Developing Tools to Inform Treatment Selection in Depression
Michele Ferrante, Ph.D.
Division of Translational Research (DTR)
This concept aims to accelerate the development of sensitive and specific markers and tools (behavioral, physiological, and biological) to predict individual response among two or more existing therapeutics for depression.
Depressed mood is a highly common symptom observed in multiple psychiatric disorders and is associated with impairment in multiple domains of function (e.g., affective, cognitive, social, sensory, and motor) that are fundamental for achieving a good quality of life. Depression can present in many forms (e.g., anxious distress, melancholic, catatonic, premenstrual, peripartum, and seasonal) and frequently co-occurs with other symptoms, such as mania and psychosis. Several treatment options exist for depression, including medications, psychosocial interventions, and various brain stimulation methods. However, treatment selection relies largely on trial-and-error approaches with each requiring up to three months to determine if the treatment is effective. Still, about 40% of patients treated with antidepressant medication do not achieve adequate symptom reduction after one year. There is an urgent need for tools that integrate pre-treatment measures to predict patients’ treatment responses and aid clinicians in selecting the most appropriate therapeutics. Until such tools are developed, validated, and deployed, rapid symptomatic relief and functional quality-of-life improvement will remain out of reach for many patients.
NIMH aims to support research promoting real-world translation by developing accurate, fast, easy-to-use, and widely accessible biomarkers/tools that can inform the treatment selection by predicting depression treatment response at the individual level. To achieve this goal, this concept aims to implement a long-term, phased, milestone-based pipeline to validate and de-risk novel technologies. This concept will be designed to support best practices for rigor and reproducibility and help accelerate the development, validation, and deployment of these tools across the following stages:
- Preliminary studies using secondary analysis of data from completed clinical trials and/or nimble, fast-fail pilot studies to develop prototype tools.
- Efficacy studies to prospectively test tools in large-scale, highly controlled clinical trials.
- Effectiveness studies in 'real-world' clinical settings e.g., Point-of-Care Technology Research Network.
Ultimately, the long-term goal of this concept is to accelerate the identification and development of a library of candidate tools - for prediction of differential treatment-response in depression - that can withstand rigorous and definitive validation in clinical trials and be confidently used in clinical practice.