Extrapolation of acenocoumarol pharmacogenetic algorithms

Jiménez-Varo, Enrique; Cañadas-Garre, Marisa; Garcés, Víctor; Gutiérrez-Pimentel, María José; Calleja‐Hernández, Miguel Ángel

doi:10.1016/j.vph.2015.06.010

Cited by 5 publications

(5 citation statements)

References 48 publications

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“…Given the fact that these patients are typically younger, have less comorbidity and take fewer concomitant medications, we believe this algorithm might not be useful for other diseases that require anticoagulation with acenocoumarol due to the differences (mainly demographic and clinical) between patients with TVD and those with other diagnoses. Nevertheless, Jimenez-Varo et al, in a recently published study, reported that implementation of the algorithm previously designed by our group led to the highest percentage of correctly classified patients (40.7%) compared with the other algorithms published to date for acenocoumarol [ 33 ].…”

Section: Discussionmentioning

confidence: 87%

A New Pharmacogenetic Algorithm to Predict the Most Appropriate Dosage of Acenocoumarol for Stable Anticoagulation in a Mixed Spanish Population

Tong¹,

Dávila-Fajardo

Borobia

et al. 2016

PLoS ONE

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There is a strong association between genetic polymorphisms and the acenocoumarol dosage requirements. Genotyping the polymorphisms involved in the pharmacokinetics and pharmacodynamics of acenocoumarol before starting anticoagulant therapy would result in a better quality of life and a more efficient use of healthcare resources. The objective of this study is to develop a new algorithm that includes clinical and genetic variables to predict the most appropriate acenocoumarol dosage for stable anticoagulation in a wide range of patients. We recruited 685 patients from 2 Spanish hospitals and 1 primary healthcare center. We randomly chose 80% of the patients (n = 556), considering an equitable distribution of genotypes to form the generation cohort. The remaining 20% (n = 129) formed the validation cohort. Multiple linear regression was used to generate the algorithm using the acenocoumarol stable dosage as the dependent variable and the clinical and genotypic variables as the independent variables. The variables included in the algorithm were age, weight, amiodarone use, enzyme inducer status, international normalized ratio target range and the presence of CYP2C9*2 (rs1799853), CYP2C9*3 (rs1057910), VKORC1 (rs9923231) and CYP4F2 (rs2108622). The coefficient of determination (R2) explained by the algorithm was 52.8% in the generation cohort and 64% in the validation cohort. The following R2 values were evaluated by pathology: atrial fibrillation, 57.4%; valve replacement, 56.3%; and venous thromboembolic disease, 51.5%. When the patients were classified into 3 dosage groups according to the stable dosage (<11 mg/week, 11–21 mg/week, >21 mg/week), the percentage of correctly classified patients was higher in the intermediate group, whereas differences between pharmacogenetic and clinical algorithms increased in the extreme dosage groups. Our algorithm could improve acenocoumarol dosage selection for patients who will begin treatment with this drug, especially in extreme-dosage patients. The predictability of the pharmacogenetic algorithm did not vary significantly between diseases.

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Section: Discussionmentioning

confidence: 87%

A New Pharmacogenetic Algorithm to Predict the Most Appropriate Dosage of Acenocoumarol for Stable Anticoagulation in a Mixed Spanish Population

Tong¹,

Dávila-Fajardo

Borobia

et al. 2016

PLoS ONE

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“…Different acenocoumarol pharmacogenetic-guided dosing algorithms have been derived in different cohorts from different populations [ 17 , 59 – 67 ]. An observational retrospective study analyzed 8 different acenocoumarol pharmacogenetic algorithms in a cohort of 189 patients [ 65 ]. The algorithm which achieved similar doses to the real stable doses was the EU-PACT algorithm [ 17 ].…”

Section: Vitamin K Antagonist (Vka)mentioning

confidence: 99%

“…However, considering the patients with over- or under-estimation of the dose and patients correctly classified (deviation from the actual stable dose of ≤20%), the algorithm which classified most patients correctly was the Borobia algorithm [ 59 ]. This algorithm correctly classified 40.7% of the 189 patients included [ 65 ]. However, an algorithm correctly classifying this percentage of patients remains far from being used in clinical practice.…”

Section: Vitamin K Antagonist (Vka)mentioning

confidence: 99%

Pharmacogenetic studies with oral anticoagulants. Genome-wide association studies in vitamin K antagonist and direct oral anticoagulants

et al. 2018

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Oral anticoagulants (OAs) are the recommended drugs to prevent cardiovascular events and recurrence in patients with atrial fibrillation (AF) and cardioembolic stroke. We conducted a literature search to review the current state of OAs pharmacogenomics, focusing on Genome Wide Association Studies (GWAs) in patients treated with vitamin K antagonists (VKAs) and direct oral anticoagulants (DOACs).VKAs: Warfarin, acenocoumarol, fluindione and phenprocoumon have long been used, but their interindividual variability and narrow therapeutic/safety ratio makes their dosage difficult. GWAs have been useful in finding genetic variants associated with VKAs response. The main genes involved in VKAs pharmacogenetics are: VKORC1, CYP2C19 and CYP4F2. Variants in these genes have been included in pharmacogenetic algorithms to predict the VKAs dose individually in each patient depending on their genotype and clinical variables.DOACs: Dabigatran, apixaban, rivaroxaban and edoxaban have been approved for patients with AF. They have stable pharmacokinetics and do not require routine blood checks, thus avoiding most of the drawbacks of VKAs. Except for a GWAs performed in patients treated with dabigatran, there is no Genome Wide pharmacogenomics data for DOACs. Pharmacogenomics could be useful to predict the better clinical response and avoid adverse events in patients treated with anticoagulants, identifying the most appropriate anticoagulant drug for each patient. Current pharmacogenomics data show that the polymorphisms affecting VKAs or DOACs are different, concluding that personalized medicine based on pharmacogenomics could be possible. However, more studies are required to implement personalized medicine in clinical practice with OA and based on pharmacogenetics of DOACs.

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“…From one coauthor we obtained an additional dataset related to an article not previously included because no data about the CYP4F2 polymorphism were present in the original publication . From the group of 38 articles retrieved from the new search (from September 1, 2011, to September 14, 2016), individual patient data were obtained from 18 studies ( Figure ) …”

Section: Resultsmentioning

confidence: 99%

Effect of CYP4F2,VKORC1, and CYP2C9 in Influencing Coumarin Dose: A Single‐Patient Data Meta‐Analysis in More Than 15,000 Individuals

Danese

Raimondi

Montagnana

et al. 2019

Clin Pharma and Therapeutics

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The cytochrome P450 (CYP)4F2 gene is known to influence mean coumarin dose. The aim of the present study was to undertake a meta-analysis at the individual patients level to capture the possible effect of ethnicity, gene-gene interaction, or other drugs on the association and to verify if inclusion of CYP4F2*3 variant into dosing algorithms improves the prediction of mean coumarin dose. We asked the authors of our previous meta-analysis (30 articles) and of 38 new articles retrieved by a systematic review to send us individual patients' data. The final collection consists of 15,754 patients split into a derivation and validation cohort. The CYP4F2*3 polymorphism was consistently associated with an increase in mean coumarin dose (+9% (95% confidence interval (CI) 7-10%), with a higher effect in women, in patients taking acenocoumarol, and in white patients. The inclusion of the CYP4F2*3 in dosing algorithms slightly improved the prediction of stable coumarin dose. New pharmacogenetic equations potentially useful for clinical practice were derived.

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Extrapolation of acenocoumarol pharmacogenetic algorithms

Cited by 5 publications

References 48 publications

A New Pharmacogenetic Algorithm to Predict the Most Appropriate Dosage of Acenocoumarol for Stable Anticoagulation in a Mixed Spanish Population

A New Pharmacogenetic Algorithm to Predict the Most Appropriate Dosage of Acenocoumarol for Stable Anticoagulation in a Mixed Spanish Population

Pharmacogenetic studies with oral anticoagulants. Genome-wide association studies in vitamin K antagonist and direct oral anticoagulants

Effect of CYP4F2,VKORC1, and CYP2C9 in Influencing Coumarin Dose: A Single‐Patient Data Meta‐Analysis in More Than 15,000 Individuals

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