A ‘bottom‐up’ approach for endo‐PK/PD analysis

Neelakantan, Srividya; Widness, John A.; Schmidt, Robert L.; Veng‐Pedersen, Peter

doi:10.1002/bdd.508

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…This analysis resulted in a subset of individual ESA responsiveness measures being negative, suggesting that this approach may not correctly represent the physiological individual responsiveness for at least a subset of the population. 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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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: 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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“…7. A “bottom-up” approach was used to estimate the value of the environmental effect functions without creating a functional relationship to any measured variables [24], since the exact mechanism that causes the reduced lifespan of the RBCs is unknown. The environmental effect functions, g ( t ) and h ( t ), were semi-parametrically represented and assumed to be equal to 1 for t ≤ t 0 , and then follow a positive and continuous linear spline with constant forward extrapolation beyond the last node.…”

Section: Methodsmentioning

confidence: 99%

Pharmacodynamic modeling of the effect of changes in the environment on cellular lifespan and cellular response

Freise

Schmidt

Widness

et al. 2008

J Pharmacokinet Pharmacodyn

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Lifespan-based pharmacodynamic (PD) models of cellular response assume that the lifespan of cells is predetermined at the time of cellular production, despite recognized changes in the cellular environment following production that may alter the survival of the cells. This work extends previously proposed cellular lifespan PD models to incorporate environmental effects on the cell lifespan by considering two basic classes of models from survival analysis: accelerated life and relative risk models. Cellular responses using both model classes were simulated using a steady-state cellular production rate with changes in the environmental effects resulting from three different basic profiles. The environmental effect models were also fitted to the red blood cell (RBC) and hemoglobin concentration data from six sheep following hematopoietic ablation by busulfan administration. The simulations indicated that the basic shapes of the cellular responses were different between the accelerated life and relative risk models. Due to the more direct physical interpretation, relatively simple steady-state relationship between the cellular response and environmental effects, and the ability to reduce the model to a "point" baseline lifespan distribution, the accelerated life model appears to be a more realistic and flexible model. The analysis of the sheep RBC and hemoglobin data indicated that the environmental effect began to decrease the survival of cells 1-2 weeks following initiation of ablation and that the average "severity" of the environmental effect increased 3.49 (29.5%) (mean (C.V.)) fold under the accelerated life model. Alternative models without an environmental effect did not describe the observed data as well. The proposed environmental effect cellular lifespan PD models allow for the incorporation of arbitrary changes in the conditions of the cellular environment and modeling of environmentally dependent cellular survival. These PD models have potential applications in hematological management of end-stage renal disease, transfusion medicine, and patients undergoing chemotherapy, among other diseases and therapies.

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Quantitative Analysis of Mechanisms That Govern Red Blood Cell Age Structure and Dynamics during Anaemia

2009

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Mathematical modelling has proven an important tool in elucidating and quantifying mechanisms that govern the age structure and population dynamics of red blood cells (RBCs). Here we synthesise ideas from previous experimental data and the mathematical modelling literature with new data in order to test hypotheses and generate new predictions about these mechanisms. The result is a set of competing hypotheses about three intrinsic mechanisms: the feedback from circulating RBC concentration to production rate of immature RBCs (reticulocytes) in bone marrow, the release of reticulocytes from bone marrow into the circulation, and their subsequent ageing and clearance. In addition we examine two mechanisms specific to our experimental system: the effect of phenylhydrazine (PHZ) and blood sampling on RBC dynamics. We performed a set of experiments to quantify the dynamics of reticulocyte proportion, RBC concentration, and erythropoietin concentration in PHZ-induced anaemic mice. By quantifying experimental error we are able to fit and assess each hypothesis against our data and recover parameter estimates using Markov chain Monte Carlo based Bayesian inference. We find that, under normal conditions, about 3% of reticulocytes are released early from bone marrow and upon maturation all cells are released immediately. In the circulation, RBCs undergo random clearance but have a maximum lifespan of about 50 days. Under anaemic conditions reticulocyte production rate is linearly correlated with the difference between normal and anaemic RBC concentrations, and their release rate is exponentially correlated with the same. PHZ appears to age rather than kill RBCs, and younger RBCs are affected more than older RBCs. Blood sampling caused short aperiodic spikes in the proportion of reticulocytes which appear to have a different developmental pathway than normal reticulocytes. We also provide evidence of large diurnal oscillations in serum erythropoietin levels during anaemia.

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A ‘bottom‐up’ approach for endo‐PK/PD analysis

Cited by 3 publications

References 34 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

Pharmacodynamic modeling of the effect of changes in the environment on cellular lifespan and cellular response

Quantitative Analysis of Mechanisms That Govern Red Blood Cell Age Structure and Dynamics during Anaemia

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