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Factors influencing pharmacokinetics of warfarin in African-Americans: implications for pharmacogenetic dosing algorithms.

ABSTRACT: AIM:This study attempted to identify predictors of S-warfarin clearance (CL[S]) and to make a pharmacokinetic evaluation of genotype-based dosing algorithms in African-Americans. METHODS:Using plasma S-warfarin concentration (Cp[S]) at a steady state and eight SNPs previously shown to influence warfarin dose in African-Americans, CL(S) and its predictors were estimated by population pharmacokinetic analysis in 60 African-Americans. The time courses of Cp(S) following either the loading dose or maintenance dose were simulated using the population pharmacokinetic estimates. RESULTS:CYP2C9*8 and body surface area or body weight were predictors of CL(S) (-30 and -5% per -0.1 m(2)/-10 kg reduction in CL[S], respectively) in African-Americans. Simulations of Cp(S) showed that Cp(S) at steady state was 1.4-times higher in patients with CYP2C9*8 than in those with CYP2C9*1/*1, irrespective of the algorithm for loading dose or maintenance dose. CONCLUSION:African-Americans possess independent predictors of CL(S), possibly leading to a prediction error of any dosing algorithm that excludes African-specific variant(s). Original submitted 3 September 2014; Revision submitted 3 November 2014.

SUBMITTER: Nagai R

PROVIDER: S-EPMC7347085 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Factors influencing pharmacokinetics of warfarin in African-Americans: implications for pharmacogenetic dosing algorithms.

Nagai Rui R Ohara Minami M Cavallari Larisa H LH Drozda Katarzyna K Patel Shitalben R SR Nutescu Edith A EA Perera Minoli A MA Hernandez Wenndy W Kaneko Naoko N Hibiya Manabu M Takahashi Harumi H

Pharmacogenomics 20150101 3

<h4>Aim</h4>This study attempted to identify predictors of S-warfarin clearance (CL[S]) and to make a pharmacokinetic evaluation of genotype-based dosing algorithms in African-Americans.<h4>Methods</h4>Using plasma S-warfarin concentration (Cp[S]) at a steady state and eight SNPs previously shown to influence warfarin dose in African-Americans, CL(S) and its predictors were estimated by population pharmacokinetic analysis in 60 African-Americans. The time courses of Cp(S) following either the lo ...[more]

PMID: 25712185

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Project description:ObjectiveMultiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared. In this literature-based study, we compared the performances of eight machine learning techniques with those of MLR in a large, racially-diverse cohort.MethodsMLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied in warfarin dose algorithms in a cohort from the International Warfarin Pharmacogenetics Consortium database. Covariates obtained by stepwise regression from 80% of randomly selected patients were used to develop algorithms. To compare the performances of these algorithms, the mean percentage of patients whose predicted dose fell within 20% of the actual dose (mean percentage within 20%) and the mean absolute error (MAE) were calculated in the remaining 20% of patients. The performances of these techniques in different races, as well as the dose ranges of therapeutic warfarin were compared. Robust results were obtained after 100 rounds of resampling.ResultsBART, MARS and SVR were statistically indistinguishable and significantly out performed all the other approaches in the whole cohort (MAE: 8.84-8.96 mg/week, mean percentage within 20%: 45.88%-46.35%). In the White population, MARS and BART showed higher mean percentage within 20% and lower mean MAE than those of MLR (all p values < 0.05). In the Asian population, SVR, BART, MARS and LAR performed the same as MLR. MLR and LAR optimally performed among the Black population. When patients were grouped in terms of warfarin dose range, all machine learning techniques except ANN and LAR showed significantly higher mean percentage within 20%, and lower MAE (all p values < 0.05) than MLR in the low- and high- dose ranges.ConclusionOverall, machine learning-based techniques, BART, MARS and SVR performed superior than MLR in warfarin pharmacogenetic dosing. Differences of algorithms' performances exist among the races. Moreover, machine learning-based algorithms tended to perform better in the low- and high- dose ranges than MLR.

Dataset Information

Factors influencing pharmacokinetics of warfarin in African-Americans: implications for pharmacogenetic dosing algorithms.

Publications

Factors influencing pharmacokinetics of warfarin in African-Americans: implications for pharmacogenetic dosing algorithms.

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