Verification of five pharmacogenomics-based warfarin administration models

Construction of warfarin population pharmacokinetics and pharmacodynamics model in Han population based on Bayesian method

The purpose of this paper is to study the genetic polymorphisms of related gene loci (CYP2C9*3, VKORC1-1639G > A) based on demographic and clinical factors, and use the maximum a posterior Bayesian method to construct a warfarin individualized dose prediction model in line with the Chinese Han population. Finally, the built model is compared and analyzed with the widely used models at home and abroad. In this study, a total of 5467 INR measurements are collected from 646 eligible subjects in our hospital, and the maximum a posterior Bayesian method is used to construct a warfarin dose prediction that conforms to the Chinese Han population on the basis of the Hamberg model. The model is verified and compared with foreign models. This study finds that body weight and concomitant use of amiodarone have a significant effect on the anticoagulant effect of warfarin. The model can provide an effective basis for individualized and rational dosing of warfarin in Han population more accurately. In the performance of comparison with different warfarin dose prediction models, the new model has the highest prediction accuracy, and the prediction percentage is as high as 72.56%. The dose predicted by the Huang model is the closest to the actual dose of warfarin. The population pharmacokinetics and pharmacodynamics model established in this study can better reflect the distribution characteristics of INR values after warfarin administration in the Han population, and performs better than the models reported in the literature.

Developing Chinese race-specific warfarin dose prediction algorithms

BACKGROUND

Numerous genotype-guided warfarin dosing algorithms have been developed to individualize warfarin doses, but they can only explain 47-52% of the variability.

AIM

This study aimed to develop new warfarin algorithms suitable to predict the stable warfarin dose for the Chinese population and to compare their prediction performance with those of the most commonly used algorithms.

METHOD

Multiple linear regression analysis with the warfarin optimal dose (WOD), logarithm (log) WOD, 1/WOD, and [Formula: see text], respectively, as the dependent variables were performed to deduce a new warfarin algorithm (NEW-Warfarin). WOD was the stable dose that maintained the international normalized ratio (INR) within the target range (2.0-3.0). Three major genotype-guided warfarin dosing algorithms were selected and compared against NEW-Warfarin predictive performance using the mean absolute error (MAE). Furthermore, patients were divided into five groups according to warfarin indications [atrial fibrillation (AF), pulmonary embolism (PE), cardiac-related disease (CRD), deep vein thrombosis (DVT), and other diseases (OD)]. Multiple linear regression analyses were also performed for each group.

RESULTS

The regression equation with [Formula: see text] as the dependent variable had the highest coefficient of determination (R² = 0.489). The NEW-Warfarin had the best predictive accuracy compared to the three algorithms selected. Group analysis, according to indications, showed that the R² of the five groups were PE (0.902) > DVT (0.608) > CRD (0.569) > OD (0.436) > AF (0.424).

CONCLUSION

Dosing algorithms based on warfarin indications are more suitable for predicting warfarin doses. Our research provides a novel strategy to develop indication-specific warfarin dosing algorithms to improve the efficacy and safety of warfarin prescribing.

Warfarin dosing algorithms: A systematic review

Aims

Numerous algorithms have been developed to guide warfarin dosing and improve clinical outcomes. We reviewed the algorithms available for various populations and the covariates, performances and risk of bias of these algorithms.

Methods

We systematically searched MEDLINE up to 20 May 2020 and selected studies describing the development, external validation or clinical utility of a multivariable warfarin dosing algorithm. Two investigators conducted data extraction and quality assessment.

Results

Of 10 035 screened records, 266 articles were included in the review, describing the development of 433 dosing algorithms, 481 external validations and 52 clinical utility assessments. Most developed algorithms were for dose initiation (86%), developed by multiple linear regression (65%) and mostly applicable to Asians (49%) or Whites (43%). The most common demographic/clinical/environmental covariates were age (included in 401 algorithms), concomitant medications (270 algorithms) and weight (229 algorithms) while CYP2C9 (329 algorithms), VKORC1 (319 algorithms) and CYP4F2 (92 algorithms) variants were the most common genetic covariates. Only 26% and 7% algorithms were externally validated and evaluated for clinical utility, respectively, with <2% of algorithm developments and external validations being rated as having a low risk of bias.

Conclusion

Most warfarin dosing algorithms have been developed in Asians and Whites and may not be applicable to under‐served populations. Few algorithms have been externally validated, assessed for clinical utility, and/or have a low risk of bias which makes them unreliable for clinical use. Algorithm development and assessment should follow current methodological recommendations to improve reliability and applicability, and under‐represented populations should be prioritized.

Genetic polymorphisms and dosing of vitamin K antagonist in Indian patients after heart valve surgery

Purpose

Vitamin K antagonists (VKAs), such as warfarin and acenocoumarol, exert their anti-coagulant effect by inhibiting the subunit 1 of vitamin K epoxide reductase complex (VKORC1). CYP2C9 is a hepatic drug-metabolizing enzyme in the CYP450 superfamily and is the primary metabolizing enzyme of warfarin. Three single nucleotide polymorphisms, two in the CYP2C9 gene, namely CYP2C9*2 and CYP2C9*3, and one in the VKORC1 gene (c.- 1639G > A, rs9923231), have been identified to reduce VKA metabolism and enhance their anti-coagulation effect. The purpose of this study is to evaluate the prevalence of CYP2C9 and VKORC1 polymorphism in Indians receiving VKA-based anti-coagulation after valve surgery and to evaluate the usefulness of genetic information in managing VKA-based anti-coagulation.

Methods

In the current prospective observational study, 150 patients who underwent heart valve surgery and had stable INR were genotyped for VKORC1 (- 1639 G > A), CYP2C9*2, and CYP2C9*3. The VKA dosage was estimated from published algorithms and compared to the clinically stabilized dosage.

Results

Out of 150 patients, 101 (67.33%) were on warfarin and 49 (32.66%) were on acenocoumarol. Majority of the patients, the 83 in warfarin group and the 40 in acenocoumarol group, had a wild CYP2C9 diplotype. The rest had a mutant (CYP2C9*2 or CYP2C9*3) diplotype. Similarly, 67 patients in the warfarin group and 35 patients in the acenocoumarol group had wild type (G/G) of VKORC1 genotype. The rest had a mutant (G/A or A/A) VKORC1 genotype. In the warfarin group, based on the genotype, 51.5% of the patients were extensive or normal metabolizers, and 47.4% of the patients were intermediate metabolizers of VKAs. In the acenocoumarol group, 61.2% of the patients were extensive or normal metabolizers, and 38.8% of the patients were intermediate metabolizers. Individually, alleles of VKORC1 (- 1639 G > A), CYP2C9*2, and CYP2C9*3 had mean dosage reduction effect on VKA dosage, which co-related to the clinically stabilized dosages (P < 0.0001). Among the VKORC1 (- 1639 G > A) cohort, the reduction in warfarin mean weekly dosage was 13.48 mg as compared to the wild-type category (P < 0.0001) and similarly, the reduction in the mean weekly acenocoumarol dose was 6.07 mg (P < 0.03) as compared to the wild type after adjusting for age, gender, and body mass index.

Conclusion

Single nucleotide polymorphism in the CYP2C9 gene and in the VKORC1 gene is present in nearly 40% of Indian patients. VKORC1 (- 1639 G > A), CYP2C9*2, and CYP2C9*3 genotypes have significant dosage-lowering effects on VKA-based anti-coagulation therapy. The trend in estimated dosages of VKAs co-related to that of observed the clinically stabilized dosage in the cohort. The pharmacogenomic calculators used in this study tend to overestimate the VKA dosages as compared to clinical dosage due to the limitations in the algorithms and in our study. A new algorithm based on a larger dataset capturing the vast genetic variability across the Indian population and relevant clinical factors could provide better results.

Creating and validating a warfarin pharmacogenetic dosing algorithm for Colombian patients

Purpose

Warfarin is an oral anticoagulant associated with adverse reaction to drugs due to wide inter- and intra-individual dosage variability. Warfarin dosage has been related to non-genetic and genetic factors. CYP2C9 and VKORC1 gene polymorphisms affect warfarin metabolism and dosage. Due to the central role of populations’ ethnical and genetic origin on warfarin dosage variability, novel algorithms for Latin American subgroups are necessary to establish safe anticoagulation therapy.

Patients and methods

We genotyped CYP2C9*2 (c.430C > T), CYP2C9*3 (c.1075A > C), CYP4F2 (c.1297G > A), and VKORC1 (−1639 G > A) polymorphisms in 152 Colombian patients who received warfarin. We evaluated the impact on the variability of patients’ warfarin dose requirements. Multiple linear regression analysis, using genetic and non-genetic variables, was used for creating an algorithm for optimal warfarin maintenance dose.

Results

Median weekly prescribed warfarin dosage was significantly lower in patients having the VKORC1-1639 AA genotype and poor CYP2C9*2/*2,*2/*3 metabolizers than their wild-type counterparts. We found a 2.3-fold increase in mean dose for normal sensitivity patients (wild-type VKORC1/CYP2C9 genotypes) compared to the other groups (moderate and high sensitivity); 31.5% of the patients in our study group had warfarin sensitivity-related genotypes. The estimated regression equation accounted for 44.4% of overall variability in regard to warfarin maintenance dose. The algorithm was validated, giving 45.9% correlation (R²=0.459).

Conclusion

Our results describe and validate the first algorithm for predicting warfarin maintenance in a Colombian mestizo population and have contributed toward the understanding of pharmacogenetics in a Latin American population subgroup.

Warfarin Pharmacogenomics in Diverse Populations

Genotype-guided warfarin dosing algorithms are a rational approach to optimize warfarin dosing and potentially reduce adverse drug events. Diverse populations, such as African Americans and Latinos, have greater variability in warfarin dose requirements and are at greater risk for experiencing warfarin-related adverse events compared with individuals of European ancestry. Although these data suggest that patients of diverse populations may benefit from improved warfarin dose estimation, the vast majority of literature on genotype-guided warfarin dosing, including data from prospective randomized trials, is in populations of European ancestry. Despite differing frequencies of variants by race/ethnicity, most evidence in diverse populations evaluates variants that are most common in populations of European ancestry. Algorithms that do not include variants important across race/ethnic groups are unlikely to benefit diverse populations. In some race/ethnic groups, development of race-specific or admixture-based algorithms may facilitate improved genotype-guided warfarin dosing algorithms above and beyond that seen in individuals of European ancestry. These observations should be considered in the interpretation of literature evaluating the clinical utility of genotype-guided warfarin dosing. Careful consideration of race/ethnicity and additional evidence focused on improving warfarin dosing algorithms across race/ethnic groups will be necessary for successful clinical implementation of warfarin pharmacogenomics. The evidence for warfarin pharmacogenomics has a broad significance for pharmacogenomic testing, emphasizing the consideration of race/ethnicity in discovery of gene-drug pairs and development of clinical recommendations for pharmacogenetic testing.