Warfarin Pharmacogenetics Research Articles

Pharmacogenetics of warfarin: A literature review

Warfarin is an oral indirect anticoagulant that is widely used for the prevention of thromboembolic events. Pharmacogenetic testing is the most promising approach to personalizing warfarin treatment. In this review, we aimed to summarize how the patients’ genetic predispositions affect the pharmacokinetics of warfarin, which determines the different dosing regimens for patients. To correctly interpret data in clinical settings, algorithms for selecting the optimal dosing regimen need to be developed that consider the patient’s age, sex, weight, height, health status, and genetic characteristics. These algorithms could help determine the optimal dose, enhance patient adherence to treatment, and increase the physician’s confidence in the treatment safety. Furthermore, although algorithms that consider SNPs in the CYP2C9, VKORC1, and CYP4F2 genes are more effective in predicting warfarin doses, their effectiveness varies according to race.

Meta-analysis of genome-wide association studies of stable warfarin dose in patients of African ancestry

AbstractWarfarin dose requirements are highly variable because of clinical and genetic factors. Although genetic variants influencing warfarin dose have been identified in European and East Asian populations, more work is needed to identify African-specific genetic variants to help optimize warfarin dosing. We performed genome-wide association studies (GWASs) in 4 African cohorts from Uganda, South Africa, and Zimbabwe, totaling 989 warfarin-treated participants who reached stable dose and had international normalized ratios within therapeutic ranges. We also included 2 African American cohorts recruited by the International Warfarin Pharmacogenetics Consortium (n = 316) and the University of Alabama at Birmingham (n = 199). After the GWAS, we performed standard error-weighted meta-analyses and then conducted stepwise conditional analyses to account for known loci in chromosomes 10 and 16. The genome-wide significance threshold was set at P < 5 × 10−8. The meta-analysis, comprising 1504 participants, identified 242 significant SNPs across 3 genomic loci, with 99.6% of these located within known loci on chromosomes 10 (top SNP: rs58800757, P = 4.27 × 10−13) and 16 (top SNP: rs9925964, P = 9.97 × 10−16). Adjustment for the VKORC1 SNP -1639G>A revealed an additional locus on chromosome 2 (top SNPs rs116057875/rs115254730/rs115240773, P = 3.64 × 10−8), implicating the MALL gene, that could indirectly influence warfarin response through interactions with caveolin-1. In conclusion, we reaffirmed the importance of CYP2C9 and VKORC1 in influencing warfarin dose requirements, and identified a new locus (MALL), that still requires direct evidence of biological plausibility.

Validating two international warfarin pharmacogenetic dosing algorithms for estimating the maintenance dose for patients in Singapore

Predicting optimal warfarin dosing is difficult due to complex pharmacodynamics and pharmacokinetics, narrow therapeutic index and susceptibility to many factors.1 Genetic variations of the CYP2C9 and VKORC1 enzymes, occurring in different frequencies in different populations, play a significant role in determining warfarin dosing.1-4 Using pharmacogenetic dosing algorithms to predict warfarin doses may shorten the time to achieve target International Normalised Ratio (INR) and stable dose.2,5 The Clinical Pharmacogenetics Implementation Consortium Guidelines 2017 Update4 recommends the Gage (WarfarinDosing.org7) and International Warfarin Pharmacogenetics Consortium (IWPC)8 pharmacogenetic algorithms.

Change plane model averaging for subgroup identification.

Central to personalized medicine and tailored therapies is discovering the subpopulations that account for treatment effect heterogeneity and are likely to benefit more from given interventions. In this article, we introduce a change plane model averaging method to identify subgroups characterized by linear combinations of predictive variables and multiple cut-offs. We first fit a sequence of statistical models, each incorporating the thresholding effect of one particular covariate. The estimation of submodels is accomplished through an iterative integration of a change point detection method and numerical optimization algorithms. A frequentist model averaging approach is then employed to linearly combine the submodels with optimal weights. Our approach can deal with high-dimensional settings involving enormous potential grouping variables by adopting the sparsity-inducing penalties. Simulation studies are conducted to investigate the prediction and subgrouping performance of the proposed method, with a comparison to various competing subgroup detection methods. Our method is applied to a dataset from a warfarin pharmacogenetics study, producing some new findings.

Targeted next-generation sequencing of genes involved in Warfarin Pharmacodynamics and pharmacokinetics pathways using the Saudi Warfarin Pharmacogenetic study (SWAP).

Warfarin is an oral anticoagulant commonly used for treatment and prophylaxis against thromboembolic events. Warfarins's narrow therapeutic index window is one of the main challenges in clinical practice; thus, it requires frequent monitoring and dose adjustment to maintain patients' therapeutic range. Warfarin dose variation and response are attributed to several inter-and intra-individuals factors, including genetic variants in enzymes involved in warfarin pharmacokinetics (PK) and pharmacodynamics (PD) pathways. Thus, we aim to utilize the next-generation sequencing (NGS) approach to identify rare and common genetic variants that might be associated with warfarin responsiveness. A predesigned NGS panel that included 16 genes involved in Warfarin PK/PD pathways was used to sequence 786 patients from the Saudi Warfarin Pharmacogenetic Cohort (SWAP). Identified variants were annotated using several annotation tools to identify the pathogenicity and allele frequencies of these variants. We conducted variants-level association tests with warfarin dose. We identified 710 variants within the sequenced genes; 19% were novel variants, with the vast majority being scarce variants. The genetic association tests showed that VKORC1 (rs9923231, and rs61742245), CYP2C9 (rs98332238, rs9332172, rs1057910, rs9332230, rs1799853, rs1057911, and rs9332119), CYP2C19 (rs28399511, and rs3758581), and CYP2C8 (rs11572080 and rs10509681) were significantly associated with warfarin weekly dose. Our model included genetics, and non-genetic factors explained 40.1% of warfarin dose variation. The study identifies novel variants associated with warfarin dose in the Saudi population. These variants are more likely to be population-specific variants, suggesting that population-specific studies should be conducted before adopting a universal warfarin genotype-guided dosing algorithm.

Genomewide Association Study Identifies Copy Number Variants Associated With Warfarin Dose Response and Risk of Venous Thromboembolism in African Americans.

The anticoagulant warfarin is commonly used to control and prevent thrombotic disorders, such as venous thromboembolism (VTE), which disproportionately afflicts African Americans. Despite the importance of copy number variants (CNVs), few studies have focused on characterizing and understanding their role in drug response and disease risk among African Americans. In this study, we conduct the first genome-wide analysis of CNVs to more comprehensively account for the contribution of genetic variation in warfarin dose requirement and VTE risk among African Americans. We used hidden Markov models to detect CNVs from high-throughput single-nucleotide polymorphism arrays for 340 African American participants in the International Warfarin Pharmacogenetics Consortium. We identified 11,570 CNVs resulting in 2,038 copy number variable regions (CNVRs) and found 3 CNVRs associated with warfarin dose requirement and 3 CNVRs associated with VTE risk in African Americans. CNVRs 1q31.2del and 6q14.1del were associated with increased warfarin dose requirement (β=11.18 and 4.94, respectively; Pemp =< 0.002); CNVR 19p13.31del was associated with decreased warfarin dose requirement (β=-1.41, Pemp =0.0004); CNVRs (2p22.1del and 5q35.1-q35.2del) were found to be associated with increased risk of VTE (odds ratios (ORs)=1.88 and 14.9, respectively; Pemp ≤0.02); and CNVR 10q26.12del was associated with a decreased risk of VTE (OR=0.6; Pemp =0.05). Modeling of the 10q26.12del in HepG2 cells revealed that this deletion results in decreased fibrinogen gene expression, decreased fibrinogen and WDR11 protein levels, and decreased secretion of fibrinogen into the extracellular matrix. We found robust evidence that CNVRs could contribute to warfarin dose requirement and risk of VTE in African Americans and for 10q26.3del describe a possible pathogenic mechanism.

Is there still room for warfarin after the appearance of direct oral anticoagulants?

After the appearance of direct oral anticoagulants (DOAC), the use of vitamin K antagonists (VKA) has become more rare, but nevertheless, there are diseases in which they remain indispensable drugs. This review is devoted to VKA and the diseases in which these drugs can be used. The article compares the mechanism of action of VKA and DOAC, discusses methods of monitoring VKA therapy. Among the diseases of non-alternative use of VKA, mechanical prosthetic heart valves, atrial fibrillation in patients with moderate and severe mitral stenosis, antiphospholipid syndrome should be mentioned. Another indication for the appointment of anticoagulants is thrombosis of the left ventricle. In patients who have survived a myocardial infarction complicated by left ventricular thrombosis, anticoagulant treatment should be continued for up to 6 months with repeated imaging control. For this purpose, warfarin is successfully used. Currently, there is insufficient data to recommend the routine use of DOAC in patients with KrCl less than 25–30 ml/min, and in routine practice, warfarin remains the main anticoagulant in such patients. Direct oral anticoagulants have practically not been studied in the so-called special groups of patients: patients with congenital thrombophilia and rare localizations of thrombosis. There were practically no such patients in randomized trials and very little is known about the effectiveness of DOAC, so today warfarin remains the main drug for their treatment. Despite the fact that DOAC has displaced VKA in patients with non-valvular AF, we should not forget that it is VKA that we are obliged to prove the effectiveness of anticoagulants in patients with AF. The article discusses the pharmacogenetics of warfarin in relation to the data of the Russian Federation, as well as the practically important question of the possibility of resuming anticoagulants in patients with bleeding, the results of their own research are presented.

Comparison of Maintenance Dose Predictions by Warfarin Dosing Algorithms Based on Chinese and Western Patients.

Warfarin has a long record of safe and effective clinical use, and it remains one of the most commonly prescribed drugs for the prevention and treatment of thromboembolic conditions even in the era of direct oral anticoagulants. To address its large interindividual variability and narrow therapeutic window, the Clinical Pharmacogenetics Implementation Consortium has recommended using pharmacogenetic dosing algorithms, such as the ones developed by the International Warfarin Pharmacogenetics Consortium (IWPC) and by Gage et al, to dose warfarin when genotype information is available. In China, dosing algorithms based on local patient populations have been developed and evaluated for predictive accuracy of warfarin maintenance doses. In this study, percentage deviations of doses predicted by 15 Chinese dosing algorithms from that by IWPC and Gage algorithms were systematically evaluated to understand the differences between Chinese and Western algorithms. In general, dose predictions by Chinese dosing algorithms tended to be lower than those predicted by IWPC or Gage algorithms for the most prevalent VKORC1 and CYP2C9 genotypes in the Chinese population. The extent of negative prediction deviation appeared to be largest in the younger age group with smaller body weight. Our findings are consistent with previous reports that Asians have a higher sensitivity to warfarin and require lower doses than Western populations.

Warfarin hypersensitivity in the internist's practice: a case report

Anticoagulants are widely used in clinical practice to reduce the risk of cardiovascular events. However, there are associated clinical conditions that require coagulation system monitoring due to an increased risk of bleeding. In clinical practice, cases of warfarin hypersensitivity due to gene polymorphisms are known. Taking warfarin in such a situation is often manifested by massive bleeding that threatens the patient's life. Sixty seven-years-old female patient was admitted to the internal medicine department of Rostov State Medical University clinic in March 2022. The day before, she noted severe abdominal pain, loose bloody stool. An outpatient examination revealed an increase in international normalized ratio to 9,42, thrombin time &gt;30 sec. There were no signs of primary gastrointestinal pathology. According to anamnesis, on January 21, 2022, the patient underwent mechanical aortic valve replacement, in connection with which warfarin was prescribed at a dose of 2,5 mg/day. After establishing hypocoagulation, warfarin was discontinued. A pharmacogenetic analysis was performed, which established the carriage of polymorphisms of cytochrome P450 system genes, the homozygous mutation 1075A&gt;C (CYP2C9 (*3/*3)), and the heterozygous mutation of the vitamin K reductase gene VKORC1 G(-1639)A (VKORC1 GA). The individual dosage of warfarin was calculated according to International Warfarin Pharmacogenetics Consortium guidelines, which was 9 mg/week. After adjusting the dose of warfarin, the level of international normalized ratio decreased to 3,61, thrombin time to 13 sec. The patient was discharged with recommendations to follow an individual warfarin regimen and monitor coagulation parameters. The presence of hypersensitivity to warfarin is not a reason for its complete withdrawal in situations requiring longterm anticoagulation. In this regard, it is necessary for the doctor to be vigilant when prescribing warfarin to a patient, understanding the causes and methods for diagnosing hypersensitivity to warfarin, and timely correction of its dosage.

Pharmacogenetics of warfarin and healthcare costs - Real-world data analysis.

Variants in CYP2C9 and VKORC1 genes have been associated with individuals' sensitivity to warfarin. The aim of this study was to investigate the differences of healthcare costs of genetically normal and genetically sensitive warfarin responder groups. This was a retrospective study linking genotype data from three Finnish biobanks (THL Biobank, Auria Biobank, Helsinki Biobank) with healthcare encounter data of the Finnish Institute of Health and Welfare (THL), drug dispensation data from the Social Insurance Institution of Finland (Kela) and laboratory data from Finnish hospital districts and municipalities. We compared the normal and sensitive warfarin responder groups in terms of healthcare costs related to bleeding and thromboembolic events, INR tests and medication purchases. We found a trend towards increased bleeding-related hospital costs in the sensitive warfarin responder group (881 patients) when compared with the normal responders (1627 patients) with a per patient difference of 150.9€/year (95% CI: -55.1, 414.6€/year, p=0.087). INR test costs were higher in the sensitive responder group with a difference of 7.2€/year (95% CI: -1.5, 16.4€/year, p=0.047). Medication costs were significantly lower in the sensitive responder group with a difference of -14.4€/year (95% CI: -15.8, -12.9€/year, p < 0.001). The difference in the costs of bleeding-related hospitalization between genetically sensitive and normal warfarin responders may justify genotype-guided warfarin dosing. Further studies with larger sample sizes would be needed to verify the result.

Abstract 10926: Warfarin Dosing Algorithms With the Information on Genotype and Amiodarone for Patients Without Extracorporeal Circulation Devices Could Be Beneficial for Those With Left Ventricular Assist Devices

Introduction: Many prediction algorithms for warfarin maintenance dose (WD) were constructed based on the clinical and genetic information of the patients without extracorporeal circulation devices (ECDs). However, it is unclear whether might be useful for the patients with ECD, such as left ventricular assist devices (LVAD), who still require warfarin. Hypothesis: The WD algorithms constructed using non-ECD patients could be useful for LVAD patients. Methods: An observational study, the Human Genome Research Ethics Committee of OU approved (#756), was conducted at the Osaka University (OU) Hospital on 108 LVAD patients receiving warfarin therapy. Genetic polymorphisms of CYP2C9*1, *3, and VKORC1-1639G&gt;A were tested using TaqMan genotyping assay kits. WD was defined as the administered dose during the periods when the PT-INR value was within its target range. We investigated the difference between the actual warfarin dose (AWD) and the calculated warfarin dose (CWD) using an algorithm proposed by the International Warfarin Pharmacogenetics Consortium (IWPC), which we verified with 125 Japanese non-ECD patients previously (Eur J Clin Pharmacol, 75:901). Results: The percentage of patients whose difference between CWD and AWD was within 20% of AWD showed no significant difference between the LVAD and the non-ECD (%; I, LVAD, 41.7, non-ECD, 49.6, p=0.23). The root mean squared percentage error (RMSPE) to the AWD was similar between the groups (%; LVAD, 42, non-ECD, 37). As the algorithm requires if amiodarone is concomitant with warfarin, we probed the effect of the amiodarone dose on the CWD. Interestingly, RMSPE of the LVAD with ≥ 200 mg/day of amiodarone (≥ 200A) was higher compared to those with &lt; 200 mg/day (&lt;200A) and without amiodarone (%; 85.5, 25.9, 36.8). The difference between AWD and CWD of ≥ 200A was significantly higher than &lt;200A (mg/day; 0.87, -0.07; p=0.012). We verified similar tendencies using other algorithms published before. Conclusions: The WD algorithm with the information on genotype and concomitant use of amiodarone for the non-ECD could be beneficial for the LVAD. Besides them, extra careful attention should be required for patients with ≥ 200 mg/day of amiodarone not to have warfarin overdosed.

HK is the apple of FXI's eye

HK is the apple of FXI's eye

Validation and comparison between two warfarin dosing clinical algorithms and warfarin fixed dosing in specialized heart center: cross-sectional study.

Warfarin is well known as a narrow therapeutic index that has prodigious variability in response which challenges dosing adjustment for the maintenance of therapeutic international normalized ratio. However, an appreciated population not on new oral anticoagulants may still need to be stabilized with warfarin dosing. The current study's main objective was to validate and compare two models of warfarin clinical algorithm models namely the Gage and the International Warfarin Pharmacogenetics Consortium (IWPC) with warfarin 5 mg fixed standard dosing strategy in a sample of Sudanese subjects. We have conducted a cross-sectional study recruited from the out-patient clinic at a tertiary specialized heart center. We included subjects with unchanged warfarin dose (stabilized), and with therapeutic international normalized ratio. The predicted doses of warfarin in the two models were calculated by three different methods (accuracy, clinical practicality, and the clinical safety of the clinical algorithms). The primary outcomes were the measurements of the clinical (accuracy, practicality, and safety) in each of the two clinical algorithms models compared to warfarin 5 mg fixed standard dose strategy. We have enrolled 71 Sudanese subjects with mean age (51.7 ± 14 years), of which (49, 69.0%) were females. There was no significant difference between the warfarin 5 mg fixed standard dose strategy and the predicted doses of the two clinical algorithm models (MAE 1.44, 1.45, and 1.49 mg/day [P =0.4]) respectively. In the clinical practicality, all of the three models had a high percent of subjects (95.0%, 51.9%, and 66.7%) in the ideal dose range in middle dose group (3-7 mg/ day) for warfarin 5 mg fixed standard dosing strategy, Gage, and IWPC clinical algorithm models respectively. However, a small percent of subjects was exhibited in the warfarin low dose group ≤ 3 mg/day (0.0%, 15.0%, and 10.0%) and warfarin high dose group ≥ 7 mg/day (0.0%, 33.3%, and 33.3%) for warfarin 5 mg fixed standard dosing strategy, Gage, and IWPC clinical algorithms respectively. In terms of clinical safety, the percent of subjects with severely over-prediction were 28.2%, 22.5%, and 22.5% for warfarin 5 mg fixed standard dosing, Gage, and IWPC, respectively. While the percent of severely under-prediction was 12.7%, 7.0%, and 5.6% for the warfarin 5 mg fixed standard dosing, Gage, and IWPC, respectively. The Gage and IWPC clinical algorithm models were accurate, more clinically practical, and clinically safe than warfarin 5 mg standard dosing in the study population. The cardiologist can use either models (Gage and IWPC) to stratify subjects for accurate, practical, and clinically safe warfarin dosing..

Warfarin sensitivity is associated with increased hospital mortality in critically Ill patients.

BackgroundWarfarin is a widely used anticoagulant with a narrow therapeutic index and large interpatient variability in the therapeutic dose. Warfarin sensitivity has been reported to be associated with increased incidence of international normalized ratio (INR) > 5. However, whether warfarin sensitivity is a risk factor for adverse outcomes in critically ill patients remains unknown. In the present study, we aimed to evaluate the utility of different machine learning algorithms for the prediction of warfarin sensitivity and to determine the impact of warfarin sensitivity on outcomes in critically ill patients.MethodsNine different machine learning algorithms for the prediction of warfarin sensitivity were tested in the International Warfarin Pharmacogenetic Consortium cohort and Easton cohort. Furthermore, a total of 7,647 critically ill patients was analyzed for warfarin sensitivity on in-hospital mortality by multivariable regression. Covariates that potentially confound the association were further adjusted using propensity score matching or inverse probability of treatment weighting.ResultsWe found that logistic regression (AUC = 0.879, 95% CI: 0.834–0.924) was indistinguishable from support vector machine with a linear kernel, neural network, AdaBoost and light gradient boosting trees, and significantly outperformed all the other machine learning algorithms. Furthermore, we found that warfarin sensitivity predicted by the logistic regression model was significantly associated with worse in-hospital mortality in critically ill patients with an odds ratio (OR) of 1.33 (95% CI, 1.01–1.77).ConclusionsOur data suggest that the logistic regression model is the best model for the prediction of warfarin sensitivity clinically and that warfarin sensitivity is likely to be a risk factor for adverse outcomes in critically ill patients.

Pharmacogenetics of clopidogrel and warfarin in the treatment of cardiovascular diseases: an overview of reviews.

Pharmacogenetics (PGx) is the relationship between an individual's genetic variations and the response to pharmacological treatment. Wechose to perform an overview of reviews on PGx testing-guided treatment for cardiovascular diseases, based on clinically relevant gene-drug pairs. We conducted a search on Medline, Embase and Cochrane Library, from their inception to 18 June 2020. The most studied gene-drug pairs were clopidogrel and warfarin associated with cytochrome p450 and vitamin K epoxide reductase complex subunit 1 genes (CYP2C19, CYP2C9 and VKORC1), classified as critically low quality. There is a need for more quality primary studies and systematic reviews that assess the risk of bias, with consistent definitions of clinical outcomes to consider the benefits of PGx testing for cardiovascular diseases.

Building and analyzing machine learning-based warfarin dose prediction models using scikit-learn.

For personalized drug dosing, prediction models may be utilized to overcome the inter-individual variability. Multiple linear regression has been used as a conventional method to model the relationship between patient features and optimal drug dose. However, linear regression cannot capture non-linear relationships and may be adversely affected by non-normal distribution and collinearity of data. To overcome this hurdle, machine learning models have been extensively adapted in drug dose prediction. In this tutorial, random forest and neural network models will be trained in tandem with a multiple linear regression model on the International Warfarin Pharmacogenetics Consortium dataset using the scikit-learn python library. Subsequent model analyses including performance comparison, permutation feature importance computation and partial dependence plotting will be demonstrated. The basic methods of model training and analysis discussed in this article may be implemented in drug dose-related studies.

Machine Learning for Prediction of Stable Warfarin Dose in US Latinos and Latin Americans.

Populations used to create warfarin dose prediction algorithms largely lacked participants reporting Hispanic or Latino ethnicity. While previous research suggests nonlinear modeling improves warfarin dose prediction, this research has mainly focused on populations with primarily European ancestry. We compare the accuracy of stable warfarin dose prediction using linear and nonlinear machine learning models in a large cohort enriched for US Latinos and Latin Americans (ULLA). Each model was tested using the same variables as published by the International Warfarin Pharmacogenetics Consortium (IWPC) and using an expanded set of variables including ethnicity and warfarin indication. We utilized a multiple linear regression model and three nonlinear regression models: Bayesian Additive Regression Trees, Multivariate Adaptive Regression Splines, and Support Vector Regression. We compared each model’s ability to predict stable warfarin dose within 20% of actual stable dose, confirming trained models in a 30% testing dataset with 100 rounds of resampling. In all patients (n = 7,030), inclusion of additional predictor variables led to a small but significant improvement in prediction of dose relative to the IWPC algorithm (47.8 versus 46.7% in IWPC, p = 1.43 × 10−15). Nonlinear models using IWPC variables did not significantly improve prediction of dose over the linear IWPC algorithm. In ULLA patients alone (n = 1,734), IWPC performed similarly to all other linear and nonlinear pharmacogenetic algorithms. Our results reinforce the validity of IWPC in a large, ethnically diverse population and suggest that additional variables that capture warfarin dose variability may improve warfarin dose prediction algorithms.

Performance Evaluation of Warfarin Dose Prediction Algorithms and Effects of Clinical Factors on Warfarin Dose in Chinese Patients.

The clinical utility of warfarin dose prediction algorithms remains controversial, our purpose is to evaluate the performance of warfarin dose prediction algorithms and the effects of clinical factors on warfarin dose in Chinese patients. Clinical data of 217 patients who received warfarin treatment were used to assess 6 warfarin dose prediction algorithms (OHNO, IWPC [International Warfarin Pharmacogenetics Consortium], HUANG, KIM, BRESS, and MIAO). The predicted dose (PD) was compared with the warfarin optimal dose (WOD, defined as the dose that maintains the international normalized ratio within the target range of 2.0-3.0). A multiple regression analysis with WOD as the dependent variable was performed to evaluate the effects of clinical factors on warfarin dose. The mean absolute error analysis ranked the predictive accuracies of the algorithms as OHNO > IWPC > HUANG > KIM > BRESS > MIAO. Stratified analysis indicated that HUANG most accurately predicted that patients required lower WODs (≤3 mg/d), whereas OHNO was the most effective in predicting medium WODs (3-5 mg/d). KIM was effective in predicting high WODs (>5 mg/d). Multiple linear regression analysis showed that VKORC1 (rs9923231) and body mass index were significantly positively correlated with WOD, whereas concurrent atrial fibrillation status, CYP2C9*3 (rs1057910), and sex were significantly negatively correlated with WOD. In Chinese patients, OHNO should be given priority during the prediction and selection of warfarin dose. When using OHNO to predict warfarin dose (≤3 mg/d or >5 mg/d), HUANG or KIM algorithms can provide precise predictions. At the same time, physicians should pay close attention to clinical factors, such as VKORC1 (rs9923231), concurrent atrial fibrillation status, CYP2C9*3 (rs1057910), body mass index, and sex, to improve warfarin dose adjustment strategies in Chinese patients.

Frequencies of polymorphisms in genes affecting the pharmacokinetics of warfarin in the Zaporizhzhia region

The aim of the study: to establish the frequencies of polymorphisms in VKORC1, CYP2C9 and CYP4F2 genes among residents of the Zaporizhzhia region. Materials and methods. A total of 150 persons (62 male, 88 female) with a median age of 46 years (26; 58) undergoing preventive examination at the Medical Educational and Scientific Center “University Clinic” of Zaporizhzhia State Medical University (ZSMU). The CYP2C9, CYP4F2, VKORC1 genes polymorphisms in atrial fibrillation patients were determined in the Department of Molecular Genetic Studies of the ZSMU Medical Laboratory Center. Amplification of DNA fragments containing polymorphic regions was performed using multiplex real time polymerase chain reaction with Warfarin Pharmacogenetics kits (LLC NPO DNA Technology) in a CFX-96 thermocycler (BioRad) with a fluorescence detection scheme. Results. It was determined that among Zaporizhzhia region residents, the frequencies of CYP2C9*2 genotypes were: C/C – 77.3 %, C/T – 22.7 %, T/T – 0; CYP2C9*3 genotypes: A/A – 88.7 %, A/C – 10.7 %, C/C – 0.6 %; CYP4F2 genotypes: C/C – 56.0 %, C/T – 35.3 %, T/T – 8.7 %; VKORC1 genotypes: G/G – 38.0 %, G/A – 50.0 %, A/A – 12.0 %. There were no statistically significant differences in the distribution of genotype frequencies between males and females and between different age groups. The frequencies of CYP2C9, CYP4F2, VKORC1 genotypes in different populations were compared. Their variability in different geographic regions was established. Conclusions. CYP4F2 and VKORC1 genes polymorphisms are more common in the Zaporizhzhia region, while the prevalence of CYP2C9*2 and CYP2C9*3 genes polymorphisms is much lower. It is necessary to take into account the prevalence of genes polymorphisms that affect warfarin metabolism for each individual population to select its dose by pharmacogenetic testing.

Evolutionary synthetic oversampling technique and cocktail ensemble model for warfarin dose prediction with imbalanced data

To improve the accuracy of warfarin daily dose prediction, we develop an evolutionary synthetic oversampling technique (ESMOTE) with a cocktail ensemble model (CEM) called ESMOTE-CEM. Different from conventional oversampling methods, ESMOTE finds the near-optimal oversampling parameters by evolving the parameter representation based on the pre-predicted warfarin dose and then synthesizes new samples to balance the data. The CEM, which improves the performance of random forest (RF) and boosted regression tree (BRT) models using a hybrid mechanism in the regression calculation, estimates the daily dose of warfarin. We test the ESMOTE-CEM on a dataset of 733 samples collected from the First Affiliated Hospital of Soochow University and the International Warfarin Pharmacogenetics Consortium (IWPC). The results show that ESMOTE outperformed the other oversampling methods by at least 6.98% for R2 and 5.03% for the mean squared error (MSE). In terms of the percentage of patients whose predicted warfarin dose is within 20% of the actual stable therapeutic dose (20%-p value), the ESMOTE-CEM achieves a 20%-p value of 50%. Moreover, compared to RF, BRT and AdaBoost models, the CEM is the most suitable base predictive model for ESMOTE.