A Decision-Theoretic Approach to Panel-Based, Preemptive Genotyping.

We discuss a decision-theoretic approach to building a panel-based, preemptive genotyping program. The method is based on findings that a large percentage of patients are prescribed medications that are known to have pharmacogenetic associations, and over time, a substantial proportion are prescribed additional such medication. Preemptive genotyping facilitates genotype-guided therapy at the time medications are prescribed; panel-based testing allows providers to reuse previously collected genetic data when a new indication arises. Because it is cost-prohibitive to conduct panel-based genotyping on all patients, we describe a three-step approach to identify patients with the highest anticipated benefit. First, we construct prediction models to estimate the risk of being prescribed one of the target medications using readily available clinical data. Second, we use literature-based estimates of adverse event rates, variant allele frequencies, secular death rates, and costs to construct a discrete event simulation that estimates the expected benefit of having an individual's genetic data in the electronic health record after an indication has occurred. Finally, we combine medication prescription risk with expected benefit of genotyping once a medication is indicated to calculate the expected benefit of preemptive genotyping. For each patient-clinic visit, we calculate this expected benefit across a range of medications and select patients with the highest expected benefit overall. We build a proof of concept implementation using a cohort of patients from a single academic medical center observed from July 2010 through December 2012. We then apply the results of our modeling strategy to show the extent to which we can improve clinical and economic outcomes in a cohort observed from January 2013 through December 2015.

Projected impact of a multigene pharmacogenetic test to optimize medication prescribing in cardiovascular patients.

AIM: To determine the projected impact of a multigene pharmacogenetic (PGx) test on medication prescribing. MATERIALS & METHODS: A retrospective analysis was conducted with 122 cardiac catheterization laboratory patients undergoing angiography for eligibility of potential PGx-guided interventions that could have occurred if multigene PGx information was pre-emptively available at the time of the procedure. Medication data and presence of actionable at-risk genotypes were used to determine eligibility of a PGx intervention. RESULTS: 20% of the study population (n = 24) would have qualified for at least one PGx-based medication intervention per US FDA or Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines within 6 months of their cardiac catheterization procedure. Commonly encountered gene-drug pairs for these interventions included: CYP2C19 for clopidogrel and antidepressants, CYP2D6 for antidepressants and codeine, SLCO1B1 for simvastatin, and VKORC1/CYP2C9 for warfarin. CONCLUSION: Pre-emptive use of a multigene PGx test in the cardiac catheterization laboratory offers potential to reduce adverse medication outcomes.

Prevalence of clinically actionable genotypes and medication exposure of older adults in the community.

This study analyzed clinically actionable pharmacogenotypes for clopidogrel, warfarin, statins, thiopurines, and tacrolimus using microarray data for 2121 participants (55-85 years) from the Australian Hunter Community Study (HCS). At least 74% of participants (95% confidence interval [CI]: 72%-76%) had strong level evidence for at least one medium- or high-risk actionable genotype that would trigger a change in standard therapy under current international recommendations. About 14% of these participants (95% CI: 12%-16%) were taking medication potentially affected by the genotype in question. Furthermore, ~2.6% of all participants with medication data (95% CI: 1.4%-3.8%) had a high-risk clinically actionable genotype for a medication to which they were exposed. This represents a considerable number of people at the population level. Although relationships between genotype and health outcomes remain contentious, pharmacogenotyping of multiple variants simultaneously may have considerable potential to improve medication safety and efficacy for older people in the community.

Genomics of Adverse Drug Reactions.

Adverse drug reactions (ADRs) are common, are associated with morbidity and mortality, and are costly to healthcare systems. Genomic factors predispose to ADRs, but these vary depending on the drug, patient, and disease. Genomic testing can not only help to predict and prevent ADRs but can also be used in other ways (diagnosis, closer monitoring of those at risk, pre-emptive genotyping, and understanding of mechanism), all of which will be important in the future to improve the benefit-risk ratio of drugs. In the era of precision medicine, such genomic data will need to be integrated with other forms of data to develop a comprehensive and integrated approach to improve responses to medicines used in patients.