PURPOSE: The aim was to determine the metabolic fingerprint that predicts warfarin response based on the international normalized ratio (INR) in patients who are already receiving warfarin (phase I: identification) and to ascertain the metabolic fingerprint that discriminates stable from unstable INR in patients starting treatment with warfarin (phase II: validation).
EXPERIMENTAL APPROACH: A total of 94 blood samples were collected for phase I: 44 patients with stable INR and 50 with unstable INR. Meanwhile, 23 samples were collected for phase II: nine patients with stable INR and 14 with unstable INR. Data analysis was performed using multivariate analysis including principal component analysis and partial least square-discriminate analysis (PLS-DA), followed by univariate and multivariate logistic regression (MVLR) to develop a model to identify unstable INR biomarkers.
KEY RESULTS: For phase I, the PLS-DA model showed the following results: sensitivity 93.18%, specificity 91.49% and accuracy 92.31%. In the MVLR analysis of phase I, ten regions were associated with unstable INR. For phase II, the PLS-DA model showed the following results: sensitivity 66.67%, specificity 61.54% and accuracy 63.64%.
CONCLUSIONS AND IMPLICATIONS: We have shown that the pharmacometabonomics technique was able to differentiate between unstable and stable INR with good accuracy. NMR-based pharmacometabonomics has the potential to identify novel biomarkers in plasma, which can be useful in individualizing treatment and controlling warfarin side effects, thus, minimizing undesirable effects in the future.
AREAS COVERED: A review of articles that evaluated the cost of prescribing conventional (e.g. vitamin K antagonists) and NOACs (e.g. direct thrombin inhibitors and direct factor Xa inhibitors) in older adults.
EXPERT COMMENTARY: While the use of NOACs significantly increases the cost of the initial treatment for thromboembolic disorders, they are still considered cost-effective relative to warfarin since they offer reduced risk of intracranial haemorrhagic events. The optimum anticoagulation with warfarin can be achieved by providing specialised care; clinics managed by pharmacists have been shown to be cost-effective relative to usual care. There are suggestions that genotyping the CYP2C9 and VKORC1 genes is useful for determining a more appropriate initial dose and thereby increasing the effectiveness and safety of warfarin.
METHODS: An open-label, non-inferiority, 1:1 randomized trial was conducted at three academic hospitals in South East Asia, involving 322 ethnically diverse patients newly indicated for warfarin (NCT00700895). Clinical follow-up was 90 days. The primary efficacy measure was the number of dose titrations within the first 2 weeks of therapy, with a mean non-inferiority margin of 0.5 over the first 14 days of therapy.
RESULTS: Among 322 randomized patients, 269 were evaluable for the primary endpoint. Compared with traditional dosing, the genotype-guided group required fewer dose titrations during the first 2 weeks (1.77 vs. 2.93, difference -1.16, 90% CI -1.48 to -0.84, P warfarin therapy, a pharmacogenetic algorithm meets criteria for both non-inferiority and superiority in reducing dose titrations compared with a traditional dosing approach, and performs well in prediction of actual maintenance doses. These findings imply that clinicians may consider applying a pharmacogenetic algorithm to personalize initial warfarin dosages in Asian patients.
TRIAL REGISTRATION: ClinicalTrials.gov NCT00700895 . Registered on June 19, 2008.
OBJECTIVES: The aim of this study was to describe trends in warfarin dosing after Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG).
SETTING: Single academic center.
METHODS: All patients chronically on warfarin anticoagulation before RYGB or SG were retrospectively identified. Indications for anticoagulation, history of bleeding or thrombotic events, perioperative complications, and warfarin dosing were collected.
RESULTS: Fifty-three patients (RYGB n = 31, SG n = 22) on chronic warfarin therapy were identified (56.6% female, mean 54.4 ± 11.7 yr of age). Of this cohort, 34.0% had prior venous thromboembolic events, 43.4% had atrial fibrillation, and 5.7% had mechanical cardiac valves. Preoperatively, the average daily dose of warfarin was similar in the RYGB group (8.3 ± 4.1 mg) and SG group (6.9 ± 2.8 mg). One month after surgery, mean daily dose of warfarin was reduced 24.1% in the RYGB group (Pwarfarin dose compared with baseline remained statistically different (RYGB: 6.8 ± 3.8 mg; SG: 6.1 ± 2.0 mg).
CONCLUSIONS: The warfarin dose is expected to be decreased by approximately 25% from preoperative levels after both RYGB and SG. Lower dose requirement within the first month after bariatric surgery is followed by a trend toward increased warfarin dose requirements, but remain less than baseline. Because dose requirements change constantly over time, frequent postoperative monitoring of the international normalized ratio is recommended.
METHODS: The warfarin maintenance doses for 140 patients were predicted using the dosing tool and compared with the observed maintenance dose. The impact of genotype was assessed by predicting maintenance doses with prior parameter values known to be altered by genetic variability (eg, EC50 for VKORC1 genotype). The prior population was evaluated by fitting the published kinetic-pharmacodynamic model, which underpins the Bayesian tool, to the observed data using NONMEM and comparing the model parameter estimates with published values.
RESULTS: The Bayesian tool produced positively biased dose predictions in the new cohort of patients (mean prediction error [95% confidence interval]; 0.32 mg/d [0.14-0.5]). The bias was only observed in patients requiring ≥7 mg/d. The direction and magnitude of the observed bias was not influenced by genotype. The prior model provided a good fit to our data, which suggests that the bias was not caused by different prior and posterior populations.
CONCLUSIONS: Maintenance doses for patients requiring ≥7 mg/d were overpredicted. The bias was not due to the influence of genotype nor was it related to differences between the prior and posterior populations. There is a need for a more mechanistic model that captures warfarin dose-response relationship at higher warfarin doses.