Validation of warfarin pharmacogenetic algorithms in clinical practice

A factor VII-based method for the prediction of anticoagulant response to warfarin

Warfarin dosing methods based on existing models for warfarin and the international normalised ratio (INR) give biased maintenance dose predictions at the upper and lower quantiles of dose requirements. The aim of this work is to propose a conceptually different approach to predict INR after warfarin dosing. Factor VII concentration was proposed as the principal driving force for the INR. The time to steady-state INR (t_SS,INR) was determined based on the INR response to changes in factor VII concentrations following warfarin initiation, and from this the steady-state INR (INR_SS) was derived. The proposed method requires timed, paired blood samples of INR and factor VII. At different simulated warfarin dose rates, the prediction error associated with the proposed method was shown to be within clinically acceptable limits for both the t_SS,INR (±2 days) and INR_SS (±0.2). The use of the method was demonstrated in two patients who were initiated with 5 mg of warfarin daily. The difference in predicted versus actual steady-state INR were 0.0 and −0.4. The proposed method represents a unique approach to predict the INR. It considers factor VII as the main driver for INR and provides valuable information about the time to steady state INR.

Evaluation of CYP2C9- and VKORC1-based pharmacogenetic algorithm for warfarin dose in Gaza-Palestine

Aim

To evaluate applicability of CYP2C9*2, *3 and VKORC1-1639G > A based algorithm to predict warfarin stable dose (WSD) in a group of Palestinian patients.

Patients & methods

Warfarin doses were retrospectively calculated for 101 Palestinian patients under warfarin therapy using three models. Performance of the three models was assessed in 47 patients found to take WSD.

Results

Frequency of CYP2C9*2, *3 and VKORC1-1639G > A alleles is 13.6, 0.0 and 46.5% respectively. The international warfarin pharmacogenetics consortium algorithm was more reliable (MAE = 8.9 ± 1.4; R² = 0.350) than both the clinical algorithm (MAE = 10.4 ± 1.4; R² = 0.128;) and the fixed-dose algorithm (MAE = 11.1 ± 1.7).

Conclusion

The international warfarin pharmacogenetics consortium algorithm can be reliably applied for predicting the WSD in Palestinian population.

Warfarin Dosing Algorithms Underpredict Dose Requirements in Patients Requiring ≥7 mg Daily: A Systematic Review and Meta-analysis

There is preliminary evidence to suggest that some published warfarin dosing algorithms produce biased maintenance dose predictions in patients who require higher than average doses. We conducted a meta-analysis of warfarin dosing algorithms to determine if there exists a systematic under- or overprediction of dose requirements for patients requiring ≥7 mg/day across published algorithms. Medline and Embase databases were searched up to September 2015. We quantified the proportion of over- and underpredicted doses in patients whose observed maintenance dose was ≥7 mg/day. The meta-analysis included 47 evaluations of 22 different warfarin dosing algorithms from 16 studies. The meta-analysis included data from 1,492 patients who required warfarin doses of ≥7 mg/day. All 22 algorithms were found to underpredict warfarin dosing requirements in patients who required ≥7 mg/day by an average of 2.3 mg/day with a pooled estimate of underpredicted doses of 92.3% (95% confidence interval 90.3-94.1, I² = 24%).

The future of pharmacogenetics in the treatment of heart failure

Heart failure is a common disease with high levels of morbidity and mortality. Current treatment comprises β-blockers, ACE inhibitors, aldosterone antagonists and diuretics. Variation in clinical response seen in patients begs the question of whether there is a pharmacogenetic component yet to be identified. To date, the genes most studied involve the β-1, β-2, α-2 adrenergic receptors and the renin-angiotensin-aldosterone pathway, mainly focusing on SNPs. However results have been inconsistent. Genome-wide association studies and next-generation sequencing are seen as alternative approaches to discovering genetic variations influencing drug response. Hopefully future research will lay the foundations for genotype-led drug management in these patients with the ultimate aim of improving their clinical outcome.

Validation of warfarin pharmacogenetic algorithms in 586 Han Chinese patients

AIM

To compare the accuracy of the nine selected algorithms for predicting warfarin dose with 586 Han Chinese patients.

MATERIALS & METHODS

Genotyping of VKORC1 1639G>A, CYP2C9*2 and CYP2C9*3 variants was performed. Both the mean absolute error and ideal estimation value were used for comparison.

RESULTS

The top three performers were from East Asians. The algorithms from Caucasians generally performed better in the medium-dose subgroup (>3 and <7 mg/day), while the algorithms from East Asians generally performed better in the low-dose subgroup (≤ 3 mg/day). None of the algorithms performed well in the high-dose subgroup (≥ 7 mg/day).

CONCLUSION

Algorithms built for specific ethnic groups and preassigned-dose groups are suggested for better prediction.

Methods for Predicting Warfarin Dose Requirements

BACKGROUND

The aim of this study was to compare the predictive performance of different warfarin dosing methods.

METHODS

Data from 46 patients who were initiating warfarin therapy were available for analysis. Nine recently published dosing tools including 8 dose prediction algorithms and a Bayesian forecasting method were compared with each other in terms of their ability to predict the actual maintenance dose. The dosing tools included 4 algorithms that were based on patient characteristics (2 clinical and 2 genotype-driven algorithms), 4 algorithms based on international normalized ratio (INR) response feedback and patient characteristics (2 clinical and 2 genotype-driven algorithms), and a Bayesian forecasting method. Comparisons were conducted using measures of bias (mean prediction error) and imprecision [root mean square error (RMSE)].

RESULTS

The 2 genotype-driven INR feedback algorithms by Horne et al and Lenzini et al produced more precise maintenance dose predictions (RMSE, 1.16 and 1.19 mg/d, respectively; P < 0.05) than the genotype-driven algorithms by Gage et al and Klein et al and the Bayesian method (RMSE, 1.60, 1.62, and 1.81 mg/d respectively). The dose predictions from clinical and genotype-driven algorithms by Gage et al, Klein et al, and Horne et al were all negatively biased. Only the INR feedback algorithms (clinical and genotype) by Lenzini et al produced unbiased dose predictions. The Bayesian method produced unbiased dose predictions overall (mean prediction error, +0.37 mg/d; 95% confidence interval, 0.89 to -0.15) but overpredicted doses in patients requiring >8 mg/d.

CONCLUSIONS

Overall, warfarin dosing methods that included some measure of INR response (INR feedback algorithms and Bayesian methods) produced unbiased and more precise dose predictions. The Bayesian forecasting method produced positively biased dose predictions in patients who required doses >8 mg/d. Further research to assess differences in clinical endpoints when warfarin doses are predicted using Bayesian or INR-driven algorithms is warranted.

Extrapolation of acenocoumarol pharmacogenetic algorithms

INTRODUCTION

Acenocoumarol (ACN) has a narrow therapeutic range that is especially difficult to control at the start of its administration. Various dosing pharmacogenetic-guided dosing algorithms have been developed, but further work on their external validation is required. The aim of this study was to evaluate the extrapolation of pharmacogenetic algorithms for ACN as an alternative to the development of a specific algorithm for a given population.

MATERIAL AND METHODS

The predictive performance, deviation, accuracy, and clinical significance of five pharmacogenetic algorithms (EU-PACT, Borobia, Rathore, Markatos, Krishna Kumar) were compared in 189 stable ACN patients representing all indications for anticoagulant treatment.

RESULTS

The correlation between the dose predictions of the five pharmacogenetic models ranged from 7.7 to 70.6% and the percentage of patients with a correct prediction (deviation ≤20% from actual ACN dose) ranged from 5.9 to 40.7%. EU-PACT and Borobia pharmacogenetic dosing algorithms were the most accurate in our setting and evidenced the best clinical performance.

CONCLUSIONS

Among the five models studied, the EU-PACT and Borobia pharmacogenetic dosing algorithms demonstrated the best potential for extrapolation.

A review of a priori regression models for warfarin maintenance dose prediction

A number of a priori warfarin dosing algorithms, derived using linear regression methods, have been proposed. Although these dosing algorithms may have been validated using patients derived from the same centre, rarely have they been validated using a patient cohort recruited from another centre. In order to undertake external validation, two cohorts were utilised. One cohort formed by patients from a prospective trial and the second formed by patients in the control arm of the EU-PACT trial. Of these, 641 patients were identified as having attained stable dosing and formed the dataset used for validation. Predicted maintenance doses from six criterion fulfilling regression models were then compared to individual patient stable warfarin dose. Predictive ability was assessed with reference to several statistics including the R-square and mean absolute error. The six regression models explained different amounts of variability in the stable maintenance warfarin dose requirements of the patients in the two validation cohorts; adjusted R-squared values ranged from 24.2% to 68.6%. An overview of the summary statistics demonstrated that no one dosing algorithm could be considered optimal. The larger validation cohort from the prospective trial produced more consistent statistics across the six dosing algorithms. The study found that all the regression models performed worse in the validation cohort when compared to the derivation cohort. Further, there was little difference between regression models that contained pharmacogenetic coefficients and algorithms containing just non-pharmacogenetic coefficients. The inconsistency of results between the validation cohorts suggests that unaccounted population specific factors cause variability in dosing algorithm performance. Better methods for dosing that take into account inter- and intra-individual variability, at the initiation and maintenance phases of warfarin treatment, are needed.

CYP2C9*3 polymorphism presenting as lethal subdural hematoma with low-dose warfarin

Warfarin is the most common and cheap oral anticoagulant currently used in clinical practice. A high inter-individual variation is seen in the response to warfarin. Recently, pharmacogenetics has gained importance in managing patients on warfarin, both in predicting the optimum required dose as well as in decreasing the risk of bleeding. This case report is a description of a 49-year-old patient who had a lethal subdural hematoma with low-dose warfarin. He was subsequently found to have CYP2C9 gene polymorphism (*1/*3). This case report stresses the importance of pre-prescription assessment of genetic analysis for those initiated on warfarin.

Pharmacogenetic testing: Current Evidence of Clinical Utility

Over the last decade, the number of clinical pharmacogenetic tests has steadily increased as understanding of the role of genes in drug response has grown. However, uptake of these tests has been slow, due in large part to the lack of robust evidence demonstrating clinical utility. We review the evidence behind four pharmacogenetic tests and discuss the barriers and facilitators to uptake: 1) warfarin (drug safety and efficacy); 2) clopidogrel (drug efficacy); 3) codeine (drug efficacy); and 4) abacavir (drug safety). Future efforts should be directed toward addressing these issues and considering additional approaches to generating evidence basis to support clinical use of pharmacogenetic tests.

Allele frequency distribution of CYP2C9 2 and CYP2C9 3 polymorphisms in six Mexican populations

Allele frequency differences of functional CYP2C9 polymorphisms are responsible for some of the variation in drug response observed in human populations. The most relevant CYP2C9 functional variants are CYP2C9*2 (rs1799853) and CYP2C9 3 (rs1057910). These polymorphisms show variation in allele frequencies among different population groups. The present study aimed to analyze these polymorphisms in 947 Mexican-Mestizo from Mexico City and 483 individuals from five indigenous Mexican populations: Nahua, Teenek, Tarahumara, Purepecha and Huichol. The CYP2C9*2 allele frequencies in the Mestizo, Nahua and Teenek populations were 0.051, 0.007 and 0.005, respectively. As for CYP2C9 3, the allelic frequencies in the Mestizo, Nahua and Teenek populations were 0.04, 0.005 and 0.005, respectively. The CYP2C9 2 and CYP2C9 3 alleles were not observed in the Tarahumara, Purepecha and Huichol populations. These findings are in agreement with previous studies reporting very low allele frequencies for these polymorphisms in American Indigenous populations.

The population attributable fraction as a measure of the impact of warfarin pharmacogenetic testing

AIM

We aimed to estimate the population impact of warfarin pharmacogenetic testing (WPGT) across multiple populations.

MATERIALS & METHODS

We used the expanded International Warfarin Pharmacogenetics Consortium data set and genotype frequencies from HapMap to simulate dose distributions for each CYP2C9/VKORC1 genotype combination in the different races, and calculated the population attributable fraction as a measure of population impact of WPGT. WPGT was compared to both clinical and fixed-dose algorithms to estimate the benefits of WPGT.

RESULTS

Our dose simulation revealed different dose requirements in difference races and considerable overlap in dose distributions of different genotype combinations. Population attributable fraction calculations suggest that complete implementation of WPGT can reduce inaccurate dosing by 18-24% in white individuals. However black, Japanese and Chinese patients do not benefit from WPGT, especially when compared against a race-specific fixed dose.

CONCLUSION

Our findings support WPGT in white individuals but not in black, Japanese and Chinese individuals.

Validation of tacrolimus equation to predict troughs using genetic and clinical factors

AIM

Tacrolimus is an immunosuppressant used in transplantation. This article reports the validation of the authors' recently developed genetics-based tacrolimus equation that predicts troughs.

METHODS

Validation was performed in an independent cohort of 795 kidney transplant recipients receiving tacrolimus. The performance of the equation to predict initial troughs was assessed by calculating the bias and precision of the equation. For all troughs in the first 6 months post-transplant, a comparison was made between the troughs predicted using the equation versus those predicted using a basic apparent clearance model with no covariates.

RESULTS

For initial troughs, the equation had a low bias (0.2 ng/ml) and high precision (1.8 ng/ml). For all troughs, the equation predicted troughs significantly better than the basic apparent clearance model.

CONCLUSION

The tacrolimus equation had good bias and precision in predicting initial troughs and performed better than a basic apparent clearance model for all the troughs.

Effect of genetic variants, especially CYP2C9 and VKORC1, on the pharmacology of warfarin

The genes encoding the cytochrome P450 2C9 enzyme (CYP2C9) and vitamin K-epoxide reductase complex unit 1 (VKORC1) are major determinants of anticoagulant response to warfarin. Together with patient demographics and clinical information, they account for approximately one-half of the warfarin dose variance in individuals of European descent. Recent prospective and randomized controlled trial data support pharmacogenetic guidance with their use in warfarin dose initiation and titration. Benefits from pharmacogenetics-guided warfarin dosing have been reported to extend beyond the period of initial dosing, with supportive data indicating benefits to at least 3 months. The genetic effects of VKORC1 and CYP2C9 in African and Asian populations are concordant with those in individuals of European ancestry; however, frequency distribution of allelic variants can vary considerably between major populations. Future randomized controlled trials in multiethnic settings using population-specific dosing algorithms will allow us to further ascertain the generalizability and cost-effectiveness of pharmacogenetics-guided warfarin therapy. Additional genome-wide association studies may help us to improve and refine dosing algorithms and potentially identify novel biological pathways.

Validation of pharmacogenetic algorithms and warfarin dosing table in Egyptian patients

BACKGROUND

Warfarin remains a difficult drug to use due to the large variability in dose response. Clear understanding of the accuracy of warfarin pharmacogenetic dosing methods might lead to appropriate control of anticoagulation.

OBJECTIVE

This study aims to evaluate the accuracy of warfarin dosing table and two pharmacogenetic algorithms, namely the algorithms of Gage et al. (Clin Pharmacol Ther 84:326-331, 2008), and the International Warfarin Pharmacogenetics Consortium algorithm (IWPC) in a real Egyptian clinical setting. Additionally, three non-pharmacogenetic dosing methods (the Gage, IWPC clinical algorithms and the empiric 5 mg/day dosing) were evaluated.

SETTING

Sixty-three Egyptian patients on a stable therapeutic warfarin dose were included. Patients were recruited from the outpatient clinic of the critical care medicine department.

METHODS

CYP2C9 and VKORC1 polymorphisms were genotyped by real time PCR system. Predicted doses by all dosing methods were calculated and compared with the actual therapeutic warfarin doses.

RESULTS

The Gage algorithm (adjusted R(2) = 0.421, and mean absolute error (MAE) = 3.3), and IWPC algorithm (adjusted R(2) = 0.419, MAE = 3.2) produced better accuracy than did the warfarin dosing table (adjusted R(2) = 0.246, MAE = 3.5), the two clinical algorithms (R(2) = 0.24, MAE = 3.7) and the fixed dose approach (MAE = 3.9). However, all dosing models produced comparable clinical accuracy with respect to proportion of patients within 1 mg/day of actual dose (ideal dose). Non-pharmacogenetic methods severely over-predicted dose (defined as ≥2 mg/day more than actual dose) compared to the three pharmacogenetic models. In comparison to non-pharmacogenetic methods, the three pharmacogenetic models performed better regarding the low dose group in terms of percentage of patients within ideal dose. In the high dose group, none of the dosing models predicted warfarin doses within ideal dose.

CONCLUSION

Our study showed that genotype-based dosing improved prediction of warfarin therapeutic dose beyond that available with the fixed-dose approach or the clinical algorithms, especially in the low-dose group. However, the two pharmacogenetic algorithms were the most accurate.

The business value and cost–effectiveness of genomic medicine

Genomic medicine offers the promise of more effective diagnosis and treatment of human diseases. Genome sequencing early in the course of disease may enable more timely and informed intervention, with reduced healthcare costs and improved long-term outcomes. However, genomic medicine strains current models for demonstrating value, challenging efforts to achieve fair payment for services delivered, both for laboratory diagnostics and for use of molecular information in clinical management. Current models of healthcare reform stipulate that care must be delivered at equal or lower cost, with better patient and population outcomes. To achieve demonstrated value, genomic medicine must overcome many uncertainties: the clinical relevance of genomic variation; potential variation in technical performance and/or computational analysis; management of massive information sets; and must have available clinical interventions that can be informed by genomic analysis, so as to attain more favorable cost management of healthcare delivery and demonstrate improvements in cost–effectiveness.