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Personalise antidepressant treatment for unipolar depression combining individual choices, risks and big data (PETRUSHKA): rationale and protocol
  1. Anneka Tomlinson1,
  2. Toshi A Furukawa2,
  3. Orestis Efthimiou3,
  4. Georgia Salanti3,
  5. Franco De Crescenzo1,
  6. Ilina Singh1,
  7. Andrea Cipriani1,4
  1. 1 Department of Psychiatry, University of Oxford, Oxford, UK
  2. 2 Graduate School of Medicine Faculty of Medicine, Kyoto University, Kyoto, Japan
  3. 3 Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
  4. 4 Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
  1. Correspondence to Professor Andrea Cipriani, University of Oxford Department of Psychiatry, Oxford OX3 7JX, UK;{at}


Introduction Matching treatment to specific patients is too often a matter of trial and error, while treatment efficacy should be optimised by limiting risks and costs and by incorporating patients’ preferences. Factors influencing an individual’s drug response in major depressive disorder may include a number of clinical variables (such as previous treatments, severity of illness, concomitant anxiety etc) as well demographics (for instance, age, weight, social support and family history). Our project, funded by the National Institute of Health Research, is aimed at developing and subsequently testing a precision medicine approach to the pharmacological treatment of major depressive disorder in adults, which can be used in everyday clinical settings.

Methods and analysis We will jointly synthesise data from patients with major depressive disorder, obtained from diverse datasets, including randomised trials as well as observational, real-world studies. We will summarise the highest quality and most up-to-date scientific evidence about comparative effectiveness and tolerability (adverse effects) of antidepressants for major depressive disorder, develop and externally validate prediction models to produce stratified treatment recommendations. Results from this analysis will subsequently inform a web-based platform and build a decision support tool combining the stratified recommendations with clinicians and patients’ preferences, to adapt the tool, increase its’ reliability and tailor treatment indications to the individual-patient level. We will then test whether use of the tool relative to treatment as usual in real-world clinical settings leads to enhanced treatment adherence and response, is acceptable to clinicians and patients, and is economically viable in the UK National Health Service.

Discussion This is a clinically oriented study, coordinated by an international team of experts, with important implications for patients treated in real-world setting. This project will form a test-case that, if effective, will be extended to non-pharmacological treatments (either face-to-face or internet-delivered), to other populations and disorders in psychiatry (for instance, children and adolescents, or schizophrenia and treatment-resistant depression) and to other fields of medicine.

  • depression and mood disorders
  • adult psychiatry

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  • Contributors AC conceived and designed the study. TAF, OE, GS and IS contributed to the design of the project. AC drafted the manuscript, and all authors critically revised the manuscript and approved the final version.

  • Funding This study is funded by the National Institute for Health Research (NIHR) Research Professorship awarded to Professor Andrea Cipriani in 2018 (RP-2017-08-ST2-006) and is also supported by the NIHR Oxford Health Biomedical Research Centre (BRC-1215-20005). The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR, or the UK Department of Health.

  • Competing interests No, there are no competing interests.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement There are no data in this work.

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