Introduction to Population Pharmacokinetic / Pharmacodynamic Analysis with Nonlinear Mixed Effects Models

Owen, Joel; Fiedler‐Kelly, Jill

doi:10.1002/9781118784860

Cited by 124 publications

(159 citation statements)

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“…Non-linear mixed effect modeling [18, 24] was applied to estimate the typical (also called mean or population) parameters and individual parameters. A normal distribution for the individual eGFR slopes eGFR Slope,i was assumed to allow negative values for 1 ≤ i ≤ n where n is the number of individuals.…”

Section: Methodsmentioning

confidence: 99%

“…For this reason a longitudinal, mathematical model was developed [18] to (i) account for individual behavior of female and male FD patients, (ii) characterize individual model parameters and the inter-individual variability, (iii) identify and quantify effects of covariates on kidney disease progression, measured as change in kidney function (eGFR) over time. In order to analyze a homogeneous group, the performed disease progression analysis focused on patients with the Classic phenotype of FD.…”

Section: Introductionmentioning

confidence: 99%

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Disease Progression Modeling to Evaluate the Effects of Enzyme Replacement Therapy on Kidney Function in Adult Patients with the Classic Phenotype of Fabry Disease

Nowak¹,

Koch²,

Huynh‐Do³

et al. 2017

Kidney Blood Press Res

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Background/Aims: Fabry disease (FD) is a rare inherited lysosomal storage disease with common and serious kidney complications. Enzyme replacement therapies (ERT) with agalsidase-α and -β were investigated to characterize their therapeutic effect on kidney function in FD patients with Classic phenotype. Methods: The prospective FD cohort consisted of 98 genetically confirmed patients (females, n = 61, males, n = 37). The median [interquartile range] follow-up time (time difference from first to last visit) was 9 [6, 12] years. The median age of ERT start was 36 [21 – 54] years for females and 39 [28 – 49] years for males. Results: A disease progression model was developed to (i) characterize the time course of estimated glomerular filtration rate (eGFR) and (ii) evaluate therapeutic effects of ERT on kidney function. Change in eGFR over time was best described by the linear model. Females had stable kidney function with and without ERT (eGFR slopes of -0.07 ml/min/1.73m^2 per year and 0.52 ml/min/1.73m^2 per year, respectively). Males with ERT showed an eGFR decrease of -3.07 ml/min/1.73m^2 per year. Conclusion: Mathematical disease progression modeling indicates that there is no clear therapeutic effect of ERT on kidney function in adult patients with Classic Phenotype of FD. Interpretation of these findings should take into account that the study is not randomized and lacks a placebo controlled group. Further investigations are warranted to clarify whether earlier ERT initiation before 18 years of age, higher ERT dose or more intensive therapies can preserve kidney function.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Disease Progression Modeling to Evaluate the Effects of Enzyme Replacement Therapy on Kidney Function in Adult Patients with the Classic Phenotype of Fabry Disease

Nowak¹,

Koch²,

Huynh‐Do³

et al. 2017

Kidney Blood Press Res

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“…Potential covariates were tested using stepwise forward selection followed by stepwise backward elimination. Changes in OFV were considered significant at p < 0.05 (Chi-square distribution, 1 df , ΔOFV > 3.84) during forward selection, and p < 0.01 (ΔOFV > 6.63) during backward elimination [28]. …”

Section: Methodsmentioning

confidence: 99%

Population Pharmacokinetics of Intravenous Paracetamol (Acetaminophen) in Preterm and Term Neonates: Model Development and External Evaluation

et al. 2015

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Objectives The aims of this study were to develop a population pharmacokinetic model for intravenous paracetamol in preterm and term neonates and to assess the generalizability of the model by testing its predictive performance in an external dataset. Methods Nonlinear mixed-effects models were constructed from paracetamol concentration–time data in NONMEM 7.2. Potential covariates included body weight, gestational age, postnatal age, postmenstrual age, sex, race, total bilirubin, and estimated glomerular filtration rate. An external dataset was used to test the predictive performance of the model through calculation of bias, precision, and normalized prediction distribution errors. Results The model-building dataset included 260 observations from 35 neonates with a mean gestational age of 33.6 weeks [standard deviation (SD) 6.6]. Data were well-described by a one-compartment model with first-order elimination. Weight predicted paracetamol clearance and volume of distribution, which were estimated as 0.348 L/h (5.5 % relative standard error; 30.8 % coefficient of variation) and 2.46 L (3.5 % relative standard error; 14.3 % coefficient of variation), respectively, at the mean subject weight of 2.30 kg. An external evaluation was performed on an independent dataset that included 436 observations from 60 neonates with a mean gestational age of 35.6 weeks (SD 4.3). The median prediction error was 10.1 % [95 % confidence interval (CI) 6.1–14.3] and the median absolute prediction error was 25.3 % (95 % CI 23.1–28.1). Conclusions Weight predicted intravenous paracetamol pharmacokinetics in neonates ranging from extreme preterm to full-term gestational status. External evaluation suggested that these findings should be generalizable to other similar patient populations.

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“…A classical pharmacokinetic approach to dealing with such datasets is to combine multi-compartment ordinary differential equation model with a mixed effects statistical model of interperson variation ( Beal and Sheiner, 1982;Owen and Fiedler-Kelly, 2014 ). The mean ("fixed effect") and standard deviation ("random effect") associated with the rate constants representing reactions between prednisone and prednisolone then provide measures of central tendency and variability in 11 β-HSD1/2 activity through the population under study.…”

Section: Introductionmentioning

confidence: 99%

Learning pharmacokinetic models for in vivo glucocorticoid activation

Bunte

Smith

Chappell

et al. 2018

Journal of Theoretical Biology

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In vivo glucocorticoid activation Probabilistic models Gaussian mixture model Expectation maximization Clustering Partially observed time series analysis a b s t r a c tTo understand trends in individual responses to medication, one can take a purely data-driven machine learning approach, or alternatively apply pharmacokinetics combined with mixed-effects statistical modelling. To take advantage of the predictive power of machine learning and the explanatory power of pharmacokinetics, we propose a latent variable mixture model for learning clusters of pharmacokinetic models demonstrated on a clinical data set investigating 11 β-hydroxysteroid dehydrogenase enzymes (11 β-HSD) activity in healthy adults. The proposed strategy automatically constructs different population models that are not based on prior knowledge or experimental design, but result naturally as mixture component models of the global latent variable mixture model. We study the parameter of the underlying multi-compartment ordinary differential equation model via identifiability analysis on the observable measurements, which reveals the model is structurally locally identifiable. Further approximation with a perturbation technique enables efficient training of the proposed probabilistic latent variable mixture clustering technique using Estimation Maximization. The training on the clinical data results in 4 clusters reflecting the prednisone conversion rate over a period of 4 h based on venous blood samples taken at 20-min intervals. The learned clusters differ in prednisone absorption as well as prednisone/prednisolone conversion. In the discussion section we include a detailed investigation of the relationship of the pharmacokinetic parameters of the trained cluster models for possible or plausible physiological explanation and correlations analysis using additional phenotypic participant measurements.

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Introduction to Population Pharmacokinetic / Pharmacodynamic Analysis with Nonlinear Mixed Effects Models

Cited by 124 publications

References 0 publications

Disease Progression Modeling to Evaluate the Effects of Enzyme Replacement Therapy on Kidney Function in Adult Patients with the Classic Phenotype of Fabry Disease

Disease Progression Modeling to Evaluate the Effects of Enzyme Replacement Therapy on Kidney Function in Adult Patients with the Classic Phenotype of Fabry Disease

Population Pharmacokinetics of Intravenous Paracetamol (Acetaminophen) in Preterm and Term Neonates: Model Development and External Evaluation

Learning pharmacokinetic models for in vivo glucocorticoid activation

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