DOI: 10.25934/pr00007541
title: Role of clinical, historical and demographic factors in the response to antidepressants versus placebo

description: Antidepressant selection is presently a trial-and-error process. One of the most common treatments for depression are selective serotonin reuptake inhibitors (SSRI), which are believed to act by increasing levels of serotonin to the brain. Data from seminal clinical trials, such as the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial, demonstrates that after failure of an SSRI, there is an equal probability of response to another SSRI, a serotonin norepinephrine reuptake inhibitor (SNRI), or bupropion. Antidepressants work by increasing the levels of key neurotransmitters in the brain. Common targets include: serotonin, a chemical produced by nerved cells and considered to be a mood stabiliser; norepinephrine, a chemical released by the nervous system in response to stress; and dopamine, produced by the nervous system to send messages between nerve cells. SSRIs increase serotonin levels, whereas SNRIs increase serotonin and norepinephrine, and bupropion increases norepinephrine and dopamine. These are all key neurotransmitters that help improve mood and other symptoms of depression. Despite these similarities and differences in the mechanisms of each of these classes of drugs, we still know relatively little about who responds best to antidepressants and why. As such patients may go through many successive trials of antidepressant medications (ADMs) before finding an effective one for them, without any information to guide this process. This unguided process of treatment selection results in substantial morbidity, increased mortality (including risk of suicide), tremendous patient and provider frustration, and a high risk of the patient dropping entirely out of treatment. As such, the need to establish which treatments are most effective for a patient with major depressive disorder (MDD) is a deeply pressing clinical and societal issue.

Little is known about whether clinical or demographic variables differentially predict treatment outcome. Information gained on this question would further our understanding of the impact and relevance of non-traditional antidepressant mechanisms of action, may help select patients who may optimally respond to one treatment or the other, or guide the development of future novel medicines. We will address this question using aggregated analyses of clinical and demographic data from randomized clinical trials treating MDD. Pooling datasets will allow high statistical power, should reduce bias from specific site / study effects, and allow a broad exploration of different drugs and drug classes. Our statistical methods will be ones common to clinical trial research, as well as those more closely related to simple forms of machine learning. Given the broad relevance of the proposed analyses, meaningful impact is expected for the general population of depressed patients (22M in the US alone), as well as patients with anxiety or trauma-related disorders in which antidepressant treatment is also a mainstay (10M's in the US). Indeed, at present it is estimated that one in eight in the US is on an antidepressant at any given time, thus illustrating both the breadth and need for a precision approach such as that described herein.