Pharmacoeconomic evaluations of pharmacogenetic and genomic screening programmes: a systematic review on content and adherence to guidelines.

The fields of pharmacogenetics and pharmacogenomics have become important practical tools to progress goals in medical and pharmaceutical research and development. As more screening tests are being developed, with some already used in clinical practice, consideration of cost-effectiveness implications is important. A systematic review was performed on the content of and adherence to pharmacoeconomic guidelines of recent pharmacoeconomic analyses performed in the field of pharmacogenetics and pharmacogenomics. Economic analyses of screening strategies for genetic variations, which were evidence-based and assumed to be associated with drug efficacy or safety, were included in the review. The 20 papers included cover a variety of healthcare issues, including screening tests on several cytochrome P450 (CYP) enzyme genes, thiopurine S-methyltransferase (TMPT) and angiotensin-converting enzyme (ACE) insertion deletion (ACE I/D) polymorphisms. Most economic analyses reported that genetic screening was cost effective and often even clearly dominated existing non-screening strategies. However, we found a lack of standardization regarding aspects such as the perspective of the analysis, factors included in the sensitivity analysis and the applied discount rates. In particular, an important limitation of several studies related to the failure to provide a sufficient evidence-based rationale for an association between genotype and phenotype. Future economic analyses should be conducted utilizing correct methods, with adherence to guidelines and including extensive sensitivity analyses. Most importantly, genetic screening strategies should be based on good evidence-based rationales. For these goals, we provide a list of recommendations for good pharmacoeconomic practice deemed useful in the fields of pharmacogenetics and pharmacogenomics, regardless of country and origin of the economic analysis.