| 1. |
- Swat, M. J., et al.
(författare)
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Pharmacometrics Markup Language (PharmML) : Opening New Perspectives for Model Exchange in Drug Development
- 2015
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Ingår i: CPT. - : American Society for Clinical Pharmacology & Therapeutics. - 2163-8306. ; 4:6, s. 316-319
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Tidskriftsartikel (refereegranskat)abstract
- The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.
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| 2. |
- Nguyen, T. H. T., et al.
(författare)
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Model Evaluation of Continuous Data Pharmacometric Models : Metrics and Graphics
- 2017
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Ingår i: CPT. - : WILEY. - 2163-8306. ; 6:2, s. 87-109
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Tidskriftsartikel (refereegranskat)abstract
- This article represents the first in a series of tutorials on model evaluation in nonlinear mixed effect models (NLMEMs), from the International Society of Pharmacometrics (ISoP) Model Evaluation Group. Numerous tools are available for evaluation of NLMEM, with a particular emphasis on visual assessment. This first basic tutorial focuses on presenting graphical evaluation tools of NLMEM for continuous data. It illustrates graphs for correct or misspecified models, discusses their pros and cons, and recalls the definition of metrics used.
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| 3. |
- 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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| 4. |
- Ernest, C. Steven, II, et al.
(författare)
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Methodological Comparison of In Vitro Binding Parameter Estimation : Sequential vs. Simultaneous Non-linear Regression
- 2010
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Ingår i: Pharmaceutical research. - : Springer Science and Business Media LLC. - 0724-8741 .- 1573-904X. ; 27:5, s. 866-877
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Tidskriftsartikel (refereegranskat)abstract
- Analysis of simulated data was compared using sequential (NLR) and simultaneous non-linear regression (SNLR) to evaluate precision and accuracy of ligand binding parameter estimation. Commonly encountered experimental error, specifically residual error of binding measurements (RE), experiment-to-experiment variability (BEV) and non-specific binding (B-NS), were examined for impact of parameter estimation using both methods. Data from equilibrium, dissociation, association and non-specific binding experiments were fit simultaneously (SNLR) using NONMEM VI compared to the common practice of analyzing data from each experiment separately and assigning these as exact values (NLR) for estimation of the subsequent parameters. The greatest contributing factor to bias and variability in parameter estimation was RE of the measured concentrations of ligand bound; however, SNLR provided more accurate and less bias estimates. Subtraction of B-NS from total ligand binding data provided poor estimation of specific ligand binding parameters using both NLR and SNLR. Additional methods examined demonstrated that the use of SNLR provided better estimation of specific binding parameters, whereas there was considerable bias using NLR. NLR cannot account for BEV, whereas SNLR can provide approximate estimates of BEV. SNLR provided superior resolution of parameter estimation in both precision and accuracy compared to NLR.
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| 5. |
- Geroldinger, Martin, et al.
(författare)
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Statistical recommendations for count, binary, and ordinal data in rare disease cross-over trials
- 2023
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Ingår i: Orphanet Journal of Rare Diseases. - : BioMed Central (BMC). - 1750-1172. ; 18:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundRecommendations for statistical methods in rare disease trials are scarce, especially for cross-over designs. As a result various state-of-the-art methodologies were compared as neutrally as possible using an illustrative data set from epidermolysis bullosa research to build recommendations for count, binary, and ordinal outcome variables. For this purpose, parametric (model averaging), semiparametric (generalized estimating equations type [GEE-like]) and nonparametric (generalized pairwise comparisons [GPC] and a marginal model implemented in the R package nparLD) methods were chosen by an international consortium of statisticians.ResultsIt was found that there is no uniformly best method for the aforementioned types of outcome variables, but in particular situations, there are methods that perform better than others. Especially if maximizing power is the primary goal, the prioritized unmatched GPC method was able to achieve particularly good results, besides being appropriate for prioritizing clinically relevant time points. Model averaging led to favorable results in some scenarios especially within the binary outcome setting and, like the GEE-like semiparametric method, also allows for considering period and carry-over effects properly. Inference based on the nonparametric marginal model was able to achieve high power, especially in the ordinal outcome scenario, despite small sample sizes due to separate testing of treatment periods, and is suitable when longitudinal and interaction effects have to be considered.ConclusionOverall, a balance has to be found between achieving high power, accounting for cross-over, period, or carry-over effects, and prioritizing clinically relevant time points.
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| 6. |
- Karlsson, Kristin C., et al.
(författare)
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Modeling subpopulations with the $MIXTURE subroutine in NONMEM : finding the individual probability of belonging to a subpopulation for the use in model analysis and improved decision making
- 2009
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Ingår i: AAPS Journal. - : Springer. - 1550-7416. ; 11:1, s. 148-154
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Tidskriftsartikel (refereegranskat)abstract
- In nonlinear mixed effects modeling using NONMEM, mixture models can be used for multimodal distributions of parameters. The fraction of individuals belonging to each of the subpopulations can be estimated, and the most probable subpopulation for each patient is output (MIXEST(k)). The objective function value (OFV) that is minimized is the sum of the OFVs for each patient (OFV(i)), which in turn is the sum across the k subpopulations (OFV(i,k)). The OFV(i,k) values can be used together with the total probability in the population of belonging to subpopulation k to calculate the individual probability of belonging to the subpopulation (IP(k)). Our objective was to explore the information gained by using IP(k) instead of or in addition to MIXEST(k) in the analysis of mixture models. Two real data sets described previously by mixture models as well as simulations were used to explore the use of IP(k) and the precision of individual parameter values based on IP(k) and MIXEST(k). For both real data-based mixture models, a substantial fraction (11% and 26%) of the patients had IP(k) values not close to 0 or 1 (IP(k) between 0.25 and 0.75). Simulations of eight different scenarios showed that individual parameter estimates based on MIXEST were less precise than those based on IP(k), as the root mean squared error was reduced for IP(k) in all scenarios. A probability estimate such as IP(k) provides more detailed information about each individual than the discrete MIXEST(k). Individual parameter estimates based on IP(k) should be preferable whenever individual parameter estimates are to be used as study output or for simulations.
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| 7. |
- 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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| 8. |
- 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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| 9. |
- 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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| 10. |
- 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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