that discuss how to assess the impact of pharmacogenomics and evaluate the benefit and risk of new genome-based technology (Burke and Zimmern, 2004; Califf, 2004; Davis and Khoury, 2006; Grosse and Khoury, 2006; Khoury et al., 2008; Phillips, 2006).
An evidence-based framework to evaluate the clinical utility of new genetic tests and treatments is lacking in the current health care infrastructure. The goal of genome-based research is personalized delivery of therapeutics that account for the genetic variation of the patient. This is a long-term new direction in medicine that, Davis said, will play out over many years. Researchers have just begun to see how complicated the genome is. There is much to be learned about the role of polymorphisms, age-dependent changes, methylation, de novo mutations, or gene copies, for example.
Gene-based diagnostic tests are very powerful. They have distinctive risk/benefit profiles, and may have significant unintended effects. Historically, however, genetic tests have been held to a less stringent regulatory standard than pharmacogenetic drugs, which require evidence of improved clinical outcomes to receive Food and Drug Administration approval. Davis stressed that the default for gathering evidence on gene-based diagnostic tests and therapeutics should be a randomized controlled trial (RCT). If an RCT is not feasible, and many times it will not be due to lack of financial and human resources, then population-based observational studies should be conducted.
HMOs, such as Kaiser, evaluate new genetic technologies in similar fashion to what has been done previously for other types of technologies. The first step is to determine if there is good evidence, either from RCTs or observational data, that the technology improves outcomes. Based on a review of the evidence, for example, HMOs are now conducting gene testing for HER-2/neu status of breast cancer tumors. However, a decision about whether to conduct gene testing for polymorphisms involved in the metabolism of the anticoagulant warfarin is still under consideration, pending the results of an ongoing RCT. The second step is to determine whether the new technology improves outcomes in a cost-effective manner. There are no set criteria for what reasonable cost is, and cost is considered relative not only to money, but also to resources and time. An example of a new test that has been determined to be cost effective is the screening test for the presence of the HLA-B*5701 allele that has been shown to be associated with hypersensitivity to the antiretroviral drug abacavir. The results of an RCT (Mallal et al., 2008) showed that HLA-B*5701 screening had a negative predictive value of 100 percent, and a positive predictive value of 47.9 percent, and estimated that 1 out of every 25 to 30 Caucasians will be hypersensitive to abacavir, leading Kaiser to conclude that this test would be cost effective.