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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.

Keywords

pharmacogenomics, pharmacogenetics, SSRIs, antipsychotics, adverse drug reactions, CYP450 enzymes.

Introduction

The number of untreated psychiatric illnesses has a high burden. The major depressive disorder (MDD) impacts more than 280 million patients in the world and is the cause of the greatest disability burden in the world, whereas schizophrenia and other psychotic disorders cause significant functional impairment and shorter life expectancy (1). The standard of care is pharmacotherapy by using SSRIs and antipsychotics. However, ironically, 50-70 per cent of patients with MDD respond to the initial trial of SSRI, and 30-50 per cent show resistance to treatment despite tolerable dosages and periods of time (1). Likewise, first-episode psychosis patients who are antipsychotic-naive respond to treatment in 60-75 per cent, i.e. one out of four- one out of three patients does not respond to the first attempt to treat (2). Moreover, 10-40% of patients require discontinuation of a specific medication due to side effects, and adverse drug reactions are the most powerful predictor of nonadherence and relapse to treatment (3).

Such clinical heterogeneity is manifested by genetic and biological differences that are disregarded by contemporary psychiatric practice. The personalization of medical treatment, or precision medicine or personalized medicine, in which medical treatment is based upon personal biological, genetic, and environmental factors, is a paradigm shift (4). Pharmacogenomics offers a rigorous production of a scientifically established basis for applying precision psychiatry. Clinicians can promptly modify dosages, choose different drugs or supplement treatment by identifying patients who are genetically prone to poor efficacy or severe adverse effects, consequently enhancing their outcome and reducing iatrogenic harm (5).

The review summarizes the existing research on genetic biomarkers predicting SSRI and antipsychotic response and side-effect profiles, highlighting evidence that can be put into clinical practice and implementation obstacles. As soon as Figure 1

Figure-1- Clinical Actionability| Evidence based Foundation| Barrier to implementation

2. PHARMACOGENETIC BASIS OF DRUG METABOLISM AND RESPONSE

2.1 Cytochrome P450 Polymorphisms and Drug Metabolism

Metabolism of the large majority of psychotropic drugs in the liver is catalysed by the cytochrome P450 (CYP450) superfamily (6). The majority of SSRIs and antipsychotics are metabolized by three CYP enzymes, which include CYP2D6, CYP2C19, and CYP3A4(7). These loci can vary genetically to generate a significant amount of phenotypic diversity: poor metabolizers (PMs, no or severely impaired enzyme activity), intermediate metabolizers (IMs, reduced activity), normal/extensive metabolizers (NMs/EMs, normal activity), rapid metabolizers (RMs), or ultrarapid metabolizers (UMs, increased gene copy number or increased promoter activity) (8).

Genotyping of CYP2D6, CYP2C19, CYP1A2, CYP3A5, and CYP2C9 of 784 Greek psychiatric patients showed that 6080% of patients harboured genotypes that necessitated dose modification of at least one psychiatric drug (9). In particular, there were 93 poor metabolizers (PMs), 633 intermediate metabolizers (IMs), 1272 normal metabolizers (NMs) and 30 ultrarapid metabolizers (UMs) in the five genes. These frequency distributions highlight the prevalence of CYP polymorphisms and how pharmacogenetic optimization can be practised on a large scale (10).

Figure 2- Genetic variation requires personalized dose adjustment

CYP2D6 and SSRIs

The most commonly used metabolic route of paroxetine and fluvoxamine is CYP2D6. CYP2D6 poor metabolizers persistently build these SSRIs up to supraphysiologic concentrations, exposing them to dose-related side effects (tremor, nausea, sexual dysfunction, hyponatremia) (11). Guidelines of Clinical Pharmacogenetics Implementation Consortium (CPIC) suggest a 25-50 per cent dose reduction in the initial and maintenance dose of paroxetine and fluvoxamine in CYP2D6 PMs(12). In case the alternative SSRIs are less reliant on CYP2D6 (e.g., citalopram, sertraline), dose changes might not be required, but close monitoring is wise (13).

Fluoxetine is a paradox to pharmacokinetics: despite being metabolized via CYP2D6, the active metabolite norfluoxetine, which is accumulated to equivalent levels irrespective of metabolizer phenotype, is a significant contributor to drug effect (14). Recent reports suggest that the CYP2D6 genotype does play a role in the ratio of fluoxetine to norfluoxetine, but the overall active moiety is constant across metabolizer phenotypes. The 2- to 3-fold greater ratio of norfluoxetine to fluoxetine in ultrarapid metabolizers is, however, linked with a two- to three-fold greater rate of treatment switches, indicating clinically significant differences of tolerability or effectiveness by the parent drug to metabolite ratio (15).

CYP2C19 and SSRIs

The principal metabolic route of citalopram and its active S-enantiomer escitalopram is CYP2C19(16). The CYP2C19 poor metabolizers and intermediate metabolizers have very low levels of medication clearance (17). Prospective pediatric trials show that CYP2C19 metabolizer activity predicts tolerability and efficacy: those with poor metabolism who take standard citalopram doses complain of a higher incidence of adverse effects (tremor, nausea, sedation) and have lower maximum doses to produce a therapeutic effect (18). The CYP2C19 poor metabolizers should have half a dose cut in the citalopram maintenance dose with slower titration schedules according to the CYP2C19 guidelines (19). Also, related in vitro functional studies have detected new CYP2C19 variants (e.g., CYP2C19 35FS) whose loss-of-function capability is comparable to the well-studied alleles, which are denoted as stars 2 and 3, implying that even further clinical prediction is possible with expanded functional genotyping (20).

2.2 Serotonergic System Biomarkers

5-HTTLPR Polymorphism

One of the most highly studied pharmacogenetic biomarkers has been the serotonin transporter-linked polymorphic region (5-HTTLPR) in the promoter of SLC6A4 (the serotonin transporter gene) (21). A 44-base-pair repeat biallelic insertion/deletion produces short (s) and long (l) alleles, the long allele having greater transcriptional efficiency than the short allele (22). A hypothesis that carriers of the short allele, which, putatively, lead to lower levels of transporter and defective serotonin reuptake inhibition, would react poorly to SSRIs elicited a lot of excitement. Early research was in favour of this association (23).

Subsequent meta-analyses have, however, produced conflicting results. A meta-analysis of 15 pooled studies that was conducted comprehensively reported a very strong, significant association that exists between the SS and worse remission rates (P = 0.0001) and SS/SL and worse response rates (P = 0.0002) (24). These findings at first confirmed 5-HTTLPR as a putative predictor of SSRI response. A bigger meta-analysis study, which summarized the findings of various prospective cohort studies, however, did not find any significant relationship between the biallelic 5-HTTLPR polymorphism and general SSRI response, but instead a low level of relationship with remission persisted (25). The discordance is probably due to publication bias and the heterogeneous definition of outcomes. The most recent consensus is that 5-HTTLPR does not predict well on its own in the response to SSRI, but perhaps information about individual side effects (26).

Brain-Derived Neurotrophic Factor (BDNF)

Brain-derived neurotrophic factor (BDNF) is a neurotrophin that is responsible for maintaining the survival of neurons, differentiation, and synaptic plasticity (27). BDNF Val66Met polymorphism has also become a strong predictor of antidepressant treatment (28). The animal models show that the BDNF gene causes impairment of neurogenesis processes of the hippocampus, which puts the animals into a depressive-like state and that the administration of antidepressants enhances the expression of the BDNF gene, especially in the hippocampus and prefrontal cortex (29). Mechanistically, SSRIs increase BDNF signalling via the tropomyosin receptor kinase B (TrkB) that subsequently facilitates neuronal growth and adaptive plasticity (30).

The meta-analysis indicates that both SSRIs and SNRIs increase the levels of peripheral BDNF, and sertraline has the quickest initial increase in serum BDNF concentration (31). The patients with the BDNF Val/Val genotype (which is linked to activity-dependent secretion of BDNF) tend to respond to antidepressants better than those with the Met allele genotype, which is characterized by more constitutive as opposed to activity-dependent BDNF secretion (32). This implies that activity-regulated BDNF release in people with a genetic predisposition to the specified treatment might be enhanced by the means of treatment that boost BDNF levels. Interactions of BDNF with other genetic variations have been identified using deep learning methods that utilise genetic as well as clinical biomarkers, which argues in favour of a polygenic profile of SSRI response (33).

2.3 Dopaminergic System Biomarkers

The mechanisms that antipsychotic drugs use to have their therapeutic effect are mainly based on dopamine D2 receptor antagonism (34). The variation in the dopaminergic genes under the influence of genetics significantly affects the response to therapy and the likelihood of their development as side effects. The dopamine receptors (DRD2, DRD3, DRD4), the dopamine transporter (DAT), the catechol-O-methyltransferase (COMT), and the monoamine oxidase A (MAOA) are the key dopaminergic genes (35).

DRD2, DRD3, and Antipsychotic Response

DRD2 Taq1A polymorphism (rs1800497, C957T) associates with antipsychotic treatment response, but with relatively small effect sizes(36). The DRD2 A1 allele is associated with increased densities of dopamine D2 receptors, and patients may respond better to the antipsychotics (37). On the other hand, DRD3 Ser9Gly (rs6280) polymorphism has had the greatest consistent association with tardive dyskinesia (TD), which is a potentially irreversible movement disorder that occurs in 0.5-65 per cent of individuals on chronic antipsychotics (38). DRD3 Gly allele predisposes to increased dopamine sensitivity, which is counterintuitive to put individuals at risk of TD mediated by dopamine (39). There is meta-analysis data among several ethnic groups that supports this relationship, and DRD3 Gly9 genotype explains a significant part of TD variance, about 30% of it, regardless of age, sex, and ethnicity (40). Furthermore, the risks of dyskinesia are predicted by BDNF polymorphisms and DRD2 haplotypes, which indicate that both interact epistatically within the dopaminergic networks (41).

COMT and Catecholaminergic Balance

The dopamine and norepinephrine are metabolized by catechol-O-methyltransferase (COMT) (42). The Val158Met polymorphism results in functional variants with 3-4 fold differences in enzyme activity: the Val allele results in higher activity of the enzyme (high rate of dopamine degradation), and the Met allele results in lower activity of the enzyme (low rate of dopamine degradation) (43). Compound relationships among COMTVal/Val, variations of DRD4 repeats and DRD2 genotypes anticipate the reaction and resistance to antipsychotics. A recent pharmacogenetic study had shown that patients with DRD4 240/240 and DRD4 120-bp allele were highly resistant to treatment, and the combination of DRD2 AA, COMT Met allele carrier and DRD4 120-bp allele carrier with DRD4 was indicative of better antipsychotic response (44)(45).

Figure 3- Gene- Gene-gene interaction Clinical heterogeneity

3. ANTIPSYCHOTIC-SPECIFIC ADVERSE EFFECT BIOMARKERS

3.1 Clozapine-Induced Agranulocytosis

Clozapine is the drug of choice against treatment-resistant schizophrenia because it is more effective in terms of its negative symptom reduction and cognitive impairment, as well as its ability to decrease suicidality (46). Nevertheless, clozapine is associated with potentially fatal agranulocytosis (absolute neutrophil count <500/ul) in 0.52% of patients treated with the drug, which makes it important to monitor hematologic parameters closely during the drug and limit its use to individuals who have failed at least two previous antipsychotics (47).

Recent genome-wide association and exome sequencing findings have revealed particular HLA variants that predispose to agranulocytosis with significantly high risks. HLA-DQB1 126Q (coded by HLA-DQB105:02) and HLA-B 158T are also independent predictors of agranulocytosis with clozapine, with an odds ratio indicating a significant risk increment in those with an allele (48). The mechanism consists of immunogenic recognition of a reactive product of nitrenium ion intermediate formed during clozapine metabolism, which can induce neutrophil destruction by T-cell-mediated haptenic killing (49). It is worth noting that the HLA-DQB1 126 glutamine residue seems to be protective, but the most frequent HLA-DQB1 105: 02 allele with the histidine at the 126 position poses increased risk (50).

Although the genetic relationships are strong, the positive predictive capability of HLA genotyping of agranulocytosis is modest (around 1-2%), thus not sufficient to apply in the immediate clinical practice to rule out patients against using clozapine (51). Rather, HLA-guided testing can help to better risk-stratify, guide more vigorous monitoring, or select candidates for other atypical antipsychotics with more favourable safety profiles. There are currently no regulatory bodies that require the practice of HLA testing before clozapine treatment, but studies suggest that it should be considered as an additional tool in addition to the current hematologic surveillance mechanisms (52).

3.2 Tardive Dyskinesia and Extrapyramidal Symptoms

In 20-30% of patients who are treated chronically, the condition that develops is tardive dyskinesia, which is involuntary, stereotypical orofacial, lingual, and extremity movements that may continue even with the withdrawal of the medications (53). Ser9Gly DRD3 polymorphism is the best-investigated genetic risk factor. Meta-analyses of data in thousands of patients of various ethnicities have repeatedly shown that there is an overall higher prevalence and severity of TD in DRD3 Gly9 carriers (54). It seems these effects are mediated by increased dopamine sensitivity in Gly carriers, which makes dopaminergic circuitry more vulnerable to sensitization by long-term antipsychotic drugs (55).

Also, DRD2 and brain-derived neurotrophic factor (BDNF) polymorphisms also play a role in TD because of their interactions with dopaminergic adaptation and neural plasticity processes (56). The routine use of DRD3 genotyping in the assessment of risk of TD in clinical practice is still not done, and partly due to the fact that the absolute risk increase, though significant at the population level, is not converted to an individual-specific recommendation on whether or not to take medication (57). Nevertheless, detection of individuals at risk can lead to increased clinical surveillance, use of less TD-liability agent (e.g., aripiprazole, quetiapine), or more vigorous early intervention with beta-blockers or benzodiazepines (58).

3.3 Antipsychotic-Induced Weight Gain and Metabolic Syndrome

Weight gain is one of the most common and most clinically problematic side effects of antipsychotics, especially the second-generation antipsychotics (SGAs) such as clozapine, olanzapine, quetiapine, and risperidone (59). Each additional pound of weight due to the use of antipsychotics places a person at higher risk of type 2 diabetes mellitus, cardiovascular disease and metabolic syndrome- all of which cause a 10-25 year decrease in the life expectancy of schizophrenic patients (60).

The twin studies and sibling design estimate the heritability of the weight gain caused by antipsychotics at 60-80 per cent, indicating significant genetic influences (61). There are several genetic systems that regulate weight gain predisposition by acting upon control of appetite, energy spending, leptin signalling and maintenance of a metabolic state. Key candidate genes include:

Serotonin 2C Receptor (HTR2C): HTR2C Cyst23Ser is the most frequent and reliable predictor of weight gain in antipsychotics (62). The 5-HT2C receptor is concentrated at the hypothalamic arcuate nucleus, where it inhibits pro-opiomelanocortin (POMC) neurons that enhance satiety and energy burning. This 5-HT2C antagonism prevents this anorexigenic signalling by antipsychotics (63). Ser allele carriers that encode lower 5-HT2C receptor activity gain weight more, especially when using clozapine and olanzapine (64).

Leptin and Leptin Receptor: Leptin suppresses appetite and energy expenditure by JAK2-STAT-mediated hypothalamic leptin receptor signalling (65). LEP and LEPR polymorphisms (−2548A/G, A19G and Q223R) are connected with the change in serum leptin levels and body mass index (66). The LEPR Q223R polymorphism correlates with both baseline obesity and augmented weight increase under antipsychotic treatment, especially with the variations in HTR2C (67).

Melanocortin 4 Receptor (MC4R): MC4R, which has been identified as a risk factor of obesity, is also a predictor of weight gain due to the use of antipsychotics (68). The receptor lies below the POMC neurons in the hypothalamic melanocortin system and is vital in the control of appetite (69).

A recent meta-analysis of CYP2D6 metabolizer status and antipsychotic weight gain gave varying results: cohort studies indicated that poor metabolizers (with higher antipsychotic blood levels) gain more weight, but cross-sectional studies and the meta-analysis overall did not offer any support to this association (70). The discordance is probably related to residual confounding because of chronicity of illness, antipsychotic preference and unmeasured lifestyle (71).

Figure 4- Genetic Susceptibility Modulates Side Effects

4. MULTIVARIATE AND POLYGENIC APPROACHES TO TREATMENT PREDICTION

4.1 Polygenic Risk Scores

Predictively, individually, single-gene pharmacogenetic markers often report small amounts of variance in treatment outcome (often less than 5 per cent) (72). Polygenic Risk Scores (PGS)-weighted aggregates of genome-wide association study (GWAS) summary statistics at a large number of loci- surround the distributed genetic vulnerability and are more predictive (73).

In response to SSRI, several studies have now shown that PGS of major depressive disorder (MDD-PGS) can reliably predict non-response: increasing the standard deviation of MDD-PGS by one unit increases the odds of non-remitting after major depressive disorder by 10-14 per cent in a variety of European cohorts and biobank studies (74). However, MDD-PGS has a percentage of the explained variance of 1% suggesting that it has a statistically significant but less valuable clinical usage at an individual level (75). However, the PGS of schizophrenia (SCZ-PGS) is found to be inversely related: high genetic susceptibility to schizophrenia is related to low SSRI responsiveness when using monoaminergic monotherapy, but possibly elevated responsiveness when using electroconvulsive therapy (ECT) or lithium augmentation (76). It indicates that there are neurobiologically different subtypes of depression that have a different pharmacological sensitivity (77).

Polygenic relationships with personality traits are also significant in the response to treatment: the high polygenic liability of neuroticism (a trait associated with dysregulation of serotonergic processes) results in a worse SSRI response (78). PGS, on the other hand, are linked to attention-deficit/hyperactivity disorder (ADHD) and heart diseases (coronary artery disease, stroke) that are linked to treatment-resistant depression, which increases the odds of non-remission twofold (79). These observations suggest the existence of common genetic makeup between psychiatric and medical disorders that could limit the effect of antidepressants (80).

4.2 Inflammatory Biomarkers

Newer findings consider high baseline inflammation as a biomarker of antidepressant responders and non-responders (81). Meta-analyses demonstrate that the patients, whose C-reactive protein (CRP >3 mg/L), interleukin-6 (IL-6), and tumour necrosis factor-alpha (TNF-α) are high, have more severe symptoms of depression, especially anhedonia and diminished motivation (82). Moreover, there are some inflammatory patterns indicating poor response to SSRI or resistance to treatment (83).

The STAR*D trial and MARS cohort studies established that polygenic risk of CRP and TNF-alpha is associated with depressive symptoms and, in certain studies, decreased antidepressant effect (84). Nevertheless, the predictive value of the inflammatory biomarkers has been varied across studies, in part because inflammation is a heterogeneous phenotype that is affected by infections, metabolic factors and lifestyle variables (85). The recent interest has been in inflammatory subtypes of depression: patients with a high level of pro-inflammatory markers (IL-1β 2, IL-6, TNF-α) and normal or decreased anti-inflammatory markers (IL-4, IL-10 ) may respond to anti-inflammatory augmentation strategies (e.g., NSAIDs, omega-3 fatty acids, cytokine antagonists) instead of SSRI monotherapy (86).

4.3 Neuroimaging Biomarkers

Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provide dynamic neuroimaging that can be used to predict response to the treatment (87). In the case of SSRIs, baseline fMRI data show that increased activity in the dorsolateral prefrontal cortex during cognitive tasks is a predictor of success in the event of remission, which presumably represents increased ability to control cognition in the context of emotionally salient stimuli (88). On the other hand, amygdala baseline reactivity to subliminal emotional cues is associated with better response to SSRI treatment and indicates that decreased affective processing is a potential source of benefit in serotonergic augmentation (89).

In the case of antipsychotics, striatum-ventrally attended network functional connectivity is the most reliable fMRI predictor of reaction (90). In a systematic review of 28 longitudinal fMRI studies, high-quality evidence of the existence of baseline striatal-cortical connectivity to predict antipsychotic response was observed in a variety of cohorts (91). It is noteworthy that fMRI functional connectivity normalises in responders and not non-responders on treatment, indicating the presence of a dynamic relationship between brain connectivity and clinical improvement (92).

Quantitative EEG biomarkers can be used to predict the quickest antidepressant response. Making 74% predictions of escitalopram response and remission after 7 days, the Antidepressant Treatment Response (ATR) index based on frontal EEG theta-to-beta ratios, compared to the 7-day predictive validity of genetic biomarkers or clinical impressions in the BRITE-MD study (93). The technical complexity in EEG standardization and inadequate availability of quantitative EEG analysis in normal clinical practices have, however, not facilitated wide application (94).

4.4 Proteomic Biomarkers

Blood-based proteomic methods determine plasma protein signatures of psychiatric disease and response to antipsychotics (95). In a large-scale proteomic focus on 1301 plasma proteins in medicated and unmedicated schizophrenic and bipolar patients, 58 proteins were found significantly dysregulated at a false-discovery level of = 0.05 (96). The antipsychotic-free patients had a downregulation of neuronal plasticity proteins (NTRK2, CNTN1, ROBO2, PLXNC1), and the neuroplasticity changes induced by antipsychotics may be the underlying cause of therapeutic effect (97). It was interesting to note that the identification of two proteins (prolactin [PRL] and mannose receptor C-type 2 [MRC2]) offered excellent diagnostic discrimination of schizophrenia with an area under the receiver operating characteristic curve (AUC) of 0.93 (97% sensitivity, 75% specificity), comparable to genetic panels in predictive validity (98).

5. CLINICAL IMPLEMENTATION AND TRANSLATION TO PRACTICE

5.1 Current Clinical Guidelines and Evidence Standards

The Clinical Pharmacogenetics Implementation Consortium (CPIC) and the Dutch Pharmacogenetics Working Group (DPWG) are the most influential organizations in the world that analyse the evidence of pharmacogenetic testing (99). Their recommendations are based on strict criteria: associations should be justified with convergent evidence from numerous studies, mechanistic plausibility has to be proven, and, of paramount importance, the clinical usefulness has to be proved (i.e. the testing should have better patient outcomes than standard care) (100).

CYP2D6 and CYP2C19 genotyping are associated with the strongest evidence and with specific dose-adjustment recommendations (level A) in the majority of SSRIs and tricyclic antidepressants (101). In the case of antipsychotics, risperidone and aripiprazole (both highly metabolised) have a high level of CYP2D6 testing evidence, but the level of evidence on the antipsychotics clozapine, olanzapine, and quetiapine is lower (102).

Moderate evidence (Level B) recommendations consist of HLA-B and HLA-DQB1 testing of the risk of clozapine agranulocytosis, but it is not clear whether it is used clinically as an exclusion criterion (103). Genotyping of tardive dyskinesia with DRD3 has been suggested; however, it has not undergone enough prospective validation and clinical actionability to meet CPIC standards (104).

The Korean Society of Laboratory Medicine (2024) and growing recommendations of the international consortia highlight:

  • Pre-emptive pharmacogenetic testing (genotyping before taking medication) demonstrates better results as compared to reactive testing (105).
  • Integration of genotyping results into electronic health record systems with automated clinical decision support alerts improves prescribing compliance and patient outcomes (106).
  • Combination of pharmacogenetic testing with therapeutic drug monitoring (TDM—measuring steady-state plasma medication concentrations) and clinical biomarkers (inflammatory markers, neuroimaging) provides superior prediction compared to single biomarker approaches (107).

Figure 5- Facilitating personalized Mental health care through guideline adherence

5.2 Barriers to Implementation

Regardless of strong scientific support and guidelines, the clinical use of pharmacogenetic testing is still insufficient throughout the world. Barriers include:

Knowledge Gaps by the Healthcare Providers: There are numerous gaps in the knowledge of most psychiatrists, primary care physicians, and pharmacists on the concepts of pharmacogenetic tests, interpretation of genetic test findings, and actionable dose adjustment principles (108). Pharmacogenomics has not been given due importance in medical education curricula in the past (109).

Cost-Benefit The cost of pharmacogenetic testing has dropped to between 500-2000 dollars per patient, but the issue of reimbursement, ROI, and cost-effectiveness compared to trial and error methods of dosing has yet to be resolved (110). Nonetheless, health economic studies show that the prevention of one severe undesirable outcome (e.g., hyponatremia, agranulocytosis) or the shortening of the remission by a few weeks justifies the costs of testing due to lower hospitalization, lost productivity, and psychosocial morbidity (111).

Poor Clinical Decision Support: There are numerous laboratory reports that give genotype data, and no actionable phenotype interpretation or medication-based recommendations (112). The incorporation of pharmacogenetic findings into clinician workflows using electronic health records (via alerts and decision support systems) is still in its early stages in most environments (113).

Ancestry and Reference Population Bias: The majority of pharmacogenetic studies and allele frequency databases are based on populations of European ancestry (114). The non-European genetic diversity is underrepresented, hindering application as well as introducing bias in the PGS-based predictions that are less accurate in non-European populations (115).

Difficulty of Polygenetic and Multifactorial Modelling: Biomarkers that are single genes are easily interpretable, whereas polygenic and multivariate prediction models remain impenetrable to clinicians (116). Better education and visualization tools are needed to enhance the means of communication of probabilistic risk and uncertainty (117).

6. INTEGRATED PHARMACOGENETIC STRATEGY FOR PRECISION PSYCHIATRIC CARE

6.1 Recommended pre-emptive Testing Approach

An overall framework of precision psychiatry must entail the following ordered steps:

1. Baseline Pharmacogenetic Panel (CYP2D6, CYP2C19, CYP3A5, CYP1A2, CYP2C9): Pre-emptive genotyping of all psychiatric pharmacotherapy patients, or, at a minimum, of those with a history of medication intolerance or resistance to therapy (118).

2. Clinical Biomarkers: Measure baseline inflammatory markers (CRP, IL-6), the level of BDNF, and (where possible) quantitative changes in EEG or fMRI-based connective regulation as a risk-stratified measure of treatment response (119).

3. Polygenic Risk Scores: MDD-PGS and SCZ-PGS are computed in the presence of genotyping data. Although each of these scores is modest in effect size individually, they will refine predictions at the group level and can help single out high-risk subgroups that should receive intensive monitoring or combination therapy (120).

4. Dynamic Therapeutic Drug Monitoring: Once the patient starts taking a drug, wait 5-7 days (fast kinetically acting drugs such as fluoxetine) or 10-14 days (accumulating drugs) to obtain steady-state plasma concentrations. Refine single dose using TDM, including CYP genotype and pharmacodynamic biomarkers (121).

5. Early Symptom Oversight and Neuroimaging: First-episode Psychosis or depression necessitates the quantification of symptom progress as validated rating scales in weeks 2, 4, and 8. Look into functional neuroimaging (fMRI or EEG) at baseline in failed treatment patients to determine neurobiologically different subtypes (122).

6.2 Gene-Drug Pairs with Highest Clinical Actionability

Gene

Drug(s)

Phenotype

Clinical Recommendation

CYP2D6 PM

Paroxetine, Fluvoxamine

Reduced metabolism → toxicity risk

Reduce dose 25–50%; select alternative SSRI (e.g., sertraline)

CYP2D6 UM

Paroxetine, Fluvoxamine

Enhanced metabolism → subtherapeutic levels

Increase dose 25–50%; consider alternative

CYP2C19 PM

Citalopram, Escitalopram

Reduced metabolism → toxicity risk

Reduce dose 50%; slower titration

CYP2C19 RM/UM

Citalopram, Escitalopram

Enhanced metabolism → inadequate response

Increase dose; consider TDM to target therapeutic range

CYP2D6 PM

Risperidone, Aripiprazole

Elevated drug levels → extrapyramidal symptoms, hyperprolactinemia

Reduce dose; monitor prolactin; assess for akathisia

HTR2C

Olanzapine, Clozapine

Weight gain risk with the Ser allele

Baseline weight and metabolic monitoring; consider ziprasidone alternative

DRD3 Gly9

All antipsychotics

Tardive dyskinesia risk

Enhanced neurological monitoring; consider agents with lower TD liability; early intervention if signs emerge

HLA-DQB105:02, HLA-B15:02

Clozapine

Agranulocytosis risk

Enhanced hematologic monitoring; discuss risks; consider alternative atypical if available

BDNF Met/Met

SSRIs

Lower treatment response

Consider augmentation strategies, higher SSRI doses, and earlier consideration of combination therapy

MDD-PGS (high)

SSRIs

Lower remission likelihood

Consider combination therapy earlier; augmentation agents; psychotherapy emphasis

Details taken from- HTR2C/DRD3 weight gain & TD: Pharmacogenomics J reviews,?HLA/clozapine agranulocytosis: Mol Psychiatry & GWAS studies?, BDNF/MDD-PGS: Meta-analyses in Front Pharmacol

7. FUTURE DIRECTIONS AND EMERGING FRONTIERS

7.1 Machine Learning and Artificial Intelligence

High-dimensional biomarker data (genetic, proteomic, neuroimaging, and inflammatory) can be combined with artificial intelligence and machine learning algorithms to have unified predictive power (123). Deep learning methods have discovered new genetic variants (e.g., ARNTL, CAMK1D, GRM8 SNPs) that relate to SSRI remission, which is superior to conventional candidate gene methods (124). Future studies need to concentrate on prospective validation of AI-generated models in independent cohorts, the addition of temporal dynamics (how biomarkers change over the course of treatment), and the generation of interpretable models that can be understood and used by clinicians (125).

7.2 Real-World Evidence and Digital Phenotyping.

EHR systems and digital biomarkers have the ability to provide unprecedented opportunities to longitudinally measure treatment response in real-world environments, beyond the limits of clinical trials, using smartphones (126). Combining genetic information with EHR phenotypes and digital biomarkers (activity levels, sleep patterns, mood-tracking app data) could result in stronger and clinically relevant predictions (127).

7.3 Multi Ethnic and Global Adaptation.

The studies of pharmacogenetics have been skewed towards the population of European ancestry (128). Future efforts should thoroughly describe the allele frequencies, pharmacokinetic associations, and treatment responses in different ancestry groups to achieve fair translation of precision psychiatry in all regions of the world (129).

7.4 Pharmacotherapy by combination based on Biomarkers.

The area is starting to look at the biomarker-inspired choice of combination treatment. As an illustration, patients with a high inflammatory bias might be preferentially responsive to SSRI-anti-inflammatory combinations, and those with a high schizophrenia genetic risk to SSRIs plus augmentation agents (lithium, antipsychotics) or other modalities (ECT) (130).

8. CONCLUSION

The pharmacogenetic biomarkers can be considered as a scientifically sound, more and more confirmed, system of psychiatric pharmacotherapy personalization. The polymorphs of cytochrome P450 (CYP2D6, CYP2C19) now justify pre-emptive testing and dosage adjustments of the SSRIs and various antipsychotics, with good CPIC/DPWG endorsements. New genetic markers such as BDNF, polymorphisms of inflammatory genes, dopaminergic variants and HLA variants have the potential to predict the response to treatment and severe adverse effects, but they have not been put into clinical use. Multivariate methods that include polygenic risk scores, inflammatory biomarkers, neuroimaging, and proteomic signatures have better predictive value than single-gene markers and need subsequent prospective studies.

A combination of pharmacogenetic tests, therapeutic drug monitoring and clinical decision support systems can be seen as a viable, feasible avenue towards precision psychiatry. The healthcare systems should invest in provider education, electronic health record integration, and reimbursement policy to hasten adoption. At the same time, studies should resolve ancestry disrepresentation, be used to validate AI-based biomarker panels and elucidate the relationships between genetic variation and clinical phenotypes.

Finally, it does not aim at genetic determinism but will be evidence-based advice: use individual biological variation to short-cut the number of drug trials, increase the speed of symptom clearance, curtail iatrogenic injury, and enhance the quality of life of millions with psychiatric diseases around the globe. This vision is becoming much more attainable and morally obligatory.

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Bijoy Ghosh
Corresponding author

Department of Pharmacology, C.T. University, Ludhiana, Punjab, India

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Meckie Khaliz Chapola
Co-author

Department of Pharmacology, C.T. University, Ludhiana, Punjab, India

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Saurav Kumar
Co-author

Department of Pharmacology, C.T. University, Ludhiana, Punjab, India

Bijoy Ghosh, Meckie Khaliz Chapola, Using Genetic Biomarkers to Predict Patient Response and Side-Effect Profiles for SSRIs and Antipsychotics: A Comprehensive Review, Int. J. of Pharm. Sci., 2026, Vol 4, Issue 1, 1001-1021. https://doi.org/10.5281/zenodo.18208788

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