Assessment of hemoglobin responsiveness to epoetin alfa in patients on hemodialysis using a population pharmacokinetic pharmacodynamic model

Wu, Lei; Mould, Diane R.; Pérez‐Ruixo, Juan José; Doshi, Sameer

doi:10.1002/jcph.527

Cited by 5 publications

(23 citation statements)

References 50 publications

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“…Pharmacokinetic/pharmacodynamic modeling of erythropoiesis 22,26-28 could be used for deriving dose-response curves, however, relative standard errors of 50% are not uncommon in PKPD model parameter estimates. 29 A limitation of our study is its observational, retrospective nature, and its focus on incident patients. It is also possible that anemia management practices at dialysis facilities other than Fresenius, over different time periods, or in prevalent hemodialysis populations, may result in different CV(EPO/wt)/CV(Hgb) ratios.…”

Section: Discussionmentioning

confidence: 98%

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The greatly misunderstood erythropoietin resistance index and the case for a new responsiveness measure

Chait

Kalim

Horowitz

et al. 2016

Hemodialysis International

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Background The optimal use of erythropoiesis stimulating agents (ESAs) to treat anemia in end stage renal disease (ESRD) remains controversial due to reported associations with adverse events. In analyzing these associations, studies often utilize ESA resistance indices (ERIs), to characterize a patient’s response to ESA. In this study, we examine whether ERI is an adequate measure of ESA resistance. Methods We used retrospective data from a non-concurrent cohort study of incident hemodialysis patients in the United States (n=9386). ERI is defined as average weekly erythropoietin (EPO) dose per kg body weight (wt) per average hemoglobin (Hgb), over a 3-month period (ERI = (EPO/wt)/Hgb). Linear regression was used to demonstrate the relationship between ERI and weight-adjusted EPO. The coefficient of variation (CV) was used to compare the variability of Hgb with that of weight-adjusted EPO in order to explain this relationship. This analysis was done for each quarter during the first year of dialysis. Results ERI is strongly linearly related with weight-adjusted EPO dose in each of the 4 quarters by the equation ERI = 0.0899*(EPO/wt) (range of R2 = 0.97–0.98) and weakly linearly related to 1/Hgb (range of R2 = 0.06–0.16). These correlations hold independent of age, sex, hgb level, ERI level, and epo-naïve stratifications. Conclusions ERI is strongly linearly related to weight-adjusted (and non-weight-adjusted) EPO dose by a “universal”, not patient-specific formula, and thus is a surrogate of EPO dose. Therefore, associations between ERI and clinical outcomes are associations between a confounded EPO dose and those outcomes.

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Section: Discussionmentioning

confidence: 98%

“…Pharmacokinetic/pharmacodynamic modelling of erythropoiesis 12,26–28 could be used for deriving dose-response curves, however, relative standard errors of 50% are not uncommon in PKPD model parameter estimates. 29 …”

Section: Discussionmentioning

confidence: 99%

The greatly misunderstood erythropoietin resistance index and the case for a new responsiveness measure

Chait

Kalim

Horowitz

et al. 2016

Hemodialysis International

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“…The reason was that HGB data (data A, part 1) collected from clinical practice were sparse with median of 5 observations per patient over 12 weeks (range: 3-12 observations) and baseline measurements were not available. The Bayesian approach allowed for incorporating informative prior distributions of the PD parameters from Wu et al 10 with similar patient population. In addition, the uncertainty of the parameters was accounted for by specifying the SDs of the priors.…”

Section: Discussionmentioning

confidence: 99%

“…As described in the model for the priors, we specified larger SDs for the priors than the results from Wu et al 10 (6.7% vs. 2.3%, 10.5% vs. 3.6%, and 29.8% vs. 6.8% for T RBC , S max , and SC 50 , respectively; and 21.1% for HBG IN ). These assignments create larger parameter space but less weight for the priors and more weight for the data to inform the parameters.…”

Section: Discussionmentioning

confidence: 99%

“…Structural PK/PD model Because drug concentrations were not available, a validated population PK model of epoetin alfa in healthy subjects 9 was utilized to simulate PK profiles of the patients. The simulated data were then used in an established PD model 10 to describe HGB response to the epoetin alfa treatment. The resulting PK/ PD model and parameters are presented in Figure 1.…”

Section: Data Setsmentioning

confidence: 99%

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Population Pharmacodynamic Modeling of Epoetin Alfa in End‐Stage Renal Disease Patients Receiving Maintenance Treatment Using Bayesian Approach

Nguyen

Meaney

Rao

et al. 2020

CPT Pharmacom & Syst Pharma

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The ability to control dosage regimens of erythropoiesis-stimulating agents (ESAs) to maintain a desired hemoglobin (HGB) target is still elusive. We utilized a Bayesian approach and informative priors to characterize HGB profiles, using simulated drug concentrations, in patients with end-stage renal disease receiving maintenance doses of epoetin alfa. We also demonstrated an adaptive Bayesian method, applied to individual patients, to improve the accuracy of HGB predictions over time. The results showed that sparse HGB data from daily clinical practice were characterized successfully. The adaptive Bayesian method effectively improved the accuracy of HGB predictions by updating the individual model with new data accounting for within-subject changes over time. The Bayesian approach presented leverages existing knowledge of the model parameters and has a potential utility in clinical practice to individualize dosage regimens of epoetin alfa and ESAs to achieve target HGB. Further studies are warranted to develop an application for practical use. Anemia is a common complication of end-stage renal disease (ESRD) associated with fatigue, weakness, dyspnea, increased morbidity, and mortality. 1 Recombinant human erythropoietin (EPO; epoetin alfa) is the first erythropoiesis-stimulating agent (ESA) introduced late 1980s for the treatment of anemia in patients with chronic kidney disease. 1 Since then, it has revolutionized anemia management, helping to prevent or minimize the use of blood transfusions and improve quality of life. 2,3 Despite its long history, the ability to control dosage regimen of epoetin alfa as well as other ESAs to maintain a target hemoglobin (HGB) concentration of 10-12 g/dL is still elusive. 4 HGB response is often observed with large oscillations and overshoots out of the desired range. 5,6 Furthermore, using high doses of ESAs to achieve the target HGB has been found associated with increased risks of death and cardiovascular events. 1 Therefore, individualized dosage regimen is recommended but sufficient guidelines are not available. 7,8 Extensive studies have been conducted in healthy volunteers and patients to characterize pharmacokinetics (PKs) and pharmacodynamics (PDs) of epoetin alfa. 9-13 However, the potential utilities of these studies have not been realized in clinical practice to individualize dosage regimen. There are challenges in bringing established PK/PD models into clinical use. These models are developed using rich data, including baseline information from individual subjects. However, in practice, only sparse HGB observations are collected, often once or twice a month, and PK data include only dose amounts without drug

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Understanding Hematological Toxicities Using Mathematical Modeling

Fornari

O’Connor

Yates

et al. 2018

Clin Pharma and Therapeutics

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Balancing antitumor efficacy with toxicity is a significant challenge, and drug-induced myelosuppression is a common dose-limiting toxicity of cancer treatments. Mathematical modeling has proven to be a powerful ally in this field, scaling results from animal models to humans, and designing optimized treatment regimens. Here we outline existing mathematical approaches for studying bone marrow toxicity, identify gaps in current understanding, and make future recommendations to advance this vital field of safety research further.

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Assessment of hemoglobin responsiveness to epoetin alfa in patients on hemodialysis using a population pharmacokinetic pharmacodynamic model

Cited by 5 publications

References 50 publications

The greatly misunderstood erythropoietin resistance index and the case for a new responsiveness measure

The greatly misunderstood erythropoietin resistance index and the case for a new responsiveness measure

Population Pharmacodynamic Modeling of Epoetin Alfa in End‐Stage Renal Disease Patients Receiving Maintenance Treatment Using Bayesian Approach

Understanding Hematological Toxicities Using Mathematical Modeling

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