| 1. |
- Ahn, Jae Eun, et al.
(författare)
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Likelihood based approaches to handling data below the quantification limit using NONMEM VI
- 2008
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 35:4, s. 401-421
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSE: To evaluate the likelihood-based methods for handling data below the quantification limit (BQL) using new features in NONMEM VI. METHODS: A two-compartment pharmacokinetic model with first-order absorption was chosen for investigation. Methods evaluated were: discarding BQL observations (M1), discarding BQL observations but adjusting the likelihood for the remaining data (M2), maximizing the likelihood for the data above the limit of quantification (LOQ) and treating BQL data as censored (M3), and like M3 but conditioning on the observation being greater than zero (M4). These four methods were compared using data simulated with a proportional error model. M2, M3, and M4 were also compared using data simulated from a positively truncated normal distribution. Successful terminations and bias and precision of parameter estimates were assessed. RESULTS: For the data simulated with a proportional error model, the overall performance was best for M3 followed by M2 and M1. M3 and M4 resulted in similar estimates in analyses without log transformation. For data simulated with the truncated normal distribution, M4 performed better than M3. CONCLUSIONS: Analyses that maximized the likelihood of the data above the LOQ and treated BQL data as censored provided the most accurate and precise parameter estimates.
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| 2. |
- Baverel, Paul, et al.
(författare)
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Evaluation of the Nonparametric Estimation Method in NONMEM VI: Application to Real Data
- 2009
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 36:4, s. 297-315
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Tidskriftsartikel (refereegranskat)abstract
- The aim of the study was to evaluate the nonparametric estimation methods available in NONMEM VI in comparison with the parametric first-order method (FO) and the first-order conditional estimation method (FOCE) when applied to real datasets. Four methods for estimating model parameters and parameter distributions (FO, FOCE, nonparametric preceded by FO (FO-NONP) and nonparametric preceded by FOCE (FOCE-NONP)) were compared for 25 models previously developed using real data and a parametric method. Numerical predictive checks were used to test the appropriateness of each model. Up to 1000 new datasets were simulated from each model and with each method to construct 90% and 50% prediction intervals. The mean absolute error and the mean error of the different outcomes investigated were computed as indicators of imprecision and bias respectively and formal statistical tests were performed. Overall, less imprecision and less bias were observed with nonparametric methods than with parametric methods. Across the 25 models, t-tests revealed that imprecision and bias were significantly lower (P < 0.05) with FOCE-NONP than with FOCE for half of the NPC outcomes investigated. Improvements were even more pronounced with FO-NONP in comparison with FO. In conclusion, when applied to real datasets and evaluated by numerical predictive checks, the nonparametric estimation methods in NONMEM VI performed better than the corresponding parametric methods (FO or FOCE).
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| 3. |
- Bergström, Mats, et al.
(författare)
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Blood-brain barrier penetration of zolmitriptan--modelling of positron emission tomography data
- 2006
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 33:1, s. 75-91
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Tidskriftsartikel (refereegranskat)abstract
- Positron emission tomography (PET) with the drug radiolabelled allows a direct measurement of brain or other organ kinetics, information which can be essential in drug development. Usually, however, a PET-tracer is administered intravenously (i.v.), whereas the therapeutic drug is mostly given orally or by a different route to the PET-tracer. In such cases, a recalculation is needed to make the PET data representative for the alternative administration route. To investigate the blood-brain barrier penetration of a drug (zolmitriptan) using dynamic PET and by PK modelling quantify the brain concentration of the drug after the nasal administration of a therapeutic dose. [11C]Zolmitriptan at tracer dose was administered as a short i.v. infusion and the brain tissue and venous blood kinetics of [11C]zolmitriptan was measured by PET in 7 healthy volunteers. One PET study was performed before and one 30 min after the administration of 5 mg zolmitriptan as nasal spray. At each of the instances, the brain radioactivity concentration after subtraction of the vascular component was determined up to 90 min after administration and compared to venous plasma radioactivity concentration after correction for radiolabelled metabolites. Convolution methods were used to describe the relationship between arterial and venous tracer concentrations, respectively between brain and arterial tracer concentration. Finally, the impulse response functions derived from the PET studies were applied on plasma PK data to estimate the brain zolmitriptan concentration after a nasal administration of a therapeutic dose. The studies shows that the PET data on brain kinetics could well be described as the convolution of venous tracer kinetics with an impulse response including terms for arterial-to-venous plasma and arterial-to-brain impulse responses. Application of the PET derived impulse responses on the plasma PK from nasal administration demonstrated that brain PK of zolmitriptan increased with time, achieving about 0.5 mg/ml at 30 min and close to a maximum of 1.5 mg/ml after 2 hr. A significant brain concentration was observed already after 5 min. The data support the notation of a rapid brain availability of zolmitriptan after nasal administration.
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| 4. |
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| 5. |
- Dansirikul, Chantaratsamon, et al.
(författare)
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Approaches to handling pharmacodynamic baseline responses
- 2008
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 35:3, s. 269-283
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Tidskriftsartikel (refereegranskat)abstract
- A few approaches for handling baseline responses are available for use in pharmacokinetic (PK)-pharmacodynamic (PD) analysis. They include: (method 1-B1) estimation of the typical value and interindividual variability (IIV) of baseline in the population, (B2) inclusion of the observed baseline response as a covariate acknowledging the residual variability, (B3) a more general version of B2 as it also takes the IIV of the baseline in the population into account, and (B4) normalization of all observations by the baseline value. The aim of this study was to investigate the relative performance of B1-B4. PD responses over a single dosing interval were simulated from an indirect response model in which a drug acts through stimulation or inhibition of the response according to an Emax model. The performance of B1-B4 was investigated under 22 designs, each containing 100 datasets. NONMEM VI beta was used to estimate model parameters with the FO and the FOCE method. The mean error (ME, %) and root mean squared error (RMSE, %) of the population parameter estimates were computed and used as an indicator of bias and imprecision. Absolute ME (|ME|) and RMSE from all methods were ranked within the same design, the lower the rank value the better method performance. Average rank of each method from all designs was reported. The results showed that with B1 and FOCE, the average of |ME| and RMSE across all typical individual parameters and all conditions was 5.9 and 31.8%. The average rank of |ME| for B1, B2, B3, and B4 was 3.7, 3.8, 3.3, and 5.2 for the FOCE method, and 4.6, 4.3, 4.7, and 6.4 for the FO method. The smallest imprecision was noted with the use of B1 (rank of 3.1 for FO, and 2.9 for FOCE) and increased, in order, with B3 (3.9-FO and 3.6-FOCE), B2 (4.8-FO; 4.7-FOCE), and B4 (6.4-FO; 6.5-FOCE). We conclude that when considering both bias and imprecision B1 was slightly better than B3 which in turn was better than B2. Differences between these methods were small. B4 was clearly inferior. The FOCE method led to a smaller bias, but no marked reduction in imprecision of parameter estimates compared to the FO method.
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| 6. |
- Dodds, Michael G, et al.
(författare)
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Robust population pharmacokinetic experiment design.
- 2005
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 32:1, s. 33-64
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Tidskriftsartikel (refereegranskat)abstract
- The population approach to estimating mixed effects model parameters of interest in pharmacokinetic (PK) studies has been demonstrated to be an effective method in quantifying relevant population drug properties. The information available for each individual is usually sparse. As such, care should be taken to ensure that the information gained from each population experiment is as efficient as possible by designing the experiment optimally, according to some criterion. The classic approach to this problem is to design "good" sampling schedules, usually addressed by the D-optimality criterion. This method has the drawback of requiring exact advanced knowledge (expected values) of the parameters of interest. Often, this information is not available. Additionally, if such prior knowledge about the parameters is misspecified, this approach yields designs that may not be robust for parameter estimation. In order to incorporate uncertainty in the prior parameter specification, a number of criteria have been suggested. We focus on ED-optimality. This criterion leads to a difficult numerical problem, which is made tractable here by a novel approximation of the expectation integral usually solved by stochastic integration techniques. We present two case studies as evidence of the robustness of ED-optimal designs in the face of misspecified prior information. Estimates from replicate simulated population data show that such misspecified ED-optimal designs recover parameter estimates that are better than similarly misspecified D-optimal designs, and approach estimates gained from D-optimal designs where the parameters are correctly specified.
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| 7. |
- Elsherbiny, Doaa A, et al.
(författare)
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A model based assessment of the CYP2B6 and CYP2C19 inductive properties by artemisinin antimalarials: implications for combination regimens.
- 2008
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Ingår i: Journal of pharmacokinetics and pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 35:2, s. 203-17
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Tidskriftsartikel (refereegranskat)abstract
- The study aim was to assess the inductive properties of artemisinin antimalarials using mephenytoin as a probe for CYP2B6 and CYP2C19 enzymatic activity. The population pharmacokinetics of S-mephenytoin and its metabolites S-nirvanol and S-4'-hydroxymephenytoin, including enzyme turn-over models for induction, were described by nonlinear mixed effects modeling. Rich data (8-16 samples/occasion/subject) were collected from 14 healthy volunteers who received mephenytoin before and during ten days of artemisinin administration. Sparse data (3 samples/occasion/subject) were collected from 74 healthy volunteers who received mephenytoin before, during and after five days administration of artemisinin, dihydroartemisinin, arteether, artemether or artesunate. The production rate of CYP2B6 was increased 79.7% by artemisinin, 61.5% by arteether, 76.1% by artemether, 19.9% by dihydroartemisinin and 16.9% by artesunate. The production rate of CYP2C19 increased 51.2% by artemisinin, 14.8% by arteether and 24.9% by artemether. In conclusion, all studied artemisinin derivatives induced CYP2B6. CYP2C19 induction by arteether and artemether as well as CYP2B6 and CYP2C19 induction by artemisinin was confirmed. The inductive capacity is different among the artemisinin drugs, which is of importance when selecting drugs to be used in antimalarial combination therapy such that the potential for drug-drug interactions is minimized.
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| 8. |
- Ernest, C. Steven, et al.
(författare)
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Population pharmacokinetics and pharmacodynamics of prasugrel and clopidogrel in aspirin-treated patients with stable coronary artery disease
- 2008
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 35:6, s. 593-618
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Tidskriftsartikel (refereegranskat)abstract
- The aim of the current analysis was to characterize the population PK of prasugrel and clopidogrel metabolites, the resulting PD response, and identification of covariates for key PK/PD parameters. Aspirin-treated subjects with coronary artery disease were randomized to double-blind treatment with clopidogrel 600 mg loading dose (LD) followed by daily 75 mg maintenance dose (MD) or prasugrel 60 mg LD and daily 10 mg MD for 28 days. Plasma concentrations of prasugrel active metabolite (Pras-AM) and prasugrel's inactive thiolactone metabolite (Pras-thiolactone) were simultaneously fit to a multicompartmental model; a similar model adequately described clopidogrel's active metabolite (Clop-AM) PK. By linking to the PK model through the active metabolite concentrations, the PK/PD model characterized the irreversible inhibition of platelet aggregation through a sigmoidal Emax model. Although dose, sex, and weight were identified as significant covariates in the prasugrel PK model, only the effect of body weight produced significant changes in Pras-AM exposure. Generally, these factors resulted in only minor changes in Pras-AM exposures such that, overall, the change in the resulting maximal platelet aggregation (MPA) was predicted to be < or =10% points on average. The clopidogrel PK model included dose as a covariate indicating that a significantly less-than-proportional increase in Clop-AM exposure is expected over the dose range of 75-600 mg, thus, the model-predicted PD response is lower than might be anticipated given an 8-fold difference in dose and lower than that typically achieved following prasugrel 60 mg LD. The greater PD response with prasugrel compared with clopidogrel was accounted for by greater conversion of dose to active metabolite.
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| 9. |
- Friberg Hietala, Sofia, 1973, et al.
(författare)
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Population pharmacokinetics of amodiaquine and desethylamodiaquine in pediatric patients with uncomplicated falciparum malaria
- 2007
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Ingår i: JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 12, s. 222-222
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Tidskriftsartikel (refereegranskat)abstract
- The study aimed to characterize the population pharmacokinetics of amodiaquine (AQ) and its major metabolite N-desethylamodiaquine (N-DEAQ), and to assess the correlation between exposure to N-DEAQ and treatment outcome. Blood samples from children in two studies in Zanzibar and one in Papua New Guinea were included in the pharmacokinetic analysis (n = 86). The children had been treated with AQ in combination with artesunate or sulphadoxine-pyrimethamine. The population pharmacokinetics of AQ and N-DEAQ were modeled using the non-linear mixed effects approach as implemented in NONMEM. Bayesian post-hoc estimates of individual pharmacokinetic parameters were used to generate individual profiles of N-DEAQ exposure. The correlation between N-DEAQ exposure and effect was studied in 212 patients and modeled with logistic regression in NONMEM. The pharmacokinetics of AQ and N-DEAQ were best described by two parallel two-compartment models with a central and a peripheral compartment for each compound. The systemic exposure to AQ was low in comparison to N-DEAQ. The t (1/2lambda) of N-DEAQ ranged from 3 days to 12 days. There was a statistically significant, yet weak, association between N-DEAQ concentration on day 7 and treatment outcome. The age-based dosing schedule currently recommended in Zanzibar appeared to result in inadequate exposure to N-DEAQ in many patients.
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| 10. |
- Kjellsson, Maria C., et al.
(författare)
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Comparison of proportional odds and differential odds models for mixed-effects analysis of categorical data
- 2008
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 35:5, s. 483-501
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Tidskriftsartikel (refereegranskat)abstract
- In this work a model for analyzing categorical data is presented; the differential odds model. Unlike the commonly used proportional odds model, this model does not assume that a covariate affects all categories equally on the log odds scale. The differential odds model was compared to the proportional odds model, by assessing statistical significance and improvement of predictive performance when applying the differential odds model to data previously analyzed using the proportional odds model. Three clinical studies; 3-category T-cell receptor density data, 5-category diarrhea data and 6-category sedation data, were re-analyzed with the differential odds model. As expected, no improvements were seen with T-cell receptor density and diarrhea data. However, for the more complex measurement sedation, the differential odds model provided both statistical improvements and improvements in simulation properties. The estimated actual critical value was for all data lower than the nominal value, using the number of added parameters as the degree of freedom, i.e. the differential odds model is statistically indicated to a less extent than expected. The differential odds model had the desired property of not being indicated when not necessary, but it may provide improvements when the data does not represent a categorization of continuous data.
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| 11. |
- Nyberg, Joakim, et al.
(författare)
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Simultaneous optimal experimental design on dose and sample times
- 2009
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 36:2, s. 125-145
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Tidskriftsartikel (refereegranskat)abstract
- Optimal experimental design can be used for optimizing new pharmacokinetic (PK)-pharmacodynamic (PD) studies to increase the parameter precision. Several methods for optimizing non-linear mixed effect models has been proposed previously but the impact of optimizing other continuous design parameters, e.g. the dose, has not been investigated to a large extent. Moreover, the optimization method (sequential or simultaneous) for optimizing several continuous design parameters can have an impact on the optimal design. In the sequential approach the time and dose where optimized in sequence and in the simultaneous approach the dose and time points where optimized at the same time. To investigate the sequential approach and the simultaneous approach; three different PK-PD models where considered. In most of the cases the optimization method did change the optimal design and furthermore the precision was improved with the simultaneous approach.
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| 12. |
- Overgaard, Rune V., et al.
(författare)
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Pharmacodynamic model of interleukin-21 effects on red blood cells in cynomolgus monkeys.
- 2007
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 34:4, s. 559-574
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Tidskriftsartikel (refereegranskat)abstract
- Interleukin-21 (IL-21) is a novel cytokine that is currently under clinical investigations as a potential anti-cancer agent. Like many other anti-cancer agents, including other interleukins, IL-21 is seen to produce a broad range of biological effects that may be related to both efficacy and safety of treatment. The present analysis investigates the observed pharmacodynamics effects on red blood cells following various treatment schedules of human IL-21 administrated to cynomolgus monkeys. These effects are described by a novel non-linear mixed-effects model that enabled separation of drug effects and sampling effects, the latter believed to be due partly to blood loss and partly to stress induced haemolysis in connection with blood sampling. Two different studies with a total of 9 different treatment groups of cynomolgus monkeys were used for model development. In conclusion, the model describes the IL-21 induced drop in red blood cells to be (1) caused by removal rather than suppression of production, consistent with increased reticulocyte concentration, and (2) considerably delayed compared to dosing, i.e. not related to the drop in red blood cells observed immediately post dose. It is believed that the structural model presented here can be used for other types of drug induced loss of red blood cells, whereas the mechanism for sampling related blood loss is relevant for investigations of anaemia in all pharmacological studies with smaller animals.
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| 13. |
- Plan, Elodie L., et al.
(författare)
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Performance in population models for count data, part I: maximum likelihood approximations.
- 2009
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 36:4, s. 353-366
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Tidskriftsartikel (refereegranskat)abstract
- There has been little evaluation of maximum likelihood approximation methods for non-linear mixed effects modelling of count data. The aim of this study was to explore the estimation accuracy of population parameters from six count models, using two different methods and programs. Simulations of 100 data sets were performed in NONMEM for each probability distribution with parameter values derived from a real case study on 551 epileptic patients. Models investigated were: Poisson (PS), Poisson with Markov elements (PMAK), Poisson with a mixture distribution for individual observations (PMIX), Zero Inflated Poisson (ZIP), Generalized Poisson (GP) and Negative Binomial (NB). Estimations of simulated datasets were completed with Laplacian approximation (LAPLACE) in NONMEM and LAPLACE/Gaussian Quadrature (GQ) in SAS. With LAPLACE, the average absolute value of the bias (AVB) in all models was 1.02% for fixed effects, and ranged 0.32-8.24% for the estimation of the random effect of the mean count (lambda). The random effect of the overdispersion parameter present in ZIP, GP and NB was underestimated (-25.87, -15.73 and -21.93% of relative bias, respectively). Analysis with GQ 9 points resulted in an improvement in these parameters (3.80% average AVB). Methods implemented in SAS had a lower fraction of successful minimizations, and GQ 9 points was considerably slower than 1 point. Simulations showed that parameter estimates, even when biased, resulted in data that were only marginally different from data simulated from the true model. Thus all methods investigated appear to provide useful results for the investigated count data models.
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| 14. |
- Ribbing, Jakob, et al.
(författare)
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Non-Bayesian Knowledge Propagation using Model Model-Based Analysis of Data from Multiple Clinical Studies
- 2008
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 35:1, s. 117-137
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Tidskriftsartikel (refereegranskat)abstract
- The ultimate goal in drug development is to establish the manner of safe and efficacious administration to patients. To achieve this in an efficient way the information contained in the clinical studies should contribute to the increasing pool of accumulated knowledge. The aim of this simulation study is to investigate different knowledge-propagation strategies when the data is analysed using a model-based approach in NONMEM. Pharmacokinetic studies were simulated according to several scenarios of the underlying model and study design, including a population-optimal design based on analysis of a previous study. Five approaches with different degrees of knowledge propagation were investigated: analysing the studies pooled into one dataset, merging the results from analysing the studies separately, fitting a pre-specified model that has been selected from a previous study on either the most recent study or on the pooled dataset, or naively analysing the most recent study without any regards to any previous study. The approaches were evaluated on what model was selected (qualitative knowledge, investigated by stepwise covariate selection within NONMEM) as well as parameter precision (quantitative knowledge) and predictive performance of the model. Pooling all studies into one dataset is the best approach for identifying the correct model and obtaining good predictive performance and merging the results of separate analyses may perform almost as well. Fitting a pre-specified model on new data is fast, without selection bias, and sanctioned for model-based confirmatory analyses. However, fitting the same pre-specified model to all available data is still fast and can be expected to perform better in terms of predictive performance than the unbiased alternative. Using ED-optimal design of sample times and stratification of subjects from different subgroups is a successful strategy which allows sparse sampling and handles prior parameter uncertainty.
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| 15. |
- Ribbing, Jakob, et al.
(författare)
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The Lasso – A Novel Method for Predictive Covariate Model Building in Nonlinear Mixed Effects Models
- 2007
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 34:4, s. 485-517
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Tidskriftsartikel (refereegranskat)abstract
- Covariate models for population pharmacokinetics and pharmacodynamics are often built with a stepwise covariate modelling procedure (SCM). When analysing a small dataset this method may produce a covariate model that suffers from selection bias and poor predictive performance. The lasso is a method suggested to remedy these problems. It may also be faster than SCM and provide a validation of the covariate model. The aim of this study was to implement the lasso for covariate selection within NONMEM and to compare this method to SCM. In the lasso all covariates must be standardised to have zero mean and standard deviation one. Subsequently, the model containing all potential covariate–parameter relations is fitted with a restriction: the sum of the absolute covariate coefficients must be smaller than a value, t. The restriction will force some coefficients towards zero while the others are estimated with shrinkage. This means in practice that when fitting the model the covariate relations are tested for inclusion at the same time as the included relations are estimated. For a given SCM analysis, the model size depends on the P-value required for selection. In the lasso the model size instead depends on the value of t which can be estimated using cross-validation. The lasso was implemented as an automated tool using PsN. The method was compared to SCM in 16 scenarios with different dataset sizes, number of investigated covariates and starting models for the covariate analysis. Hundred replicate datasets were created by resampling from a PK-dataset consisting of 721 stroke patients. The two methods were compared primarily on the ability to predict external data, estimate their own predictive performance (external validation), and on the computer run-time. In all 16 scenarios the lasso predicted external data better than SCM with any of the studied P-values (5%, 1% and 0.1%), but the benefit was negligible for large datasets. The lasso cross-validation provided a precise and nearly unbiased estimate of the actual prediction error. On a single processor, the lasso was faster than SCM. Further, the lasso could run completely in parallel whereas SCM must run in steps. In conclusion, the lasso is superior to SCM in obtaining a predictive covariate model on a small dataset or on small subgroups (e.g. rare genotype). Run in parallel the lasso could be much faster than SCM. Using cross-validation, the lasso provides a validation of the covariate model and does not require the user to specify a P-value for selection.
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| 16. |
- Savic, Radojka M., et al.
(författare)
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Implementation of a Transit Compartment Model for Describing Drug Absorption in Pharmacokinetic Studies
- 2007
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 34:5, s. 711-726
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To compare the performance of the standard lag time model (LAG model) with the performance of an analytical solution of the transit compartment model (TRANSIT model) in the evaluation of four pharmacokinetic studies with four different compounds. Methods: The population pharmacokinetic analyses were performed using NONMEM on concentration–time data of glibenclamide, furosemide, amiloride, and moxonidine. In the TRANSIT model, the optimal number of transit compartments was estimated from the data. This was based on an analytical solution for the change in drug concentration arising from a series of transit compartments with the same first-order transfer rate between each compartment. Goodness-of-fit was assessed by the decrease in objective function value (OFV) and by inspection of diagnostic graphs. Results: With the TRANSIT model, the OFV was significantly lower and the goodness-of-fit was markedly improved in the absorption phase compared with the LAG model for all drugs. The parameter estimates related to the absorption differed between the two models while the estimates of the pharmacokinetic disposition parameters were similar. Conclusion: Based on these results, the TRANSIT model is an attractive alternative for modeling drug absorption delay, especially when a LAG model poorly describes the drug absorption phase or is numerically unstable.
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| 17. |
- Silber, Hanna E., et al.
(författare)
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Optimization of the intravenous glucose tolerance test in T2DM patients using optimal experimental design
- 2009
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 36:3, s. 281-295
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Tidskriftsartikel (refereegranskat)abstract
- Intravenous glucose tolerance test (IVGTT) provocations are informative, but complex and laborious, for studying the glucose-insulin system. The objective of this study was to evaluate, through optimal design methodology, the possibilities of more informative and/or less laborious study design of the insulin modified IVGTT in type 2 diabetic patients. A previously developed model for glucose and insulin regulation was implemented in the optimal design software PopED 2.0. The following aspects of the study design of the insulin modified IVGTT were evaluated; (1) glucose dose, (2) insulin infusion, (3) combination of (1) and (2), (4) sampling times, (5) exclusion of labeled glucose. Constraints were incorporated to avoid prolonged hyper- and/or hypoglycemia and a reduced design was used to decrease run times. Design efficiency was calculated as a measure of the improvement with an optimal design compared to the basic design. The results showed that the design of the insulin modified IVGTT could be substantially improved by the use of an optimized design compared to the standard design and that it was possible to use a reduced number of samples. Optimization of sample times gave the largest improvement followed by insulin dose. The results further showed that it was possible to reduce the total sample time with only a minor loss in efficiency. Simulations confirmed the predictions from PopED. The predicted uncertainty of parameter estimates (CV) was low in all tested cases, despite the reduction in the number of samples/subject. The best design had a predicted average CV of parameter estimates of 19.5%. We conclude that improvement can be made to the design of the insulin modified IVGTT and that the most important design factor was the placement of sample times followed by the use of an optimal insulin dose. This paper illustrates how complex provocation experiments can be improved by sequential modeling and optimal design.
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| 18. |
- Silber, Hanna E, 1977-, et al.
(författare)
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The impact of misspecification of residual error or correlation structure on the type I error rate for covariate inclusion
- 2009
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 36:1, s. 81-99
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Tidskriftsartikel (refereegranskat)abstract
- It has been shown that when using the FOCE method in NONMEM, the likelihood ratio test (LRT) can be sensitive to the use of an inappropriate estimation method in that ignoring an existing eta-epsilon interaction leads to actual significance levels for type I errors being higher than the nominal levels. The objective of this study was to assess through simulations the LRT sensitivity to various types of residual error model misspecifications in both continuous and categorical data. The study contained two parts, simulations based on continuous and categorical data. Data sets containing 250 individuals with up to 24 observations per individual were simulated multiple times (1000) with different types of residual error models for the continuous data and different strength of correlation between observations for the categorical data. The data sets were analyzed using either the correct or a simpler (incorrect) model with or without addition of a covariate. The type I error rate of inclusion of the non-informative covariate on the 5% level was calculated as the number of runs where the drop in the objective function value (OFV) was larger than 3.84 when the covariate relationship was included in the model using the correct or the incorrect model. The difference in OFV between the model with the correct and the incorrect structure was also calculated as a measure of the residual error model misspecification. For continuous data the FOCE method was used in most cases (with interaction when appropriate). The Laplacian estimation method was used for one of the continuous models and for categorical data. The results showed that the residual error model misspecifications when the erroneous model was used were pronounced, as indicated by the OFV being substantially higher than for the corresponding correct models. The significance levels of the LRT with the incorrect model were appropriate in all cases but ignoring (serial) correlations between observations (continuous and categorical data) as well as when the eta-epsilon interaction was ignored (which has previously been shown, continuous data). When ignoring correlation, the type I error rates were shown to be sensitive to the correlation strength, the number of observations per individual and the magnitude of the inter-individual variability on clearance. We conclude that the LRT appears robust towards all tested cases, but ignoring (serial) correlations between observations and eta-epsilon interaction.
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| 19. |
- Sällström, Björn, et al.
(författare)
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A Pharmacodynamic Turnover Model Capturing Asymmetric Circadian Baselines of Body Temperature, Heart Rate and Blood Pressure in Rats : Challenges in Terms of Tolerance and Animal-handling Effects
- 2005
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 32:5-6, s. 835-859
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Tidskriftsartikel (refereegranskat)abstract
- This study presents development and behaviour of a feedback turnover model that mimics asymmetric circadian oscillations of body temperature, blood pressure and heart rate in rats.The study also includes an application to drug-induced hypothermia, tolerance and handling effects. Data were collected inn normotensive Sprague-Dawley rats, housed at 25 degrees C with a 12:12 hr light dark cycle (light on at 06:00 am) and with free access of food and water. The model consisted of two intertwined parallel compartments which captured a free-running rhythm with a period close to but not exactly 24 hrs. The free-running rhythm was synchronised to exactly 24 hrs by the environmental timekeeper (12:12 hr light on/off cycle) in experimental settings. The baseline model was fitted to a standardised 24-hr period derived from mean data of six animals over a period of nine consecutive days. The first-order rate constants related to the turnover of the baseline temperature, alpha and beta, were 0.026 min(-1) (+/-5%) and 0.0037 min(-1) (+/-3%). The alpha and beta parameters are approximately 2/transition time between day and night and 2/night time, respectively. The day:night timekeeper g(t), reference point T(ref) and amplitude were 0.053(+/-2%),37.3(+/-0.02%) and 3.3% (+/-2%), respectively. Simulations with the baseline model revealed stable oscillations (free-running rhythm) in the absence of the timekeeper. This temperature-time profile was then symmetric and had a smaller amplitude, with a slightly shorter period and less pronounced temperature shift as compared to the profile in the presence of an external Timekeeper. Fitting the model to 96 hr mean profiles of blood pressure and heart rate from 10 control animals demonstrated the usefulness of the model.Simulations of the integrated temperature model succeeded in mimicking other modes of administration such as oral dosing.
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| 20. |
- Trocóniz, Iñaki F, et al.
(författare)
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Modelling overdispersion and Markovian features in count data.
- 2009
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 36:5, s. 461-477
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Tidskriftsartikel (refereegranskat)abstract
- The number of counts (events) per unit of time is a discrete response variable that is generally analyzed with the Poisson distribution (PS) model. The PS model makes two assumptions: the mean number of counts (lambda) is assumed equal to the variance, and counts occurring in non-overlapping intervals are assumed independent. However, many counting outcomes show greater variability than predicted by the PS model, a phenomenon called overdispersion. The purpose of this study was to implement and explore, in the population context, different distribution models accounting for overdispersion and Markov patterns in the analysis of count data. Daily seizures count data obtained from 551 subjects during the 12-week screening phase of a double-blind, placebo-controlled, parallel-group multicenter study performed in epileptic patients with medically refractory partial seizures, were used in the current investigation. The following distribution models were fitted to the data: PS, Zero-Inflated PS (ZIP), Negative Binomial (NB), and Zero-Inflated Negative Binomial (ZINB) models. Markovian features were introduced estimating different lambdas and overdispersion parameters depending on whether the previous day was a seizure or a non-seizure day. All analyses were performed with NONMEM VI. All models were successfully implemented and all overdispersed models improved the fit with respect to the PS model. The NB model resulted in the best description of the data. The inclusion of Markovian features in lambda and in the overdispersion parameter improved the fit significantly (P < 0.001). The plot of the variance versus mean daily seizure count profiles, and the number of transitions, are suggested as model performance tools reflecting the capability to handle overdispersion and Markovian features, respectively.
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| 21. |
- Tunblad, Karin, et al.
(författare)
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The use of clinical irrelevance criteria in covariate model building with application to dofetilide pharmacokinetic data
- 2008
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 35:5, s. 503-526
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Tidskriftsartikel (refereegranskat)abstract
- To characterise the pharmacokinetics of dofetilide in patients and to identify clinically relevant parameter-covariate relationships. To investigate three different modelling strategies in covariate model building using dofetilide as an example: (1) using statistical criteria only or in combination with clinical irrelevance criteria for covariate selection, (2) applying covariate effects on total clearance or separately on non-renal and renal clearances and (3) using separate data sets for covariate selection and parameter estimation. Pooled concentration-time data (1,445 patients, 10,133 observations) from phase III clinical trials was used. A population pharmacokinetic model was developed using NONMEM. Stepwise covariate model building was applied to identify important covariates using the strategies described above. Inclusion and exclusion of covariates using clinical irrelevance was based on reduction in interindividual variability and changes in parameters at the extremes of the covariate distribution. Parametric separation of the elimination pathways was accomplished using creatinine clearance as an indicator of renal function. The pooled data was split in three parts which were used for covariate selection, parameter estimation and evaluation of predictive performance. Parameter estimations were done using the first-order (FO) and the first-order conditional estimation (FOCE) methods. A one-compartment model with first order absorption adequately described the data. Using clinical irrelevance criteria resulted in models containing less parameter-covariate relationships with a minor loss in predictive power. A larger number of covariates were found significant when the elimination was divided into a renal part and a non-renal part, but no gain in predictive power could be seen with this data set. The FO and FOCE estimation methods gave almost identical final covariate model structures with similar predictive performance. Clinical irrelevance criteria may be valuable for practical reasons since stricter inclusion/exclusion criteria shortens the run times of the covariate model building procedure and because only the covariates important for the predictive performance are included in the model.
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| 22. |
- Zingmark, Per-Henrik, et al.
(författare)
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Modelling a spontaneously reported side effect by use of a Markov mixed-effects model.
- 2005
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 32:2, s. 261-281
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Tidskriftsartikel (refereegranskat)abstract
- Aims: To present a method for analyzing side-effect data where change in severity is spontaneouslyreported during the experiment. Methods: A clinical study in 12 healthy volunteers aimed toinvestigate the concentration-response characteristics of a CNS-specific side-effect was conducted.After an open session where the subjects experienced the side-effect and where the individualpharmacokinetic parameters were evaluated they were randomized to a sequence of three differentinfusion rates of the drug in a double-blinded crossover way. The infusion rates were individualizedto achieve the same target concentration in all subjects and different drug input rates wereselected to mimic absorption profiles from different formulations. The occurrence of the specificside-effect and any subsequent change in severity was self-reported by the subjects. Severity wasrecorded as 0 = no side-effect, 1 = mild side-effect and 2 = moderate or severe side-effect.Results: The side-effect data were analyzed using a mixed-effects model for ordered categoricaldata with and without Markov elements. The former model estimated the probability of having acertain side-effect score conditioned on the preceding observation and drug exposure. The observednumbers of transitions between scores were from 0 ->1: 24, from 0 ->2: 11, from 1 ->2: 23, from2 ->1: 1, from 2 ->0: 32 and from 1 ->0: 2. The side-effect model consisted of an effect-compartmentmodel with a tolerance compartment. The predictive performance of the Markov model wasinvestigated by a posterior predictive check (PPC), where 100 datasets were simulated from thefinal model. Average number of the different transitions from the PPC was from 0 ->1: 26, from0 ->2: 11, from 1 ->2: 25, from 2 ->1: 1, from 2 ->0: 35 and from 1 ->0: 1. A similar PPCfor the model without Markov elements was at considerable disparity with the data. Conclusion:This approach of incorporating Markov elements in an analysis of spontaneously reported categoricalside-effect data could adequately predict the observed side-effect time course and could beconsidered in analyses of categorical data where dependence between observations is an issue.
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| 23. |
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| 24. |
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| 25. |
- Nyström, Kristina
(författare)
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Entry, market turbulence and industry employment growth
- 2009
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Ingår i: Empirica. - : Springer Science+Business Media B.V.. - 0340-8744 .- 1573-6911. ; 36:3, s. 293-308
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Tidskriftsartikel (refereegranskat)abstract
- This paper investigates the relationship between industrial dynamics in terms of firm entry, market turbulence and employment growth. Do entry of firms, the composition of industry dynamics (net entry) and market turbulence (entry and exit) influence industrial employment growth? This paper provides an empirical investigation, using unique data for 42 disaggregated Swedish industrial sectors during the period 1997-2001. It is hypothesised that the importance of entering firms, net entry and market turbulence may differ significantly across industries. A quantile regression method is used in order to detect industrial differences in the response to industrial employment growth. The empirical evidence shows that, on the one hand, firm entry and market turbulence have a positive effect on employment for fast growing industries and that the effect is larger for high growth industries. On the other hand, the composition of industry dynamics in terms of net entry rates has a more dispersed effect across all industries, even though the effect of net entry is larger for high growth industries.
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