Active Mental Health NIHR-supported project Psychology & Behaviour

The PRADA project (Prescribing the Right Antidepressant for Depression in Adult)

Funder NIHR (part of DHSC)
Recipient organisation NIHR Oxford Health Biomedical Research Centre
NIHR support Recorded as supported by this research centre
Period May 2025 — May 2030

In plain English

AI plain-English summary

Doctors currently choose antidepressants for depressed patients largely by trial and error, often cycling through several drugs before finding one that works. The PRADA project is building a web-based tool that adds a patient’s genetic information to clinical and social factors, aiming to predict which antidepressant will work for that person from the start. The problem is stark: roughly half of patients do not respond to their first antidepressant, and the delay in finding an effective drug can worsen depression and increase suicide risk. Current prescribing ignores the genetic differences that influence how people metabolise and respond to medications. The researchers have already developed a tool using clinical and social data; this project adds low-cost DNA sequencing to make it more precise. If successful, the tool could transform depression treatment from guesswork into personalised medicine. A SMART trial with 2,055 participants in Nigeria, Pakistan, and the UK will test whether pharmacogenetic information improves real-world outcomes, with the primary measure being treatment discontinuation at eight weeks. The team will also collect mood and quality-of-life data over 52 weeks using the mindLAMP app, and study how the tool works across different cultural contexts. This could make effective prescribing routine even in low-resource countries.

View original technical description
Having developed a web-based tool that integrates socio-demographic and clinical measures, and includes patient preferences to personalise pharmacological treatment for depression in adults, we now aim to create an accessible multi-modal tool that will be scalable and widely usable by adding genetic predictors. We hypothesize it will result in better clinical outcomes. Pilot data show that easy-to-collect pharmacogenetic and polygenic score information can aid stratification of people experiencing depression. This can be collected using low-cost DNA-sequencing technology, in all clinical settings. A sequential, multiple-assignment, randomised (SMART) factorial trial with 2,055 depressed participants from Nigeria, Pakistan and UK will address these questions: (1) Can pharmacogenetic information personalise pharmacological treatment and improve real-world clinical outcomes? (2) What is the best adaptive therapeutic strategy in the early treatment process? (3) Can polygenic scores improve the long-term outcome of patients with depression? The primary outcome is treatment discontinuation at 8 weeks. We will also collect passive (sensor) and active (survey/questionnaire) outcome data on mood, anxiety and quality of life using mindLAMP (docs.lamp.digital) over 52 weeks. We will investigate the tool's acceptability and optimisation for different cultural contexts and gather insights regarding perception of depression. These data will support implementation globally, including low resource countries.

Researchers

Andrea Cipriani (Principal Investigator)

Related Research

Grants with similar aims, by meaning.

Using adverse events and patient preferences to personalise pharmacological treatment for depression
Patient-reported outcome measures for monitoring primary care patients with depression: PROMDEP randomised controlled trial
The Computational Psychiatry of Major Depressive Disorder
Digital delivery of Behavioural Activation to overcome depression and facilitate social and economic transitions
Decoding Depression: Integrating Genetics, Environment, and Clinical Data to Predict Prognosis and Treatments

Original classification

Data Science

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