| 1. |
- Ueckert, Sebastian, 1983-, et al.
(författare)
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Challenges and potential of optimal design in late phase clinical trials through application in Alzheimer’s disease
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Optimal design is a methodology that can be a valuable tool for the planning of clinical studies. Current applications however, are largely limited to early phases of the drug development process. The increasing complexity in late phase trials is a major reason why optimal design is not applied at these stages. This work uses the example of Alzheimer's disease to investigate challenges and potential of applying optimal design in late phase clinical trials.Information from several sources was used to construct a disease progression model for Alzheimer's disease. The resulting model was used to optimize the study design of an Alzheimer's trial for three distinct metrics: maximal information, minimal number of samples and maximal power to detect a drug effect. Challenges encountered and addressed during the implementation included covariates, dropout and clinical constraints.Depending on the optimization criterion used, the optimal designs had 35% a higher efficiency, needed 33% fewer samples to obtain the same amount of information or required 70% fewer individuals to achieve 80% power compared to the reference design.Optimal design can improve the design and therefore reduce the costs of late phase trials. Several tools and techniques have been identified to address the main challenges connected to this application.
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| 2. |
- Acharya, Chayan, et al.
(författare)
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A diagnostic tool for population models using non-compartmental analysis : The ncappc package for R
- 2016
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Ingår i: Computer Methods and Programs in Biomedicine. - : Elsevier BV. - 0169-2607 .- 1872-7565. ; 127, s. 83-93
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Tidskriftsartikel (refereegranskat)abstract
- Background and objective: Non-compartmental analysis (NCA) calculates pharmacokinetic (PK) metrics related to the systemic exposure to a drug following administration, e.g. area under the concentration time curve and peak concentration. We developed a new package in R, called ncappc, to perform (i) a NCA and (ii) simulation-based posterior predictive checks (ppc) for a population PK (PopPK) model using NCA metrics. Methods: The nca feature of ncappc package estimates the NCA metrics by NCA. The ppc feature of ncappc estimates the NCA metrics from multiple sets of simulated concentration time data and compares them with those estimated from the observed data. The diagnostic analysis is performed at the population as well as the individual level. The distribution of the simulated population means of each NCA metric is compared with the corresponding observed population mean. The individual level comparison is performed based on the deviation of the mean of any NCA metric based on simulations for an individual from the corresponding NCA metric obtained from the observed data. The ncappc package also reports the normalized prediction distribution error (NPDE) of the simulated NCA metrics for each individual and their distribution within a population. Results: The ncappc produces two default outputs depending on the type of analysis performed, i.e., NCA and PopPK diagnosis. The PopPK diagnosis feature of ncappc produces 8 sets of graphical outputs to assess the ability of a population model to simulate the concentration time profile of a drug and thereby evaluate model adequacy. In addition, tabular outputs are generated showing the values of the NCA metrics estimated from the observed and the simulated data, along with the deviation, NPDE, regression parameters used to estimate the elimination rate constant and the related population statistics. Conclusions: The ncappc package is a versatile and flexible tool-set written in R that successfully estimates NCA metrics from concentration time data and produces a comprehensive set of graphical and tabular output to summarize the diagnostic results including the model specific outliers. The output is easy to interpret and to use in evaluation of a population PK model. ncappc is freely available on CRAN (http://crantoprojectorg/web/packages/ncappc/index.html/) and GitHub (https://github.comicacha0227/ncappc/).
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| 3. |
- 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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| 4. |
- Aoki, Yasunori, 1982-, et al.
(författare)
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PopED lite : an optimal design software for preclinical pharmacokinetic and pharmacodynamic studies
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Optimal experimental design approaches are seldom used in pre-clinical drug discovery. Main reasons for this lack of use are that available software tools require relatively high insight in optimal design theory, and that the design-execution cycle of in vivo experiments is short, making time-consuming optimizations infeasible. We present the publicly available software PopED lite in order to increase the use of optimal design in pre-clinical drug discovery. PopED lite is designed to be simple, fast and intuitive. Simple, to give many users access to basic optimal design calculations. Fast, to fit the short design-execution cycle and allow interactive experimental design (test one design, discuss proposed design, test another design, etc). Intuitive, so that the input to and output from the software can easily be understood by users without knowledge of the theory of optimal design. In this way, PopED lite is highly useful in practice and complements existing tools. Key functionality of PopED lite is demonstrated by three case studies from real drug discovery projects.
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| 5. |
- Aoki, Yasunori, et al.
(författare)
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PopED lite: an optimal design software for preclinical pharmacokinetic and pharmacodynamic studies
- 2016
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Ingår i: Computer Methods and Programs in Biomedicine. - : Elsevier BV. - 0169-2607 .- 1872-7565. ; 127, s. 126-143
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Tidskriftsartikel (refereegranskat)abstract
- Background and ObjectiveOptimal experimental design approaches are seldom used in preclinical drug discovery. The objective is to develop an optimal design software tool specifically designed for preclinical applications in order to increase the efficiency of drug discovery in vivo studies.MethodsSeveral realistic experimental design case studies were collected and many preclinical experimental teams were consulted to determine the design goal of the software tool. The tool obtains an optimized experimental design by solving a constrained optimization problem, where each experimental design is evaluated using some function of the Fisher Information Matrix. The software was implemented in C++ using the Qt framework to assure a responsive user-software interaction through a rich graphical user interface, and at the same time, achieving the desired computational speed. In addition, a discrete global optimization algorithm was developed and implemented.ResultsThe software design goals were simplicity, speed and intuition. Based on these design goals, we have developed the publicly available software PopED lite (http://www.bluetree.me/PopED_lite). Optimization computation was on average, over 14 test problems, 30 times faster in PopED lite compared to an already existing optimal design software tool. PopED lite is now used in real drug discovery projects and a few of these case studies are presented in this paper.ConclusionsPopED lite is designed to be simple, fast and intuitive. Simple, to give many users access to basic optimal design calculations. Fast, to fit a short design-execution cycle and allow interactive experimental design (test one design, discuss proposed design, test another design, etc). Intuitive, so that the input to and output from the software tool can easily be understood by users without knowledge of the theory of optimal design. In this way, PopED lite is highly useful in practice and complements existing tools.
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| 6. |
- Aoki, Yasunori, et al.
(författare)
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Preconditioning of Nonlinear Mixed Effects Models for Stabilisation of Variance-Covariance Matrix Computations
- 2016
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Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 18:2, s. 505-518
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Tidskriftsartikel (refereegranskat)abstract
- As the importance of pharmacometric analysis increases, more and more complex mathematical models are introduced and computational error resulting from computational instability starts to become a bottleneck in the analysis. We propose a preconditioning method for non-linear mixed effects models used in pharmacometric analyses to stabilise the computation of the variance-covariance matrix. Roughly speaking, the method reparameterises the model with a linear combination of the original model parameters so that the Hessian matrix of the likelihood of the reparameterised model becomes close to an identity matrix. This approach will reduce the influence of computational error, for example rounding error, to the final computational result. We present numerical experiments demonstrating that the stabilisation of the computation using the proposed method can recover failed variance-covariance matrix computations, and reveal non-identifiability of the model parameters.
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| 7. |
- 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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| 8. |
- Bergstrand, Martin, 1977-, et al.
(författare)
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Prediction-Corrected Visual Predictive Checks for Diagnosing Nonlinear Mixed-Effects Models
- 2011
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Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 13:2, s. 143-151
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Tidskriftsartikel (refereegranskat)abstract
- Informative diagnostic tools are vital to the development of useful mixed-effects models. The Visual Predictive Check (VPC) is a popular tool for evaluating the performance of population PK and PKPD models. Ideally, a VPC will diagnose both the fixed and random effects in a mixed-effects model. In many cases, this can be done by comparing different percentiles of the observed data to percentiles of simulated data, generally grouped together within bins of an independent variable. However, the diagnostic value of a VPC can be hampered by binning across a large variability in dose and/or influential covariates. VPCs can also be misleading if applied to data following adaptive designs such as dose adjustments. The prediction-corrected VPC (pcVPC) offers a solution to these problems while retaining the visual interpretation of the traditional VPC. In a pcVPC, the variability coming from binning across independent variables is removed by normalizing the observed and simulated dependent variable based on the typical population prediction for the median independent variable in the bin. The principal benefit with the pcVPC has been explored by application to both simulated and real examples of PK and PKPD models. The investigated examples demonstrate that pcVPCs have an enhanced ability to diagnose model misspecification especially with respect to random effects models in a range of situations. The pcVPC was in contrast to traditional VPCs shown to be readily applicable to data from studies with a priori and/or a posteriori dose adaptations.
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| 9. |
- Bizzotto, Roberto, et al.
(författare)
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Multinomial Logistic Functions in Markov Chain Models of Sleep Architecture : Internal and External Validation and Covariate Analysis
- 2011
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Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 13:3, s. 445-463
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Tidskriftsartikel (refereegranskat)abstract
- Mixed-effect Markov chain models have been recently proposed to characterize the time course of transition probabilities between sleep stages in insomniac patients. The most recent one, based on multinomial logistic functions, was used as a base to develop a final model combining the strengths of the existing ones. This final model was validated on placebo data applying also new diagnostic methods and then used for the inclusion of potential age, gender, and BMI effects. Internal validation was performed through simplified posterior predictive check (sPPC), visual predictive check (VPC) for categorical data, and new visual methods based on stochastic simulation and estimation and called visual estimation check (VEC). External validation mainly relied on the evaluation of the objective function value and sPPC. Covariate effects were identified through stepwise covariate modeling within NONMEM VI. New model features were introduced in the model, providing significant sPPC improvements. Outcomes from VPC, VEC, and external validation were generally very good. Age, gender, and BMI were found to be statistically significant covariates, but their inclusion did not improve substantially the model's predictive performance. In summary, an improved model for sleep internal architecture has been developed and suitably validated in insomniac patients treated with placebo. Thereafter, covariate effects have been included into the final model.
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| 10. |
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