Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Jun;141(6):541-552.
doi: 10.1111/acps.13131. Epub 2019 Dec 5.

Personalized medicine begins with the phenotype: identifying antipsychotic response phenotypes in a first-episode psychosis cohort

S Mas  1   2   3 , P Gassó  1   2   3 , N Rodríguez  4 , B Cabrera  2   5 , G Mezquida  3   6   7 , A Lobo  8 , A González-Pinto  2   9   10   11 , M Parellada  12 , I Corripio  2   13   14   15 , E Vieta  2   3   16   17 , J Castro-Fornieles  2   3   17   18 , J Bobes  2   19   20 , J Usall  21   22 , J Saiz-Ruiz  23 , F Contreras  2   3   24   25 , E Parellada  2   3   17   26 , M Bernardo  2   3   17   26 ; PEPs group
Collaborators, Affiliations

Personalized medicine begins with the phenotype: identifying antipsychotic response phenotypes in a first-episode psychosis cohort

S Mas et al. Acta Psychiatr Scand. 2020 Jun.

Abstract

Aims: Here, we present a clustering strategy to identify phenotypes of antipsychotic (AP) response by using longitudinal data from patients presenting first-episode psychosis (FEP).

Method: One hundred and ninety FEP with complete data were selected from the PEPs project. The efficacy was assessed using total PANSS, and adverse effects using total UKU, during one-year follow-up. We used the Klm3D method to cluster longitudinal data.

Results: We identified four clusters: cluster A, drug not toxic and beneficial; cluster B, drug beneficial but toxic; cluster C, drug neither toxic nor beneficial; and cluster D, drug toxic and not beneficial. These groups significantly differ in baseline demographics, clinical, and neuropsychological characteristics (PAS, total PANSS, DUP, insight, pIQ, age of onset, cocaine use and family history of mental illness).

Conclusions: The results presented here allow the identification of phenotypes of AP response that differ in well-known simple and classic clinical variables opening the door to clinical prediction and application of personalized medicine.

Keywords: antipsychotic; clustering; first-episode; personalized medicine; predictive factors; psychosis.

PubMed Disclaimer

References

    1. Fond G, d'Albis MA, Jamain S et al. The promise of biological markers for treatment response in first-episode psychosis: a systematic review. Schizophr Bull 2015;41:559-573.
    1. Leucht S, Cipriani A, Spineli L et al. Comparative efficacy and tolerability of 15 antipsychotic drugs in schizophrenia: a multiple-treatments meta-analysis. Lancet 2013;382:951-962.
    1. Zhang JP, Malhotra AK. Recent progress in pharmacogenomics of antipsychotic drug response. Curr Psychiatry Rep 2018;20:24.
    1. Zai CC, Tiwari AK, Zai GC, Maes MS, Kennedy JL. New findings in pharmacogenetics of schizophrenia. Curr Opin Psychiatry 2018;31:200-212.
    1. Serretti A. The present and future of precision medicine in psychiatry: focus on clinical psychopharmacology of antidepressants. Clin Psychopharmacol Neurosci 2018;16:1-6.

Publication types

Substances