Progress and promise of attention-deficit hyperactivity disorder pharmacogenetics
Tanya E Froehlich1, James J McGough, Mark A Stein
1Division of Developmental and Behavioral Pediatrics, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
None:
One strategy for understanding variability in attention-deficit hyperactivity disorder (ADHD) medication response, and therefore redressing the current trial-and-error approach to ADHD medication management, is to identify genetic moderators of treatment. This article summarizes ADHD pharmacogenetic investigative efforts to date, which have primarily focused on short-term response to methylphenidate and largely been limited by modest sample sizes. The most well studied genes include the dopamine transporter and dopamine D(4) receptor, with additional genes that have been significantly associated with stimulant medication response including the adrenergic alpha(2A)-receptor, catechol-O-methyltransferase, D(5) receptor, noradrenaline (norepinephrine) transporter protein 1 and synaptosomal-associated protein 25 kDa. Unfortunately, results of current ADHD pharmacogenetic studies have not been entirely consistent, possibly due to differences in study design, medication dosing regimens and outcome measures. Future directions for ADHD pharmacogenetics investigations may include examination of drug-metabolizing enzymes and a wider range of stimulant and non-stimulant medications. In addition, researchers are increasingly interested in going beyond the individual candidate gene approach to investigate gene-gene interactions or pathways, effect modification by additional environmental exposures and whole genome approaches. Advancements in ADHD pharmacogenetics will be facilitated by multi-site collaborations to obtain larger sample sizes using standardized protocols. Although ADHD pharmacogenetic efforts are still in a relatively early stage, their potential clinical applications may include the development of treatment efficacy and adverse effect prediction algorithms that incorporate the interplay of genetic and environmental factors, as well as the development of novel ADHD treatments.
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