Biomedical subjects

E N Jonsson

Publications and source records attributed to E N Jonsson.

12 recordsLinked to original sources

PubMed2006

Diurnal variation in serum levels of cartilage oligomeric matrix protein in patients with knee osteoarthritis or rheumatoid arthritis.

OBJECTIVE: To monitor changes in serum concentrations of cartilage oligomeric matrix protein (COMP) during a 24-h period to determine any diurnal variation, and to estimate the half life of COMP in the circulation in patients with symptomatic knee osteoarthritis and in those with rheumatoid arthritis. METHODS: Serum samples were drawn every 4 h (7 samples/patient over 24 h) in 10 patients with knee osteoarthritis and 14 patients with rheumatoid arthritis. Osteoarthritis was defined radiographically and clinically (American College of Rheumatology (ACR) criteria) and rheumatoid arthritis according to the 1987 ACR criteria. Serum COMP was measured by sandwich ELISA. A statistical model for the diurnal variation in the COMP levels was developed using the computer program NONMEM. RESULTS: No considerable changes in COMP levels were observed during the day between 08:00 and 21:00 in either group. A significant decrease in serum COMP was apparent during bed rest at night, reaching the lowest levels between 04:00 and 05:00 (p<0.03 or better v all other time points) in patients with osteoarthritis and in those with rheumatoid arthritis. From the rate of decreasing serum COMP levels, a putative half life of COMP in the circulation was estimated to be 7.4 h. CONCLUSION: During normal daytime activities, serum COMP levels are constant. The decrease during the night indicates a rapid elimination of COMP once it has reached the circulation. The stable COMP levels during the day suggest that it is not necessary to further standardise the time of serum sampling in clinical practice.

Arthritis, Rheumatoid↗

PubMed2001

Probing sponge-derived terpenoids for human 15-lipoxygenase inhibitors.

A human 15-lipoxygenase (15-HLO) assay has been employed to discover new marine-sponge-derived bioactive compounds. Extracts from two different sponges, Jaspis splendens (order Choristida, family Jaspidae) and Suberea sp. (order Verongida, family Aplysinellidae), exhibited potent IC(50) values of 0.4 and 0.1 microg/mL, respectively. Both are sources of terpenoids, and the former is a known source of (+)-jasplakinolide (7), which is inactive as a 15-HLO inhibitor. The terpenoids included (+)-(5S,6S)-subersin (1, IC(50) > 100 microM), (-)-(5R,10R)-subersic acid (2, IC(50) = 15 microM), jaspaquinol (3, IC(50) = 0.3 microM), and (-)-jaspic acid (4, IC(50) = 1.4 microM). Structure elucidations and lipoxygenase activity studies of these compounds are reported.

Animals↗

PubMed2001

Assessment of actual significance levels for covariate effects in NONMEM.

The objectives of this study were to assess the difference between actual and nominal significance levels, as judged by the likelihood ratio test, for hypothesis tests regarding covariate effects using NONMEM, and to study what factors influence these levels. Also, a strategy for obtaining closer agreement between nominal and actual significance levels was investigated. Pharmacokinetic (PK) data without covariate relationships were simulated from a one compartment i.v. bolus model for 50 individuals. Models with and without covariate relationships were then fitted to the data, and differences in the objective function values were calculated. Alterations were made to the simulation settings; the structural and error models, the number of individuals, the number of samples per individual and the covariate distribution. Different estimation methods in NONMEM were also tried. In addition, a strategy for estimating the actual significance levels for a specific data set, model and parameter was investigated using covariate randomization and a real data set. Under most conditions when the first-order (FO) method was used, the actual significance level for including a covariate relationship in a model was higher than the nominal significance level. Among factors with high impact were frequency of sampling and residual error magnitude. The use of the first-order conditional estimation method with interaction (FOCE-INTER) resulted in close agreement between actual and nominal significance levels. The results from the covariate randomization procedure of the real data set were in agreement with the results from the simulation study. With the FO method the actual significance levels were higher than the nominal, independent of the covariate type, but depending on the parameter influenced. When using FOCE-INTER the actual and nominal levels were similar. The most important factors influencing the actual significance levels for the FO method are the approximation of the influence of the random effects in a nonlinear model, a heteroscedastic error structure in which an existing interaction between interindividual and residual variability is not accounted for in the model, and a lognormal distribution of the residual error which is approximated by a symmetric distribution. Estimation with FOCE-INTER and the covariate randomization procedure provide means to achieve agreement between nominal and actual significance levels.

Adult↗

PubMed2001

Population pharmacokinetic analysis resulting in a tool for dose individualization of busulphan in bone marrow transplantation recipients.

The aims of the present study were (1) to investigate and quantify the pharmacokinetics, including inter-occasion variability and covariate relationships, of busulphan in BMT patients and (2) to develop a user-friendly initial dosing and therapeutic drug monitoring (TDM) strategy for the treatment of those patients with busulphan. The pharmacokinetics of busulphan was studied in 64 adults and 12 children who received busulphan (1 mg/kg) four times daily for 4 days. A one-compartment model with first order absorption and a lag time was sufficient in describing the concentration-time profile. Oral clearance (CL/F) was found to be correlated to weight (+1.2%/kg), ALT (-13%/microcat/l) and concomitant phenytoin treatment (+21%). CL/F and the volume of distribution (V/F) were estimated to 9.23 l/h and 39.3 l, respectively, in a typical individual. Inter-occasion variability (9.4%) in CL/F was estimated to be less than inter-individual variability (28%), a prerequisite for the value of TDM. Bayesian CL/F estimates based on three samples were in good accordance with those based on all samples. The final population model was implemented into the program Excel. The resulting flexible and easy to use dosing program might be used for both initial and, requiring only three plasma samples, maintenance dose individualization of busulphan therapy.

Administration, Oral↗

PubMed2000

Nonlinearity detection: advantages of nonlinear mixed-effects modeling.

The purpose of this study was to address the question of whether the use of nonlinear mixed-effect models has an impact on the detection and characterization of nonlinear processes (pharmacokinetic and pharmacodynamic) in rich data obtained from a few subjects. Simulations were used to assess the difference between applying population analysis, ie, nonlinear mixed-effects models as implemented in NONMEM, and the standard 2-stage (STS) method as the data analysis method for detection and characterization of nonlinearities. Three situations were considered, 2 pharmacokinetic and 1 pharmacodynamic. Both the first-order (FO) and FO conditional estimation (FOCE) algorithms were used for the population analyses. Within each situation, rich data were simulated for 8 subjects at multiple dose levels. The true nonlinear model and a simpler linear model were fit to each data set using each of the STS, FO, and FOCE methods. Criteria were prespecified to determine when each data analysis method detected the true nonlinear model. For all 3 simulated situations, the application of population analysis with the FOCE algorithm enabled the detection and characterization of the true nonlinear models in at least a 4-fold lower dose level than the STS approach. For both of the pharmacokinetic settings, population analysis with the FO algorithm performed much more poorly than the STS approach. The superior detection and characterization of nonlinearities provided by population analysis with the FOCE algorithm should allow drug developers to better predict and define how a drug should be used in clinical practice in such situations.

Algorithms↗

PubMed1999

Xpose--an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM.

The building of population pharmacokinetic/pharmacodynamic (PK/PD) models is a time consuming and complicated task. This is partly due the lack of specialized tools for the visualization and exploration requirements of this type of analysis. In this paper we present Xpose, a model building aid for population PK/PD analysis using NONMEM, which simplifies the task of producing documentation, data set checkout plots, goodness of fit plots and graphical model comparison. It also facilitates covariate model building by the use of stepwise generalized additive modeling (GAM), bootstrap of the GAM analyses and tree based modeling. The plots and analyses are presented in the form of a text based menu system and the only thing the user has to do is to make NONMEM produce one or more table files named in a specific way.

Computer Simulation↗

PubMed1999

Likelihood-based diagnostics for influential individuals in non-linear mixed effects model selection.

PURPOSE: Data from single individuals, or a small group of subjects may influence non-linear mixed effects model selection. Diagnostics routinely applied in model building may identify such individuals, but these methods are not specifically designed for that purpose and are, therefore, not optimal. We describe two likelihood-based diagnostics for identifying individuals that can influence the choice between two competing models. METHODS: One method is based on a jackknife of the raw data on the individual level and refitting the model to each new data set. The second method is a calculation which utilises the contribution each individual make to the objective function values under each of the two models. The two methods were applied to model selection during analysis of a real data set. RESULTS: The agreement between the methods was high. Individuals for whom there was a discrepancy between the methods tended to be those for which neither of the contending models described the data appropriately. Both methods identified individuals that influenced the model selection. CONCLUSIONS: Two objective, specific and quantitative methods for identifying influential individuals in nonlinear mixed effects model selection have been presented. One of the methods doesn't require additional model fitting and is therefore particularly attractive.

Age Factors↗

PubMed1998

Automated covariate model building within NONMEM.

PURPOSE: One important task in population pharmacokinetic/pharmacodynamic model building is to identify the relationships between the parameters and demographic factors (covariates). The purpose of this study is to present an automated procedure that accomplishes this. The benefits of the proposed procedure over other commonly used methods are (i) the covariate model is built for all parameters simultaneously, (ii) the covariate model is built within the population modeling program (NONMEM) giving familiar meaning to the significance levels used, (iii) it can appropriately handle covariates that varies over time and (iv) it is not dependent on the quality of the posterior Bayes estimates of the individual parameter values. For situations in which the computer run-times are a limiting factor, a linearization of the non-linear mixed effects model is proposed and evaluated. METHODS: The covariate model is built in a stepwise fashion in which both linear and non-linear relationships between the parameters and covariates are considered. The linearization is basically a linear mixed effects model in which the population predictions and their derivatives with respect to the parameters are fixed from a model without covariates. The stepwise procedure as well as the linearization was evaluated using simulations in which the covariates were taken from a real data set. RESULTS: The covariate models identified agreed well with what could be expected based on the covariates that were actually supported in each of the simulated data sets. The predictive performance of the linearized model was close to that of the non-linearized model. CONCLUSIONS: The proposed procedure identifies covariate models that are close to the model supported by the data set as well as being useful in the prediction of new data. The linearized model performs nearly as well as the non-linearized model.

Mathematical Computing↗

PubMed1998

Assumption testing in population pharmacokinetic models: illustrated with an analysis of moxonidine data from congestive heart failure patients.

Deriving a population pharmacokinetic model from real data is always associated with numerous assumptions. Violations of these assumptions, especially if undetected, may lead to inappropriate conclusions being made from the analysis. Routinely, only a few of the assumptions are explicitly stated and justified in the reporting of a population model. Here, we attempt to be exhaustive in the presentation of the assumptions made in the course of an analysis of moxonidine pharmacokinetics. The different ways that assumptions were justified, through experience, graphical examination, or additional modeling, are outlined. Models for relaxing assumptions regarding the covariate and statistical submodels, not previously reported in the area of population pharmacokinetic modeling, are also described.

Adult↗

PubMed1998

Artemisinin population pharmacokinetics in children and adults with uncomplicated falciparum malaria.

AIMS: To investigate the pharmacokinetics of the antimalarial artemisinin in the field setting using sparsely collected data. METHODS: Artemisinin concentrations were determined by h.p.l.c. in a total of 107 capillary plasma samples collected on the first day and in 33 samples on the last day of a 5-day oral artemisinin regimen of 10 mg kg(-1) day(-1) in 23 paediatric (aged 2-12 years) and 31 adult (aged 16-45 years) Vietnamese patients with uncomplicated falciparum malaria. The population model was developed using NONMEM, incorporating interoccasion variability and accounting for a systematic change in artemisinin pharmacokinetics with time, modelled as a change in oral bioavailability. RESULTS: Clinical efficacy, in terms of parasite clearance and fever subsidence times, was comparable between children and adults. A one-compartment model with separate pharmacokinetic estimates for children and adults was found best to describe the disposition of artemisinin after oral administration. The population estimates for artemisinin clearance and distribution volume, respectively, were 432 1 h(-1) and 16001 for adults and 14.41 h(-1) kg(-1) and 37.91 kg(-1) for children, with an intersubject variability (collectively for both age groups) of 45% and 104%, respectively. The oral bioavailability was estimated to decrease from Day 1 to Day 5 by a factor of 6.9, a value found to be similar for children and adults. CONCLUSIONS: Artemisinin pharmacokinetic data was successfully derived in both paediatric and adult patients using 2-3 capillary blood samples taken in conjunction with parasitaemia monitoring. This study's findings advocated the dosing of artemisinin to children according to bodyweight and to adults according to a standard dose.

Administration, Oral↗

PubMed1997

Discrimination between rival dosing histories.

PURPOSE: In population pharmacokinetic studies, the dosing history is sometimes recorded in more than one way. The purpose of this study was to develop and evaluate a procedure for discriminating between rival dosing histories, i.e., for each individual in a data set, identify the dosing history that is the most plausible. METHODS: The procedure consists of four steps. In the first step we identify individuals whose dosing histories produce predictions that are consistent. In the second step these individuals are used to build a population pharmacokinetic model which is used, in step three, to select the dosing history for the individuals not identified in step one. In step four the population model is refined using the best available dosing histories for all individuals. The proposed procedure was evaluated using both simulations and a real data set, in which two dosing histories, based on patient diaries and electronic monitoring devices (MEMS) were available. RESULTS: In the real data set, estimated variabilities were almost always lower when the selected dosing histories were used compared to when no selection procedure was used. The diary dosing histories were selected more often than the MEMS dosing histories. In the simulations, the parameter estimates obtained using the selection procedure were closer to the true parameter values compared to when only one of the dosing histories was used. CONCLUSIONS: The proposed procedure appears to be robust and should be beneficial in at least two respects: improved parameter estimation of population pharmacokinetic and PK/PD models and objective information by which dosage recording methodologies can be compared and patient dose recording behavior can be assessed.

Algorithms↗

PubMed1996

Comparison of some practical sampling strategies for population pharmacokinetic studies.

Using population analysis, sparsely sampled Phase 3 clinical data can be utilized to determine the pharmacokinetic characteristics of the target population. Data arising from such studies are likely to be constrained to certain sampling windows, i.e., the visiting hours at the study clinic. When the sampling window is narrow compared to the half-life of the drug, the advantage of taking more than one sample is not obvious. Study designs with one or two samples per visit have been compared with respect to (i) precision and bias of the population parameter estimates, (ii) the ability to identify the underlying pharmacokinetic model, and (iii) the estimation of individual parameter values. The first point was assessed using simulated data while the latter two were studied using a real data set. Results show: (i) Parameter estimates are more biased and imprecise when only one sample is taken compared to when two samples are obtained, this is true irrespective of the time span between the two samples. (ii) Ability to identify a more complex model is increased if two samples are taken. Specifically, the variability between occasions can be quantified. (iii) Two-sample designs are generally better with respect to prediction of individual parameter values. Even minor changes to commonly employed study designs, in this case the addition of one sample at each study occasion, can improve quality and quantity of the information obtained.

Computer Simulation↗