Metabolic syndrome (MetS) presents a significant global health challenge, characterized by a cluster of metabolic alterations including obesity, hypertension, insulin resistance/dysglycemia, and atherogenic dyslipidemia. Advances in understanding and pharmacotherapy have added complexity to MetS management, particularly concerning drug interactions and pharmacogenetic variations. Limited literature exists on drug–drug–gene interactions (DDGIs) and drug–drug–transporter gene interactions (DDTGIs), which can significantly impact pharmacokinetics and pharmacodynamics, affecting treatment outcomes. This narrative review aims to address the following three key objectives: firstly, shedding a light on the PK metabolism, transport, and the pharmacogenetics (PGx) of medicines most commonly used in the MetS setting (relevant lipid-lowering drugs, antihypertensives and antihyperglycemics agents); secondly, exemplifying potential clinically relevant pharmacokinetic drug interactions, including drug–drug interactions, DDGIs, and DDTGIs; and, thirdly, describing and discussing their potential roles in clinical practice. This narrative review includes relevant information found with the use of interaction checkers, pharmacogenetic databases, clinical pharmacogenetic practice guidelines, and literature sources, guided by evidence-based medicine principles.

Non-vitamin K antagonist oral anticoagulants’ interindividual trough concentration variability affects efficacy and safety, especially in bleeding events. Rivaroxaban is metabolised via CYP3A4/5-, CYP2J2-, and CYP-independent mechanisms and is a substrate of two transporter proteins: ABCB1 (MDR1, P-glycoprotein) and ABCG2 (BCRP; breast-cancer-resistance protein). The polymorphisms of these genes may possibly affect the pharmacokinetics of rivaroxaban and, consequently, its safety profile. Rivaroxaban variability may be associated with age, liver and kidney function, concomitant illness and therapy, and pharmacogenetic predisposition. This case series is the first, to our knowledge, that presents multiple risk factors for rivaroxaban-related bleeding (RRB) including age, renal function, concomitant diseases, concomitant treatment, and pharmacogenetic data. It presents patients with RRB, along with their complete clinical and pharmacogenetic data, as well as an evaluation of possible risk factors for RRB. Thirteen patients were carriers of ABCB1, ABCG2, CYP2J2, and/or CYP3A4/5 gene polymorphisms. Possible drug–drug interactions with increased bleeding risk were identified in nine patients. Six patients had eGFR <60 mL/min/1.73 m². Our data suggest a possible role of multiple factors and their interactions in predicting RRB; however, they also indicate the need for further comprehensive multidisciplinary research to enable safer use of this product based on a personalised approach.

Although the role of pharmacogenomics (PGx) in personalized pharmacotherapy has been well established, its implementation in clinical practice lags behind. In this article, we present an overview of important achievements in the field of PGx of ...

A recently discovered haplotype—CYP2C:TG—determines the ultrarapid metabolism of several CYP2C19 substrates. The platelet inhibitor clopidogrel requires CYP2C19-mediated activation: the risk of ischemic events is increased in patients with a poor (PM) or intermediate (IM) CYP2C19 metabolizer phenotype (vs. normal, NM; rapid, RM; or ultrarapid, UM). We investigated whether the CYP2C:TG haplotype affected efficacy/bleeding risk in clopidogrel-treated patients. Adults (n = 283) treated with clopidogrel over 3–6 months were classified by CYP2C19 phenotype based on the CYP2C19*2*17 genotype, and based on the CYP2C19/CYP2C cluster genotype, and regarding carriage of the CYP2:TG haplotype, and were balanced on a number of covariates across the levels of phenotypes/haplotype carriage. Overall, 45 (15.9%) patients experienced ischemic events, and 49 (17.3%) experienced bleedings. By either classification, the incidence of ischemic events was similarly numerically higher in PM/IM patients (21.6%, 21.8%, respectively) than in mutually similar NM, RM, and UM patients (13.2–14.8%), whereas the incidence of bleeding events was numerically lower (13.1% vs. 16.6–20.5%). The incidence of ischemic events was similar in CYP2C:TG carries and non-carries (14.1% vs. 16.1%), whereas the incidence of bleedings appeared mildly lower in the former (14.9% vs. 20.1%). We observed no signal to suggest a major effect of the CYP2C19/CYP2C cluster genotype or CYP2C:TG haplotype on the clinical efficacy/safety of clopidogrel.

Background: Treatment-resistant depression (TRD) affects up to 30–40% of patients with major depressive disorder and remains a major therapeutic challenge. Genetic and epigenetic factors are increasingly recognized as key contributors to both vulnerability and treatment response. Methods: We conducted a narrative review of studies published between 2021 and 2025, focusing exclusively on DNA- and RNA-based biomarkers of TRD. Twelve studies met the inclusion criteria, covering candidate gene analyses, genome-wide association studies (GWAS), neuroimaging–genetic approaches, and microRNA profiling. Results: Genetic investigations consistently implicate neuroplasticity-related genes (BDNF, NTRK2, PTEN, SYN1, MAPK1, and GSK3B) in the risk of TRD and its relapse. Variants in glutamatergic receptor genes (GRIN2A, GRIN2B, GRIA2, GRIA3) were predicted to result in a rapid and sustained response to ketamine. Genomic approaches further demonstrated that composite genetic panels outperform single-variant predictors. In parallel, microRNAs such as miR-1202, miR-16, miR-135, miR-124, miR-223, and miR-146a emerged as dynamic biomarkers of treatment response, particularly in cohorts treated with ketamine or electroconvulsive therapy. Conclusions: DNA- and RNA-based biomarkers provide promising avenues for improving the understanding and management of TRD. Their integration into clinical frameworks could support patient stratification, individualized treatment selection, and real-time monitoring of therapeutic efficacy. Future research should prioritize replication, methodological harmonization, and longitudinal validation to facilitate the translation of findings into precision psychiatry.