Logistics and cost-effectiveness of pharmacogenomics (PGx)-guided prescribing may be optimized when delivered in a preemptive panel approach.
Barriers impeding implementation of a preemptive PGx-panel approach include the lack of evidence of (cost-)effectiveness, the undetermined optimal target population and timing for delivering PGx, and the lack of tools supporting implementation.
Developments in sequencing and artificial intelligence will further improve the predictive utility of genetic variation to predict drug response.
Although drug treatment is often successful, adverse drug reactions (ADRs) and lack of efficacy present a significant burden for individual patients and society as a whole. ADRs are an important cause of emergency department visits and hospital admissions. A study in 2 large UK hospitals showed that 6.5% of hospital admissions were attributable to ADRs
. In the United States, ADR-related morbidity and mortality have been estimated at $30 billion to $136 billion annually . In parallel, lack of efficacy also results in a significant burden. Its magnitude can be estimated by inspecting the number needed to treat of commonly used drugs , which are commonly more than 10. As a result, most patients will not benefit from drug treatment and, in contrast, may experience harm from unsuccessfully treated disease. It has been estimated that $100 billion a year is wasted on ineffective drug treatment
Precision medicine aims to individualize or stratify application of pharmacotherapy, as opposed to the current population-based application, in an effort to optimize the benefit/risk ratio
, . By enabling identification of individuals who are at higher risk for ADRs or lack of efficacy, before drug initiation and potential harm, an individualized dose and drug selection may be applied to reduce this risk. An individual’s germline genetic variation is a particularly promising predictive factor that can enable drug response prediction. This notion is supported by its pharmacologic plausibility and has been demonstrated in various studies
. Drug-gene interactions (DGIs) can be categorized into 3 groups ( Fig. 1 A–C ): pharmacokinetic-dependent ADRs (see Fig. 1 A), pharmacodynamic-dependent ADRs (see Fig. 1 B), and idiosyncratic ADRs (see Fig. 1 C).