Pharmacogenomics selection

It is imperative, however, to understand the probabilistic nature of such experiments a promising profile on pharmacogenomic and toxicogenomic screens will enhance the likelihood of having selected an ultimately successful compound, and will achieve this goal quicker than conventional animal experimentation, but will do so only with a certain likelihood of success. The less reductionist approach of the animal experiment will still be needed. It is to be anticipated, however, that such approaches will constitute an important, time- and resource-saving first evaluation or screening step that will help to focus and reduce the number of animal experiments that will ultimately need to be conducted. 

Advances in measurement of gene expression have also been phenomenal. In five years, the numbers of genes, sensitivity of the assays, and reproducibility of results have also increased significantly as has the ability to analyze the data. The measurement of gene expression has been used in several ways in cancer genetics and cancer pharmacogenomics. Gene expression has led to better ways to classify cancers and to select the appropriate therapy (Miyazato et al., 2001 Birner et al., 2001). 

Cancer pharmacogenomics inclndes stndies on biomarkers snch as thiopnrine methyltransferase (TPMT) and epidermal growth factor receptor (EGFR). Research methods snch as germline and tnmor DNA stndies, polymorphism selection, and biomarker screening as well as genotyping systems are described. 

Fig. 5. Selection of candidate genes for selection in a either a study examining the role of pharmacogenomics in drug disposition and/or action or alternatively, use as a chnical tool to individualize drug therapy. Those genes prioritized for inclusion should he those shown to contribute markedly to drug pharmacokinetics and/or dynamics.

New genomic and proteomic technologies provide powerful tools for the selection of patients likely to benefit from a therapeutic program without unacceptable adverse events. In this chapter we attempt to clarify how pharmacogenomic biomarker classifiers of the patients most likely to benefit from a drug can be identified and utilized during clinical development. 

Physicians need improved tools for selecting treatments for individual patients. For example, many cancer treatments benefit only a minority of the patients to whom they are administered. Being able to predict which patients are most likely to benefit would not only save patients from uimecessary toxicity and inconvenience, but might facilitate their receiving drugs that are more likely to help them. In addition, the current over-treatment of patients results in major expense for individuals and society, an expense that may not be indefinitely sustainable. In this discussion we will address some key issues in the validation of pharmacogenomic classifiers. 

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