Validation of warfarin pharmacogenetic algorithms in 586 Han Chinese patients

Comparison of the prediction performance of different warfarin dosing algorithms based on Chinese patients

Aim: To compare the prediction performance of different warfarin dosing algorithms based on Chinese patients. Materials & methods: A total of 18 algorithms were tested in 325 patients. The predictive efficacy of selected algorithms was evaluated by calculating the percentage of patients whose predicted dose fell within ±20% of their actual stable warfarin dose and the mean absolute error. Results: The percentage within ± 20% and the mean absolute error of the algorithms ranged from 11.9 to 41.2% and -0.20 (-0.29 to -0.11) mg/d to -1.63 (-1.75 to -1.50) mg/d. The algorithms established by Miao et al. and Wei et al. had optimal predictive performance. Conclusion: Algorithms based on geographical populations might be more suitable for the prediction of stable warfarin doses in local patients.

Pharmacogenetic-Guided Algorithm to Improve Daily Dose of Warfarin in Elder Han-Chinese Population

Objectives

To verify the accuracy of the International Warfarin Pharmacogenetics Consortium (IWPC) algorithm, identify the effects of genetic and clinical factors on warfarin stable dose, and to establish a new warfarin stable dose prediction algorithm for the elderly Han-Chinese population under the guidance of pharmacogenetics.

Methods

According to the inclusion criteria, 544 non-valvular atrial fibrillation patients taking warfarin for anticoagulation treatment were enrolled. Data information of three groups including the whole population, people under 65 years old and over 65 years old were substituted into the IWPC algorithm respectively to verify its accuracy. The basic data and clinical information of 360 elderly people were collected for statistical analysis and the genotypes of VKORC1-G1639A and CYP2C9 were detected by Sanger sequencing. The new algorithm of the elder pharmacogenetics warfarin dosing was obtained by stepwise multiple regression. The determination coefficient (R2), root mean squared error (RMSE), and the proportion of the predicted value within the true value range of ±20%(20%-p) were used to evaluate the accuracy of the IWPC algorithm and the new algorithm.

Results

Among the three different age groups, the warfarin stable dose predictive accuracy of IWPC algorithm was the lowest in the elderly patients above 65-year-old. In this study, the important factors influencing the stable dose of warfarin in the elderly Han-Chinese were height, weight, body surface area, serum creatinine level, amiodarone usage, CYP2C9 (*1*2, *1*3), and VKORC1 (GG/GA) genotypes. By means of stepwise multiple regression analysis, we established a new elder warfarin dosing algorithm (R²=0.3714) containing height, creatinine, amiodarone usage, CYP2C9 (*1*2 or *1*3), and VKORC1 (GA or GG) genotypes. The prediction accuracy and clinical availability of the Elderly algorithm was significantly better than that of IWPC algorithm verified by RMSE, R2, and (20%-p) methods.

Conclusions

The IWPC model may not be suitable for the elder Han-Chinese population. Polymorphism of CYP2C9 and VKORC1 obviously affected warfarin stable dose of the elder Han-Chinese. Combination of genetic data with demographic and clinical factors could help to better improve warfarin doses in the elder Han-Chinese population.

Development and Validation of a Novel Warfarin Dosing Algorithm for Korean Patients With VKORC1 1173C

BACKGROUND

Differences in the performance of suggested warfarin dosing algorithms among different ethnicities and genotypes have been reported; this necessitates the development of an algorithm with enhanced performance for specific population groups. Previous warfarin dosing algorithms underestimated warfarin doses in VKORC1 1173C carriers. We aimed to develop and validate a new warfarin dosing algorithm for Korean patients with VKORC1 1173C.

METHODS

A total of 109 patients carrying VKORC1 1173CT (N=105) or 1173CC (N=4) were included in this study. Multiple regression analysis was performed to deduce a new dosing algorithm. Following literature searches for genotype-guided warfarin dosing algorithms, 21 algorithms were selected and evaluated using the correlation coefficient (ρ) of actual dose and estimated dose, mean error, and root mean square error.

RESULTS

The developed algorithm is as follows: maintenance dose (mg/week)=exp [3.223-0.009×(age)+0.577×(body surface area [BSA])+0.178×(sex)-0.481×(CYP2C9 genotype)+0.227×(VKORC1 genotype)]. Integrated variables explained 44% of the variance in the maintenance dose. The predicted and actual doses showed moderate correlation (ρ=0.641) with the best performance with a mean error of -1.30 mg/week. The proportion of underestimated groups was 17%, which was lower than with the other algorithms.

CONCLUSIONS

This is the first study to develop and validate a warfarin dosing algorithm based on data from VKORC1 1173C carriers; it showed superior predictive performance compared with previously published algorithms.

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.

Precision medicine: does ethnicity information complement genotype-based prescribing decisions?

Inter-ethnic differences in drug response are all too well known. These are underpinned by a number of factors, including pharmacogenetic differences across various ethnic populations. Precision medicine relies on genotype-based prescribing decisions with the aim of maximizing efficacy and mitigating the risks. When there is no access to genotyping tests, ethnicity is frequently regarded as a proxy of the patient's probable genotype on the basis of overall population-based frequency of genetic variations in the ethnic group the patient belongs to, with some variations being ethnicity-specific. However, ever-increasing transcontinental migration of populations and the resulting admixing of populations have undermined the utility of self-identified ethnicity in predicting the genetic ancestry, and therefore the genotype, of the patient. An example of the relevance of genetic ancestry of a patient is the inadequate performance of European-derived pharmacogenetic dosing algorithms of warfarin in African Americans, Brazilians and Caribbean Hispanics. Consequently, genotyping a patient potentially requires testing for all known clinically actionable variants that the patient may harbour, and new variants that are likely to be identified using state-of the art next-generation sequencing-based methods. Furthermore, self-identified ethnicity is associated with a number of ethnicity-related attributes and non-genetic factors that potentially influence the risk of phenoconversion (genotype-phenotype discordance), which may adversely impact the success of genotype-based prescribing decisions. Therefore, while genotype-based prescribing decisions are important in implementing precision medicine, ethnicity should not be disregarded.

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%).

Evaluation of genotype-guided acenocoumarol dosing algorithms in Russian patients

BACKGROUND

Acenocoumarol dose is normally determined via step-by-step adjustment process based on International Normalized Ratio (INR) measurements. During this time, the risk of adverse reactions is especially high. Several genotype-based acenocoumarol dosing algorithms have been created to predict ideal doses at the start of anticoagulant therapy.

METHODS

Nine dosing algorithms were selected through a literature search. These were evaluated using a cohort of 63 patients with atrial fibrillation receiving acenocoumarol therapy.

RESULTS

None of the existing algorithms could predict the ideal acenocoumarol dose in 50% of Russian patients. The Wolkanin-Bartnik algorithtm based on European population was the best-performing one with the highest correlation values (r=0.397), mean absolute error (MAE) 0.82 (±0.61). EU-PACT also managed to give an estimate within the ideal range in 43% of the cases. The two least accurate results were yielded by the Indian population-based algorithms. Among patients receiving amiodarone, algorithms by Schie and Tong proved to be the most effective with the MAE of 0.48±0.42 mg/day and 0.56±0.31 mg/day, respectively.

CONCLUSIONS

Patient ethnicity and amiodarone intake are factors that must be considered when building future algorithms. Further research is required to find the perfect dosing formula of acenocoumarol maintenance doses in Russian patients.

A novel acenocoumarol pharmacogenomic dosing algorithm for the Greek population of EU-PACT trial

Aim: To generate and validate a pharmacogenomic-guided (PG) dosing algorithm for acenocoumarol in the Greek population. To compare its performance with other PG algorithms developed for the Greek population. Patients & methods: A total of 140 Greek patients participants of the EU-PACT trial for acenocoumarol, a randomized clinical trial that prospectively compared the effect of a PG dosing algorithm with a clinical dosing algorithm on the percentage of time within INR therapeutic range, who reached acenocoumarol stable dose were included in the study. Results: CYP2C9 and VKORC1 genotypes, age and weight affected acenocoumarol dose and predicted 53.9% of its variability. EU-PACT PG algorithm overestimated acenocoumarol dose across all different CYP2C9/VKORC1 functional phenotype bins (predicted dose vs stable dose in normal responders 2.31 vs 2.00 mg/day, p = 0.028, in sensitive responders 1.72 vs 1.50 mg/day, p = 0.003, in highly sensitive responders 1.39 vs 1.00 mg/day, p = 0.029). The PG algorithm previously developed for the Greek population overestimated the dose in normal responders (2.51 vs 2.00 mg/day, p < 0.001). Conclusion: Ethnic-specific dosing algorithm is suggested for better prediction of acenocoumarol dosage requirements in patients of Greek origin.

Implementing Precision Medicine: The Ethical Challenges

Precision medicine aims to individualize care by understanding differences in genetics, lifestyle, and environment. Pharmacogenomics and cancer genetics represent two promising areas for this approach. Pharmacogenomic tests have the potential to direct drug prescribing to increase safety and effectiveness because individuals vary on a genetic basis in their response to many drugs. Similarly, tests to identify people with an inherited cancer risk can guide prevention. For both, a few tests have entered clinical practice and more are under development. Implementation challenges include the limited evidence base available to guide clinical use and the lack of data from diverse populations. Accordingly, ongoing research should prioritize procedures that enhance the trustworthiness of clinical practice guidelines and create decision support for clinicians and patients that address their needs and accommodate flexibility. Each step involves choices with ethical implications.