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Combinatorial Pharmacogenetics

Nature Reviews Drug Discovery volume 4, pages 911–918 (2005)Cite this article

Key Points

  • Polymorphic drug-metabolizing enzymes (DME) are likely to represent some of the most common inheritable risk factors associated with common 'disease' phenotypes, such as adverse drug reactions.

  • The relatively high degree of association between DME polymorphisms and clinical phenotypes, examples of which are discussed here, suggests that research into this class of polymorphisms will provide near-term benefits to patients.

  • Given the complexity of the body's reactions to pharmacological agents and the combinatoric magnitude of the resulting analysis problem, traditional analysis methods will often break down. Novel analysis techniques, such as multifactor dimensionality reduction (MDR), offer viable options for evaluating complex pharmacogenetic data.

  • The MDR method, which has been successfully applied to identifying gene–gene and gene–environment interactions for a variety of complex clinical endpoints, can be implemented as part of a comprehensive and flexible four-step framework for combinatorial data mining. The open-source and platform-independent version of the MDR software is freely available for download.

  • We propose the application of MDR to defining gene–gene interactions in a similar context directed toward the characterization of drug-treatment outcomes. The application of this method, and others like it, could lead to the development of improved gene-based dosing models, and facilitate safer drug prescribing through the prospective application of individual drug susceptibility profiles.

Abstract

Combinatorial pharmacogenetics seeks to characterize genetic variations that affect reactions to potentially toxic agents within the complex metabolic networks of the human body. Polymorphic drug-metabolizing enzymes are likely to represent some of the most common inheritable risk factors associated with common 'disease' phenotypes, such as adverse drug reactions. The relatively high concordance between polymorphisms in drug-metabolizing enzymes and clinical phenotypes indicates that research into this class of polymorphisms could benefit patients in the near future. Characterization of other genes affecting drug disposition (absorption, distribution, metabolism and elimination) will further enhance this process. As with most questions concerning biological systems, the complexity arises out of the combinatorial magnitude of all the possible interactions and pathways. The high-dimensionality of the resulting analysis problem will often overwhelm traditional analysis methods. Novel analysis techniques, such as multifactor dimensionality reduction, offer viable options for evaluating such data.

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Figure 1: Competing metabolic pathways.
Figure 2: Drug disposition.

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Acknowledgements

This work was supported by grants from the National Institutes of Health (NIH). D.M.R. was supported by an NIH training grant.

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Authors and Affiliations

  1. Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA

    Russell A. Wilke

  2. Department of Genetics, Computational Genetics Laboratory, Dartmouth Medical School, Lebanon, New Hampshire, USA

    David M. Reif

  3. Departments of Genetics, Computational Genetics Laboratory, Norris-Cotton Cancer Center, and Community and Family Medicine, Dartmouth Medical School, Lebanon

    Jason H. Moore

  4. Department of Biological Sciences, Dartmouth College, Hanover

    Jason H. Moore

  5. Department of Computer Science, University of New Hampshire, New Hampshire

    Jason H. Moore

  6. 706 Rubin, HB7937, Dartmouth-Hitchcock Medical Center, One Medical Center Drive, Lebanon, 03756, New Hampshire, USA

    Jason H. Moore

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Correspondence to Jason H. Moore.

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DATABASES

Entrez Gene

ACE

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CYP2D6

OATP-C

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FURTHER INFORMATION

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Wilke, R., Reif, D. & Moore, J. Combinatorial Pharmacogenetics. Nat Rev Drug Discov 4, 911–918 (2005). https://doi.org/10.1038/nrd1874

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