Julia Korell

An Approach for Identifiability of Population Pharmacokinetic–Pharmacodynamic Models

Mathematical models are routinely used in clinical pharmacology to study the pharmacokinetic and pharmacodynamic properties of a drug in the body. Identifiability of these models is an important requirement for the success of these clinical studies. Identifiability is classified into two types, structural identifiability related to the structure of the mathematical model and deterministic identifiability which is related to the study design. There are existing approaches for assessment of structural identifiability of fixed‐effects models, although their use appears uncommon in the literature. In this study, we develop an informal unified approach for simultaneous assessment of structural and deterministic identifiability for fixed and mixed‐effects pharmacokinetic or pharmacokinetic–pharmacodynamic models. This approach uses an information theoretic framework. The method is applied both to simple examples to explore known identifiability properties and to a more complex example to illustrate its utility.

A statistical model for red blood cell survival

A statistical model for the survival time of red blood cells (RBCs) with a continuous distribution of cell lifespans is presented. The underlying distribution of RBC lifespans is derived from a probability density function with a bathtub-shaped hazard curve, and accounts for death of RBCs due to senescence (age-dependent increasing hazard rate) and random destruction (constant hazard), as well as for death due to initial or delayed failures and neocytolysis (equivalent to early red cell mortality). The model yields survival times similar to those of previously published studies of RBC survival and is easily amenable to inclusion of drug effects and haemolytic disorders.

Modeling red blood cell survival data.

Anaemia of chronic kidney disease (CKD) is a common complication in patients with renal impairment, especially in end-stage renal failure. As well as erythropoietin deficiency, decreased red blood cell survival is a contributing factor. However, it remains unclear which mechanism underlies the altered survival of red blood cells (RBCs). In this work a previously developed statistical model for RBC survival was applied to clinical data obtained from 14 patients with CKD undergoing hemodialysis as well as 14 healthy controls using radioactive chromium (51Cr) as random labelling method. A classical two-stage approach and a full population analysis were applied to estimate the key parameters controlling random destruction and senescence in the model. Estimating random destruction was preferred over estimating an accelerated senescence in both approaches and both groups as it provided the better fit to the data. Due to significant nonspecific random loss of the label from the cells that cannot be quantified directly only a relative RBC survival can be obtained from data using 51Cr as labelling method. Nevertheless, RBC survival was found to be significantly reduced in CKD patients compared to the controls with a relative reduction of 20–30% depending on the analysis method used.

A Population Pharmacokinetic Model for Low-Dose Methotrexate and its Polyglutamated Metabolites in Red Blood Cells

BackgroundMeasurement of intracellular concentrations of methotrexate (MTX) and its polyglutamated metabolites (MTXGlu2–5) in red blood cells (RBCs) has been suggested as a potential means of monitoring low-dose MTX treatment of rheumatoid arthritis (RA). However, a possible correlation between RBC MTX and MTXGlu2–5 concentrations and clinical outcomes of MTX treatment in RA is debated. A better understanding of the dose-concentration–time relationship of MTX and MTXGlu2–5 in RBCs by population pharmacokinetic modelling is desirable and will facilitate assessing a potential RBC concentration–effect relationship in the future.AimThe purpose of this analysis was to describe the pharmacokinetics of MTX and MTXGlu2–5 in RBCs. Secondary objectives included investigation of deglutamation reactions and the loss of MTX and MTXGlu2–5 from the RBC.MethodsA model was developed using NONMEM® version 7.2 based on RBC data obtained from 48 patients with RA receiving once-weekly low-dose MTX treatment. This model was linked to a fixed two-compartment model that was used to describe the pharmacokinetics of MTX in the plasma. A series of five compartments were used to describe the intracellular pharmacokinetics of MTX and MTXGlu2–5 in RBCs. Biologically plausible covariates were tested for a significant effect on MTX plasma clearance and the intracellular volume of distribution of all MTX species in RBCs (

Evaluation of red blood cell labelling methods based on a statistical model for red blood cell survival

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Determination of Plasma Concentration Reference Ranges for Risperidone and Paliperidone.

). The developed model was used to test hypotheses related to the enzymatic deglutamation of MTXGlu2–5 and potential loss of MTXGlu2–5 from RBCs.ResultsThe final RBC pharmacokinetic model required the intracellular volumes of distribution for the parent and metabolites to be set to the value estimated for the parent drug MTX alone, and the rate constants describing the polyglutamation steps were fixed at literature values. Significant covariates included effect of body surface area-adjusted estimated glomerular filtration rate on renal plasma clearance and effect of allometrically scaled total body weight with a fixed exponent of 0.75 on non-renal plasma clearance of MTX. The only significant covariate with an effect on

Optimizing Antipsychotic Patient Management Using Population Pharmacokinetic Models and Point‐of‐Care Testing

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