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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Gitlin, R. S., & Mieczkowski, A. E. (2020) · Springer eBooks · doi:10.1007/978-3-030-50695-7_33 · Cited by 3
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“In clinical practice, treatments for PDD are commonly selected in an unsystematic matter, often based on subjective clinical experience, treatment-preference of patients or trial-and-error approaches [12, 15, 16].”
Background
Depressive symptom factors and their differential association with psychotherapy outcomes in late-life depression: Results of the CBTlate study
Heser, K., Dafsari, F. S., Kleineidam, L., Kuss-Gondorf, K., Luppa, M., Peters, O., Froelich, L., Riedel-Heller, S. G., Schramm, E., Hautzinger, M., et al. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.119790 · Cited by 2
Cross-platform Prediction of Depression Treatment Outcome Using Location Sensory Data on Smartphones
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“However, very few clinical characteristics, biomarkers, or genetic variations have been identified that can reliably predict differential effectiveness of specific depression treatments [12, 29, 54].”
An umbrella review of inflammatory biomarkers and their relationship to treatment response in MDD
Baxter, L., Utulu, N., Utulu, F., Utulu, S., Li, L., Strawbridge, R., Powell, T. R., & Rucker, J. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106806 · Cited by 2
Review: In older adults with acute major depression, SNRIs, but not SSRIs, increase adverse events vs placebo
Simon, G. E. (2019) · Annals of Internal Medicine · doi:10.7326/acpj201910150-039 · Cited by 2
Self-citation
Patient characteristics associated with retrospectively self-reported treatment outcomes following psychological therapy for anxiety or depressive disorders - a cohort of GLAD study participants
Rayner, C., Coleman, J. R. I., Skelton, M., Armour, C., Bradley, J. R., Buckman, J. E. J., Davies, M. R., Hirsch, C. R., Hotopf, M. H., Hübel, C., et al. (2022) · BMC Psychiatry · doi:10.1186/s12888-022-04275-6 · Cited by 2
“Despite evidence for many associated factors, few statistical models can prospectively predict mental health treatment outcomes outside of the settings used to develop the models [22, 23].”
Background
Should We Prevent Off-Label Drug Prescriptions? Empirical Evidence from France
Tunçel, T. (2024) · The Review of Economic Studies · doi:10.1093/restud/rdae060 · Cited by 2
Effect of age on the response to serotonergic and noradrenergic antidepressants: A systematic review, meta-regression and individual participant data pooled analysis
Ossola, P., Gerra, M. L., Luviè, L., Piacente, A., Marchesi, C., Schoretsanitis, G., & Stewart, J. W. (2025) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2025.02.013 · Cited by 2
Status and Prospects for Psychopharmacology
Baldessarini, R. J. (2012) · Springer eBooks · doi:10.1007/978-1-4614-3710-9_5 · Cited by 2
Psychiatric Pharmacogenomics
Perlis, R. H. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-800105-9.00044-5 · Cited by 2
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Stepped Care for Depression: A Systematic Review and Feasibility Study
Hill, J. J. (2016) · Open Research Exeter (University of Exeter) · Cited by 2
Does psychological process change during psychotherapy predict long-term depression outcome after successful cognitive therapy or interpersonal psychotherapy? Secondary analysis of a randomized trial
Huibers, M. J. H., van Bronswijk, S. C., Peeters, F. P. M. L., & Lemmens, L. H. J. M. (2022) · Psychotherapy Research · doi:10.1080/10503307.2022.2064251 · Cited by 2
“However, individual responses to treatment vary greatly (Cohen & DeRubeis, 2018; Simon & Perlis, 2010) and to this date, the causal pathways of change through which these therapies exert their beneficial effects have © 2022 The Author(s).”
Background
Depression im Alter – Eine Frage der Emotionsregulation
Becker-Hingst, N. (2023) · Springer eBooks · doi:10.1007/978-3-658-41449-8 · Cited by 1
Grant Report on SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics
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Experience, Perception, and Depression
Simon, G. E. (2012) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2011.11091374 · Cited by 2
“We have no evidence suggesting that a history of childhood trauma is useful in selecting from among different types of psychotherapy or different antidepressant medications (9).”
BackgroundSelf-citation
A clustering-based reinforcement learning approach for tailored personalization of e-Health interventionsPreprint
el Hassouni, A., Hoogendoorn, M., van Otterlo, M., Eiben, A. E., Muhonen, V., & Bárbaro, E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.03592 · Cited by 2
“6 and 7 respectively. We close with a discussion. 2. Related work In recent years personalization using RL has seen a signiﬁcant upward trend in many domains and especially in healthcare applications [22, 30, 31, 32, 33, 34, 35, 36]. Judging from the systematic literature review by [den Hengst et al., 2020], several i…”
Background
Should We Prevent Off-Label Drug Prescriptions? Empirical Evidence from FrancePreprint
Tunҫel, T. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3694632 · Cited by 2
“Heterogeneity in treatment impact of drugs across patients in depression treatment is a well-documented result in the medical literature (see, e.g., Simon and Perlis (2010), Perlis (2014), Uher et al. (2012)).”
Background
Individual Prediction of Optimal Treatment Allocation Between Electroconvulsive Therapy or Ketamine using the Personalized Advantage IndexPreprint
Wade, B., Pindale, R., Camprodon, J. A., Luccarelli, J., Li, S., Meisner, R. C., Seiner, S. J., & Henry, M. (2023) · Research Square · doi:10.21203/rs.3.rs-3682009/v1 · Cited by 2
Validity and reliability of the Depression Information Needs Scale among the Iranian general population
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“Regular assessment of treatment outcomes, coupled with ongoing refinement of personalization approaches, helps maximize the impact of interventions while minimizing the risk of treatment failure in this vulnerable population [50].”
Sounding Depressed? Personalized Deep Learning Model for Depression Detection From Speech and Text
Zhang, H., Liu, Z., Yuan, J., Li, G., Ding, Z., & Hu, B. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3668527 · Cited by 1
“Current research often uses demographic information, clinical features (including symptom patterns or comorbidities), or biomarkers (such as neuroimaging) as personalized information [7].”
Predictors and moderators of treatment outcomes from cognitive behavioural interventions for insomnia in adults: A systematic review
Smith, M. L., Oldenhof, E., Byrne, J. E. M., McNeill, I. M., Sweetman, A., & Staiger, P. K. (2026) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2026.102269 · Cited by 1
Clinical Predictors of Antidepressant Effects of Ketamine and Esketamine in Treatment-Resistant Unipolar and Bipolar Depression: A Systematic Review
Syed, O. A., Sobolenko, V., Nestor, S. M., Husain, M. I., Lipsman, N., Alam, F., & Giacobbe, P. (2026) · CNS Drugs · doi:10.1007/s40263-026-01296-7 · Cited by 1
“With several effective psychiatric interventions for TRD, the ability to personalize treatment selection and parameters would likely improve therapeutic response [21–23].”
Cognitive therapy plus medication management is better than antidepressants alone for patients with severe depression
Sung, S. C. (2015) · Evidence-Based Mental Health · doi:10.1136/eb-2014-102012 · Cited by 1
“WHAT NEXT IN RESEARCH Additional research is still needed to achieve the goal of personalised medicine (ie, which treatment(s), for which patient, in what order).(4) This may be best achieved through clinical trials that employ sequential, multiple assignment, randomised trial designs that can more closely match the t…”
Background
Should This Patient Receive an Antidepressant?
Burns, R. B., Smetana, G. W., & Brady, R. O. (2017) · Annals of Internal Medicine · doi:10.7326/m17-0966 · Cited by 1
Future Directions in Developing and Evaluating Psychological Interventions
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Depression management and pharmacoepigenetics
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Cognitive improvement in patients with major depressive disorder after personalised multi domain training in the CERT-D study
Hawighorst, A., Knight, M. J., Fourrier, C., Sampson, E., Hori, H., Cearns, M., Jörgens, S., & Baune, B. T. (2023) · Psychiatry Research · doi:10.1016/j.psychres.2023.115590 · Cited by 1
Neural signatures of emotional biases predict clinical outcomes in difficult-to-treat depression
Fennema, D., Barker, G. J., O’Daly, O., Godlewska, B. R., Carr, E., Goldsmith, K., Young, A. H., Moll, J., & Zahn, R. (2024) · Research Directions Depression · doi:10.1017/dep.2024.6 · Cited by 1
“Such biomarkers are thought to represent underlying biological substrates of depression, which can be used to predict general prognosis regardless of treatment, better outcome with any treatment, or differential treatment response (Simon and Perlis 2010).”
Can Participants’ Characteristics Predict Benefit from a Multimodal Burnout Prevention Program? Secondary Analysis of a Randomized Controlled Trial Conducted in Germany
Vanajan, A., Stier‐Jarmer, M., Ivandić, I., Schuh, A., & Sabariego, C. (2019) · Behavioral Medicine · doi:10.1080/08964289.2019.1594671 · Cited by 1
Enchantment in psychiatric medicine
Redinger, M. J. (2019) · Asian Journal of Psychiatry · doi:10.1016/j.ajp.2019.10.003 · Cited by 2
Type of Trauma Exposure Impacts Neural Reactivity to Errors
Jones, E. E., Kreutzer, K. A., Manzler, C. A., Evans, E. G., & Gorka, S. M. (2023) · Journal of Psychophysiology · doi:10.1027/0269-8803/a000318 · Cited by 1
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Baldessarini, R. J. (2012) · Springer eBooks · doi:10.1007/978-1-4614-3710-9_4 · Cited by 1
Wider, faster, more: Re-envisioning the treatment and research of depression to address its public health burden in the United StatesPreprint
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Interindividual Differences in Treatment Effects based on Structural Equation Models with Latent Variables: An EffectLiteR TutorialPreprint
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The experience of recalling Christian spiritual songs on affect dysregulation in the mood and anxiety disorders
Cochran, N. J. (2015) · Digital Commons (Liberty University)
Latent Variable Approaches for Understanding Heterogeneity in Depression: A Dissertation
Ulbricht, C. M. (2015) · The Journal of the American Medical Association (JAMA) Network (American Medical Association) · doi:10.13028/m2fg68
“Identifying homogenous subgroups based on clinically observable characteristics could improve the ability to efficiently predict who will benefit from which treatments.(11,27,28) Latent class analysis (LCA) is a person-centered analytic approach which can be”
BackgroundInfluential

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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