Cited by 369Treatment of Major DepressionPsychotherapy Techniques and ApplicationsAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes

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Personalized Medicine for Depression: Can We Match Patients With Treatments?

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Simon, Gregory E., Perlis, Roy H.·2010·American Journal of Psychiatry

DOI 10.1176/appi.ajp.2010.09111680PMC PMC3723328

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Abstract

OBJECTIVE: Response to specific depression treatments varies widely among individuals. Understanding and predicting that variation could have great benefits for people living with depression. METHOD: The authors describe a conceptual model for identifying and evaluating evidence relevant to personalizing treatment for depression. They review evidence related to three specific treatment decisions: choice between antidepressant medication and psychotherapy, selection of a specific antidepressant medication, and selection of a specific psychotherapy. They then discuss potential explanations for negative findings as well as implications for research and clinical practice. RESULTS: Many previous studies have examined general predictors of outcome, but few have examined true moderators (predictors of differential response to alternative treatments). The limited evidence indicates that some specific clinical characteristics may inform the choice between antidepressant medication and psychotherapy and the choice of specific antidepressant medication. Research to date does not identify any biologic or genetic predictors of sufficient clinical utility to inform the choice between medication and psychotherapy, the selection of specific medication, or the selection of a specific psychotherapy. CONCLUSIONS: While individuals vary widely in response to specific depression treatments, the variability remains largely unpredictable. Future research should focus on identifying true moderator effects and should consider how response to treatments varies across episodes. At this time, our inability to match patients with treatments implies that systematic follow-up assessment and adjustment of treatment are more important than initial treatment selection.

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Interindividual differences in treatment effects based on structural equation models with latent variables: an EffectLiteR tutorial
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“since the introduction of the Hamilton Depression Rating Scale (HDRS), and we lack useful markers of disease or treatment response which is a serious challenge to the hope of being better able to match treatment to individual circumstances or illness profile (Simon and Perlis 2010).”
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Pretreatment Differences in BOLD Response to Emotional Faces Correlate with Antidepressant Response to Scopolamine
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“Currently utilized antidepressant agents produce varied clinical responses in patients with major depressive disorder (MDD), and often multiple trials are necessary before an effective therapy is identified (Insel and Wang, 2009; Simon and Perlis, 2010).”
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A Patient Stratification Approach to Identifying the Likelihood of Continued Chronic Depression and Relapse Following Treatment for Depression
Saunders, R., Cohen, Z. D., Ambler, G., DeRubeis, R. J., Wiles, N., Kessler, D., Gilbody, S., Hollon, S. D., Kendrick, T., Watkins, E., et al. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11121295 · Cited by 21
“Although pharmacogenetics may hold promise for personalizing antidepressant treatments [15,16], it is becoming increasingly common that antidepressants are prescribed indefinitely for all patients with a history of previous relapses, to mitigate the risks of chronic illness and relapse [11,12,17,18].”
Background
A systematic review of predictors and moderators of treatment outcomes in internet- and mobile-based interventions for depression
Sextl-Plötz, T., Steinhoff, M., Baumeister, H., Cuijpers, P. J., Ebert, D. D., & Zarski, A. (2024) · Internet Interventions · doi:10.1016/j.invent.2024.100760 · Cited by 26
“A solution to optimize individual outcomes of IMIs for depression is to identify and explain differential treatment responses and personalize interventions by assigning and tailoring them according to patients' needs (Insel, 2009; Simon and Perlis, 2010).”
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Wang, Y., Wei, J., Chen, T., Yang, X., Zhao, L., Wang, M., Dou, Y., Du, Y., Ni, R., Li, T., et al. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.907034 · Cited by 21
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Differences in Quality of Life Outcomes Among Depressed Spinal Cord Injury Trial Participants
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¡Enjaulad a Dodo, por favor! El cuento de que todas las psicoterapias son igual de eficaces
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“Treatment-related predictors and variables have been of increased focus in research, and aspects such as credibility and adherence have been associated with positive treatment outcome (Brandenburg, 2017; Hedman et al., 2012; Meyer et al., 2002; Simon & Perlis, 2010).”
Background
The impact of personality disorder pathology on the effectiveness of Cognitive Therapy and Interpersonal Psychotherapy for Major Depressive Disorder
van Bronswijk, S. C., Lemmens, L. H. J. M., Viechtbauer, W., Huibers, M. J. H., Arntz, A. R., & Peeters, F. P. M. L. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2017.08.043 · Cited by 20
“A major challenge in health care research today is to select the best treatment option for a given individual, a concept referred to as personalized medicine (Simon and Perlis, 2010).”
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“Identifying TRD may be key to developing personalized depression treatment that can improve the efficiency of treatment planning and resource allocation as well as outcomes (Simon and Perlis, 2010).”
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Response to Past Depression Treatments Is Not Accurately Recalled
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“At this time we have no evidence-based criteria for selecting specific treatments for individual patients (5, 6).”
Self-citation
Genome-wide approaches to antidepressant treatment: working towards understanding and predicting response
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“Simon GE, Perlis RH: Personalized medicine for depression: can we match patients with treatments?”
BackgroundInfluential
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“Psychological scientists have long called for idiographic research methods 13–15 , with such calls receiving renewed enthusiasm as the field of clinical psychology moves towards personalized-medicine approaches 8,16,17 .”
Background
Moderators of the Efficacy of a Psychosocial Group Intervention for Children With Chronic Illness and Their Parents: What Works for Whom?
Scholten, L., Willemen, A. M., Napoleone, E., Maurice‐Stam, H., Last, B. F., van Dijk‐Lokkart, E. M., Zandbelt, N., Ensink, E., Grootenhuis, M. A., & Schuengel, C. (2014) · Journal of Pediatric Psychology · doi:10.1093/jpepsy/jsu092 · Cited by 17
Do comorbid social and other anxiety disorders predict outcomes during and after cognitive therapy for depression?
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“Following the concept outlined by Simon and Perlis (2010), these connectivities could be characterized as general predictors of prognosis or general predictors of treatment response.”
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“Whereas establishing the average effectiveness of a treatment is a necessary step, a major goal in the era of personalized and precision medicine is to identify the treatment best-suited for each individual (Simon and Perlis, 2010).”
Background
Personality Disturbances in Melancholic and Nonmelancholic Unipolar Major Depression
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“In addition, the inconclusiveness of the results might also be related to inadequate statistical power in some studies (Simon and Perlis, 2010).”
Result
Leveraging Single-Case Experimental Designs to Promote Personalized Psychological Treatment: Step-by-Step Implementation Protocol with Stakeholder Involvement of an Outpatient Clinic for Personalized Psychotherapy
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“In this regard and analogous to personalized medicine (Simon & Perlis, 2010), personalizing psychological treatments to the individual patient might be a promising approach to achieve this goal (Chekroud et al., 2021).”
N100 as a response prediction biomarker for accelerated 1 Hz right DLPFC-rTMS in major depression
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A translational model of research-practice integration.
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“…whom a particular treatment might have differential effects) have been proposed as a top priority for research by National Institute of Mental Health (2008, Strategy 3.4, http://www.nimh.nih.gov/ research-funding/research-priorities/strategy-3-4.shtml; Hollon & Ponniah, 2010; Simon & Perlis, 2010).”
Influential
Functional Impairment and Changes in Depression Subtypes for Women in STAR*D: A Latent Transition Analysis
Ulbricht, C. M., Rothschild, A. J., & Lapane, K. L. (2015) · Journal of Women s Health · doi:10.1089/jwh.2015.5361 · Cited by 13
“Identifying distinct depression subtypes based on clinically observable characteristics could improve the ability to predict who will benefit from which treatments.(15,20,21) The extent to which proposed subtypes or even individual symptoms change over time is uncertain.”
Background
Imagine your mood: Study design and protocol of a randomized controlled micro-trial using app-based experience sampling methodology to explore processes of change during relapse prevention interventions for recurrent depression
Slofstra, C., Klein, N. S., Nauta, M. H., Wichers, M., Batalas, N., & Bockting, C. (2017) · Contemporary Clinical Trials Communications · doi:10.1016/j.conctc.2017.07.003 · Cited by 12
A personalized index to inform selection of a trauma-focused or non-trauma-focused treatment for PTSD
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Covariate Selection for Estimating Individual Treatment Effects in Psychotherapy Research: A Simulation Study and Empirical Example
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“Although multiple treatments may be equally effective on average, patients differ substantially in how much they profit from a specific treatment (Kravitz et al., 2004; Schwartz et al., 2021; Simon & Perlis, 2010).”
Background
Moderators of Treatment for Adolescent Depression
Meyer, A. E., & Curry, J. F. (2020) · Journal of Clinical Child & Adolescent Psychology · doi:10.1080/15374416.2020.1796683 · Cited by 12
“Personalized medicine (Simon & Perlis, 2010) involves matching treatments to the individual characteristics of patients, recalling the question posted more than a halfcentury ago regarding psychotherapy: “What treatment, by whom, is most effective for this individual with that specific problem, and…”
Background
A large-scale genome-wide gene expression analysis in peripheral blood identifies very few differentially expressed genes related to antidepressant treatment and response in patients with major depressive disorder
Nøhr, A. K., Lindow, M., Forsingdal, A., Demharter, S., Nielsen, T. T., Buller, R., Moltke, I., Vitezic, M., & Albrechtsen, A. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01002-9 · Cited by 12
“Individual response to a specific treatment can vary widely, and today limited evidence can guide the choice of one medication over another based on greater effectiveness and efficacy [7, 8].”
Background
How well do clinicians and patients agree on depression treatment outcomes? Implications for personalized medicine
Dunlop, B. W., McCabe, B., Eudicone, J. M., Sheehan, J. J., & Baker, R. A. (2014) · Human Psychopharmacology Clinical and Experimental · doi:10.1002/hup.2428 · Cited by 12
“Personalized medicine aims to measure biomarkers or clinical variables that can be used to choose between treatment options, selecting the one that is most likely to maximize the outcome for an individual, rather than the “average” outcome (Simon and Perlis, 2010).”
Background
Latent Profiles of Cognitive and Interpersonal Risk Factors for Adolescent Depression and Implications for Personalized Treatment
Gunlicks-Stoessel, M., Eckshtain, D., Lee, S., Reigstad, K. M., Mufson, L., & Weisz, J. (2019) · Journal of Abnormal Child Psychology · doi:10.1007/s10802-019-00552-3 · Cited by 11
Role of autobiographical memory in patient response to cognitive behavioural therapies for depression: protocol of an individual patient data meta-analysis
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Nitrous oxide as a putative novel dual-mechanism treatment for bipolar depression: Proof-of-concept study design and methodology
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Impact of Medicare Part D Plan Features on Use of Generic Drugs
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“Although antidepressants have similar comparative effectiveness, on average, these agents are not equally effective at the individual level and patients with depression may try multiple antidepressants before finding one that works.(40,41) As a result, physicians may be reluctant to engage in therapeutic substitution…”
Background
The predictors of change in reflective parenting therapy: Uncovering the influence of parental reflective functioning and child temperament in predicting the improvement in parent–child relationship and child outcome following DUET group intervention
Menashe‐Grinberg, A., & Atzaba‐Poria, N. (2022) · Development and Psychopathology · doi:10.1017/s0954579422000566 · Cited by 14
Temporal multi-step predictive modeling of remission in major depressive disorder using early stage treatment data; STAR*D based machine learning approach
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A mathematical formula of plasticity: Measuring susceptibility to change in mental health and data science
Branchi, I. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105272 · Cited by 10
Congruency of multimodal data-driven personalization with shared decision-making for StayFine: individualized app-based relapse prevention for anxiety and depression in young people
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“So far, however, identiﬁcation of treatment moderators has borne less robust results (14, 22–24 ).”
Background

Authors: Simon, G. E., & Perlis, R. H.
Affiliations: Kaiser Permanente Washington Health Research Institute (US)
ORCID: Simon, Gregory E.
Perlis, Roy H.
Venue: American Journal of Psychiatry
Publisher: American Psychiatric Association Publishing
Published: 2010-12
Volume: 167
Issue: 12
Pages: 1445-1455
ISSN: 0002-953X (ISSN-L)
1535-7228
DOI: 10.1176/appi.ajp.2010.09111680
PMC: PMC3723328
OpenAlex: W2148933229
Semantic Scholar: ca7063c77ba1833df2d5629f27cb2392af0da971
Cited by: 365
References: 89
Citations by year: 2026: 17 · 2025: 16 · 2024: 34 · 2023: 21 · 2022: 29 · 2021: 29 · 2020: 30 · 2019: 24 · 2018: 21 · 2017: 17 · 2016: 25 · 2015: 24 · 2014: 25 · 2013: 17 · 2012: 26
FWCI: 1.87
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: American Journal of Psychiatry (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: Eli Lilly and Company
National Institute of Mental Health (P20MH068572, R01MH085930, R01MH086026)
Topics: Health Sciences › Medicine › Pharmacology (medical) › Treatment of Major Depression
Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Keywords: depression, personalized medicine, treatment response variability, antidepressants, psychotherapy, treatment selection, treatment moderators, predictors of treatment outcome, genetic predictors, treatment modification
MeSH terms: Antidepressive Agents, Clinical Trials as Topic, Decision Support Techniques, Depression, Humans, Psychotherapy, Precision Medicine

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Personalized Medicine for Depression: Can We Match Patients With Treatments?

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