Structural equation modelling of nucleotide polymorphisms and pharmacokinetics in direct oral anticoagulant use for stroke and embolism prevention in atrial fibrillation.

BACKGROUND

Genetic factors affect DOAC pharmacokinetics and efficacy in atrial fibrillation (AF) patients, yet no pharmacogenomic guidelines exist. This study aims to assess their impact on supporting personalized therapy.

METHODS

Following PRISMA-2020 (PROSPERO: CRD42024592412), a systematic review analysed 31 studies with 8558 AF patients across Asia and Europe. Structural Equation Modelling (SEM) in R Studio (v2024.12) assessed 331 genotypes affecting pharmacokinetics and clinical events. Monte Carlo simulations, pathway enrichment, PCA clustering, and logistic regression further refined the analysis.

RESULTS

Dabigatran metabolism is influenced by ABCB1 and CES1 polymorphisms, with ABCB1 (rs1045642, AAAG), CES1 (rs761128900, T), and CES1 (rs71647871, AA/AG) linked to low bleeding risk (<30 %), ensuring stable efficacy. Apixaban's safety is associated with ABCB1 (rs2032582, AA), ABCB1 (rs1045642, CT/CT), and PXR haplotype1B, minimizing bleeding/stroke risk (<20 %). Rivaroxaban effectiveness is enhanced by ABCG2 (rs546230660, AG), ABCB1 (c.61236, CT), CYP3A4 (rs4646440, WT), and CYP3A4 (rs55808883, CT), reducing stroke/bleeding risk (<20 %). Edoxaban clearance depends on SLCO1B1 (rs11045879, TCCC) and SLCO1B1 (c.388A > G, rs12317268, AGGG), maintaining stable plasma levels (stroke/bleeding risk <40 %). SEM results (CFI = 0.997, TLI = 0.990, RMSEA = 0.037, SRMR = 0.031) confirm model stability, logistic regression (AUC = 0.996) reinforcing findings. PCA clustering identified low risk (stable drug levels, minimal bleeding risk) and moderate risk (suboptimal pharmacokinetics, higher stroke/bleeding risks) 69.7 % of the total variance.

CONCLUSION

genetics significantly influences DOAC pharmacokinetics, particularly for dabigatran and apixaban, where stroke and bleeding risks correlate with Cmin and Cmax. Pharmacogenomic profiling and therapeutic drug monitoring are crucial for optimizing therapy, especially in high-risk patients.