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Multi-parameter phenotypic profiling: using cellular effects to characterize small-molecule compounds

Nature Reviews Drug Discovery volume 8, pages 567–578 (2009)Cite this article

Key Points

  • Major hurdles in early-stage drug discovery include how to triage hit compounds identified from cell-based screens, as well as how to rapidly evaluate their cellular efficacy and pinpoint side effects of compounds from target-driven screens.

  • Multi-parameter phenotypic profiling of small molecules can provide important insights into the mechanisms of action of hit compounds, thus expediting the selection of lead compounds for drug development.

  • Current phenotypic profiling technologies, including mRNA-, protein- and imaging-based technologies, together with data analysis methods that can be applied to multidimensional data sets, are reviewed and compared in terms of their information content, throughput, cost and scalability.

  • The most important challenge is how to integrate phenotypic profiles with genetic mutations, chemical similarity and biochemical activity profiles in a way that improves decision making and enables rapid hypothesis and model testing.

  • It is predicted that systematic integration of phenotypic approaches into the existing linear 'disease to target to drug' approach will improve success rates of lead selection and optimization early in the drug discovery process.

Abstract

Multi-parameter phenotypic profiling of small molecules provides important insights into their mechanisms of action, as well as a systems level understanding of biological pathways and their responses to small molecule treatments. It therefore deserves more attention at an early step in the drug discovery pipeline. Here, we summarize the technologies that are currently in use for phenotypic profiling — including mRNA-, protein- and imaging-based multi-parameter profiling — in the drug discovery context. We think that an earlier integration of phenotypic profiling technologies, combined with effective experimental and in silico target identification approaches, can improve success rates of lead selection and optimization in the drug discovery process.

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Figure 1: Multiplexity and throughput of phenotypic profiling technologies.
Figure 2: Modalities of profiling technologies.
Figure 3: Single-cell analysis.
Figure 4: Integration of information.

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Authors and Affiliations

  1. Developmental and Molecular Pathways, Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, 02139, Massachusetts, USA

    Yan Feng & John A. Tallarico

  2. Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, 02115, Massachusetts, USA

    Timothy J. Mitchison

  3. Medicinal Chemistry Division, Leiden/Amsterdam Center for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands

    Andreas Bender

  4. Wolf, Greenfield and Sacks, 600 Atlantic Avenue, Boston, 02210, Massachusetts, USA

    Daniel W. Young

  5. Global Discovery Chemistry, Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, 02139, Massachusetts, USA

    John A. Tallarico

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Correspondence to Yan Feng or Timothy J. Mitchison.

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Glossary

Multiplexing

A process by which many single-output assays are carried out in parallel.

Gene signature

A small subset of gene products (mRNA or protein) that are consistently upregulated or downregulated in certain disease states or following treatment with known drugs. The gene signatures can be used to mine expression profiling data for compound or disease association, or applied directly in higher-throughput detection methods, such as the quantitative polymerase chain reaction.

iTRAQ

(Isobaric tag for relative and absolute quantitation). A technique that uses isotope-coded covalent tags to quantify protein from different sources in one single mass spectrometry experiment.

High-content assay

An automated fluorescence microscopy-based assay that measures many parameters of cellular marker intensity and morphology pertaining to compound activity and toxicity.

Unsupervised clustering

A method used in machine learning to determine how data are organized without using predetermined training data.

Kolmogorov–Smirnov score

The minimum difference between the empirical distribution function of the sample and the cumulative distribution function of the reference.

Support vector machine

(SVM). A supervised learning algorithm, which is used for classification. An SVM constructs a separating hyperplane to maximize the difference between the treated and control cell data sets.

Factor analysis

A statistical method that explains the variability of observed variables in terms of reduced numbers of unobserved variables called common factors. The observed variables are modelled as linear combinations of the factors, plus 'error'. The factors often carry more interpretable meaning than the observed variables themselves.

Principal component analysis

(PCA). A data transformation method that is used to reduce multidimensional data sets to lower dimensions for analysis. PCA can reveal the internal structure of the data in a way that best explains the variance in the data.

Structure–activity relationship

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

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Feng, Y., Mitchison, T., Bender, A. et al. Multi-parameter phenotypic profiling: using cellular effects to characterize small-molecule compounds. Nat Rev Drug Discov 8, 567–578 (2009). https://doi.org/10.1038/nrd2876

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