Edoxaban Exposure‐Response Analysis and Clinical Utility Index Assessment in Patients With Symptomatic Deep‐Vein Thrombosis or Pulmonary Embolism

Nyberg, Joakim; Karlsson, Kristin; Jönsson, Siv; Yin, Ophelia; Miller, Raymond; Karlsson, Mats O.; Simonsson, Ulrika S. H.

doi:10.1002/psp4.12077

Cited by 11 publications

(7 citation statements)

References 22 publications

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“…Under the proportional hazard assumption, a hazard ratio can be calculated by exponentiating the coefficient. 18 Alternatively, a covariate/treatment can affect the hazard function's individual parameters for which the application has a relative input per parameter. Both slider and numerical Shiny input widgets are built into the application for function parameters and covariate/ treatment effect as well as for the simulation time to give the user full control.…”

Section: Exploration Of Hazard Function Parameter Valuesmentioning

confidence: 99%

“…The Gompertz model reduces to the exponential model for γ = 0. Its application in pharmacometrics include the characterization of the time to recurrent venous thromboembolism, acute deep vein thrombosis, and pulmonary embolism 18 ; the time to analgesic remedication after surgery, 19–21 which can be expected to decrease with time after surgery; and the time to recurrent Clostridium difficile infection 22 …”

Section: Hazard Functions For Parametric Time‐to‐event Analysismentioning

confidence: 99%

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Finding the right hazard function for time‐to‐event modeling: A tutorial and Shiny application

Wijk

Simonsson

2022

CPT Pharmacom & Syst Pharma

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Parametric time‐to‐event analysis is an important pharmacometric method to predict the probability of an event up until a certain time as a function of covariates and/or drug exposure. Modeling is performed at the level of the hazard function describing the instantaneous rate of an event occurring at that timepoint. We give an overview of the parametric time‐to‐event analysis starting with graphical exploration by Kaplan–Meier plotting for the event data including censoring and nonparametric hazard estimators such as the kernel‐based visual hazard comparison for the underlying hazard. The most common hazard functions including the exponential, Gompertz, Weibull, log‐normal, log‐logistic, and circadian functions are described in detail. A Shiny application was developed to graphically guide the modeler which of the most common hazard functions presents a similar shape compared to the data in order to guide which hazard functions to test in the parametric time‐to‐event analysis. For the chosen hazard function(s), the Shiny application can additionally be used to explore corresponding parameter values to inform on suitable initial estimates for parametric modeling as well as on possible covariate or treatment relationships to certain parameters. Moreover, it can be used for the dissemination of results as well as communication, training, and workshops on time‐to‐event analysis. By guiding the modeler on which functions and what parameter values to test and compare as well as to assist in dissemination, the Shiny application developed here greatly supports the modeler in complicated parametric time‐to‐event modeling.

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Section: Exploration Of Hazard Function Parameter Valuesmentioning

confidence: 99%

Section: Hazard Functions For Parametric Time‐to‐event Analysismentioning

confidence: 99%

Finding the right hazard function for time‐to‐event modeling: A tutorial and Shiny application

Wijk

Simonsson

2022

CPT Pharmacom & Syst Pharma

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“…74,75 In patients with moderate renal impairment who were given a dose reduction (30 mg OD) in the Hokusai-VTE study, clinically relevant bleeding was lower than in the 60-mg OD dose group (7.91% and 8.60%, respectively), although recurrent VTE occurred more frequently (1.77% and 1.57%, respectively). 76 A third modeling analysis determined that in patients with severe renal impairment, a dose reduction to 30 mg OD resulted in similar predicted exposure levels to those of patients with normal or mild renal impairment receiving the standard 60-mg OD dose. 77 Clinical data supporting dose reduction come from a 12-week openlabel study in which Japanese patients with nonvalvular AF and severe renal impairment received edoxaban 15 mg OD and patients with normal renal function or mild renal impairment were randomized to receive edoxaban 30 or 60 mg OD.…”

Section: Influence Of Renal Function On Edoxaban Pk/pdmentioning

confidence: 99%

Influence of Renal Function on the Pharmacokinetics, Pharmacodynamics, Efficacy, and Safety of Non–Vitamin K Antagonist Oral Anticoagulants

Weir

Kreutz

2018

Mayo Clinic Proceedings

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With the growing integration of nonevitamin K antagonist oral anticoagulants (NOACs) into clinical practice, questions have arisen regarding their use in special populations, including groups that may have been underrepresented in clinical trials. Patients with renal impairment, particularly in the lower echelons of renal function, are one such group. In an effort to elucidate the current evidence regarding the use of NOACs in patients with renal impairment, a systematic assessment of the literature was performed. The MEDLINE database was interrogated for studies and analyses evaluating the influence of renal function on the pharmacokinetics, pharmacodynamics, efficacy, and safety of NOACs published from January 1, 2000, through August 2, 2017. The 82 relevant publications retrieved highlight the diversity in the NOAC class regarding the impact of renal function on drug clearance, drug exposures, and clinical trial outcomes. In several large clinical trials, subgroup analyses revealed no significant differences when patients were stratified by creatinine clearance as a measure of renal function. Efficacy findings, in particular, were largely aligned with the overall population in the included studies. However, relative risks of bleeding were shown to vary, sometimes driven by changes in bleeding event rates in the comparator arm (eg, warfarin, enoxaparin). With few exceptions, minimal influence of mild renal impairment was observed on the relative efficacy and safety of NOACs. Taken together, the evidence suggests that the presence of renal impairment merits careful consideration of anticoagulant choice but should not deter physicians from appropriate use of NOACs.

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“…Similar to the current findings with rivaroxaban, those analyses supported administration of edoxaban and apixaban at approved doses without routine therapeutic drug monitoring. 14–17…”

Section: Discussionmentioning

confidence: 99%

Influence of model-predicted rivaroxaban exposure and patient characteristics on efficacy and safety outcomes in patients with acute coronary syndrome

Zhang

Yan

Nandy

et al. 2019

Therapeutic Advances in Cardiovascular Disease

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Background: This analysis aimed to evaluate the impact of rivaroxaban exposure and patient characteristics on efficacy and safety outcomes in patients with acute coronary syndrome (ACS) and to determine whether therapeutic drug monitoring might provide additional information regarding rivaroxaban dose, beyond what patient characteristics provide. Methods: A post hoc exposure–response analysis was conducted using data from the phase III ATLAS ACS 2 Thrombolysis in Myocardial Infarction (TIMI) 51 study, in which 15,526 randomized ACS patients received rivaroxaban (2.5 mg or 5 mg twice daily) or placebo for a mean of 13 months (maximum follow up: 31 months). A multivariate Cox model was used to correlate individual predicted rivaroxaban exposures and patient characteristics with time-to-event clinical outcomes. Results: For the incidence of myocardial infarction (MI), ischemic stroke, or nonhemorrhagic cardiovascular death, hazard ratios (HRs) for steady-state maximum plasma concentration (Cmax) in the 5th and 95th percentiles versus the median were statistically significant but close to 1 for both rivaroxaban doses. For TIMI major bleeding events, a statistically significant association was observed with Cmax [HR, 1.08; 95% CI, 1.06–1.11 (95th percentile versus median, 2.5 mg twice daily)], sex [HR, 0.56; 95% CI, 0.38–0.84 (female versus male)], and previous revascularization [HR, 0.62; 95% CI, 0.44–0.87 (no versus yes)]. Conclusions: The shallow slopes of the exposure–response relationships and the lack of a clear therapeutic window render it unlikely that therapeutic drug monitoring in patients with ACS would provide additional information regarding rivaroxaban dose beyond that provided by patient characteristics.

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Edoxaban Exposure‐Response Analysis and Clinical Utility Index Assessment in Patients With Symptomatic Deep‐Vein Thrombosis or Pulmonary Embolism

Cited by 11 publications

References 22 publications

Finding the right hazard function for time‐to‐event modeling: A tutorial and Shiny application

Finding the right hazard function for time‐to‐event modeling: A tutorial and Shiny application

Influence of Renal Function on the Pharmacokinetics, Pharmacodynamics, Efficacy, and Safety of Non–Vitamin K Antagonist Oral Anticoagulants

Influence of model-predicted rivaroxaban exposure and patient characteristics on efficacy and safety outcomes in patients with acute coronary syndrome

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