Using Genetic Biomarkers to Predict Patient Response and Side-Effect Profiles for SSRIs and Antipsychotics: A Comprehensive Review

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Authors: Bijoy Ghosh*, Meckie Khaliz Chapola, Saurav Kumar
Cited by: 0

Abstract

Selective serotonin reuptake inhibitors (SSRIS) and antipsychotic drugs continue to form the basis of pharmacotherapy of major depressive disorder, anxiety disorders, and psychotic spectrum disorders. Significant interindividual differences in drug response and adverse drug reactions (ADRs), however, restrict their clinical importance and add to their treatment discontinuation rates, relapse, and psychiatric morbidity. Pharmacogenomics, the study of the effect that genetic variation has on drug response, provides a logical scientific model upon which individual responses to drugs and the selection and dosing of drugs can be predicted. The present review of the literature on the research question will analyse the existing evidence regarding genetic biomarkers of SSRI and antipsychotic response, meta-analyses, and randomized controlled trials, and recommend clinical strategies based on the global pharmacogenetics recommendations. Our targets include cytochrome P450 (CYP2D6, CYP2C19) polymorphisms, serotonergic (HTTLPR, BDNF), dopaminergic (DRD2, DRD3, COMT), immune-related (HLA variants), and multivariate biomarker. The material justifies the fact that, when combined with therapeutic drug monitoring and clinical decision support systems, pharmacogenetic testing can contribute to a considerably enhanced treatment outcome, decreased adverse effects, and a faster recovery of the patient. Although there is strong scientific evidence, clinical application is still limited, indicating that education and integration of testing in standard psychiatric practice as well as subsequent validation studies, are necessary.

Fields of Study & Subjects

Pharmacogenetics and Drug Metabolism Treatment of Major Depression Genetic Associations and Epidemiology

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
10 January 2026