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Predicting the effectiveness of antidepressants

Research awardFellows Award programme

Funding period2017 - 2020

Institution Trinity College Dublin



Dr Claire Gillan is creating an internet-based tool which could predict how effective antidepressants will be for different individuals.

The project

Imagine if we could accurately predict which types of antidepressant would be most effective for people living with depression

Right now, one in ten people experience depression in the UK. Millions turn to their doctor each year – but without precise tools to know which treatment is most suitable, many may be given treatment that doesn't work for them. For drug treatments, such as antidepressants - it often takes several attempts before finding something that is works.

This trial and error approach means many people may endure months of little improvement and unnecessary side effects. Recovery and wellbeing often suffers as a result.

So, Dr Claire Gillan at Trinity College Dublin will be investigating whether a simple new tool that uses internet-based techniques could provide the information needed to transform treatment of depression. She’s designing a tool that could make sure the right treatments are reaching the right patients – by predicting how well each individual will respond to treatment.

The process

To create this tool, Claire will collect data online from people who are new to antidepressants.

Participants will share information about themselves online and take part in online cognitive tests to determine and track their symptoms.

This data will enable Claire to create an algorithm to estimate how well people with certain characteristics will respond to specific treatments.

The algorithm will then be tested in local clinics with patients who have been diagnosed with depression – and with other conditions like Obsessive Compulsive Disorder (OCD).

By using the internet in this way, Claire will be exploring important new ways to conduct treatment research.

The potential

Claire’s project has the power to transform the way we personalise treatment for depression – meaning millions of people could be given more effective support.  

As well as enabling people to get better faster, the work could also enable health services to radically reduce the amount spent on ineffective treatments. 

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