| 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. |
- Albitar, Orwa, et al.
(författare)
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Pharmacometric modeling of drug adverse effects : an application of mixture models in schizophrenia spectrum disorder patients treated with clozapine
- 2023
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Nature. - 1567-567X .- 1573-8744. ; 50:1, s. 21-31
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Tidskriftsartikel (refereegranskat)abstract
- Clozapine has superior efficacy to other antipsychotics yet is underutilized due to its adverse effects, such as neutropenia, weight gain, and tachycardia. The current investigation aimed to introduce a pharmacometric approach to simultaneously model drug adverse effects, with examples from schizophrenia spectrum patients receiving clozapine. The adverse drug effects were represented as a function of time by incorporating a mixture model to describe individual susceptibility to the adverse effects. Applications of the proposed method were presented by analyzing retrospective data from patients’ medical records in Psychiatric Clinic, Penang General Hospital. Tachycardia, weight gain, and absolute neutrophils count (ANC) decrease were best described by an offset, a piecewise linear, and a transient surge function, respectively. 42.9% of the patients had all the adverse effects, including weight gain (0.01 kg/m2 increase every week over a baseline of 24.7 kg/m2 until stabilizing at 279 weeks), ANC decrease (20% decrease from 4540 cells/µL week 12-20.8), and tachycardia (14% constant increase over a baseline of 87.9 bpm for a clozapine maintenance dose of 450 mg daily). 32.5% of the patients had only tachycardia, while the remaining 24.6% had none of the adverse effects. A new pharmacometric approach was proposed to describe adverse drug effects with examples of clozapine-induced weight gain, ANC drop, and tachycardia. The current approach described the longitudinal time changes of continuous data while assessing patient susceptibility. Furthermore, the model revealed the possible co-existence of ANC drop and weight gain; thus, neutrophil monitoring might predict future changes in body weight.
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| 3. |
- Almquist, Joachim, 1980, et al.
(författare)
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Using sensitivity equations for computing gradients of the FOCE and FOCEI approximations to the population likelihood
- 2015
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 42:3, s. 191-209
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Tidskriftsartikel (refereegranskat)abstract
- The first order conditional estimation (FOCE) method is still one of the parameter estimation workhorses for nonlinear mixed effects (NLME) modeling used in population pharmacokinetics and pharmacodynamics. However, because this method involves two nested levels of optimizations, with respect to the empirical Bayes estimates and the population parameters, FOCE may be numerically unstable and have long run times, issues which are most apparent for models requiring numerical integration of differential equations. We propose an alternative implementation of the FOCE method, and the related FOCEI, for parameter estimation in NLME models. Instead of obtaining the gradients needed for the two levels of quasi-Newton optimizations from the standard finite difference approximation, gradients are computed using so called sensitivity equations. The advantages of this approach were demonstrated using different versions of a pharmacokinetic model defined by nonlinear differential equations. We show that both the accuracy and precision of gradients can be improved extensively, which will increase the chances of a successfully converging parameter estimation. We also show that the proposed approach can lead to markedly reduced computational times. The accumulated effect of the novel gradient computations ranged from a 10-fold decrease in run times for the least complex model when comparing to forward finite differences, to a substantial 100-fold decrease for the most complex model when comparing to central finite differences. Considering the use of finite differences in for instance NONMEM and Phoenix NLME, our results suggests that significant improvements in the execution of FOCE are possible and that the approach of sensitivity equations should be carefully considered for both levels of optimization.
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| 4. |
- Alskär, Oskar, et al.
(författare)
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A pharmacokinetic model for the glycation of albumin
- 2012
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 39:3, s. 273-282
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Tidskriftsartikel (refereegranskat)abstract
- Glycated haemoglobin (HbA1c) concentrations can be falsely lowered in circumstances when red blood cell (RBC) survival is reduced, e.g. in patients with chronic kidney disease (CKD). Glycated albumin (GA) has been suggested as an alternative marker of glycaemic control in these patients since it is independent of the RBC life span. The primary aim of this work was to develop a pharmacokinetic model that describes the time course of GA. The secondary aim was to assess the performance of GA as marker for glycaemic control in comparison to HbA1c based on simulations. For the second aim, three different scenarios were considered in the simulations: 1) assessment of the effect of large intra-day fluctuations in mean blood glucose on GA concentrations, 2) initiation of antidiabetic treatment on the GA profile, and 3) a hypothetical phase II study for a new antidiabetic compound. The GA model, as well as a previously developed HbA1c model described literature data well. GA concentrations appear to be stable even in the presence of high intra-day fluctuations in mean blood glucose concentrations. Simulation of a decrease in mean blood glucose concentrations resulted in a faster change in GA compared to HbA1c. GA also provided a time to 90 % power of the effect of a hypothetical antidiabetic drug that was 16 days shorter than when using HbA1c. These results indicate that GA could be used as alternative marker to assess blood glucose control in diabetic patients with CKD and also to follow an individual patient over time.
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| 5. |
- Andersson, R., et al.
(författare)
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Modeling of free fatty acid dynamics: insulin and nicotinic acid resistance under acute and chronic treatments
- 2017
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1573-8744 .- 1567-567X. ; 44:3, s. 203-222
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Tidskriftsartikel (refereegranskat)abstract
- Nicotinic acid (NiAc) is a potent inhibitor of adipose tissue lipolysis. Acute administration results in a rapid reduction of plasma free fatty acid (FFA) concentrations. Sustained NiAc exposure is associated with tolerance development (drug resistance) and complete adaptation (FFA returning to pretreatment levels). We conducted a meta-analysis on a rich pre-clinical data set of the NiAc-FFA interaction to establish the acute and chronic exposure-response relations from a macro perspective. The data were analyzed using a nonlinear mixed-effects framework. We also developed a new turnover model that describes the adaptation seen in plasma FFA concentrations in lean Sprague-Dawley and obese Zucker rats following acute and chronic NiAc exposure. The adaptive mechanisms within the system were described using integral control systems and dynamic efficacies in the traditional model. Insulin was incorporated in parallel with NiAc as the main endogenous co-variate of FFA dynamics. The model captured profound insulin resistance and complete drug resistance in obese rats. The efficacy of NiAc as an inhibitor of FFA release went from 1 to approximately 0 during sustained exposure in obese rats. The potency of NiAc as an inhibitor of insulin and of FFA release was estimated to be 0.338 and 0.436 , respectively, in obese rats. A range of dosing regimens was analyzed and predictions made for optimizing NiAc delivery to minimize FFA exposure. Given the exposure levels of the experiments, the importance of washout periods in-between NiAc infusions was illustrated. The washout periods should be 2 h longer than the infusions in order to optimize 24 h lowering of FFA in rats. However, the predicted concentration-response relationships suggests that higher AUC reductions might be attained at lower NiAc exposures.
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| 6. |
- Aoki, Yasunori, 1982-, et al.
(författare)
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Model selection and averaging of nonlinear mixed-effect models for robust phase III dose selection
- 2017
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 44:6, s. 581-597
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Tidskriftsartikel (refereegranskat)abstract
- Population model-based (pharmacometric) approaches are widely used for the analyses of phase IIb clinical trial data to increase the accuracy of the dose selection for phase III clinical trials. On the other hand, if the analysis is based on one selected model, model selection bias can potentially spoil the accuracy of the dose selection process. In this paper, four methods that assume a number of pre-defined model structure candidates, for example a set of dose-response shape functions, and then combine or select those candidate models are introduced. The key hypothesis is that by combining both model structure uncertainty and model parameter uncertainty using these methodologies, we can make a more robust model based dose selection decision at the end of a phase IIb clinical trial. These methods are investigated using realistic simulation studies based on the study protocol of an actual phase IIb trial for an oral asthma drug candidate (AZD1981). Based on the simulation study, it is demonstrated that a bootstrap model selection method properly avoids model selection bias and in most cases increases the accuracy of the end of phase IIb decision. Thus, we recommend using this bootstrap model selection method when conducting population model-based decision-making at the end of phase IIb clinical trials.
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| 7. |
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| 8. |
- Arrington, Leticia, et al.
(författare)
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Performance of longitudinal item response theory models in shortened or partial assessments.
- 2020
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 47:5, s. 461-471
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Tidskriftsartikel (refereegranskat)abstract
- This work evaluates the performance of longitudinal item response (IR) theory models in shortened assessments using an existing model for part II and III of the MDS-UPDRS score. Based on the item information content, the assessment was reduced by removal of items in multiple increments and the models' ability to recover the item characteristics of the remaining items at each level was evaluated. This evaluation was done for both simulated and real data. The metric of comparison in both cases was the item information function. For real data, the impact of shortening on the estimated disease progression and drug effect was also studied. In the simulated data setting, the item characteristics did not differ between the full and the shortened assessments down to the lowest level of information remaining; indicating a considerable independence between items. In contrast when reducing the assessment in a real data setting, a substantial change in item information was observed for some of the items. Disease progression and drug effect estimates also decreased in the reduced assessments. These changes indicate a shift in the measured construct of the shortened assessment and warrant caution when comparing results from a partial assessment with results from the full assessment.
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| 9. |
- Arshad, Usman, et al.
(författare)
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Development of visual predictive checks accounting for multimodal parameter distributions in mixture models
- 2019
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : SPRINGER/PLENUM PUBLISHERS. - 1567-567X .- 1573-8744. ; 46:3, s. 241-250
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Tidskriftsartikel (refereegranskat)abstract
- The assumption of interindividual variability being unimodally distributed in nonlinear mixed effects models does not hold when the population under study displays multimodal parameter distributions. Mixture models allow the identification of parameters characteristic to a subpopulation by describing these multimodalities. Visual predictive check (VPC) is a standard simulation based diagnostic tool, but not yet adapted to account for multimodal parameter distributions. Mixture model analysis provides the probability for an individual to belong to a subpopulation (IPmix) and the most likely subpopulation for an individual to belong to (MIXEST). Using simulated data examples, two implementation strategies were followed to split the data into subpopulations for the development of mixture model specific VPCs. The first strategy splits the observed and simulated data according to the MIXEST assignment. A shortcoming of the MIXEST-based allocation strategy was a biased allocation towards the dominating subpopulation. This shortcoming was avoided by splitting observed and simulated data according to the IPmix assignment. For illustration purpose, the approaches were also applied to an irinotecan mixture model demonstrating 36% lower clearance of irinotecan metabolite (SN-38) in individuals with UGT1A1 homo/heterozygote versus wild-type genotype. VPCs with segregated subpopulations were helpful in identifying model misspecifications which were not evident with standard VPCs. The new tool provides an enhanced power of evaluation of mixture models.
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| 10. |
- 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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