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Precision Medicine in Psychiatric Disorders

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Precision Medicine in Clinical Practice

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Abstract

While mental disorder causes great suffering and psychosocial burdens, its diagnosis, and treatment, unlike in other medical specialties, is based on a subjective evaluation of symptoms and trial-and-error medication choices. As a consequence, the implementation of precision psychiatry significantly lags behind precision approaches in other fields. The current chapter focuses on special characteristics of psychiatric disorders including their heterogeneity, multifactorial background, the problems of finding and relying on peripheral biomarkers instead of sampling the brain, and the special case of psychotherapeutic treatment. It explains the need for precise predictive models for diagnosis, prognosis, and treatment and overviews the needs and current state of clinical implementation in the case of affective and schizophrenia spectrum disorders. Finally, the chapter also mentions several potential obstacles in the way of precision psychiatry which are related to the nature of psychiatric disorders.

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References

  1. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372(9):793–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Fernandes BS, Williams LM, Steiner J, Leboyer M, Carvalho AF, Berk M. The new field of ‘precision psychiatry’. BMC Med. 2017;15(1):80. https://doi.org/10.1186/s12916-017-0849-x.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Council NR. Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease; 2011.

    Google Scholar 

  4. Zanardi R, Prestifilippo D, Fabbri C, Colombo C, Maron E, Serretti A. Precision psychiatry in clinical practice. Int J Psychiatry Clin Pract. 2021;25(1):19–27.

    Article  CAS  PubMed  Google Scholar 

  5. Terry SF. Obama’s precision medicine initiative. Genet Test Mol Biomarkers. 2015;19(3):113–4.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Manchia M, Pisanu C, Squassina A, Carpiniello B. Challenges and future prospects of precision medicine in psychiatry. Pharmgenomics Pers Med. 2020;13:127–40. https://doi.org/10.2147/PGPM.S198225.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Schumann G, Binder EB, Holte A, de Kloet ER, Oedegaard KJ, Robbins TW, et al. Stratified medicine for mental disorders. Eur Neuropsychopharmacol. 2014;24(1):5–50.

    Article  CAS  PubMed  Google Scholar 

  8. Whiteford HA, Degenhardt L, Rehm J, Baxter AJ, Ferrari AJ, Erskine HE, et al. Global burden of disease attributable to mental and substance use disorders: findings from the global burden of disease study 2010. Lancet. 2013;382(9904):1575–86.

    Article  PubMed  Google Scholar 

  9. Wittchen H-U, Jacobi F, Rehm J, Gustavsson A, Svensson M, Jönsson B, et al. The size and burden of mental disorders and other disorders of the brain in Europe 2010. Eur Neuropsychopharmacol. 2011;21(9):655–79.

    Article  CAS  PubMed  Google Scholar 

  10. Trautmann S, Rehm J, Wittchen HU. The economic costs of mental disorders: do our societies react appropriately to the burden of mental disorders? EMBO Rep. 2016;17(9):1245–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Raber J, O'Shea RD, Bloom FE, Campbell IL. Modulation of hypothalamic-pituitary-adrenal function by transgenic expression of interleukin-6 in the CNS of mice. J Neurosci. 1997;17(24):9473–80. https://doi.org/10.1523/jneurosci.17-24-09473.1997.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Serretti A. The present and future of precision medicine in psychiatry: focus on clinical psychopharmacology of antidepressants. Clin Psychopharmacol Neurosci. 2018;16(1):1–6. https://doi.org/10.9758/cpn.2018.16.1.1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Fried EI, Nesse RM. Depression is not a consistent syndrome: an investigation of unique symptom patterns in the STAR* D study. J Affect Disord. 2015;172:96–102.

    Article  PubMed  Google Scholar 

  14. Stephan KE, Bach DR, Fletcher PC, Flint J, Frank MJ, Friston KJ, et al. Charting the landscape of priority problems in psychiatry, part 1: classification and diagnosis. Lancet Psychiatry. 2016;3(1):77–83.

    Article  PubMed  Google Scholar 

  15. Tamminga CA. Approaching human neuroscience for disease understanding. World Psychiatry. 2014;13(1):41.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Kendler KS. The nature of psychiatric disorders. World Psychiatry. 2016;15(1):5–12.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Roche D, Russell V. Can precision medicine advance psychiatry? Ir J Psychol Med. 2021;38(3):163–8. https://doi.org/10.1017/ipm.2020.79.

    Article  PubMed  Google Scholar 

  18. Quinlan EB, Banaschewski T, Barker GJ, Bokde ALW, Bromberg U, Buchel C, et al. Identifying biological markers for improved precision medicine in psychiatry. Mol Psychiatry. 2020;25(2):243–53. https://doi.org/10.1038/s41380-019-0555-5.

    Article  CAS  PubMed  Google Scholar 

  19. Insel TR. The NIMH research domain criteria (RDoC) project: precision medicine for psychiatry. Am J Psychiatr. 2014;171(4):395–7.

    Article  PubMed  Google Scholar 

  20. Lee Y, Ragguett RM, Mansur RB, Boutilier JJ, Rosenblat JD, Trevizol A, et al. Applications of machine learning algorithms to predict therapeutic outcomes in depression: a meta-analysis and systematic review. J Affect Disord. 2018;241:519–32. https://doi.org/10.1016/j.jad.2018.08.073.

    Article  PubMed  Google Scholar 

  21. Passos IC, Ballester P, Rabelo-da-Ponte FD, Kapczinski F. Precision psychiatry: the future is now. Can J Psychiatr. 2021;67 https://doi.org/10.1177/0706743721998044.

  22. Salazar de Pablo G, Studerus E, Vaquerizo-Serrano J, Irving J, Catalan A, Oliver D, et al. Implementing precision psychiatry: a systematic review of individualized prediction models for clinical practice. Schizophr Bull. 2021;47(2):284–97. https://doi.org/10.1093/schbul/sbaa120.

    Article  PubMed  Google Scholar 

  23. Vieta E. Personalised medicine applied to mental health: precision psychiatry; 2015.

    Google Scholar 

  24. Le-Niculescu H, Roseberry K, Gill SS, Levey DF, Phalen PL, Mullen J, et al. Precision medicine for mood disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs. Mol Psychiatry. 2021;26(7):2776–804. https://doi.org/10.1038/s41380-021-01061-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Tretter F, Gebicke-Haerter PJ. Systems biology in psychiatric research: from complex data sets over wiring diagrams to computer simulations. In: Psychiatric disorders. Cham: Springer; 2012. p. 567–92.

    Chapter  Google Scholar 

  26. Godlewska B, Browning M, Norbury R, Cowen PJ, Harmer CJ. Early changes in emotional processing as a marker of clinical response to SSRI treatment in depression. Transl Psychiatry. 2016;6(11):e957.

    Google Scholar 

  27. Shiroma PR, Thuras P, Wels J, Erbes C, Kehle-Forbes S, Polusny M. A proof-of-concept study of subanesthetic intravenous ketamine combined with prolonged exposure therapy among veterans with posttraumatic stress disorder. J Clin Psychiatry. 2020;81(6) https://doi.org/10.4088/JCP.20l13406.

  28. Schumann G, Benegal V, Yu C, Tao S, Jernigan T, Heinz A, et al. Precision medicine and global mental health. Lancet Glob Health. 2019;7(1) https://doi.org/10.1016/s2214-109x(18)30406-6.

  29. Bzdok D, Meyer-Lindenberg A. Machine learning for precision psychiatry: opportunities and challenges. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3(3):223–30. https://doi.org/10.1016/j.bpsc.2017.11.007.

    Article  PubMed  Google Scholar 

  30. Fusar-Poli P, Hijazi Z, Stahl D, Steyerberg EW. The science of prognosis in psychiatry: a review. JAMA Psychiat. 2018;75(12):1289–97.

    Article  Google Scholar 

  31. Abbott NJ, Patabendige AA, Dolman DE, Yusof SR, Begley DJ. Structure and function of the blood-brain barrier. Neurobiol Dis. 2010;37(1):13–25. https://doi.org/10.1016/j.nbd.2009.07.030.

    Article  CAS  PubMed  Google Scholar 

  32. Kealy J, Greene C, Campbell M. Blood-brain barrier regulation in psychiatric disorders. Neurosci Lett. 2020;726:133664. https://doi.org/10.1016/j.neulet.2018.06.033.

    Article  CAS  PubMed  Google Scholar 

  33. Pollak TA, Drndarski S, Stone JM, David AS, McGuire P, Abbott NJ. The blood-brain barrier in psychosis. Lancet Psychiatry. 2018;5(1):79–92. https://doi.org/10.1016/s2215-0366(17)30293-6.

    Article  PubMed  Google Scholar 

  34. Erdő F, Denes L, de Lange E. Age-associated physiological and pathological changes at the blood–brain barrier: a review. J Cereb Blood Flow Metab. 2016;37(1):4–24. https://doi.org/10.1177/0271678X16679420.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Greene C, Hanley N, Campbell M. Claudin-5: gatekeeper of neurological function. Fluids Barriers CNS. 2019;16(1):3. https://doi.org/10.1186/s12987-019-0123-z.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Greene C, Kealy J, Humphries M, Gong Y, Hou J, Hudson N, et al. Dose-dependent expression of claudin-5 is a modifying factor in schizophrenia. Mol Psychiatry. 2018;23(11):2156–66.

    Article  CAS  PubMed  Google Scholar 

  37. Ray A, Tennakoon L, Keller J, Sarginson JE, Ryan HS, Murphy GM, et al. ABCB1 (MDR1) predicts remission on P-gp substrates in chronic depression. Pharmacogenomics J. 2015;15(4):332–9. https://doi.org/10.1038/tpj.2014.72.

    Article  CAS  PubMed  Google Scholar 

  38. Uhr M, Tontsch A, Namendorf C, Ripke S, Lucae S, Ising M, et al. Polymorphisms in the drug transporter gene <em>ABCB1</em> predict antidepressant treatment response in depression. Neuron. 2008;57(2):203–9. https://doi.org/10.1016/j.neuron.2007.11.017.

    Article  CAS  PubMed  Google Scholar 

  39. Kuzman MR, Medved V, Bozina N, Grubišin J, Jovanovic N, Sertic J. Association study of MDR1 and 5-HT2C genetic polymorphisms and antipsychotic-induced metabolic disturbances in female patients with schizophrenia. Pharmacogenomics J. 2011;11(1):35–44. https://doi.org/10.1038/tpj.2010.7.

    Article  CAS  PubMed  Google Scholar 

  40. Geers LM, Pozhidaev IV, Ivanova SA, Freidin MB, Schmidt AF, Cohen D, et al. Association between 8 P-glycoprotein (MDR1/ABCB1) gene polymorphisms and antipsychotic drug-induced hyperprolactinaemia. Br J Clin Pharmacol. 2020;86(9):1827–35. https://doi.org/10.1111/bcp.14288.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Pedersen CB, Mors O, Bertelsen A, Waltoft BL, Agerbo E, McGrath JJ, et al. A comprehensive nationwide study of the incidence rate and lifetime risk for treated mental disorders. JAMA Psychiat. 2014;71(5):573–81.

    Article  Google Scholar 

  42. Manchia M, Squassina A, Pisanu C, Congiu D, Garzilli M, Guiso B, et al. Investigating the relationship between melatonin levels, melatonin system, microbiota composition and bipolar disorder psychopathology across the different phases of the disease. Int J Bipolar Disord. 2019;7(1):1–7.

    Article  Google Scholar 

  43. Van Ijzendoorn MH, Bakermans-Kranenburg MJ. Genetic differential susceptibility on trial: meta-analytic support from randomized controlled experiments. Dev Psychopathol. 2015;27(1):151–62.

    Article  PubMed  Google Scholar 

  44. Bakker J, Lieverse R, Menne-Lothmann C, Viechtbauer W, Pishva E, Kenis G, et al. Therapygenetics in mindfulness-based cognitive therapy: do genes have an impact on therapy-induced change in real-life positive affective experiences? Translational Psychiatry. 2014;4(4):e384.

    Google Scholar 

  45. Hudson JL, Lester KJ, Lewis CM, Tropeano M, Creswell C, Collier DA, et al. Predicting outcomes following cognitive behaviour therapy in child anxiety disorders: the influence of genetic, demographic and clinical information. J Child Psychol Psychiatry. 2013;54(10):1086–94.

    Article  PubMed  Google Scholar 

  46. Coleman JR, Lester KJ, Keers R, Roberts S, Curtis C, Arendt K, et al. Genome-wide association study of response to cognitive–behavioural therapy in children with anxiety disorders. Br J Psychiatry. 2016;209(3):236–43.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Keers R, Coleman JR, Lester KJ, Roberts S, Breen G, Thastum M, et al. A genome-wide test of the differential susceptibility hypothesis reveals a genetic predictor of differential response to psychological treatments for child anxiety disorders. Psychother Psychosom. 2016;85(3):146–58.

    Article  PubMed  Google Scholar 

  48. Rayner C, Coleman JR, Purves KL, Hodsoll J, Goldsmith K, Alpers GW, et al. A genome-wide association meta-analysis of prognostic outcomes following cognitive behavioural therapy in individuals with anxiety and depressive disorders. Transl Psychiatry. 2019;9(1):1–13.

    Article  CAS  Google Scholar 

  49. Marx P, Antal P, Bolgar B, Bagdy G, Deakin B, Juhasz G. Comorbidities in the diseasome are more apparent than real: what Bayesian filtering reveals about the comorbidities of depression. PLoS Comput Biol. 2017;13(6):e1005487. https://doi.org/10.1371/journal.pcbi.1005487.

  50. Carvalho AF, Sharma MS, Brunoni AR, Vieta E, Fava GA. The safety, tolerability and risks associated with the use of newer generation antidepressant drugs: a critical review of the literature. Psychother Psychosom. 2016;85(5):270–88. https://doi.org/10.1159/000447034.

    Article  PubMed  Google Scholar 

  51. Juruena MF, Bocharova M, Agustini B, Young AH. Atypical depression and non-atypical depression: is HPA axis function a biomarker? A systematic review. J Affect Disord. 2018;233:45–67. https://doi.org/10.1016/j.jad.2017.09.052.

    Article  CAS  PubMed  Google Scholar 

  52. Toro R, Poline J-B, Huguet G, Loth E, Frouin V, Banaschewski T, et al. Genomic architecture of human neuroanatomical diversity. Mol Psychiatry. 2015;20(8):1011–6.

    Article  CAS  PubMed  Google Scholar 

  53. Quinlan E, Barker E, Luo Q, Banaschewski T, Bokde A, Bromberg U, Consortium, I.et al. Peer victimization and its impact on adolescent brain development and psychopathology. Mol Psychiatry. 2018;25(11):366–76.

    Google Scholar 

  54. Quinlan EB, Cattrell A, Jia T, Artiges E, Banaschewski T, Barker G, et al. Psychosocial stress and brain function in adolescent psychopathology. Am J Psychiatr. 2017;174(8):785–94.

    Article  PubMed  Google Scholar 

  55. Hullam G, Antal P, Petschner P, Gonda X, Bagdy G, Deakin B, et al. The UKB envirome of depression: from interactions to synergistic effects. Sci Rep. 2019;9(1):1–19.

    Article  CAS  Google Scholar 

  56. Nanni V, Uher R, Danese A. Childhood maltreatment predicts unfavorable course of illness and treatment outcome in depression: a meta-analysis. Am J Psychiatr. 2012;169(2):141–51.

    Article  PubMed  Google Scholar 

  57. Xu Z, Zhang Z, Shi Y, Pu M, Yuan Y, Zhang X, et al. Influence and interaction of genetic polymorphisms in catecholamine neurotransmitter systems and early life stress on antidepressant drug response. J Affect Disord. 2011;133(1–2):165–73.

    Article  CAS  PubMed  Google Scholar 

  58. Xu Z, Zhang Z, Shi Y, Pu M, Yuan Y, Zhang X, et al. Influence and interaction of genetic polymorphisms in the serotonin system and life stress on antidepressant drug response. J Psychopharmacol. 2012;26(3):349–59.

    Article  CAS  PubMed  Google Scholar 

  59. Mandelli L, Marino E, Pirovano A, Calati R, Zanardi R, Colombo C, et al. Interaction between SERTPR and stressful life events on response to antidepressant treatment. Eur Neuropsychopharmacol. 2009;19(1):64–7.

    Article  CAS  PubMed  Google Scholar 

  60. Keers R, Uher R. Gene–environment interaction in major depression and antidepressant treatment response. Curr Psychiatry Rep. 2012;14(2):129–37.

    Article  PubMed  Google Scholar 

  61. Keers R, Uher R, Huezo-Diaz P, Smith R, Jaffee S, Rietschel M, et al. Interaction between serotonin transporter gene variants and life events predicts response to antidepressants in the GENDEP project. Pharmacogenomics J. 2011;11(2):138–45.

    Article  CAS  PubMed  Google Scholar 

  62. Maron E, Baldwin DS, Balõtšev R, Fabbri C, Gaur V, Hidalgo-Mazzei D, et al. Manifesto for an international digital mental health network. Digital Psychiatry. 2019;2(1):14–24.

    Article  Google Scholar 

  63. Vos T, Barber RM, Bell B, Bertozzi-Villa A, Biryukov S, Bolliger I, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the global burden of disease study 2013. Lancet. 2015;386(9995):743–800.

    Article  Google Scholar 

  64. Trivedi MH, Rush AJ, Wisniewski SR, Nierenberg AA, Warden D, Ritz L, et al. Evaluation of outcomes with citalopram for depression using measurement-based care in STAR* D: implications for clinical practice. Am J Psychiatr. 2006;163(1):28–40.

    Article  PubMed  Google Scholar 

  65. Souery D, Serretti A, Calati R, Oswald P, Massat I, Konstantinidis A, et al. Switching antidepressant class does not improve response or remission in treatment-resistant depression. J Clin Psychopharmacol. 2011;31(4):512–6.

    Article  CAS  PubMed  Google Scholar 

  66. Trivedi MH, Morris DW, Wisniewski SR, Lesser I, Nierenberg AA, Daly E, et al. Increase in work productivity of depressed individuals with improvement in depressive symptom severity. Am J Psychiatr. 2013;170(6):633–41.

    Article  PubMed  Google Scholar 

  67. Hirschfeld RM, Vornik LA. Perceptions and impact of bipolar disorder: how far have we really come? Results of the national depressive and manic-depressive association 2000 survey of individuals with bipolar disorder. J Clin Psychiatry. 2003;64(2):161–74.

    Article  PubMed  Google Scholar 

  68. Berk M, Dodd S, Callaly P, Berk L, Fitzgerald P, De Castella A, et al. History of illness prior to a diagnosis of bipolar disorder or schizoaffective disorder. J Affect Disord. 2007;103(1–3):181–6.

    Article  CAS  PubMed  Google Scholar 

  69. Tansey K, Rucker JJ, Kavanagh D, Guipponi M, Perroud N, Bondolfi G, et al. Copy number variants and therapeutic response to antidepressant medication in major depressive disorder. Pharmacogenomics J. 2014;14(4):395–9.

    Article  CAS  PubMed  Google Scholar 

  70. Border R, Johnson EC, Evans LM, Smolen A, Berley N, Sullivan PF, et al. No support for historical candidate gene or candidate gene-by-interaction hypotheses for major depression across multiple large samples. Am J Psychiatr. 2019;176(5):376–87.

    Article  PubMed  Google Scholar 

  71. Serretti A, Smeraldi E. Neural network analysis in pharmacogenetics of mood disorders. BMC Med Genet. 2004;5(1):1–6.

    Article  Google Scholar 

  72. Fabbri C, Zohar J, Serretti A. Pharmacogenetic tests to guide drug treatment in depression: comparison of the available testing kits and clinical trials. Prog Neuro-Psychopharmacol Biol Psychiatry. 2018;86:36–44.

    Article  CAS  Google Scholar 

  73. Rosenblat JD, Lee Y, McIntyre RS. Does pharmacogenomic testing improve clinical outcomes for major depressive disorder? A systematic review of clinical trials and cost-effectiveness studies. J Clin Psychiatry. 2017;78(6):720–9.

    Article  PubMed  Google Scholar 

  74. Nie Z, Vairavan S, Narayan VA, Ye J, Li QS. Predictive modeling of treatment resistant depression using data from STAR* D and an independent clinical study. PLoS One. 2018;13(6):e0197268.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Perlis RH. A clinical risk stratification tool for predicting treatment resistance in major depressive disorder. Biol Psychiatry. 2013;74(1):7–14. https://doi.org/10.1016/j.biopsych.2012.12.007.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Le-Niculescu H, Kurian S, Yehyawi N, Dike C, Patel S, Edenberg H, et al. Identifying blood biomarkers for mood disorders using convergent functional genomics. Mol Psychiatry. 2009;14(2):156–74.

    Article  CAS  PubMed  Google Scholar 

  77. Kéri S, Szabó C, Kelemen O. Blood biomarkers of depression track clinical changes during cognitive-behavioral therapy. J Affect Disord. 2014;164:118–22.

    Article  PubMed  Google Scholar 

  78. Niculescu A, Levey D, Phalen P, Le-Niculescu H, Dainton H, Jain N, et al. Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach. Mol Psychiatry. 2015;20(11):1266–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Levey DF, Niculescu EM, Le-Niculescu H, Dainton H, Phalen PL, Ladd TB, et al. Towards understanding and predicting suicidality in women: biomarkers and clinical risk assessment. Mol Psychiatry. 2016;21(6):768–85.

    Article  CAS  PubMed  Google Scholar 

  80. Niculescu AB, Le-Niculescu H. Dissecting suicidality using a combined genomic and clinical approach; 2017.

    Google Scholar 

  81. Howard DM, Adams MJ, Clarke TK, Hafferty JD, Gibson J, Shirali M, et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci. 2019;22(3):343–52. https://doi.org/10.1038/s41593-018-0326-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Davies C, Cipriani A, Ioannidis JP, Radua J, Stahl D, Provenzani U, et al. Lack of evidence to favor specific preventive interventions in psychosis: a network meta-analysis. World Psychiatry. 2018;17(2):196–209.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Carpiniello B, Pinna F. The reciprocal relationship between suicidality and stigma. Front Psych. 2017;8:35.

    Google Scholar 

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Acknowledgments

This work was supported by the Hungarian Brain Research Program (Grants: 2017-1.2.1-NKP-2017-00002; KTIA_13_NAPA-II/14); the National Development Agency (Grant: KTIA_NAP_13-1-2013-0001); the Hungarian Academy of Sciences, Hungarian National Development Agency, Semmelweis University, and the Hungarian Brain Research Program (Grant: KTIA_NAP_13-2-2015-0001) (MTA-SE-NAP B Genetic Brain Imaging Migraine Research Group); the Thematic Excellence Program (Tématerületi Kiválósági Program, 2020-4.1.1.-TKP2020) of the Ministry for Innovation and Technology in Hungary, within the framework of the Neurology and Translational Biotechnology thematic programs of the Semmelweis University; the National Research, Development and Innovation Office, Hungary (2019-2.1.7-ERA-NET-2020-00005), under the frame of ERA PerMed (ERAPERMED2019-108); and the TKP2021-EGA-25 project (Ministry of Innovation and Technology of Hungary, National Research, Development and Innovation Fund, financed under the TKP2021-EGA funding scheme).

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Authors and Affiliations

  1. NAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary

    Xenia Gonda

  2. Department of Psychiatry and Psychotherapy, Faculty of Medicine, Semmelweis University, Budapest, Hungary

    Xenia Gonda

  3. Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary

    Kinga Gecse, Zsofia Gal & Gabriella Juhasz

  4. SE-NAP 2 Genetic Brain Imaging Migraine Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary

    Kinga Gecse & Gabriella Juhasz

  5. TRAJECTOME Research Group, Budapest, Hungary

    Zsofia Gal & Gabriella Juhasz

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  1. Xenia Gonda
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  4. Gabriella Juhasz
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Correspondence to Gabriella Juhasz .

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  1. Medical Genomics Research Center, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran

    Mandana Hasanzad

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Gonda, X., Gecse, K., Gal, Z., Juhasz, G. (2022). Precision Medicine in Psychiatric Disorders. In: Hasanzad, M. (eds) Precision Medicine in Clinical Practice. Springer, Singapore. https://doi.org/10.1007/978-981-19-5082-7_6

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