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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“or contradict thewisdomof heeding a depressedpatient’s preference for a specific treatment, or of favoring an antidepressant to which a patient has responded in the past.(40)”
Divergent outcomes in cognitive-behavioral therapy and pharmacotherapy for adult depression
Vittengl, J. R., Jarrett, R. B., Weitz, E., Hollon, S. D., Twisk, J., Cristea, I., David, D., DeRubeis, R. J., Dimidjian, S., Dunlop, B. W., et al. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2015.15040492 · Cited by 97
“Even so, empirical guidance for selecting CBT or pharmacotherapy for a given patient based on average post-treatment symptom scores is available [16,38].”
Background
Which treatment worked better for whom? Moderators of group cognitive behavioral therapy versus adapted mindfulness based stress reduction for anxiety disorders
Arch, J. J., & Ayers, C. R. (2013) · Behaviour Research and Therapy · doi:10.1016/j.brat.2013.04.004 · Cited by 94
“Previous difficulties in identifying clinical moderators of treatment outcome (e.g., Simon & Perlis, 2010) may stem in part from a failure to examine such non-linear relationships.”
BackgroundInfluential
Translational applications of personality science for the conceptualization and treatment of psychopathology.
Lengel, G. J., Helle, A. C., DeShong, H. L., Meyer, N. A., & Mullins‐Sweatt, S. N. (2016) · Clinical Psychology Science and Practice · doi:10.1111/cpsp.12166 · Cited by 92
Progress monitoring in mental health and addiction treatment: A means of improving care.
Goodman, J. D., McKay, J. R., & DePhilippis, D. (2013) · Professional Psychology Research and Practice · doi:10.1037/a0032605 · Cited by 90
“With regard to affective disorders, Simon and Perlis (2010) argued that personalized medicine for depression would be better served by monitoring outcomes and adjusting treatment over time than by attempting to personalize via information collected before treatment is initiated.”
Background
Pharmacogenetics in psychiatry: translating research into clinical practice
Ak, M., Zhang, J.-P., & Lencz, T. (2011) · Molecular Psychiatry · doi:10.1038/mp.2011.146 · Cited by 85
Finding factors that predict treatment-resistant depression: results of a cohort study
Cepeda, M. S., Reps, J., & Ryan, P. (2018) · Depression and Anxiety · doi:10.1002/da.22774 · Cited by 80
“…studyhundredsof thousands of subjects, a sample sizemany times larger than the traditional studies (De Carlo, Calati, & Serretti, 2016; Kautzky et al., 2017; Souery et al., 2007), and assess a much larger number of potential predictors compared with the traditional studies (Simon & Perlis, 2010).”
BackgroundResult
Precision medicine for long-term depression outcomes using the Personalized Advantage Index approach: cognitive therapy or interpersonal psychotherapy?
van Bronswijk, S. C., DeRubeis, R. J., Lemmens, L. H. J. M., Peeters, F. P. M. L., Keefe, J. R., Cohen, Z. D., & Huibers, M. J. H. (2021) · Psychological Medicine · doi:10.1017/s0033291719003192 · Cited by 75
Heterogeneity of treatment effects in trials on psychotherapy of depression.
Kaiser, T., Volkmann, C., Volkmann, A., Karyotaki, E., Cuijpers, P. J., & Brakemeier, E. (2022) · Clinical Psychology Science and Practice · doi:10.1037/cps0000079 · Cited by 87
Biomarkers Predicting Antidepressant Treatment Response: How Can We Advance the Field?
Labermaier, C., Masana, M., & Müller, M. B. (2013) · Disease Markers · doi:10.1155/2013/984845 · Cited by 80
Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression
Geraci, J., Wilansky, P., Luca, V. D., Roy, A., Kennedy, J. L., & Strauss, J. (2017) · Evidence-Based Mental Health · doi:10.1136/eb-2017-102688 · Cited by 72
Does dissociation moderate treatment outcomes of narrative exposure therapy for PTSD? A secondary analysis from a randomized controlled clinical trial
Halvorsen, J. Ø., Stenmark, H. I., Neuner, F., & Nordahl, H. M. (2014) · Behaviour Research and Therapy · doi:10.1016/j.brat.2014.03.010 · Cited by 70
“…moderators of treatment outcomes (Kraemer, Frank, & Kupfer, 2006; Kraemer, Wilson, Fairburn, & Agras, 2002) in an effort to personalise treatments (Simon & Perlis, 2010) for PTSD. Dissociation has been suggested by a number of researchers and trauma therapists as an important moderator of…”
Background
Transdiagnostic Treatment Personalization: The Feasibility of Ordering Unified Protocol Modules According to Patient Strengths and Weaknesses
Sauer-Zavala, S., Cassiello-Robbins, C., Ametaj, A. A., Wilner, J. G., & Pagan, D. (2019) · Behavior Modification · doi:10.1177/0145445518774914 · Cited by 67
“…evidence cited to support these personalization strategies has come from post hoc examinations of pretreatment characteristics that predict differential response to interventions in randomized trials and the literature is mixed with regard to the most advantageous approach (Simon & Perlis, 2010).”
Background
RETRACTED: Individual Differences in Response to Antidepressants
Maslej, M. M., Furukawa, T. A., Cipriani, A., Andrews, P. W., & Mulsant, B. H. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.4815 · Cited by 65
Retracted
Moderators of remission with interpersonal counselling or drug treatment in primary care patients with depression: randomised controlled trial
Menchetti, M., Rucci, P., Bortolotti, B., Bombi, A., Scocco, P., KRAEMER, H. C., Berardi, D., & the DEPICS group (2013) · The British Journal of Psychiatry · doi:10.1192/bjp.bp.112.122663 · Cited by 62
Pharmacogenetics of antipsychotics: recent progress and methodological issues
Zhang, J., & Malhotra, A. K. (2012) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1517/17425255.2013.736964 · Cited by 61
“, testing the genotype treatment interaction [49].”
EEG-Based Personalized Medicine in ADHD: Individual Alpha Peak Frequency as an Endophenotype Associated with Nonresponse
Arns, M. (2012) · Journal of Neurotherapy · doi:10.1080/10874208.2012.677664 · Cited by 60
“Therefore, a move beyond data regarding the average effectiveness of treatment, to identify the best treatment for any individual (Simon & Perlis, 2010) or personalized medicine, is crucial.”
Background
Reinforcement learning for personalization: A systematic literature review
den Hengst, F., Grua, E. M., el Hassouni, A., & Hoogendoorn, M. (2020) · Data Science · doi:10.3233/ds-200028 · Cited by 62
“These reach far and wide, including medicine [5,71,77,84,178,179], human-computer interaction [61,100,118], product, news, music and video recommendations [163,164,217] and even manufacturing [39,154].”
Individual Differences in Response to Antidepressants
Maslej, M. M., Furukawa, T. A., Cipriani, A., Andrews, P. W., Sanches, M., Tomlinson, A., Volkmann, C., McCutcheon, R. A., Howes, O. D., Guo, X., et al. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.4564 · Cited by 55
Is it worth it to personalize the treatment of PTSD? – A variance-ratio meta-analysis and estimation of treatment effect heterogeneity in RCTs of PTSD
Herzog, P., & Kaiser, T. (2022) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2022.102611 · Cited by 64
Single-subject research in psychiatry: facts and fictions
Zuidersma, M., Riese, H., Snippe, E., Booij, S. H., Wichers, M., & Bos, E. H. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.539777 · Cited by 54
Older Adults With Severe, Treatment-Resistant Depression
Unützer, J., & Park, M. (2012) · JAMA · doi:10.1001/2012.jama.10690 · Cited by 56
A narrative review of emotion regulation process in stress and recovery phases
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Non-linear EEG analyses predict non-response to rTMS treatment in major depressive disorder
Arns, M., Cerquera, A., Gutiérrez, R. M., Hasselman, F., & Freund, J. A. (2013) · Clinical Neurophysiology · doi:10.1016/j.clinph.2013.11.022 · Cited by 54
Blue genes? Understanding and mitigating negative consequences of personalized information about genetic risk for depression
Lebowitz, M. S., & Ahn, W. (2017) · Journal of Genetic Counseling · doi:10.1007/s10897-017-0140-5 · Cited by 46
“In particular, there has been significant interest in using genetic information and other biomarkers to find the best antidepressant medication for individual patients (Simon and Perlis 2010; Uher et al. 2010; Williams et al. 2011).”
Background
Predicting Barriers to Treatment for Depression in a U.S. National Sample: A Cross-Sectional, Proof-of-Concept Study
Chekroud, A. M., Foster, D. F., Zheutlin, A. B., Gerhard, D. M., Roy, B., Koutsouleris, N., Chandra, A., Esposti, M. D., Subramanyan, G., Gueorguieva, R. V., et al. (2018) · Psychiatric Services · doi:10.1176/appi.ps.201800094 · Cited by 51
“Because low motivation, hypersomnia, and low energy are cardinal symptoms of depression, aggressive outreach may be required to encourage some individuals to begin and remain in care (46,49), and thus better targeting of patients in need of encouragement may make outreach cost-effective.”
Background
Inflammatory Biomarker and Response to Antidepressant in Major Depressive Disorder: a Systematic Review and Meta-Analysis
Gasparini, A., Callegari, C., Lucca, G., Bellini, A., Caselli, I., & Ielmini, M. (2025) · Psychopharmacology Bulletin · doi:10.64719/pb.4425 · Cited by 49
Robust symptom networks in recurrent major depression across different levels of genetic and environmental risk
van Loo, H. M., van Borkulo, C. D., Peterson, R. E., Fried, E. I., Aggen, S. H., Borsboom, D., & Kendler, K. S. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2017.10.038 · Cited by 52
“Major depression (MD) is a heterogeneous disorder in terms of presentation, course of illness and treatment response (Cuijpers et al., 2012; Eaton et al., 2008; Hardeveld et al., 2013; Holma et al., 2008; Oquendo et al., 2004; Simon and Perlis, 2010).”
What are the chances for personalised treatment with antidepressants? Detection of patient-by-treatment interaction with a variance ratio meta-analysis
Plöderl, M., & Hengartner, M. P. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-034816 · Cited by 50
How and for whom does web-based acceptance and commitment therapy work? Mediation and moderation analyses of web-based ACT for depressive symptoms
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“Since the analyses were exploratory and post-hoc, the absence of significant moderators could well be a result of inadequate statistical power [67].”
Background
Customizing Emotional Support: How Do Individuals Construct and Interact With LLM-Powered Chatbots
Zheng, X., Li, Z., Gui, X., & Luo, Y. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713453 · Cited by 73
“These varying needs often warrant personalized support tailored to individuals’ personal experience, sources of stress, etc [21, 77, 82, 83].”
The importance of transdiagnostic symptom level assessment to understanding prognosis for depressed adults: analysis of data from six randomised control trials
O’Driscoll, C., Buckman, J. E. J., Fried, E. I., Saunders, R., Cohen, Z. D., Ambler, G., DeRubeis, R. J., Gilbody, S. M., Hollon, S. D., Kendrick, T., et al. (2021) · BMC Medicine · doi:10.1186/s12916-021-01971-0 · Cited by 51
Developmental psychopathology in an era of molecular genetics and neuroimaging: A developmental neurogenetics approach
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“…greater understanding of who, when, and how individuals are at most risk for maladaptive outcomes, or who, when, and how some individuals are resilient, studies can push forward more targeted and personalized prevention and intervention strategies (Simon & Perlis, 2010; Willard & Ginsburg, 2009).”
Background
Biomarkers Predicting Treatment Outcome in Depression: What is Clinically Significant?
Uher, R., Tansey, K. E., Malki, K., & Perlis, R. H. (2012) · Pharmacogenomics · doi:10.2217/pgs.11.161 · Cited by 44
Self-citation
Differentially expressed genes related to major depressive disorder and antidepressant response: genome-wide gene expression analysis
Woo, H. I., Lim, S.-W., Myung, W., Kim, D. K., & Lee, S.-Y. (2018) · Experimental & Molecular Medicine · doi:10.1038/s12276-018-0123-0 · Cited by 42
Predicting antidepressant treatment outcome based on socioeconomic status and citalopram dose
Viglione, A., Chiarotti, F., Poggini, S., Giuliani, A., & Branchi, I. (2019) · The Pharmacogenomics Journal · doi:10.1038/s41397-019-0080-6 · Cited by 42
Interpersonal Problems Predict Differential Response to Cognitive Versus Behavioral Treatment in a Randomized Controlled Trial
Newman, M. G., Jacobson, N. C., Erickson, T. M., & Fisher, A. J. (2017) · Behavior Therapy · doi:10.1016/j.beth.2016.05.005 · Cited by 41
“Keywords: GAD; interpersonal problems; CBT; cognitive therapy; behavioral therapy AN IMPORTANT FOCUS OF PSYCHOTHERAPY research has been which treatments work for whom (Paul, 1967), which is the core theme of “personalized medicine” (Simon & Perlis, 2010).”
Novel drug-regulated transcriptional networks in brain reveal pharmacological properties of psychotropic drugs
Korostyński, M., Piechota, M., Dzbek, J., Młynarski, W. F., Szklarczyk, K., Ziółkowska, B., & Przewłocki, R. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-606 · Cited by 41
“This diversity reflects their diverse pharmacological and neurobiological mechanisms of action [12], as well as significant differences in the efficacy of individual compounds in the treatment of different forms of depression [2,59].”
Aifred Health, a Deep Learning Powered Clinical Decision Support System for Mental Health
Benrimoh, D. A., Fratila, R., Israel, S. A., Perlman, K., Mirchi, N., Desai, S., Rosenfeld, A., Knappe, S., Behrmann, J., Rollins, C. P. E., et al. (2018) · The Springer series on challenges in machine learning · doi:10.1007/978-3-319-94042-7_13 · Cited by 41
Antidepressant treatment and emotional processing: can we dissociate the roles of serotonin and noradrenaline?
Pringle, A., McCabe, C., Cowen, P., & Harmer, C. (2013) · Journal of Psychopharmacology · doi:10.1177/0269881112474523 · Cited by 37
“In depression, it has been suggested that it will be necessary to consider a range of factors including sociodemographic factors, biological markers (for example neural changes and genetic variation) and differences in clinical presentation (Simon and Perlis, 2010).”
Background
Symptom networks in acute depression across bipolar and major depressive disorders: A network analysis on a large, international, observational study
Corponi, F., Anmella, G., Verdolini, N., Pacchiarotti, I., Samalin, L., Popović, D., Azorin, J., Angst, J., Bowden, C. L., Mosolov, S. N., et al. (2020) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2020.03.017 · Cited by 42
Personalized medicine in major depressive disorder — Opportunities and pitfalls
Miller, D. B., & O'Callaghan, J. P. (2012) · Metabolism · doi:10.1016/j.metabol.2012.08.021 · Cited by 39
“This limits their opportunity for treatment with a different agent or the addition of a second drug; both strategies are used in non-responders and can be effective [19].”
Background
Neuroimaging for precision medicine in psychiatry
Williams, L. M., & Whitfield, S. (2024) · Neuropsychopharmacology · doi:10.1038/s41386-024-01917-z · Cited by 42
Acceptance and commitment therapy for the treatment of anxiety disorders: A concise review
Landy, L. N., Schneider, R. L., & Arch, J. J. (2015) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2014.11.004 · Cited by 35
“Is it just a matter of personal preference, or do certain patient– level characteristics make someone a particularly good candidate for ACT? Rather than solely investigating whether treatments work, the field has increasingly begun examining for whom and under what conditions they work [15,16].”
Background
Advancing Personalized Medicine: Application of a Novel Statistical Method to Identify Treatment Moderators in the Coordinated Anxiety Learning and Management Study
Niles, A. N., Guinyard, A. G. L., Krull, J. L., Roy‐Byrne, P., Sullivan, G., Sherbourne, C. D., Bystritsky, A., & Craske, M. G. (2017) · Behavior Therapy · doi:10.1016/j.beth.2017.02.001 · Cited by 39
“…for personalized medicine, where the most effective treatment is selected based on individual characteristics (i.e. treatment moderators), has received increasing attention (Simon & Perlis, 2010) as evidenced by the $215 million Precision Medicine Initiative (National Institute of Health, 2016).”
Background
A quantitative review on outcome-to-antidepressants in melancholic unipolar depression
Valerio, M. P., Szmulewicz, A., & Martino, D. J. (2018) · Psychiatry Research · doi:10.1016/j.psychres.2018.03.088 · Cited by 37
Biomarker-based subtyping of depression and anxiety disorders using Latent Class Analysis. A NESDA study
Beijers, L., Wardenaar, K. J., Bosker, F. J., Lamers, F., van Grootheest, G., de Boer, M. K., Penninx, B. W. J. H., & Schoevers, R. A. (2018) · Psychological Medicine · doi:10.1017/s0033291718001307 · Cited by 37
“Identification of more homogeneous biomarker-based subtypes in the patient population could help to gain more insight into patient-specific etiological mechanisms and to better target treatments to those that are likely to benefit (Meyer and Ginsburg, 2002; Bartova et al., 2010; Leuchter et al., 2010; Simon and Perlis…”
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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