Pill cutter: using genetic analysis to optimise antidepressant use
Major depressive disorder (MDD) is ranked by the World Health Organization as the single largest contributor to global disability, responsible for 8.1 % of all years lived with disability in the EU. Each year, 25 % of the EU population suffer from depression or anxiety, with the annual cost of these disorders estimated at EUR 170 billion. Just one third of patients see a remission following their first round of antidepressant treatment, and a further third do not respond to multiple regimens. Correctly predicting which patients will respond to antidepressants could lead to more efficient allocation of resources. It would also enable a more effective response to the needs of resistant patients who could then be directed to alternative treatments more quickly. “We use antidepressants frequently, but we have poor guidance in how to choose these treatments,” says Chiara Fabbri, ESTREA project investigator. The project sought to identify genetic markers that indicate whether a patient can benefit from a course of antidepressants. “It sounds easy, but the thing is this process is influenced not just by one gene, but by many different variants across the genome,” explains Fabbri. The psychiatrist studied genetic and clinical data from 1 346 patients diagnosed with MDD. Using a machine-learning model, she built a statistical framework that can correctly identify up to 73 % of patients with treatment-resistant depression. This system could be used to direct physicians during their first encounter with a patient who exhibits signs of depression. “The standard approach is psychopharmaceuticals, and it’s only when the doctor sees that a patient is not responding to multiple treatments that they decide to try something different,” says Fabbri. These alternatives include pairing antidepressants with psychotherapy, such as cognitive behavioural therapy. “This costs more, but we can target it better,” she adds. “Even if we have to invest more resources and money in the beginning, this is probably going to save money in the medium to long term.” The model would also help those with treatment-resistant depression get the help they need faster, reducing the amount of time they are burdened with the illness. “In this way, we reduce the disability associated with depression, and reduce the direct and indirect costs associated with it as well,” notes Fabbri. Fabbri was supported in her work by the EU’s Marie Skłodowska-Curie Actions programme. “Thanks to this funding, I could work on this project at King’s College London, a leading institution for this kind of genetic analysis,” she adds. “I also developed an improved approach that nobody had used before to analyse this data, and that wouldn’t have been possible either.” Results of the work were published in the journal ‘Translational Psychiatry’. Her approach has been adopted by a team at the University of Bonn in Germany that is also looking into predictors of treatment-resistant depression, and Fabbri hopes to refine her models using data from larger biobanks in the future. “We really lack a way to personalise treatment, so I think it’s an area where there is a real possibility of improvement,” concludes Fabbri. “It’s something that could really make a change, make a difference.”
Keywords
ESTREA, depression, anxiety, genetic, marker, disorder, treatment, antidepressants
