Analysis of Pharmacokinetics, Pharmacodynamics, and Pharmacogenomics Data Sets Using VizStruct, a Novel Multidimensional Visualization Technique

Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, New York 14260, USA.
Pharmaceutical Research (Impact Factor: 3.42). 06/2004; 21(5):777-80. DOI: 10.1023/B:PHAM.0000026427.30177.61
Source: PubMed


Data visualization techniques for the pharmaceutical sciences have not been extensively investigated. The purpose of this study was to evaluate the usefulness of VizStruct, a multidimensional visualization tool, for applications in pharmacokinetics, pharmacodynamics, and pharmacogenomics.
The VizStruct tool uses the first harmonic of the discrete Fourier transform to map multidimensional data to two dimensions for visualization. The mapping was used to visualize several published pharmacokinetic, pharmacodynamic, and pharmacogenomic data sets. The VizStruct approach was evaluated using simulated population pharmacokinetics data sets, the data from Dalen and colleagues (Clin. PharmacoL Ther. 63:444-452, 1998) on the kinetics of nortriptyline and its 10-hydroxynortriptyline metabolite in subjects with differing number of copies of the CYP2D6, and the gene expression profiling data of Bohen and colleagues (Proc. Natl. Acad. Sci. USA 100:1926-1930, 2003) on follicular lymphoma patients responsive and nonresponsive to rituximab.
The VizStruct mapping preserves the key characteristics of multidimensional data in two dimensions in a manner that facilitates visualization. The mapping is computationally efficient and can be used for cluster detection and class prediction in pharmaceutical data sets. The VizStruct visualization succinctly summarized the salient similarities and differences in the nortriptyline and 10-hydroxynortriptyline pharmacokinetic profiles in subjects with increasing number of CYP2D6 gene copies. In the simulated population pharmacokinetic data sets, it was capable of discriminating the subtle differences between pharmacokinetic profiles derived from 1- and 2-compartment models with the same area under the curve. The two-dimensional VizStruct mapping computed from a subset of 102 informative genes from the Bohen and colleagues data set effectively separated the rituximab responder, rituximab nonresponder, and control subject groups.
The VizStruct approach is a computationally efficient and effective approach for visualizing complex, multidimensional data sets. It could have many useful applications in the pharmaceutical sciences.

Full-text preview

Available from:
  • [Show abstract] [Hide abstract]
    ABSTRACT: The problem of adverse drug reactions is a well-documented global public health problem. Recent withdrawals of several widely used prescription medications in the USA and other countries have raised concerns among patients, clinicians, scientists and policy makers. The increasing interest and concern regarding withdrawal of previously approved prescription medications and drug safety has prompted renewed research efforts aimed at improving surveillance of approved drugs and reducing adverse drug reactions. Pharmacogenomics research is increasingly directed at developing genomic diagnostics and tests with predictive ability for adverse drug reactions. This paper focuses on the problem of adverse drug reactions and reviews the evidence and the state of the science for the application of pharmacogenomics to adverse drug reactions.
    No preview · Article · Mar 2008 · Expert Review of Clinical Pharmacology
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We developed an information-theoretic metric called the Interaction Index for prioritizing genetic variations and environmental variables for follow-up in detailed sequencing studies. The Interaction Index was found to be effective for prioritizing the genetic and environmental variables involved in GEI for a diverse range of simulated data sets. The metric was also evaluated for a 103-SNP Crohn's disease dataset and a simulated data set containing 9187 SNPs and multiple covariates that was modeled on a rheumatoid arthritis data set. Our results demonstrate that the Interaction Index algorithm is effective and efficient for prioritizing interacting variables for a diverse range of epidemiologic data sets containing complex combinations of direct effects, multiple GGI and GEI.
    Full-text · Article · Apr 2009 · European journal of human genetics: EJHG