Researchers develop algorithm able to identify antidepressant suitability

pharmafile | July 18, 2018 | News story | Research and Development McLean, algorithm, antidepressants, research 

A study published by researchers at McLean Hospital in Belmont, Massachusetts has led to the development of a statistical algorithm that is able to identify which patients may respond best to antidepressants before they begin treatment.

The study published on 2 July 2018 sought to determine which individuals respond best to antidepressants. Participants were randomly administered with either a common antidepressant medication or a placebo.

The demographic and clinical characteristics of individuals were collected prior to the start of treatment by the study team. Using this information, an algorithm was developed which predicted that approximately one-third of individuals would derive a meaningful therapeutic benefit from antidepressant medication in comparison with a placebo.

Dr Christian Webb, Director of Treatment and Etiology of Depression in Youth Laboratory at McLean Hospital explained that the results were like many previous clinical trials in that, “we found relatively little difference in average symptom improvement between those individuals randomly assigned to the medication versus placebo”. However, he noted that, “for the one-third of individuals predicted to be better suited to antidepressants, they had significantly better outcomes if they happened to be assigned to the medication rather than the placebo.”

The study found that those who responded best to antidepressants were individuals who were older and more likely to be employed and that they were characterised by more severe depression and negative emotionality while also demonstrating better cognitive control on a computerised task.

Dr Madhukar Trivedi of UT Southwestern Medical Centre added that: “These results bring us closer to identifying groups of patients very likely to benefit preferentially from an SSRI and could realise the goal of personalising antidepressant treatment selection.”

Webb’s team are now adapting the algorithm for use in a clinical setting.

Webb commented that: “Our mission is to use these data-driven algorithms to provide clinicians and patients with useful information about which treatment is expected to yield the best outcome for this specific individual,” noting that research similar to this may further the goal of creating “personalised medicine” in health care. “Rather than using a one-size-fits-all approach, we’d like to optimise our treatment recommendations for individual patients.”

Louis Goss

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