Warfarin Pharmacogenetics Reevaluated: Subgroup Analysis Reveals a Likely Underestimation of the Maximum Pharmacogenetic Benefit by Clinical Trials

Change surface regression for nonlinear subgroup identification with application to warfarin pharmacogenomics data

Pharmacogenomics stands as a pivotal driver toward personalized medicine, aiming to optimize drug efficacy while minimizing adverse effects by uncovering the impact of genetic variations on inter-individual outcome variability. Despite its promise, the intricate landscape of drug metabolism introduces complexity, where the correlation between drug response and genes can be shaped by numerous nongenetic factors, often exhibiting heterogeneity across diverse subpopulations. This challenge is particularly pronounced in datasets such as the International Warfarin Pharmacogenetic Consortium (IWPC), which encompasses diverse patient information from multiple nations. To capture the between-patient heterogeneity in dosing requirement, we formulate a novel change surface model as a model-based approach for multiple subgroup identification in complex datasets. A key feature of our approach is its ability to accommodate nonlinear subgroup divisions, providing a clearer understanding of dynamic drug-gene associations. Furthermore, our model effectively handles high-dimensional data through a doubly penalized approach, ensuring both interpretability and adaptability. We propose an iterative 2-stage method that combines a change point detection technique in the first stage with a smoothed local adaptive majorize-minimization algorithm for surface regression in the second stage. Performance of the proposed methods is evaluated through extensive numerical studies. Application of our method to the IWPC dataset leads to significant new findings, where 3 subgroups subject to different pharmacogenomic relationships are identified, contributing valuable insights into the complex dynamics of drug-gene associations in patients.

ABCB1 and DRD3 polymorphism as a response predicting biomarker and tool for pharmacogenetically guided clozapine dosing in Asian Indian treatment resistant schizophrenia patients

PURPOSE

To investigate association of two single nucleotide polymorphisms (SNPs) ABCB1(rs1045462) and DRD3(rs6280) with clozapine response and the dose in treatment resistant schizophrenia (TRS) patients.

METHODS

200 TRS patients were enrolled in the study during their follow up visit post clozapine initiation. SNP assessment was performed for DRD3 (rs6280) and ABCB1(rs1045462) by sequencing. Blood sample for genotyping was collected with disease and treatment related variables recording on case record form. Patients were classified as responders or nonresponders based upon Andreasen criteria and Positive and Negative Syndrome Scale (PANSS).

RESULTS

Mean clozapine dose, the genotype frequency distribution of ABCB1, DRD3 SNPs were significantly different in clozapine responder and non-responder study population (p < 0.05). CT genotype of ABCB1 and AG genotype of DRD3 were observed to be more prevalent in the responder group. TT genotype of ABCB1 and AG genotype of DRD3 were prevalent in the nonresponder group. Clozapine dosing equations for responder and nonresponder TRS populations were developed through logistic regression analysis. 27% variability in clozapine dose was explained by possible combinations of ABCB1 and DRD3 SNP analysis.

CONCLUSION

Differential ABCB1(rs1045462) and DRD3(rs6280) genotype frequencies among the clozapine responders and non-responders explained clear feasibility of response predictor potential along with clozapine dose variability association. Pharmacogenetically guided clozapine dosing is possible if more SNPs are considered together with ABCB1(rs1045462) and DRD3(rs6280) in TRS patients.

Chinese Patients With Heart Valve Replacement Do Not Benefit From Warfarin Pharmacogenetic Testing on Anticoagulation Outcomes

BACKGROUND

Genotype-guided warfarin dosing has been shown in some randomized trials to improve anticoagulation outcomes in individuals of European ancestry; yet, its utility in Chinese patients with heart valve replacement remains unresolved.

METHODS

A total of 2264 patients who underwent heart valve replacement at Wuhan Asia Heart Hospital were enrolled in this study. Patients were randomly divided into 2 groups, namely, a genotype-guided and a traditional clinically guided warfarin dosing group. In the genotype-guided group (n = 1134), genotyping for CYP2C9 and VKORC1 (-1639 G→A) was performed using TaqMan genotyping assay. Warfarin doses were predicted with the International Warfarin Pharmacogenetics Consortium algorithm. Patients in the control group (n = 1130) were clinically guided. The primary outcome was to compare the incidence of adverse events (major bleeding and thrombotic) during a 90-day follow-up period between 2 groups. Secondary objectives were to describe effects of the pharmacogenetic intervention on the first therapeutic-target-achieving time, the stable maintenance dose, and the hospitalization days.

RESULTS

A total of 2245 patients were included in the analysis. Forty-nine events occurred during follow-up. Genotype-guided dosing strategy did not result in a reduction in major bleeding (0.26% versus 0.63%; hazard ratio, 0.44; 95% confidence interval, 0.13-1.53; P = 0.20) and thrombotic events (0.89% versus 1.61%; hazard ratio, 0.56; 95% confidence interval, 0.27-1.17; P = 0.12) compared with clinical dosing group. Compared with traditional dosing, patients in the genotype-guided group reached their therapeutic international normalized ratio in a shorter time (3.8 ± 2.0 versus 4.4 ± 2.0 days, P < 0.001). There was no difference in hospitalization days (P = 0.28).

CONCLUSIONS

Warfarin pharmacogenetic testing according to the International Warfarin Pharmacogenetics Consortium algorithm cannot improve anticoagulation outcomes in Chinese patients with heart valve replacement.