Biomedical subjects

Siv Jönsson

Publications and source records attributed to Siv Jönsson.

5 recordsLinked to original sources

PubMed2005

Estimation of dosing strategies aiming at maximizing utility or responder probability, using oxybutynin as an example drug.

Methods for optimizing dosing strategies for individualization with a limited number of discrete doses, in terms of maximizing the expected utility of treatment or responder probability, are presented. The optimality criteria require models for both beneficial and adverse effects that are part of the utility definition and published population models describing those effects for oxybutynin (urge urinary incontinence episodes per week and severity of dry mouth, respectively) were used for illustration. Dosing strategies with two dosing categories were defined in terms of sizes of the daily doses (low and high dose) and the proportion of patients that can be expected to be preferentially treated at the low dose level. Utility and responder definitions were varied to investigate the influence on the resulting dosing strategy. By minimizing a risk function, describing the seriousness of deviations from the predefined target, optimal dosing strategies were estimated using mixture models in NONMEM. The estimated dose ranges for oxybutynin were similar to those recommended. The optimal individualization conditions were dependent on the definitions of responder and utility. The predicted gain of individualization given utility and responder definitions used was greater, when a responder criteria was maximized compared with maximizing utility.

Chemistry, Pharmaceutical↗

PubMed2005

Population pharmacokinetic modelling and estimation of dosing strategy for NXY-059, a nitrone being developed for stroke.

BACKGROUND AND OBJECTIVES: NXY-059 (disufenton sodium, Cerovive, a nitrone with neuroprotective and free radical trapping properties (in experimental stroke) is under development for the treatment of acute stroke. The objectives of this study were to develop a population pharmacokinetic model for NXY-059 in acute stroke patients and to estimate individualised dosing strategies for NXY-059 using preclinical pharmacological and clinical pharmacokinetic information and knowledge of characteristics of the patient population. METHODS: NXY-059 was given as a continuous intravenous infusion for 72 hours, including a 1-hour loading infusion. Maintenance infusion rates were individualised based on creatinine clearance (CL(CR)). Population pharmacokinetic models were derived using NONMEM software. Optimal dosing strategies, individualised based on CL(CR) or bodyweight, were estimated using the population pharmacokinetic models, empirical covariate distributions relevant for the target population, and a target definition. Dosing strategies were selected based on target fulfillment criteria and parsimony. PATIENTS: Pharmacokinetic data from 179 patients with acute ischaemic or haemorrhagic stroke, included in two clinical studies, were used for the analyses. Patients were aged 34-92 years with varying degrees of renal impairment (estimated CL(CR) 20-143 mL/min). MAIN OUTCOME MEASURES AND RESULTS: The final population model based on data from both studies comprised a two-compartment model with unexplained interpatient variability for clearance (23% coefficient of variation [CV]) and central volume of distribution (40% CV). Part of the variability in clearance and volume of distribution was explained by CL(CR) and bodyweight, respectively. Typical clearance was estimated to 4.54 L/h in a patient with CL(CR) of 70 mL/min. The preferred dosing strategy for NXY-059 comprised an initial loading infusion (the same for all patients) followed by an individualised maintenance infusion on the basis of CL(CR) (three dosing categories) with cut-off values (at which infusion rates are incremented or decremented) of 50 and 80 mL/min. CONCLUSION: The results illustrate how an individualised dosing strategy, given a pharmacokinetic target, for NXY-059 was successfully optimised through estimation using the increasing pharmacokinetic and pharmacodynamic knowledge during a clinical drug development programme. The chosen dosing strategy of NXY-059 provides an easily adapted treatment regimen for acute stroke, resulting in early achievement of target plasma concentrations.

Adult↗

PubMed2004

The back-step method--method for obtaining unbiased population parameter estimates for ordered categorical data.

A significant bias in parameters, estimated with the proportional odds model using the software NONMEM, has been reported. Typically, this bias occurs with ordered categorical data, when most of the observations are found at one extreme of the possible outcomes. The aim of this study was to assess, through simulations, the performance of the Back-Step Method (BSM), a novel approach for obtaining unbiased estimates when the standard approach provides biased estimates. BSM is an iterative method involving sequential simulation-estimation steps. BSM was compared with the standard approach in the analysis of a 4-category ordered variable using the Laplacian method in NONMEM. The bias in parameter estimates and the accuracy of model predictions were determined for the 2 methods on 3 conditions: (1) a nonskewed distribution of the response with low interindividual variability (IIV), (2) a skewed distribution with low IIV, and (3) a skewed distribution with high IIV. An increase in bias with increasing skewness and IIV was shown in parameters estimated using the standard approach in NONMEM. BSM performed without appreciable bias in the estimates under the 3 conditions, and the model predictions were in good agreement with the original data. Each BSM estimation represents a random sample of the population; hence, repeating the BSM estimation reduces the imprecision of the parameter estimates. The BSM is an accurate estimation method when the standard modeling approach in NONMEM gives biased estimates.

Bias↗

PubMed2004

Estimating bias in population parameters for some models for repeated measures ordinal data using NONMEM and NLMIXED.

The application of proportional odds models to ordered categorical data using the mixed-effects modeling approach has become more frequently reported within the pharmacokinetic/pharmacodynamic area during the last decade. The aim of this paper was to investigate the bias in parameter estimates, when models for ordered categorical data were estimated using methods employing different approximations of the likelihood integral; the Laplacian approximation in NONMEM (without and with the centering option) and NLMIXED, and the Gaussian quadrature approximations in NLMIXED. In particular, we have focused on situations with non-even distributions of the response categories and the impact of interpatient variability. This is a Monte Carlo simulation study where original data sets were derived from a known model and fixed study design. The simulated response was a four-category variable on the ordinal scale with categories 0, 1, 2 and 3. The model used for simulation was fitted to each data set for assessment of bias. Also, simulations of new data based on estimated population parameters were performed to evaluate the usefulness of the estimated model. For the conditions tested, Gaussian quadrature performed without appreciable bias in parameter estimates. However, markedly biased parameter estimates were obtained using the Laplacian estimation method without the centering option, in particular when distributions of observations between response categories were skewed and when the interpatient variability was moderate to large. Simulations under the model could not mimic the original data when bias was present, but resulted in overestimation of rare events. The bias was considerably reduced when the centering option in NONMEM was used. The cause for the biased estimates appears to be related to the conditioning on uninformative and uncertain empirical Bayes estimate of interindividual random effects during the estimation, in conjunction with the normality assumption.

Bias↗

PubMed2003

A rational approach for selection of optimal covariate-based dosing strategies.

BACKGROUND: At present, there is no rational approach for choosing a dosing strategy for individualization based on a covariate. An approach to use in establishment of an a priori dosing strategy for individualization is presented. Factors influencing the choice of such a dosing strategy are identified. METHODS: The approach requires definition of the following: target variable, seriousness of deviations from the target (ie, risk function), population model, covariate distributions, and constraints. Minimizing the total risk yields an optimal dosing strategy, estimated as dose sizes for different subpopulations and covariate cutoff values at which doses are increased or decreased. The method was illustrated with the use of simulated and real drug examples for the situation in which clearance is related to creatinine clearance. RESULTS: The estimated optimal cutoff(s) paralleled the median creatinine clearance in the population. The extent of variability in clearance explained by creatinine clearance was the main factor influencing the optimal ratios between adjacent dose sizes. An optimal dosing strategy was possible to estimate for the real drug. CONCLUSIONS: The method is simple to perform, although one difficulty lies in defining the target variable and risk function. Our results imply that commonly used constraints in dosing strategies based on renal function (ie, dose ratio of 2 and predetermined cutoffs) are nonoptimal in the sense we propose. Because an optimal dosing strategy may not be practical to use, the therapeutic cost that would result with any constraint can be assessed by comparison of the outcome after the desired and the optimal strategy.

Creatinine↗