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Review

Machine learning, pharmacogenomics, and clinical psychiatry: predicting antidepressant response in patients with major depressive disorder

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Pages 927-944 | Received 18 Apr 2022, Accepted 09 Aug 2022, Published online: 21 Aug 2022

References

  • GBD. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1789–1858.
  • Bromet E, Andrade LH, Swang I, et al. Cross-national epidemiology of DSM-IV major depressive episode. BMC Med. 2011;9(1):90.
  • Murray CJ, Lopez AD. Global morality, disability, and the contribution of risk factors: global burden of disease study. Lancet. 1997;349(9063):1436–1442.
  • World Health Organization (WHO). The global burden of disease: 2004 update. [cited 2021 Mar 9]. Available from: https://www.who.int/healthinfo/global_burden_disease/GBD_report_2004update_full.pdf?ua=1.
  • Friedrich MJ. Depression is the leading cause of disability around the world. JAMA. 2017;317:1517.
  • Liu Q, He H, Yang J, et al. Changes in the global burden of depression from 1990 to 2017: findings from the global burden of disease study. J Psychiatr Res. 2020;126:134–140.
  • Smith K. Mental health: a world of depression. Nature. 2014;515(7526):181.
  • Wittchen HU, Jacobi F, Rehm J, et al. The size and burden of mental disorders and other disorders of the brain in Europe 2010. Eur Neuropsychopharmacol. 2011;21(9):655–679.
  • Greenberg PE, Fournier -A-A, Sisitsky T, et al. The economic burden of adults with major depressive disorder in the United States (2005 and 2010). J Clin Psychiatry. 2015;76(2):155–162.
  • Otte C, Gold SM, Penninx BW, et al. Major depressive disorder. Nat Rev Dis Primers. 2016;2(1):160–165.
  • Rush AJ, Trivedi MH, Wisniewski SR, et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatry. 2006;163(11):1905–1917.
  • Zimmerman M, McGlinchey JB, Posternak MA, et al. How should remission from depression be defined? The depressed patient’s perspective. Am J Psychiatry 2006;163:148–150.
  • Blier P. Optimal use of antidepressants: when to act? J Psychiatry Neurosci. 2009;34(1):80.
  • Perlis RH. Abandoning personalization to get to precision in the pharmacotherapy of depression. World Psychiatry. 2016;15(3):228–235.
  • Gillett G, Tomlinson A, Efthimiou O, et al. Predicting treatment effects in unipolar depression: a meta-review. Pharmacol Ther. 2020;212:107557.
  • Fava M, Uebelacker LA, Alpert JE, et al. Major depressive subtypes and treatment response. Biol Psychiatry. 1997;42(7):568–576.
  • Parker G, Wilhelm K, Mitchell P, et al. Subtyping depression: testing algorithms and identification of a tiered model. J Nerv Ment Dis. 1999;187(10):610–617.
  • Arnow BA, Blasey C, Williams LM, et al. Depression subtypes in predicting antidepressant response: a report from the iSPOT-D trial. Am J Psychiatry. 2015;172(8):743–750.
  • Perlman K, Benrimoh D, Israel S, et al. A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder. J Affect Disord. 2019;243:503–515.
  • Lee Y, Ragguett RM, Mansur RB, et al. Applications of machine learning algorithms to predict therapeutic outcomes in depression: a meta-analysis and a systematic review. J Affect Disord. 2018;241:519–532.
  • Saveanu RV, Nemeroff CB. Etiology of depression: genetic and environmental factors. Psychiatr Clin North Am. 2012;35(1):51–71.
  • American Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM-5. 5th ed. Washington, DC, USA: American Psychiatric Association; 2013.
  • Zimmerman M, Ellison W, Young D, et al. How many different ways do patients meeting diagnostic criteria for major depressive disorder? Compr Psychiatry. 2015;56:29–34.
  • Cuthbert BN, Insel TR. Toward the future of psychiatric diagnosis: the seven pillars of RDoC. BMC Med. 2013;11(1):126.
  • Milaneschi Y, Lamers F, Peyrot WJ, et al. Polygenetic dissection of major depression clinical heterogeneity. Mol Psychiatry. 2016;21(4):516–522.
  • Athreya A, Iyer R, Wang L, et al. Integration of machine learning and pharmacogenomic biomarkers for predicting response to antidepressant treatment: can computation all intelligence be used to augment clinical assessments? Pharmacogenomics. 2019;20(14):983–988.
  • Carter GC, Cantrell RA, Zarotsky V, et al. COMPREHENSIVE REVIEW OF FACTORS IMPLICATED IN THE HETEROGENEITY OF RESPONSE IN DEPRESSION. Depress Anxiety. 2012;29(4):340–354.
  • Dwyer DB, Falkai P, Koutsouleris N. Machine learning approaches for clinical psychology and psychiatry. Annu Rev Psychology. 2018;14(1):91–118
  • Feczko E, Miranda-Dominguez O, Marr M, et al. The heterogeneity problem: approaches to identify psychiatric subtypes. Trends Cogn Sci. 2019;23(7):584–601.
  • James G, Witten D, Hastie T, et al. An introduction to statistical learning. Vol. 112. 18.
  • Doupe P, Faghmous J, Basu S. Machine learning for health services researchers. Value Health. 2019;22(7):808–815.
  • Lin E, Lin C-H, Lane H-Y. Machine learning and deep learning for the pharmacogenomics of antidepressant treatments. Clin Psychopharmacol Neurosci. 2021;19(4):577–588.
  • Tai AMY, Albuquerque A, Carmona NE, et al. Machine learning and big data: implications for disease modeling and a therapeutic discovery in the psychiatry. Artif Intell Med. 2019;99:101704.
  • Bagby RM, Ryder AG, Cristi C. Psychosocial and clinical predictors of response to pharmacotherapy for depression. Journal of psychiatry & neuroscience: JPN. 2002;27(4):250–257.
  • Chekroud AM, Zotti RJ, Shehzad Z, et al. Cross‐trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry. 2016;3(3):243–250.
  • Chekroud AM, Gueorguieva R, Krumholz HM, et al. Reevaluating the efficacy and predictability of antidepressant treatments: a symptom clustering approach. JAMA Psychiatry. 2017;74(4):370–378.
  • Iniesta R, Malki K, Maier W, et al. Combining clinical variables to optimize prediction of antidepressant treatment outcomes. J Psychiatr Res. 2016;78:94–102.
  • Kautzky A, Moller H-J, Dold M, et al. Combining machine learning algorithms for prediction of antidepressant treatment response. Acta Psychiatr Scand. 2021;143(1):36–49.
  • Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):83.
  • Tansey KE, Guipponi M, Hu X, et al. Contribution of Kallmann genetic variants to antidepressant response. Biol Psychiatry. 2013;73(7):679–682.
  • Kato M, Serretti A. Review and meta-analysis of antidepressant pharmacogenetic findings in major depressive disorder. Mol Psychiatry. 2010;15(5):473–500.
  • Perlis RH. Pharmacogenomic testing and personalized treatment of depression. Clin Chem. 2014;60(1):53–59.
  • GENDEP Investigators, Mars Investigators, STAR*D Investigators. Common genetic variation and antidepressant efficacy in major depressive disorder: a meta-analysis of 3 genome-wide pharmacogenetic studies. Am J Psychiatry. 2013;170(2):207–217.
  • Greden JF, Parikh WV, Rothschild AJ, et al. Impact of pharmacogenomics on clinical outcomes in major depressive disorder in the GUIDED trial: a large, patient- and rater-blinded, randomized, controlled study. J Psychiatr Res. 2019;111:59–67.
  • Bousman CA, Arandjelovic K, Mancuso SG, et al. Pharmacogenetic tests and depressive symptoms remission: a meta-analysis of randomized controlled trials. Pharmacogenomics. 2019;20(1):37–47.
  • Rosenblat JD, Lee Y, McIntyre RS. The effect of pharmacogenomic testing on response and remission rates in the acute treatment of major depressive disorder: a meta-analysis. J Affect Disord. 2018;241:484–491.
  • Zeier Z, Carpenter LL, Kalin NH, et al. Clinical implementation of pharmacogenetic decision support tools for antidepressant drug prescribing. Am J Psychiatry. 2018;175(9):j873–886.
  • Maciukiewicz M, Marshe VS, Hauschild A-C, et al. GWAS-based machine learning approach to predict duloxetine response in major depressive disorder. J Psychiatr Res. 2018;99:62–68.
  • Montgomery SA, Asberg M. A new depression scale designed to be sensitive to change. Br J Psychiatry. 1979;134(4):382–389.
  • Bao Z, Zhao X, Li J, et al. Prediction of repeated-dose intravenous ketamine response in major depressive disorder using the GWAS-based machine learning approach. J Psychiatr Res. 2021;138:284–290.
  • Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23(1):56–62.
  • Athreya AP, Neavin D, Carrillo-Roa T, et al., Pharmacogenomics-driven prediction of antidepressant treatment outcomes: a machine-learning approach with multi-trial replication. Clin Pharmacol Ther. 2019; 106(4): 855–865.
  • Gupta M, Neavin D, Liu D, et al. TSPAN5, ERICH3 and selective serotonin reuptake inhibitors in major depressive disorder: pharmacometabolomics-informed pharmacogenomics. Mol Psychiatry. 2016;21(12):1717–1725.
  • Liu D, Ray B, Neavin DR, et al. Beta-defensin 1, aryl hydrocarbon receptor and plasma kynurenine in major depressive disorder: metabolomics-informed genomics. Transl Psychiatry. 2018;8(1):10.
  • Ji Y, Biernacka JM, Hebbring S, et al. Pharmacogenomics of selective serotonin reuptake inhibitor treatment for major depressive disorder: genome-wide associations and functional genomics. Pharmacogenomics J. 2013;13(5):456–463.
  • Rush AJ, Trivdei MH, Ibrahim HM, et al. The 16-item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol Psychiatry. 2003;54(5):573–583.
  • Biernacka JM, Sangkuhl K, Jenkins G, et al. The International SSRI Pharmacogenomics Consortium (ISPC): a genome-wide association study of antidepressant treatment response. Transl Psychiatry. 2016;5(4):e553.
  • Trivedi MH, Rush AJ, Wisniewski SR, et al. Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. Am J Psychiatry. 2006;163(1):28–40.
  • Kautzky A, Baldinger P, Souery D, et al. The combined effect of genetic polymorphisms and clinical parameters on treatment outcome in treatment-resistant depression. Eur Neuropsychopharmacol. 2015;25(4):441–453.
  • Souery D, Oswald P, Massat I, et al. Clinical factors associated with treatment resistance in major depressive disorder: results from a European multi-center study. J Clin Psychiatry. 2007;68(7):1062–1070.
  • Sheehan DV, Lecrubier Y, Sheehan KH, et al. The Mini-International Neuropsychiatric Interview (MINI): the development and validation of a structured diagnostic psychiatric interview for DSM0IV and ICD-10. J Clin Psychiatry. 1998;59(20):S22–S33.
  • Lin E, Kuo P-H, Liu Y-L, et al. A deep learning approach for predicting antidepressant response in major depression using clinical and genetic biomarkers. Front Psychiatry. 2018;9:290.
  • Lin E, Kuo P-H, Liu Y-L, et al. Prediction of antidepressant treatment response and remission using an ensemble machine learning framework. Pharmaceuticals. 2020;13(10):305.
  • Taliaz D, Spinrad A, Barzilay R, et al. Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data. Transl Psychiatry. 2021;11(1):381.
  • Fabbri C, Corponi F, Albani D, et al. Pleiotropic genes in psychiatry: calcium channels and stress-related FKBP5 gene in antidepressant resistance. Progr Neuropsychopharmacol Biol Psychiatry. 2018;81:203–210.
  • Iniesta R, Hodgson K, Stahl D, et al. Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables. Sci Rep. 2018;8(1):5530.
  • Uher R, Perroud N, Ng MYM, et al. Genome-wide pharmacogenetics of antidepressant response in the GENDEP project. Am J Psychiatry. 2010;167(5):555–564.
  • Shumake J, Mallard JT, McGeary JE, et al. Inclusion of genetic variants in an ensemble of gradient boosting decision trees does not improve the prediction of citalopram treatment response. Sci Rep. 2021;11(1):3780.
  • Bi Y, Ren D, Guo Z, et al. Influence and interaction of genetic, cognitive, neuroendocrine and personalistic markers to antidepressant response in Chinese patients with major depression. Progr Neuropsychopharmacol Biol Psychiatry. 2021;104:110036.
  • Pei C, Sun Y, Zhu J, et al. Ensemble learning for early-response prediction of antidepressant treatment in major depressive disorder. J Magn Reson Imaging. 2020;52(1):161–171.
  • Fabbri C, Kasper S, Kautzky A, et al. A polygenic predictor of treatment-resistant depression using whole exome sequencing and genome-wide genotyping. Transl Psychiatry. 2020;10(1):50.
  • Lim S-W, Won -H-H, Kim H, et al. Genetic prediction of antidepressant drug response and nonresponse in Korean patients. PLoS ONE. 2014;9(9):e107098.
  • Yin L, Zhang YY, Zhang X, et al. TPH, SLC6A2, SLC6A3, DRD2 and DRD4 polymorphisms and neuroendocrine factors predict SSRIs treatment outcome in the Chinese population with major depression. Pharmacopsychiatry. 2015;48(3):95–103.
  • Joyce JB, Grant CW, Liu D, et al. Multi-omics drive predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication. Transl Psychiatry. 2021;11(1):513.
  • Rush AJ, Trivedi MH, Stewart JW, et al. Combining medications to enhance depression outcomes (CO-MED): acute and long term outcomes of a single-blind randomized study. Am J Psychiatry. 2011;168(7):689–701.
  • Bhattacharyya S, Dunlop BW, Mahmoudiandehkordi S, et al. Pilot study of metabolomic clusters as state markers of major depression and outcomes to CBT treatment. Front Neurosci. 2019;13:926.
  • Mahmoudiandehkordi S, Ahmed AT, Bhattacharyya S, et al. Alterations in acylcarnitines, amines, and lipids inform about the mechanism of action of citalopram/escitalopram in major depression. Transl Psychiatry. 2021;11(1):153.
  • Czysz AH, South C, Gadad BS, et al. Can targeted metabolomics predict depression recovery? Results from the CO-MED trial. Transl Psychiatry. 2019;9(1):11.
  • Perna G, Grassi M, Caldirola D, et al. The revolution of personalized psychiatry: will technology make it happen sooner? Psychol Med. 2018;48(5):705–713.
  • Fernandes BS, Williams LM, Steiner J, et al. The new field of ‘precision psychiatry. BMC Med. 2017;15(1):80.
  • Ritchie MD, Holzinger ER, Li R, et al. Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genetics. 2015;16(2):85–97.
  • Bzdok D, Meyer-Lindenberg A. Machine learning for precision psychiatry: opportunities and challenges. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3:223–230.
  • Athreya A, Iyer R, Neavin D, et al. Augmentation of physician assessments with multi-omics enhances predictability of drug response: a case study of major depressive disorder. IEEE Comput Intell Mag. 2018;13(3):20–31.
  • The Lancet Respiratory Medicine. Opening the black box of machine learning. Lancet Respir Med. 2018;6(11):801.
  • Chen JH, Asch SM. Machine learning and prediction in medicine – beyond the peak of inflated expectations. N Engl J Med. 2017;376(26):2507–2509.
  • Porcelli S, Drago A, Fabbri C, et al. Pharmacogenetics of antidepressant response. J Psychiatry Neurosci. 2011;36(2):87–113.
  • Mirza B, Wang W, Wang J, et al. Machine learning and integrative analysis of biomedical big data. Genes (Basel). 2019;10(2):87.
  • Reel PS, Reel S, Pearson E, et al. Using machine learning approaches for multi-omics data analysis: a review. Biotechnol Adv. 2021;49:107739.
  • Dantzer, O’Connor JC, Lawson MA, et al. Inflammation-associated depression: from serotonin to kynurenine. Dantzer R, O’Connor JC, Lawson MA, et al. Psychoneuroendocrinology. 2011;36(3):426–436.
  • Lam RW, Milev R, Rotzinger S, et al. Discovering biomarkers for antidepressant response: protocol from the Canadian biomarker integration network in depression (CAN-BIND) and clinical characteristics of the first patient cohort. BMC Psychiatry. 2016;16(1):105.
  • Menke A, Weber H, Deckert J. Roadmap for routine pharmacogenetic testing in a psychiatric university hospital. Pharmacopsychiatry. 2020;53(4):179–183.
  • Wang L, Scherer SE, Bielinski SJ, et al. Implementation of preemptive DNA sequence-based pharmacogenomic testing across a large academic medical center: the Mayo-Baylor RIGHT 10K Study. Genet Med. 2022;24(5):1062–1072.
  • Fatumo S, Chikowore T, Choudhury A, et al. A roadmap to increase diversity in genomic studies. Nat Med. 2022;28(2):243–250.
  • McIntyre RS, Lee Y, Mansur RB. Treating to target in major depressive disorder: response to remission to functional recovery. CNS Spectr. 2015;20(S1):20–30.
  • Dietterich T. Overfitting and undercomputing in machine learning. ACM Comput Surv. 1995;27(3):326–327.
  • Hofman JM, Watts DJ, Athey S, et al. Integrating explanation and prediction in computational social science. Nature. 2021;595(7866):181–188.
  • Aradhya S, Nussbaum RL. Genetics in mainstream medicine: finally within grasp to influence healthcare globally. Mol Genet Genomic Med. 2018;6(4):473–480.
  • Cameron LD, Muller C. Psychosocial aspects of genetic testing. Curr Opin Psychiatry. 2009;99(2):218–223.
  • Johansen Taber KA, Dickinson BD. Pharmacogenomic knowledge gaps and educational resource needs among physicians in selected specialties. Pharmacogenomics Pers Med. 2014;7:145–162.
  • Selkirk CG, Weissman SM, Anderson A, et al. Physicians’ preparedness for integration of genomic and pharmacogenetic testing into practice within a major healthcare system. Genet Test Mol Biomarkers. 2013;17(3):219–225.
  • Stanek EJ, Sanders CL, Frueh FW. physician awareness and utilization of food and drug administration (FDA)-approved labeling for pharmacogenomic testing information. J Pers Med. 2013;3(2):111–123.
  • Stanek EJ, Sanders CL, Taber KAJ, et al. Adoption of pharmacogenomic testing by US physicians: results of a nationwide survey. Clin Pharmacol Ther. 2012;91(3):450–458.
  • Salm M, Abbate K, Appelbaum P, et al. Use of genetic tests among neurologists and psychiatrists: knowledge, attitudes, behaviors, and needs for training. J Genet Couns. 2014;23(2):156–163.
  • Thompson C, Steven PH, Catriona H. Psychiatrist attitudes towards pharmacogenetic testing, direct-to-consumer genetic testing, and integrating genetic counseling into psychiatric patient care. Psychiatry Res. 2015;226(1):68–72.
  • Walden LM, Brandl EJ, Changasi A, et al. Physicians’ opinions following pharmacogenetic testing for psychotropic medication. Psychiatry Res. 2015;229(3):913–918.
  • Jacobs M, Pradier MF, McCoy TH, et al. How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection. Transl Psychiatry. 2021;11(1):108.
  • Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–46.

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