| 1. |
- 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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| 2. |
- Bauer, Robert J., et al.
(författare)
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Tutorial for $DESIGN in NONMEM : Clinical trial evaluation and optimization
- 2021
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Ingår i: CPT. - : John Wiley & Sons. - 2163-8306. ; 10:12, s. 1452-1465
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Tidskriftsartikel (refereegranskat)abstract
- This NONMEM tutorial shows how to evaluate and optimize clinical trial designs, using algorithms developed in design software, such as PopED and PFIM 4.0. Parameter precision and model parameter estimability is obtained by assessing the Fisher Information Matrix (FIM), providing expected model parameter uncertainty. Model parameter identifiability may be uncovered by very large standard errors or inability to invert an FIM. Because evaluation of FIM is more efficient than clinical trial simulation, more designs can be investigated, and the design of a clinical trial can be optimized. This tutorial provides simple and complex pharmacokinetic/pharmacodynamic examples on obtaining optimal sample times, doses, or best division of subjects among design groups. Robust design techniques accounting for likely variability among subjects are also shown. A design evaluator and optimizer within NONMEM allows any control stream first developed for trial design exploration to be subsequently used for estimation of parameters of simulated or clinical data, without transferring the model to another software. Conversely, a model developed in NONMEM could be used for design optimization. In addition, the $DESIGN feature can be used on any model file and dataset combination to retrospectively evaluate the model parameter uncertainty one would expect given that the model generated the data, particularly if outliers of the actual data prevent a reasonable assessment of the variance-covariance. The NONMEM trial design feature is suitable for standard continuous data, whereas more elaborate trial designs or with noncontinuous data-types can still be accomplished in optimal design dedicated software like PopED and PFIM.
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| 3. |
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| 4. |
- Bjugård Nyberg, Henrik, 1984-
(författare)
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Garnishing the smorgasbord of pharmacometric methods
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The smorgasbord of methods that we use within the field of pharmacometrics has developed steadily over several decades and is now a well-laid-out buffet. This thesis adds some garnish to the table in the form of small improvements to the handling of certain problems.The first problem tackled by the thesis was the challenge of saddle points and local non-identifiability when estimating pharmacometric model parameters. Substituting the common method of randomly perturbing the initial parameter estimates with one saddle-reset step enhances the accuracy of maximum likelihood estimates by overcoming saddle points parameter values, a common issue in nonlinear mixed-effects models. This algorithm, as implemented in the NONMEM software, was applied to various identifiable and nonidentifiable pharmacometric models, showing improved performance over traditional methods.Part of the thesis was dedicated to the development of a paediatric pharmacokinetic model for ethionamide, a drug used in treating multidrug-resistant tuberculosis. The resulting model was then used to simulate drug exposure under different dosing regimens, a new dosing regimen for children was proposed. The developed model, and therefore the proposed paediatric dosing regimen, considers factors like maturation of pharmacokinetic pathways and, administration by nasogastric tube, and concurrent rifampicin treatment. The regimen, with some modifications, was adopted in the 2022 update to the World Health Organization operational handbook on tuberculosis.Finally, the thesis explored novel model-integrated evidence (MIE) approaches for bioequivalence (BE) determination. Such methods could offer more robust alternatives to standard BE approached using non-compartmental analysis (NCA). Model-based methods have been shown to be advantageous in sparse data situations, such as is found in studies of ophthalmic formulations, but have suffered from inflated type I error rates. MIE BE approaches using a single model or using model averaging were presented and shown to control type I error at the nominal level while demonstrating increased power in bioequivalence determination.
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| 5. |
- Bjugård Nyberg, Henrik, 1984-, et al.
(författare)
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Population Pharmacokinetics and Dosing of Ethionamide in Children with Tuberculosis
- 2020
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Ingår i: Antimicrobial Agents and Chemotherapy. - : American Society for Microbiology. - 0066-4804 .- 1098-6596. ; 64:3
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Tidskriftsartikel (refereegranskat)abstract
- Ethionamide has proven efficacy against both drug-susceptible and some drug-resistant strains of Mycobacterium tuberculosis. Limited information on its pharmacokinetics in children is available, and current doses are extrapolated from weight-based adult doses. Pediatric doses based on more robust evidence are expected to improve antituberculosis treatment, especially in small children. In this analysis, ethionamide concentrations in children from 2 observational clinical studies conducted in Cape Town, South Africa, were pooled. All children received ethionamide once daily at a weight-based dose of approximately 20 mg/kg of body weight (range, 10.4 to 25.3 mg/kg) in combination with other first- or second-line antituberculosis medications and with antiretroviral therapy in cases of HIV coinfection. Pharmacokinetic parameters were estimated using nonlinear mixed-effects modeling. The MDR-PK1 study contributed data for 110 children on treatment for multidrug-resistant tuberculosis, while the DATiC study contributed data for 9 children treated for drug-susceptible tuberculosis. The median age of the children in the studies combined was 2.6 years (range, 0.23 to 15 years), and the median weight was 12.5 kg (range, 2.5 to 66 kg). A one-compartment, transit absorption model with first-order elimination best described ethionamide pharmacokinetics in children. Allometric scaling of clearance (typical value, 8.88 liters/h), the volume of distribution (typical value, 21.4 liters), and maturation of clearance and absorption improved the model fit. HIV coinfection decreased the ethionamide bioavailability by 22%, rifampin coadministration increased clearance by 16%, and ethionamide administration by use of a nasogastric tube increased the rate, but the not extent, of absorption. The developed model was used to predict pediatric doses achieving the same drug exposure achieved in 50- to 70-kg adults receiving 750-mg once-daily dosing. Based on model predictions, we recommend a weight-banded pediatric dosing scheme using scored 125-mg tablets.
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| 6. |
- Bjugård Nyberg, Henrik, et al.
(författare)
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Saddle-Reset for Robust Parameter Estimation and Identifiability Analysis of Nonlinear Mixed Effects Models
- 2020
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Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 22:4
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Tidskriftsartikel (refereegranskat)abstract
- Parameter estimation of a nonlinear model based on maximizing the likelihood using gradient-based numerical optimization methods can often fail due to premature termination of the optimization algorithm. One reason for such failure is that these numerical optimization methods cannot distinguish between the minimum, maximum, and a saddle point; hence, the parameters found by these optimization algorithms can possibly be in any of these three stationary points on the likelihood surface. We have found that for maximization of the likelihood for nonlinear mixed effects models used in pharmaceutical development, the optimization algorithm Broyden-Fletcher-Goldfarb-Shanno (BFGS) often terminates in saddle points, and we propose an algorithm, saddle-reset, to avoid the termination at saddle points, based on the second partial derivative test. In this algorithm, we use the approximated Hessian matrix at the point where BFGS terminates, perturb the point in the direction of the eigenvector associated with the lowest eigenvalue, and restart the BFGS algorithm. We have implemented this algorithm in industry standard software for nonlinear mixed effects modeling (NONMEM, version 7.4 and up) and showed that it can be used to avoid termination of parameter estimation at saddle points, as well as unveil practical parameter non-identifiability. We demonstrate this using four published pharmacometric models and two models specifically designed to be practically non-identifiable.
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| 7. |
- Brekkan, Ari, et al.
(författare)
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A Population Pharmacokinetic-Pharmacodynamic Model of Pegfilgrastim
- 2018
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Ingår i: AAPS Journal. - : SPRINGER. - 1550-7416. ; 20:5
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Tidskriftsartikel (refereegranskat)abstract
- Neutropenia and febrile neutropenia (FN) are serious side effects of cytotoxic chemotherapy which may be alleviated with the administration of recombinant granulocyte colony-stimulating factor (GCSF) derivatives, such as pegfilgrastim (PG) which increases absolute neutrophil count (ANC). In this work, a population pharmacokinetic-pharmacodynamic (PKPD) model was developed based on data obtained from healthy volunteers receiving multiple administrations of PG. The developed model was a bidirectional PKPD model, where PG stimulated the proliferation, maturation, and margination of neutrophils and where circulating neutrophils in turn increased the elimination of PG. Simulations from the developed model show disproportionate changes in response with changes in dose. A dose increase of 10% from the 6 mg therapeutic dose taken as a reference leads to area under the curve (AUC) increases of similar to 50 and similar to 5% for PK and PD, respectively. A full random effects covariate model showed that little of the parameter variability could be explained by sex, age, body size, and race. As a consequence, little of the secondary parameter variability (C-max and AUC of PG and ANC) could be explained by these covariates.
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| 8. |
- Brekkan, Ari, et al.
(författare)
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Reduced and optimized trial designs for drugs described by a target mediated drug disposition model
- 2018
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : SPRINGER/PLENUM PUBLISHERS. - 1567-567X .- 1573-8744. ; 45:4, s. 637-647
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Tidskriftsartikel (refereegranskat)abstract
- Monoclonal antibodies against soluble targets are often rich and include the sampling of multiple analytes over a lengthy period of time. Predictive models built on data obtained in such studies can be useful in all drug development phases. If adequate model predictions can be maintained with a reduced design (e.g. fewer samples or shorter duration) the use of such designs may be advocated. The effect of reducing and optimizing a rich design based on a published study for Omalizumab (OMA) was evaluated as an example. OMA pharmacokinetics were characterized using a target-mediated drug disposition model considering the binding of OMA to free IgE and the subsequent formation of an OMA-IgE complex. The performance of the reduced and optimized designs was evaluated with respect to: efficiency, parameter uncertainty and predictions of free target. It was possible to reduce the number of samples in the study by 30% while still maintaining an efficiency of almost 90%. A reduction in sampling duration by two-thirds resulted in an efficiency of 75%. Omission of any analyte measurement or a reduction of the number of dose levels was detrimental to the efficiency of the designs (efficiency ae 51%). However, other metrics were, in some cases, relatively unaffected, showing that multiple metrics may be needed to obtain balanced assessments of design performance.
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| 9. |
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| 10. |
- Chasseloup, Estelle
(författare)
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Methods for handling model misspecification in pharmacometric model-based approaches for the analysis of longitudinal clinical trial data
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Pharmacometrics model-based analyses proved to be increasingly useful in drug discovery and development. This usefulness was made evident by the formalisation of the model-integrated drug discovery and development framework (MID3). The overall objective of this thesis was to assess and improve the robustness of longitudinal pharmacometric-model-based approaches toward model misspecification. IMA is a new approach using mixture models to test for a drug effect. It was presented and assessed together with other approaches, including model averaging, in the context of controlled balanced two-armed parallel trials. The assessment (type I error, power, and accuracy) carried out over real and simulated data demonstrated problems with the existing method, caused by model misspecification and selection bias, and the robustness of IMA towards these issues. In the same context another novel approach presented and assessed among the alternatives, rcLRT, also showed robustness and good performance. IMA was generalised to unbalanced designs and dose-response scenarios where it confirmed its good performance. A new extension of the visual predictive checks diagnostic tool to the specific case of mixture models was presented. It was able to diagnose the mixture proportion aspect, enabling the diagnostic of model misspecification in that aspect. PACS is a new approach balancing data information and prior information in the selection process between correlated covariates, in which the prior was introduced as a penalty to the objective function. PACS was able to influence to some extent the final covariate selection process and resulted in a strategy not previously described as the best under several conditions This approach is an interesting tool against data-driven misspecification when strong prior knowledge is available. A new methodology revolving around "digital twins" in pharmacometrics was presented as another alternative to making analysis results robust with respect to model approximations. The method filtered the simulations based on observed data. The selected avatars are both insurance that some of the simulated data are mimicking reasonably well the observations at clinical endpoints of interest, and a diagnostic tool for model misspecification when the avatar subset is too different from the raw model simulations. In conclusion, the new pharmacometric approaches provide robust methods towards model misspecification, which opens up new possibilities for the use of pharmacometrics models in drug development.
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