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11 September 2025

Pharmacogenetics and the Response to Antidepressants in Major Depressive Disorder

,
and
1
Graduate Program in Neurosciences, Federal University of Santa Catarina, Florianópolis 88040-900, SC, Brazil
2
Laboratory of Physiology, Pharmacology, and Psychopathology, Graduate Program in Biomedical Sciences, Federal University of Fronteira Sul, Chapecó 89815-899, SC, Brazil
3
Translational Neuropsychobiology Laboratory, Graduate Program in Biomedical Sciences, Federal University of Fronteira Sul, Passo Fundo 99010-020, RS, Brazil
*
Author to whom correspondence should be addressed.
Pharmaceuticals2025, 18(9), 1360;https://doi.org/10.3390/ph18091360
This article belongs to the Special Issue Treatment and Molecular Mechanisms of Depression
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Abstract

Purpose: Genetic polymorphisms within specific genes play a role in both the genetic predisposition to Major Depressive Disorder (MDD) and the variation observed in responses to antidepressant treatments. Pharmacogenetics examines how these polymorphisms affect medication response. This review highlights significant disparities in the pharmacogenetic influences on antidepressant response, with a focus on ethnic and sex-based differences. Methods: This review synthesizes findings from a comprehensive literature search conducted between 2000 and 2025. It utilized databases such as PubMed, Scopus, and Web of Science, using search terms including “pharmacogenetics”, “antidepressants”, “Major Depressive Disorder”, “CYP450”, “neuroplasticity”, and “genetic variations”. This review integrates pharmacogenetics with neurotransmitters and their transporters, neuroplasticity, growth factors, and the cytochrome P450 family, providing promising insights for personalized MDD treatment strategies. We analyzed and synthesized findings from over 50 relevant studies, focusing on those with a clear emphasis on genetic associations with antidepressant efficacy and adverse effects. Results: Pharmacogenetic analysis facilitates personalized antidepressant prescriptions by identifying key genetic variants that influence treatment outcomes. Specifically, variations in CYP2D6 and CYP2C19 can significantly impact drug metabolism and tolerability. A high percentage of patients with non-normal metabolizer phenotypes are predisposed to adverse drug reactions or ineffective responses. Furthermore, this review identifies significant ethnic and sex-based disparities in treatment response. For example, the L allele of the 5-HTTLPR polymorphism confers a higher likelihood of response and remission following SSRI treatment in white people compared to Asians. Additionally, in women, specific 5-HTTLPR polymorphisms have a more pronounced influence on mood and MDD pathophysiology, with a significant reduction in mood in response to tryptophan depletion. Conclusions: Integrating pharmacogenetic insights, encompassing genetic factors, neurotransmitter pathways, neuroplasticity, and the influence of ethnicity and sex, is crucial for developing personalized antidepressant treatment strategies. This will ultimately optimize patient recovery and minimize adverse effects.

1. Introduction

Pharmacogenetics, related to antidepressants, is an area of research focusing on how individual genetic variations influence the response to antidepressant medications. Given the significant variability in antidepressant efficacy among patients, personalizing treatment based on genetic profiles offers a more effective strategy for managing Major Depressive Disorder (MDD) [1].
The most commonly used classes of classic antidepressants are selective serotonin reuptake inhibitors (SSRIs), such as fluoxetine, sertraline, and citalopram; serotonin and norepinephrine reuptake inhibitors (SNRIs), such as duloxetine, desipramine, and maprotiline; and tricyclic antidepressants (TCAs), such as amitriptyline, imipramine, and clomipramine [2,3].
Despite available treatments, MDD exhibits a high rate of non-response or inadequate response to antidepressants, with less than 50% of the patients achieving adequate response to the initial classic antidepressant. Around 30% of the patients do not achieve remission of MDD symptoms despite multiple therapeutic attempts and are therefore classified as having treatment-resistant depression (TRD) [4], which can result in morbidities for patients, besides significant costs [5]. TRD is strongly associated with different factors, such as childhood adversity, traumatic events, and bullying victimization. However, it can affect individuals without these histories [6] and genetic factors that seem responsible for approximately 50% of the variance in response to antidepressants [7].
Pharmacogenetics offers an opportunity to identify in advance which antidepressants may be most effective for a patient based on their unique genetics, helping to avoid unsuccessful treatment attempts [5]. Two widely used methodologies for research in the area are hypothesis-based candidate analysis and hypothesis-free Genome-Wide Association Studies (GWASs). While candidate analysis is a more targeted approach focused on specific genes based on previous hypotheses, GWASs explore the genome in an undirected way, seeking to identify genetic associations on a large scale without needing prior knowledge about the genes involved. Additionally, pharmacogenetic studies enable the identification of potential heterogeneities and the creation of clinical genetic panels that correlate antidepressants with specific genes and variants [8,9,10].
Pharmacogenetic-guided antidepressant treatment personalization enhances medication efficacy and accelerates the identification of optimal therapeutic strategies, which is particularly crucial for patients with severe TRD who have not responded to conventional approaches. Consequently, pharmacogenetics facilitates more informed and targeted MDD treatment decisions, improving patient outcomes [11].
While foundational reviews by Porcelli et al. [12] and Keers and Aitchison [13] explored the pharmacogenetics of antidepressant response, they were published at a time when research was still grappling with inconsistent findings and modest effect sizes. The Porcelli review, for example, highlighted a focus on pharmacodynamic genes despite inconsistencies. In contrast, the Keers review noted the disappointing failure in replicating some findings and a strong need for standardized methodologies. Over the past decade, significant advances in both research and technology have generated a wealth of new data. This review distinguishes itself by providing a holistic and integrated perspective, synthesizing the fragmented literature on the complex interactions between genetic variations, neurobiological factors (such as neurotransmitter systems, neuroplasticity, and inflammation), and key demographic variables (including ethnicity and sex). By doing so, this review addresses a critical gap and offers a unique framework for understanding personalized MDD treatment.

2. Methodology

This narrative review synthesizes and critically evaluates the existing literature on the pharmacogenetics of antidepressant response in MDD, focusing on the intricate interplay between genetic variations, key neurobiological systems, and demographic factors such as ethnicity and sex. A comprehensive search was conducted on PubMed, Scopus, and Web of Science for studies published between 2000 and 2025. The search strategy involved a combination of keywords, including “pharmacogenetics”, “antidepressant”, “Major Depressive Disorder”, “genetic polymorphism”, “5-HTTLPR”, “BDNF”, “CYP2D6”, “CYP2C19”, “ethnicity”, and “sex”. Only peer-reviewed articles in English involving human subjects, including original research, meta-analyses, and comprehensive reviews that investigated the association between genetic polymorphisms and antidepressant efficacy or adverse effects, were included. Articles not directly related to the topic were excluded. The selected studies were then analyzed, and key data on study design, population demographics, genes, and findings were extracted and compiled.

3. Genetic Influence on Major Depressive Disorder and Antidepressant Treatment

The etiology of MDD is a complex interaction of genetic, biological, psychological, and environmental factors. Family history of depression, chemical imbalances in the brain, personality traits, history of traumatic events, chronic stress, lack of social support, limited access to resources, and low self-esteem are psychological factors that may contribute to the etiology of depression. These factors can also interact with genetic predisposition [14].
A genetic influence on MDD has been a subject of significant research, and several studies have explored the role of genetics in the development and treatment of this complex mental health condition. A GWAS involving 807,553 individuals, 246,363 with depression and 561,190 controls, identified 102 independent genetic variants and implicated 269 genes, supporting the polygenic nature of depression. Additionally, the study identified 15 gene sets associated with depression, encompassing individual genes and genetic pathways related to synaptic structure and neurotransmission, primarily within the prefrontal cortex [15].
Diseases with an inflammatory pathophysiology, such as depression, are more likely to develop due to genetic problems that control lipid metabolism and consequently affect inflammatory mechanisms. The literature indicates that people with MDD have altered levels of phospholipids, including arachidonic acid. At least one study has identified a shared genetic etiology between this biological alteration and MDD [16].
Polymorphisms of the mineralocorticoid receptor (MR) and glucocorticoid receptor (GR) genes also influence the risk of MDD, considering their influence on the levels of the biomarkers aldosterone and cortisol, both of which are altered in the disorder. The polymorphism of the MR and GR genes is associated with stress at the beginning of life, a risk situation for the development of MDD [17] because it stimulates changes that can even influence the response to antidepressants [18].
Polymorphisms of the ATP-binding cassette (ABC) B1 gene, which encodes P-glycoprotein (P-gp), an ATP-driven efflux pump at the blood–brain barrier, were also associated with the physiopathology and the response to antidepressants [19]. It is essential to consider that carriers of the minor allele for rs2032583 with elevated plasma levels of antidepressant have more sleep-related side effects compared to homozygotes for the major allele with high plasma levels. It is suggested that the elevation in plasma levels may be related to a reduced or altered P-gp efflux activity in patients who are carriers of the minor allele, emphasizing that the plasma levels of antidepressants of the SSRI, SSNRi, or TCA classes should not exceed the recommended range to obtain an optimal therapeutic result [20].

8. Final Considerations and Future Directions

Genes and genetic polymorphisms are inherently linked, regulating gene expression and determining organismal characteristics. This intricate interplay is particularly crucial in understanding the genetic predisposition to depression, the variability in the response to antidepressant treatment, and the identification of potential therapeutic targets.
The genetic influence on antidepressant response is complex and may vary according to the specific genes, ethnicity, and neurotransmitters considered. It is essential to consider that research with small samples does not usually demonstrate significant differences between carriers of genetic alterations and individuals without these alterations [35]. Still, these differences are evident in research with larger samples [36]. Researchers have demonstrated in different samples that ethnicity influences the effect of polymorphisms on drug response [33], and thus, we must consider ethnic differences. Moreover, sex also influences this, with genetic polymorphisms associated with MDD predominating in women [33,72]. Although several researchers have studied genetic polymorphisms in the SLC6A4 gene, their impact on the pathophysiology of MDD has yet to be elucidated.
It is necessary to improve the number of studies on other neurotransmitters, such as dopamine and noradrenaline, and their transporters. Still, research has failed to replicate previously verified results, possibly due to differences in the antidepressant analyzed or its class [73]. Therefore, more research with large samples is needed to fully understand how individual genetic variations can affect the effectiveness of antidepressant treatments and how this can inform clinical practice in the area of mental health.
The intersection of pharmacogenetics, neuroplasticity, and growth factor research has yielded valuable insights for personalized approaches to depression treatment. Understanding genetic variations linked to neuroplasticity-related genes, such as BDNF and VEGFA, holds the promise of tailoring the most effective antidepressant interventions for individuals with MDD. Similarly, alterations in the inflammatory response contribute to the disorder and may be related to the pharmacogenetics of the reaction to classical antidepressants.
The CYP450 family of hepatic enzymes plays a crucial role in the metabolism of several antidepressants, with genetic variations in these genes responsible for significant impacts on the serum concentrations of these drugs. In turn, the serum concentrations can modulate the effects of antidepressants, increasing or decreasing their effectiveness. The study of the CYP450 genotype contributes to a more appropriate and personalized choice of medications, minimizing the rates of treatment resistance and adverse effects. The influence of the genetic variations in the CYP450 family extends to different classes of antidepressants, affecting the release and elimination of substances. However, an exclusion criterion in clinical investigations about genetic variations presents challenges, especially in regions such as Sub-Saharan Africa, where the lack of comprehensive studies makes the access to more effective medicines difficult, highlighting the need for extensive research to ensure appropriate results for different ethnicities.
In the context of clinical application, the recommended pharmacogenetic tests for antidepressants focus on two key biological systems [74]. The best-established and most clinically relevant tests examine genes that code for drug-metabolizing enzymes, primarily within the CYP450 system. These tests, which include genotyping for CYP2D6 and CYP2C19 variants, help predict how quickly a patient will metabolize a drug, guiding dosage to minimize the adverse effects and ensure therapeutic levels. Genetic variants in these genes can classify patients into different metabolic profiles, such as poor, intermediate, standard, rapid, or ultra-rapid metabolizers, which directly affect drug efficacy and the risk of side effects. Following genotype-informed recommendations, particularly for drugs like escitalopram, sertraline, and paroxetine, can reduce adverse drug reactions and improve treatment efficacy, especially in patients who have experienced prior treatment failure [75,76].
Additionally, significant progress has been made in testing drug targets, which are the genes related to the pharmacological action of the antidepressants themselves. These include pharmacodynamic genes such as SLC6A4 and BDNF. For SLC6A4, genetic variants like the 5-HTTLPR polymorphism have been associated with antidepressant response, with the L allele predicting better response and remission, especially in Caucasian populations. In the BDNF gene, the Val66Met (rs6265) polymorphism is also associated with a better SSRI response, particularly in Asian populations [77]. Beyond these variants, epigenetic modifications such as DNA methylation of the SLC6A4 and BDNF genes show promise as biomarkers for predicting treatment outcomes [78]. However, despite these promising findings, the evidence is often inconsistent and not yet robust enough for routine clinical decision-making. While integrating information from both metabolic genes and these pharmacodynamic markers could theoretically enhance personalized treatment strategies in the future, the current clinical guidelines do not yet support the routine use of SLC6A4 or BDNF testing for the prescription of antidepressants [79].
Studies suggest that pharmacogenetic analyses help avoid adverse effects that specific gene variations may cause or exacerbate [80,81,82,83]. A pragmatic randomized clinical trial evaluated the effectiveness of a particular, commercially available pharmacogenetic test to guide antidepressant prescribing at 22 sites in the United States. The results indicate that healthcare professionals require more excellent education and support to implement pharmacogenetics in clinical practice, particularly for antidepressant prescription, and the study’s conclusion reveals the practice’s promise [65].
Studies have shown that polymorphic variants of SERT and BDNF individually contribute to severe and resistant depression in people who suffer from childhood stress. Further, researchers discovered that variations in both SERT and BDNF genes modify the risk of depression conferred by childhood maltreatment when analyzed together [84]. Future studies should investigate the interaction between consistent polymorphisms and environmental factors, offering an interesting direction for research in this area.
Pharmacogenetics related to antidepressants seems to offer a promising possibility of personalizing antidepressant treatments based on the individual genetic makeup of patients. This can be particularly beneficial due to the high rate of non-responsiveness to classic treatment strategies, improving the effectiveness of antidepressants and providing a more efficient path to recovery for patients with depression. However, although some results have highlighted the importance of pharmacogenetics for treatments, others point to the need for further investigations, observing the interaction between genetic variables and other biological or environmental variables.

Author Contributions

A.G.B. conceptualized the study and wrote the first draft. R.M. reviewed the manuscript. Z.M.I. supervised and reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by grants in Brazil from FAPESC (Z.M.I.; Grant 2023TR001508 and Grant 2024TR002554) and UFFS (Z.M.I.; Grant PES-2024-0533); Z.M.I. is a C CNPq Research Fellow.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

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