A European Union-funded team of researchers is using pharmacogenomics technologies to identify genetic differences in patients with depression that may help predict how individuals respond to certain antidepressants.
The researchers are also trying to line up Roche Diagnostics to help with the research — a catch that would increase the likelihood that results from this study may eventually lead to a molecular diagnostic for drug response.
“The idea is that we should be able to tailor the treatments to individuals based on their genetic profile,” said Peter McGuffin, a professor and director at the Institute of Psychiatry at King’s College London, and the study’s coordinator.
The three-year project, called Gendep, is funded with €7.5 million ($8.9 million) and involves researchers at 16 different medical schools, research centers, and companies in eight European countries.
The two-pronged project comprises a human and an animal study. For the human study, researchers will study 1,000 individuals with recent-onset depression who are otherwise healthy. They will be randomly assigned to receive either nortriptyline, a tricyclic antidepressant that mainly affects noradrenaline reuptake, which goes by the brand names Aventil and Pamelor, or escitalopram, a selective serotonin reuptake inhibitor also known as Lexapro.
Today, around 60 percent of patients prescribed antidepressants respond to them, according to McGuffin, and as many as half of all patients develop side effects ranging from headache and nausea to heart arrhythmias and sexual dysfunction. Approximately 18.8 million adults in the United States, or about 9.5 percent of the US population aged 18 and older in a given year, have a depressive disorder, according to the National Institute of Mental Health.
“This has been one of the goals” of psychiatric research, said Jules Asher, a spokesman for NIMH, referring to the development of molecular diagnostics that can predict drug response.
Initially, the scientists will pinpoint variations in about a dozen genes in these patients and perform association studies to find links between drug response and adverse events. These genes will include “the usual suspects,” according to McGuffin, for example cytochrome genes, the serotonin transporter, and the noradrenaline transporter.
“In the second stage of genotyping, we hope to be much more exploratory,” he said. To come up with candidate genes for this stage, the team will analyze peripheral blood mononuclear cells and look for changes in gene and protein expression after drug treatment.
Proteome Sciences, a London-based company, will pocket €438,000 of the funding to perform the proteomics analysis. Gene- and protein-expression studies may turn up not only additional candidates for genotyping, but also potentially novel drug targets for depression.
The researchers will also study drug response in rats and mice using a number of behavioral tests. By measuring gene-expression changes in the brain and peripheral cells of the animals, the researchers hope to find out if gene expression in human peripheral cells accurately reflects changes in the brain. GlaxoSmithKline in Italy and England is going to perform the gene expression analysis in animals.
Eventually, the scientists hope to develop a prognostic test, based on a genotyping profile. Originally, a small Irish company called HiberGen was going to develop a diagnostic kit, McGuffin said, but the company went out of business.
Currently, the research team is negotiating with Roche Diagnostics, which would join the group but not receive any of its government funding. The company has already won “approval in principle” from the EU to join the consortium, McGuffin said. This would enable the researchers to gain access to Roche’s clinical material and the Affymetrix system for genotyping cytochrome genes, McGuffin said. “We hope that that may lead on to a partnership with them about developing diagnostic testing kits,” he added.
According to McGuffin, this is the first depression genotyping study of this scale, providing enough power to detect many of the effects of gene variations.