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

Warfarin-A natural anticoagulant: A review of research trends for precision medication

BACKGROUND

Warfarin is a widely prescribed anticoagulant in the clinic. It has a more considerable individual variability, and many factors affect its variability. Mathematical models can quantify the quantitative impact of these factors on individual variability.

PURPOSE

The aim is to comprehensively analyze the advanced warfarin dosing algorithm based on pharmacometrics and machine learning models of personalized warfarin dosage.

METHODS

A bibliometric analysis of the literature retrieved from PubMed and Scopus was performed using VOSviewer. The relevant literature that reported the precise dosage of warfarin calculation was retrieved from the database. The multiple linear regression (MLR) algorithm was excluded because a recent systematic review that mainly reviewed this algorithm has been reported. The following terms of quantitative systems pharmacology, mechanistic model, physiologically based pharmacokinetic model, artificial intelligence, machine learning, pharmacokinetic, pharmacodynamic, pharmacokinetics, pharmacodynamics, and warfarin were added as MeSH Terms or appearing in Title/Abstract into query box of PubMed, then humans and English as filter were added to retrieve the literature.

RESULTS

Bibliometric analysis revealed important co-occuring MeShH and index keywords. Further, the United States, China, and the United Kingdom were among the top countries contributing in this domain. Some studies have established personalized warfarin dosage models using pharmacometrics and machine learning-based algorithms. There were 54 related studies, including 14 pharmacometric models, 31 artificial intelligence models, and 9 model evaluations. Each model has its advantages and disadvantages. The pharmacometric model contains biological or pharmacological mechanisms in structure. The process of pharmacometric model development is very time- and labor-intensive. Machine learning is a purely data-driven approach; its parameters are more mathematical and have less biological interpretation. However, it is faster, more efficient, and less time-consuming. Most published models of machine learning algorithms were established based on cross-sectional data sourced from the database.

CONCLUSION

Future research on personalized warfarin medication should focus on combining the advantages of machine learning and pharmacometrics algorithms to establish a more robust warfarin dosage algorithm. Randomized controlled trials should be performed to evaluate the established algorithm of warfarin dosage. Moreover, a more user-friendly and accessible warfarin precision medicine platform should be developed.

Exploring the complex relationship between vitamin K, gut microbiota, and warfarin variability in cardiac surgery patients

BACKGROUND AND OBJECTIVES

Due to the high individual variability of anticoagulant warfarin, this study aimed to investigate the effects of vitamin K concentration and gut microbiota on individual variability of warfarin in 246 cardiac surgery patients.

METHODS

The pharmacokinetics and pharmacodynamics (PKPD) model predicted international normalized ratio (INR) and warfarin concentration. Serum and fecal samples were collected to detect warfarin and vitamin K [VK1 and menaquinone-4 (MK4)] concentrations and gut microbiota diversity, respectively. In addition, the patient's medical records were reviewed for demographic characteristics, drug history, and CYP2C9, VKORC1, and CYP4F2 genotypes.

RESULTS

The PKPD model predicted ideal values of 62.7% for S-warfarin, 70.4% for R-warfarin, and 76.4% for INR. The normal VK1 level was 1.34±1.12 nmol/ml (95% CI: 0.33-4.08 nmol/ml), and the normal MK4 level was 0.22±0.18 nmol/ml (95% CI: 0.07-0.63 nmol/ml). The MK4 to total vitamin K ratio was 16.5±9.8% (95% CI: 4.3-41.5%). The S-warfarin concentration of producing 50% of maximum anticoagulation and the half-life of prothrombin complex activity tended to increase with vitamin K. Further, Prevotella and Eubacterium of gut microbiota identified as the main bacteria associated with individual variability of warfarin. The results suggest that an increase in vitamin K concentration can decrease anticoagulation, and gut microbiota may influence warfarin anticoagulation through vitamin K2 synthesis.

CONCLUSION

This study highlights the importance of considering vitamin K concentration and gut microbiota when prescribing warfarin. The findings may have significant implications for the personalized use of warfarin. Further research is needed to understand better the role of vitamin K and gut microbiota in warfarin anticoagulation.

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.

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.

Impact of CYP2C19 gene polymorphisms on warfarin dose requirement: a systematic review and meta-analysis

Background: Various genetic factors influence warfarin maintenance dose. Methods: A literature search was performed on PubMed, Embase and the Cochrane Library, and a meta-analysis to analyze the impact of CYP2C19 polymorphisms on warfarin maintenance dose was conducted. Results: From nine studies encompassing 1393 patients, three CYP2C19 SNPs were identified: rs4244285, rs4986893 and rs3814637. Warfarin maintenance dose was significantly reduced by 10% in individuals with the rs4986893 A allele compared with the GG carriers and was 34%, 16% and 18% lower in patients with rs3814637 TT and CT genotypes and T allele, respectively, than that in CC carriers. No significant dose difference was observed among the rs4244285 genotypes. Conclusion: CYP2C19 rs4986893 and rs3814637 are associated with significantly reduced warfarin dose requirements.

A novel, rapid and simple UHPLC-MS/MS method for quantification of warfarin in dried blood spots

Warfarin is a common first line anticoagulant with a narrow therapeutic window. Because of the large blood volume needed, previous warfarin determination methods were not applicable to small animals, such as mice. To reduce the number of small animals used needed, we developed and validated a sensitive rapid assay for the simultaneous detection of warfarin enantiomers in mouse dried blood spot (DBS) samples. Analytes were extracted by tert-butyl methyl ether and then separated by a chiral Cellulose-1 column with a mobile phase of 75% acetonitrile (containing 0.1% formic acid). The total chromatographic run time was 3 min. Negative mode electrospray ionization was used for MS/MS detection, where the monitored ion transitions were m/z 307.1 → 161.0 and 341.1 → 284.0 for warfarin and coumachlor (internal standard) respectively. The calibration curves were linear with a correlation coefficient of ≥0.994 for both enantiomers over a concentration range of 10-1000 ng/mL. The satisfactory accuracy and adequate reproducibility of both warfarin enantiomers were validated in terms of intra- and interday precision with mouse DBS cards. The samples were stable at room temperature for at least 14 days. The validated method was applied to a pharmacokinetic study in mice.

Determining the adjusted initial treatment dose of warfarin anticoagulant medicine using kernel-based support vector regression

BACKGROUND AND OBJECTIVE

A novel research field in bioinformatics is pharmacogenomics and the corresponding applications of artificial intelligence tools. Pharmacogenomics is the study of the relationship between genotype and responses to medical measures such as drug use. One of the most effective drugs is warfarin anticoagulant, but determining its initial treatment dose is challenging. Mistakes in the determination of the initial treatment dose can result directly in patient death.

METHODS

Some of the most successful techniques for estimating the initial treatment dose are kernel-based methods. However, all the available studies use pre-defined and constant kernels that might not necessarily address the problem's intended requirements. The present study seeks to define and present a new computational kernel extracted from a data set. This process aims to utilize all the data-related statistical features to generate a dose determination tool proportional to the data set with minimum error rate. The kernel-based version of the least square support vector regression estimator was defined. Through this method, a more appropriate approach was proposed for predicting the adjusted dose of warfarin.

RESULTS AND CONCLUSION

This paper benefits from the International Warfarin Pharmacogenomics Consortium (IWPC) Database. The results obtained in this study demonstrate that the support vector regression with the proposed new kernel can successfully estimate the ideal dosage of warfarin for approximately 68% of patients.

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.