| 1. |
- 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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| 2. |
- Buatois, Simon, et al.
(författare)
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cLRT-Mod : An efficient methodology for pharmacometric model-based analysis of longitudinal phase II dose finding studies under model uncertainty
- 2021
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Ingår i: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 40:10, s. 2435-2451
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Tidskriftsartikel (refereegranskat)abstract
- Within the challenging context of phase II dose-finding trials, longitudinal analyses may increase drug effect detection power compared to an end-of-treatment analysis. This work proposes cLRT-Mod, a pharmacometric adaptation of the MCP-Mod methodology, which allows the use of nonlinear mixed effect models to first detect a dose-response signal and then identify the doses for the confirmatory phase while accounting for model structure uncertainty. The method was evaluated through extensive clinical trial simulations of a hypothetical phase II dose-finding trial using different scenarios and comparing different methods such as MCP-Mod. The results show an increase in power using cLRT with longitudinal data compared to an EOT multiple contrast tests for scenarios with small sample size and weak drug effect while maintaining pre-specifiability of the models prior to data analysis and the nominal type I error. This work shows how model averaging provides better coverage probability of the drug effect in the prediction step, and avoids under-estimation of the size of the confidence interval. Finally, for illustration purpose cLRT-Mod was applied to the analysis of a real phase II dose-finding trial.
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| 3. |
- Buatois, Simon, et al.
(författare)
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Comparison of Model Averaging and Model Selection in Dose Finding Trials Analyzed by Nonlinear Mixed Effect Models
- 2018
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Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 20:3
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Tidskriftsartikel (refereegranskat)abstract
- In drug development, pharmacometric approaches consist in identifying via a model selection (MS) process the model structure that best describes the data. However, making predictions using a selected model ignores model structure uncertainty, which could impair predictive performance. To overcome this drawback, model averaging (MA) takes into account the uncertainty across a set of candidate models by weighting them as a function of an information criterion. Our primary objective was to use clinical trial simulations (CTSs) to compare model selection (MS) with model averaging (MA) in dose finding clinical trials, based on the AIC information criterion. A secondary aim of this analysis was to challenge the use of AIC by comparing MA and MS using five different information criteria. CTSs were based on a nonlinear mixed effect model characterizing the time course of visual acuity in wet age-related macular degeneration patients. Predictive performances of the modeling approaches were evaluated using three performance criteria focused on the main objectives of a phase II clinical trial. In this framework, MA adequately described the data and showed better predictive performance than MS, increasing the likelihood of accurately characterizing the dose-response relationship and defining the minimum effective dose. Moreover, regardless of the modeling approach, AIC was associated with the best predictive performances.
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| 7. |
- Matuozzo, Daniela, et al.
(författare)
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Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19.
- 2023
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Ingår i: Genome medicine. - 1756-994X. ; 15:1
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Tidskriftsartikel (refereegranskat)abstract
- We previously reported that impaired type I IFN activity, due to inborn errors of TLR3- and TLR7-dependent type I interferon (IFN) immunity or to autoantibodies against type I IFN, account for 15-20% of cases of life-threatening COVID-19 in unvaccinated patients. Therefore, the determinants of life-threatening COVID-19 remain to be identified in~80% of cases.We report here a genome-wide rare variant burden association analysis in 3269 unvaccinated patients with life-threatening COVID-19, and 1373 unvaccinated SARS-CoV-2-infected individuals without pneumonia. Among the 928 patients tested for autoantibodies against type I IFN, a quarter (234) were positive and were excluded.No gene reached genome-wide significance. Under a recessive model, the most significant gene with at-risk variants was TLR7, with an OR of 27.68 (95%CI 1.5-528.7, P=1.1×10-4) for biochemically loss-of-function (bLOF) variants. We replicated the enrichment in rare predicted LOF (pLOF) variants at 13 influenza susceptibility loci involved in TLR3-dependent type I IFN immunity (OR=3.70[95%CI 1.3-8.2], P=2.1×10-4). This enrichment was further strengthened by (1) adding the recently reported TYK2 and TLR7 COVID-19 loci, particularly under a recessive model (OR=19.65[95%CI 2.1-2635.4], P=3.4×10-3), and (2) considering as pLOF branchpoint variants with potentially strong impacts on splicing among the 15 loci (OR=4.40[9%CI 2.3-8.4], P=7.7×10-8). Finally, the patients with pLOF/bLOF variants at these 15 loci were significantly younger (mean age [SD]=43.3 [20.3] years) than the other patients (56.0 [17.3] years; P=1.68×10-5).Rare variants of TLR3- and TLR7-dependent type I IFN immunity genes can underlie life-threatening COVID-19, particularly with recessive inheritance, in patients under 60years old.
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| 8. |
- Mentre, France, et al.
(författare)
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Pharmacometrics and Systems Pharmacology 2030
- 2020
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Ingår i: Clinical Pharmacology and Therapeutics. - : Wiley. - 0009-9236 .- 1532-6535. ; 107:1, s. 76-78
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- In 2012, a new journal was launched from the ASCPT family, CPT: Pharmacometrics and Systems Pharmacology (PSP) as both quantitative system pharmacology (QSP) and pharmacometrics were growing fields in pharmacology, drug development, and drug use. In this Perspective, the present editors and associate editors of PSP want to share their strategic vision of where these two fields, separately and together, should, would, or could be 10 years from now.
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| 9. |
- Mentre, France, et al.
(författare)
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Software for optimal design in population pharmacokinetics and pharmacodynamics : a comparison
- 2007
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Konferensbidrag (refereegranskat)abstract
- Introduction: Following the first theoretical work on optimal design for nonlinear mixed effect models, this research theme has rapidly grown both in methodological and application developments. There are now several different software tools that implement an evaluation of the Fisher information matrix for population PK and PD models and proposed optimization of the experimental designs. In 2006, the Population Optimal Design of Experiments workshop was created with a meeting every year in May (www.maths.qmul.ac.uk/~bb/PODE/PODE2007.html). This year at PODE07 a special session was organized to present different software tools for population PK/PD optimal design and to compare them with respect to their statistical methodology. Objectives: 1) To present the different software tools; 2) To compare the statistical methods implemented in these tools; 3) To report the conclusion of the PODE07 meeting with respect to future software development in population PK/PD design. Methods: The software tools will be compared with respect to: a) their availability, b) required language, c) library of PK or PD models, d) ability to deal with multiresponse models and/or with models defined by differential equations, e) approximations made to compute the Fisher information matrix, f) optimisation criteria, g)optimisation algorithms, h) ability to optimize design structure, i) ability to deal with constraints in sampling times, j) availability of optimisation trough sampling windows, k) assessment of user specified designs, l) ability to deal with unbalanced multiresponse designs, m) ability to deal with correlations between random effects, o) provided outputs ... Results: The five software tools discussed at PODE07 are (in alphabetical order): PFIM (S. Retout & F. Mentré), PkStaMP (S. Leonov), PopDes (K. Ogungbenro & I. Gueorguieva) PopED (A. Hooker), and WinPOPT (S. Duffull). Tables comparing the software with respect to the different aspects described in the method section will be reported. The conclusions of the PODE07 meeting regarding future software development for optimal design in population PK/PD will be presented.
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| 10. |
- Nyberg, Joakim, et al.
(författare)
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Methods and software tools for design evaluation for population pharmacokinetics-pharmacodynamics studies
- 2015
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Ingår i: British Journal of Clinical Pharmacology. - : Wiley. - 0306-5251 .- 1365-2125. ; 79:1, s. 6-17
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Tidskriftsartikel (refereegranskat)abstract
- Population Pharmacokinetic (PK)-Pharmacodynamic (PD) (PKPD) models are increasingly used in drug development and in academic research. Hence designing efficient studies is an important task. Following the first theoretical work on optimal design for nonlinear mixed effect models, this research theme has grown rapidly. There are now several different software tools that implement an evaluation of the Fisher information matrix for population PKPD. We compared and evaluated five software tools: PFIM, PkStaMP, PopDes, PopED, and POPT. The comparisons were performed using two models: i) a simple one compartment warfarin PK model; ii) a more complex PKPD model for Pegylated-interferon (peg-interferon) with both concentration and response of viral load of hepatitis C virus (HCV) data. The results of the software were compared in terms of the standard error values of the parameters (SE) predicted from the software and the empirical SE values obtained via replicated clinical trial simulation and estimation. For the warfarin PK model and the peg-interferon PKPD model all software gave similar results. Of interest it was seen, for all software, that the simpler approximation to the Fisher information matrix, using the block diagonal matrix, provided predicted SE values that were closer to the empirical SE values than when the more complicated approximation was used (the full matrix). For most PKPD models, using any of the available software tools will provide meaningful results, avoiding cumbersome simulation and allowing design optimization.
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| 11. |
- Plan, Elodie L., et al.
(författare)
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Performance comparison of various maximum likelihood nonlinear mixed-effects estimation methods for dose-response models
- 2012
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Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 14:3, s. 420-432
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Tidskriftsartikel (refereegranskat)abstract
- Estimation methods for nonlinear mixed-effects modelling have considerably improved over the last decades. Nowadays, several algorithms implemented in different software are used. The present study aimed at comparing their performance for dose–response models. Eight scenarios were considered using a sigmoid E max model, with varying sigmoidicity and residual error models. One hundred simulated datasets for each scenario were generated. One hundred individuals with observations at four doses constituted the rich design and at two doses, the sparse design. Nine parametric approaches for maximum likelihood estimation were studied: first-order conditional estimation (FOCE) in NONMEM and R, LAPLACE in NONMEM and SAS, adaptive Gaussian quadrature (AGQ) in SAS, and stochastic approximation expectation maximization (SAEM) in NONMEM and MONOLIX (both SAEM approaches with default and modified settings). All approaches started first from initial estimates set to the true values and second, using altered values. Results were examined through relative root mean squared error (RRMSE) of the estimates. With true initial conditions, full completion rate was obtained with all approaches except FOCE in R. Runtimes were shortest with FOCE and LAPLACE and longest with AGQ. Under the rich design, all approaches performed well except FOCE in R. When starting from altered initial conditions, AGQ, and then FOCE in NONMEM, LAPLACE in SAS, and SAEM in NONMEM and MONOLIX with tuned settings, consistently displayed lower RRMSE than the other approaches. For standard dose–response models analyzed through mixed-effects models, differences were identified in the performance of estimation methods available in current software, giving material to modellers to identify suitable approaches based on an accuracy-versus-runtime trade-off.
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| 12. |
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| 13. |
- Smith, Mike K., et al.
(författare)
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Model Description Language (MDL) : A Standard for Modeling and Simulation
- 2017
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Ingår i: CPT. - : WILEY. - 2163-8306. ; 6:10, s. 647-650
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Tidskriftsartikel (refereegranskat)abstract
- Recent work on Model Informed Drug Discovery and Development (MID3) has noted the need for clarity in model description used in quantitative disciplines such as pharmacology and statistics. 1-3 Currently, models are encoded in a variety of computer languages and are shared through publications that rarely include original code and generally lack reproducibility. The DDMoRe Model Description Language (MDL) has been developed primarily as a language standard to facilitate sharing knowledge and understanding of models.
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| 14. |
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| 15. |
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| 16. |
- Zingmark, Per-Henrik, 1972-
(författare)
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Models for Ordered Categorical Pharmacodynamic Data
- 2005
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In drug development clinical trials are designed to investigate whether a new treatment is safe and has the desired effect on the disease in the target patient population. Categorical endpoints, for example different ranking scales or grading of adverse events, are commonly used to measure effects in the trials. Pharmacokinetic/Pharmacodynamic (PK/PD) models are used to describe the plasma concentration of a drug over time and its relationship to the effect studied. The models are utilized both in drug development and in discussions with drug regulating authorities. Methods for incorporation of ordered categorical data in PK/PD models were studied using a non-linear mixed effects modelling approach as implemented in the software NONMEM. The traditionally used proportional odds model was used for analysis of a 6-grade sedation scale in acute stroke patients and for analysis of a T-cell receptor expression in patients with Multiple Sclerosis, where the results also were compared with an analysis of the data on a continuous scale. Modifications of the proportional odds model were developed to enable analysis of a spontaneously reported side-effect and to analyze situations where the scale used is heterogeneous or where the drug affects the different scores in the scale in a non-proportional way. The new models were compared with the proportional odds model and were shown to give better predictive performances in the analyzed situations. The results in this thesis show that categorical data obtained in clinical trials with different design and different categorical endpoints successfully can be incorporated in PK/PD models. The models developed can also be applied to analyses of other ordered categorical scales than those presented.
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