Dopamine Receptor Gene Influences Response to Methylphenidate Therapy in ADHD
DRD4 7-repeat allele is associated with greater behavioral improvement according to reports
Attention-deficit hyperactivity disorder (ADHD) is a relatively common juvenile neurobehavioral disease, thought to affect 3–5% of children worldwide. Its main symptoms are ‘inattention, hyperactivity and impulsivity‘, although, as with many such behavioral disorders, precise classification can be somewhat subjective. Although research into this disorder has attracted controversy on several occasions, progress has nevertheless been made in its diagnosis and treatment. Methylphenidate (MPH) is a commonly prescribed ADHD medication, generally producing good results, although there is a great deal of interindividual variation in terms of optimal dosage.
In a presentation given at the 2010 Annual Meeting of the Pediatric Academic Societies (Vancouver, BC, Canada), Professor Tanya Froehlich (University of Cincinnati College of Medicine, OH, USA) explained the recent progress that she and her colleagues had made in determining the mechanism behind this interindividual variation.
At present, the method for determining optimal MPH dose involves simply starting at a standard initial dose and titrating from there. In Professor Froehlich‘s words, “We don‘t have a good way of predicting who will experience great improvement in ADHD symptoms with a particular medication, so we use a trial-and-error approach”. This may have started to change, however, with the recent discovery by Froehlich‘s group of a relationship between the 7-repeat variant of the dopamine receptor D4 (DRD4) gene and response to MPH.
The receptor coded for by DRD4 is a common target for a variety of psychiatric medications, and the 7-repeat variant – claimed to interact less strongly with dopamine – has previously been analyzed for association with novelty-seeking behavior, with inconclusive overall results. Froehlich‘s group genotyped 89 children diagnosed with ADHD for the DRD4 7-repeat allele and then collected parent and teacher assessments of ADHD symptoms over a 4-week period, comparing MPH treatment against placebo. The outcome, as it was expressed in the presentation, was that “children with the DRD4 7-repeat allele showed more improvement in teacher-reported ADHD symptoms at increasing MPH doses than those lacking the 7-repeat”.
This finding has the potential to open up ADHD treatment to further pharmacogenomic analysis, possibly leading to the identification of more genetic variations with an impact on treatment, and eventually the addition of ADHD to the list of diseases for which personalized therapy can be fully implemented.
As Professor Froehlich concluded, “…we might be able to predict treatment course, tailor our approach to each child, and improve symptom response while decreasing health care costs.”
Sources: Cincinnati Children‘s Hospital press release: www.cincinnatichildrens.org/about/news/release/2010/ADHD-drug-05–01–2010.htm; Froehlich TE, Kahn RS, Nick TG et al.: Dopamine receptor D4 genotype and methylphenidate dose response in children with ADHD. Presented at: 2010 Annual Meeting of the Pediatric Academic Societies. Vancouver, BC, Canada, 1 May 2010.
Low-Cost Complete Genome Sequencing Followed up with Comprehensive Clinical Risk Analysis
Although the sequencing and analysis of the relevant portions of a patient‘s genome in order to help decide how best to treat their illness is not a novel concept (indeed, it is one of the basic practices of pharmacogenomics), the recent activity of Professors Euan Ashley and Stephen Quake (Stanford School of Medicine, CA, USA) has attracted considerable attention.
While Professor Quake is currently healthy, a decision was taken to fully sequence his genome and subsequently analyze it in order to assess his risk for developing numerous diseases, and his likely reaction to a variety of medications. This prospective testing and analysis of an individual‘s genotype is unprecedented, and may signal the start of a trend for each individual to obtain an affordable, personalized genomic and clinical ‘risk assessment‘. Professor Ashley expressed this more succinctly: “The $1,000 genome is coming fast”.
Possessing an individual‘s genome sequence data and clinical records is one thing, but being able to make meaningful use of that information is quite another. “The challenge lies in knowing what to do with all that information”, commented Professor Ashley, “We‘ve focused on establishing priorities that will be most helpful when a patient and a physician are sitting together looking at the computer screen”.
In terms of potential for usefulness, the practical benefits are likely to be significant should such combined risk assessment become widespread.
“Patients at risk for certain diseases will be able to receive closer monitoring and more frequent testing, while those who are at lower risk will be spared unnecessary tests”, opined Professor Hank Greely (Stanford Center for Law and the Biosciences, CA, USA). “This will have important economic benefits as well, because it improves the efficiency of medicine.”
Despite this positive outlook, a potential pitfall of the process is likely to arrive when individuals begin to receive less positive test results; the long-standing ethical issues surrounding this area have suddenly jumped further from the theoretical to the practical.
Sources: Stanford University Press Release. http://med.stanford.edu/ism/2010/april/genome.html; Ashley EA, Butte AJ, Wheeler MT et al.: Clinical assessment incorporating a personal genome. Lancet 375(9725), 1525–1535 (2010)
HMGCR Polymorphism Determines Efficacy of Statins as Preventative Therapy for Colon Cancer
Statins, a class of drugs used primarily to lower plasma cholesterol levels, have been known for some time to possess several other significant properties in terms of disease prevention and treatment. One such alternative application that has been suggested is their use as a preventative therapy for colon cancer, although studies in the field have thus far produced conflicting and/or weak results.
The cause of these underwhelming results may be a polymorphism in the gene that codes for HMG–CoA reductase (HMGCR), which is claimed to influence the protective effect statins exert against colorectal cancer. HMG–CoA reductase is the rate-limiting enzyme in cholesterol synthesis, and a target for statins in cholesterol level therapy.
The group led by Professor Stephen Gruber (Director, University of Michigan Comprehensive Cancer Center, MI, USA) genotyped over 4000 individuals in Northern Israel, split approximately evenly between colorectal cancer patients and nonsufferers, and asked them about their statin use. The investigators concluded that the gene that determines the efficacy of statins in cholesterol control also determines their efficacy in preventing colorectal cancer, with the long version of the gene providing greater effectiveness in both cases.
Professor Gruber was keen to point out, however, that the test concerns prevention rather than diagnosis; “The gene test by itself doesn‘t predict whether you‘re at an increased risk of colon cancer; it predicts only how well statins lower the risk”.
This development is good news for those individuals at risk of colorectal cancer who possess the correct genotype to gain increased benefit from statin therapy, especially given the relatively minor side effects. While plasma cholesterol levels can be monitored and managed with a variety of different methods, cancer risk is much more difficult to quantify, and impossible to assess specifically at a given time with any great degree of accuracy, making this genetic test extremely useful.
Sources: University of Michigan press release: http://www2.med.umich.edu/prmc/media/newsroom/details.cfm?ID=1557; Lipkin SM, Chao L, Moreno V et al.: Genetic variation in 3-hydroxy-3-methylglutaryl CoA reductase modifies the chemopreventive activity of statins for colorectal cancer. Cancer Prev. Res. 3(5), DOI: 10.1158/1940-6207.CAPR-10-0007 (2010).
Systems Biology Provides Novel Genetic Framework for ADR Prediction in Long QT Syndrome and Beyond
Adverse drug reactions (ADRs) are a major problem claimed to account for a significant proportion of hospital visits worldwide. When determining therapy, the risk factor of the available drugs must be taken into account in order to make the best selection. Along with lifestyle, environment and other medications, a significant factor in the occurrence of ADRs is the patient‘s genotype, and it has long been a goal of pharmacogenomics research to elucidate the genetic mechanisms and risk factors involved.
Professor Ravi Iyengar (Mount Sinai School of Medicine, NY, USA) and his group of researchers may have produced another tool for this effort with their announcement of a new genetic framework for associating adverse reactions with the gene–drug combinations that can cause them.
The group initially focused on drugs that may cause cardiac arrhythmia via long QT syndrome (LQTS). LQTS is a disorder that can be congenital or drug induced or both, and which causes a delay in the repolarization of heart tissue after a heartbeat, potentially leading to fatal arrhythmia. There are several genes associated with LQTS, each of which can produce the end result detailed above via the modification of various cellular ion channels in different ways.
Given the seriousness of the potential side effects, investigation of this disorder is a high priority, and LQTS studies are nothing new. The work of Professor Iyengar‘s group, however, is interesting in that it “demonstrates a practical, real-life application of systems biology”, according to Dr Sarah Dunsmore (National Institute of General Medical Sciences, MD, USA). Although the potential applications of systems biology are extremely interesting, most have yet to be implemented, so progress towards this goal is certainly noteworthy.
The group isolated the 1629 proteins they believed to be associated with QT from the human interactome, a significant fraction of which were also known to be associated with other disorders, including several with relevance to cardiac arrhythmia. This was interesting in itself, as it helped to delineate the borders of each category of disorder, as well as highlighting proteins ‘shared‘ between more than one, thus providing information about potential comorbidity.
The next step was to apply the information concerning LQTS-associated proteins to a variety of databases in order to link them to previously reported ADRs and therefore to the relevant drugs. From here, it may be possible to discern patterns that reveal further potentially risky drugs or SNPs, which may be useful to both individual doctors when making decisions about how best to treat their patients, and to companies in the early stages of drug development. As Professor Iyengar commented, “By doing a network analysis, one can start to figure out the common mechanism”.
Although the focus of this project has been on LQTS, there is no reason why the framework cannot be adapted in order to apply to other areas. Indeed, Professor Iyengar has announced that the next move will be to analyze cancer drug ADRs.
Sources: National Institute of General Medical Sciences press release: http://publications.nigms.nih.gov/computinglife/genetic_framework.htm; Berger SI, Ma‘ayan A, Iyengar R. Systems pharmacology of arrhythmias. Sci. Signal. 3(118), RA30 (2010).