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Impact of high-throughput screening in biomedical research

Nature Reviews Drug Discovery volume 10, pages 188–195 (2011)Cite this article

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Abstract

High-throughput screening (HTS) has been postulated in several quarters to be a contributory factor to the decline in productivity in the pharmaceutical industry. Moreover, it has been blamed for stifling the creativity that drug discovery demands. In this article, we aim to dispel these myths and present the case for the use of HTS as part of a proven scientific tool kit, the wider use of which is essential for the discovery of new chemotypes.

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Figure 1: Examples of high-throughput screening in drug discovery.
Figure 2: Size of corporate screening collections over time.
Figure 3: Comparison of average molecular mass and clogP of leads identified in 2009 by HTS or other strategies in three pharmaceutical companies.

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Acknowledgements

The authors are grateful to the many colleagues in the HTS community who have contributed data and opinions presented in this article.

Author information

Author notes

  1. Jeff W. Paslay

    Present address: G.Sitta Sittampalam is at the Department of Pharmacology, Toxicology & Therapeutics, Institute for Advancing Medical Innovations, The University of Kansas Cancer Center, 3901 Rainbow Blvd., Kansas City, Kansas 66160, USA,

  2. G. Sitta Sittampalam

    Present address: Jeff W.Paslay was previously at the Department of Screening Sciences, Research Centers of Emphasis, Pfizer; Present address: 302 Old Barn Circle, Phoenixville, Pennsylvania 19460, USA,

Authors and Affiliations

  1. Ricardo Macarron is at the Department of Sample Management Technologies, GlaxoSmithKline, Mail Stop UP 12-200, 1250S Collegeville Rd, Collegeville, Pennsylvania 19426, USA,

    Ricardo Macarron

  2. Martyn N. Banks is at the Department of Applied Biotechnologies, Bristol-Myers Squibb Co., 5 Research Parkway, Wallingford, Connecticut 06492, USA,

    Martyn N. Banks

  3. Dejan Bojanic is at the Lead Finding Platform, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA,

    Dejan Bojanic

  4. David J.Burns is with the Early Pain Discovery Team, Abbott Laboratories, R4DI AP52N 200 Abbott Park Road, Abbott Park, Illinois 60064, USA,

    David J. Burns

  5. Dragan A.Cirovic is at the Department of Lead Generation and Candidate Realization, Sanofi-Aventis, 1041 Route 202-206, Mail Stop BRJR1-002A, Bridgewater, New Jersey 08807, USA,

    Dragan A. Cirovic

  6. Tina Garyantes is at the Department of Lead Generation and Candidate Realization, Sanofi-Aventis, 1041 Route 202-206, P.O. BOX 6800, JR-303D, Bridgewater, New Jersey 08807, USA,

    Tina Garyantes

  7. Darren V.S.Green is at the Department of Computational and Structural Chemistry, GlaxoSmithKline, Stevenage, Hertfordshire SG1 2NY, UK,

    Darren V. S. Green

  8. Robert P.Hertzberg is at the Department of Screening and Compound Profiling, GlaxoSmithKline, Mail Stop UP 12-L05, 1250 S Collegeville Rd, Collegeville, Pennsylvania 19426, USA,

    Robert P. Hertzberg

  9. William P.Janzen is at the Division of Medicinal Chemistry and Natural Products, Center for Integrative Chemical Biology and Drug Discovery, Eshelman School of Pharmacy, The University of North Carolina, Chapel Hill, North Carolina 27599-7363, USA,

    William P. Janzen

  10. Ulrich Schopfer is at the Lead Finding Platform, Novartis Institutes for BioMedical Research, Forum 1, Novartis Campus, CH-4056 Basel, Switzerland,

    Ulrich Schopfer

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Correspondence to Ricardo Macarron.

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Supplementary information

Supplementary information Table S1

Molecular Libraries HTS Project Success- Key Indicators and Metrics 2004-2009* (PDF 242 kb)

Glossary

Cell-based luciferase reporter screen

A popular reporter gene assay that uses a luciferase gene to detect metabolites (for example, cyclic AMP levels) or changes in expression of a gene of interest.

Chemical space

The space spanned by all energetically stable stoichiometric combinations of electrons, atomic nuclei and topologies in molecules. It is calculated to contain up to 1 × 1060 distinct molecules. Drug-like space may contain up to 1 × 1030 molecules.

clogP

The calculated logarithm of the partition coefficient between n-octanol and water for a given compound. This parameter is an estimation of the lipophilicity of the compound.

Combinatorial chemistry

Rapid and parallel synthesis of large collections of compounds to facilitate the identification of new active compounds for drug targets by high-throughput screening techniques.

Constrained optimization

The process of finding the most favourable condition that satisfies all conditions (or constraints) that frame the problem.

Drug-like properties

Sharing certain characteristics — such as size, shape and solubility in water and organic solvents — with other molecules that act in the same way as drugs. Lipinski's rule of five provides a commonly used definition of these properties for oral drugs.

eADMET

Computational models designed to predict the ADMET (absorption, distribution, metabolism, excretion and toxicity) of molecules.

Fragment screening

The identification of bioactive substances by screening small-molecule fragments (<300 Da). It requires high-resolution structural techniques to guide the optimization of weak efficient hits into leads.

Lead-like properties

Sharing certain characteristics — such as size, shape and solubility in water and organic solvents — with other molecules that act as precursors of drugs (leads). Lead-likeness is typically associated with small size (molecular mass <400 Da) and low lipophilicity (clogP <4).

Plate pattern recognition algorithms

Microtitre plates may suffer from heterogeneous temperature, air flow, reader and liquid handler bias, and so on, leading to systematic assay errors that need to be detected and corrected by ad hoc algorithms.

Phenotypic cell-based screen

A screen based on whole cells that measures an observable change in cell physiology or morphology in the presence of active compounds. Phenotypic assays cannot distinguish direct compound interactions with the specific targets or signalling pathways in the cell.

Structure–activity relationship

Correlations that are constructed between the features of chemical structures in a set of candidate compounds and parameters of biological activity, such as potency, selectivity and toxicity.

Structure-based design

The use of three-dimensional structural information and molecular-modelling techniques to design a series of possible pharmacological modulators that can, for example, block an active site of an enzyme.

Target focused selection

The selection of chemical compounds that are related to either known ligands of a target or to the target class of interest.

Virtual screening

The selection of potential bioactive substances from a much larger list of candidate molecules using in silico models typically based on known structures and/or ligands of the target of interest.

Z′ trend monitoring

Z′ is a relative indication of the separation of the signal and background controls and is widely used in high-throughput screening (HTS) to assess the quality of an assay. Every microtitre plate in a run will exhibit a distinct Z′ value and monitoring its trends in a campaign is a standard quality control practice.

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Macarron, R., Banks, M., Bojanic, D. et al. Impact of high-throughput screening in biomedical research. Nat Rev Drug Discov 10, 188–195 (2011). https://doi.org/10.1038/nrd3368

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