartis Horsham Research Centre, Novartis Pharmaceuticals UK Ltd., Wimblehurst Road, Horsham, West Sussex RH12 5AB, UK.
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13
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Abt M, Lim Y, Sacks J, Xie M, Young SS. Sequential Approach for Identifying Lead Compounds in Large Chemical Databases. Stat Sci 2001. [DOI: 10.1214/ss/1009213288] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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