1.
Papathanasiou, Theodoros, et al.
(författare)
Feasibility of Exposure-Response Analyses for Clinical Dose-Ranging Studies of Drug Combinations
2018
Ingår i: AAPS Journal. - : SPRINGER. - 1550-7416. ; 20:3
Tidskriftsartikel (refereegranskat) abstract
The exposure-response relationship of combinatory drug effects can be quantitatively described using pharmacodynamic interaction models, which can be used for the selection of optimal dose combinations. The aim of this simulation study was to evaluate the reliability of parameter estimates and the probability for accurate dose identification for various underlying exposure-response profiles, under a number of different phase II designs. An efficacy variable driven by the combined exposure of two theoretical compounds was simulated and model parameters were estimated using two different models, one estimating all parameters and one assuming that adequate previous knowledge for one drug is readily available. Estimation of all pharmacodynamic parameters under a realistic, in terms of sample size and study design, phase II trial, proved to be challenging. Inaccurate estimates were found in all exposure-response scenarios, except for situations where no pharmacodynamic interaction was present, with the drug potency and interaction parameters being the hardest to estimate. When previous knowledge of the exposure-response relationship of one of the monocomponents is available, such information should be utilized, as it enabled relevant improvements in parameter estimation and in correct dose identification. No general trends for classification of the performance of the tested study designs across different scenarios could be identified. This study shows that pharmacodynamic interactions models can be used for the exposure-response analysis of clinical endpoints especially when accompanied by appropriate dose selection in regard to the expected drug potencies and appropriate trial size and if information regarding the exposure-response profile of one monocomponent is available.
2.
Aoki, Yasunori, 1982-, et al.
(författare)
PopED lite : an optimal design software for preclinical pharmacokinetic and pharmacodynamic studies
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.
3.
Aoki, Yasunori, et al.
(författare)
PopED lite: an optimal design software for preclinical pharmacokinetic and pharmacodynamic studies
2016
Ingår i: Computer Methods and Programs in Biomedicine. - : Elsevier BV. - 0169-2607 .- 1872-7565. ; 127, s. 126-143
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.
4.
Aoki, Yasunori, et al.
(författare)
Preconditioning of Nonlinear Mixed Effects Models for Stabilisation of Variance-Covariance Matrix Computations
2016
Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 18:2, s. 505-518
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.
5.
Bergstrand, Martin, 1977-, et al.
(författare)
Prediction-Corrected Visual Predictive Checks for Diagnosing Nonlinear Mixed-Effects Models
2011
Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 13:2, s. 143-151
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.
6.
Bjugård Nyberg, Henrik, 1984-, et al.
(författare)
Population Pharmacokinetics and Dosing of Ethionamide in Children with Tuberculosis
2020
Ingår i: Antimicrobial Agents and Chemotherapy. - : American Society for Microbiology. - 0066-4804 .- 1098-6596. ; 64:3
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.
7.
Bjugård Nyberg, Henrik, et al.
(författare)
Saddle-Reset for Robust Parameter Estimation and Identifiability Analysis of Nonlinear Mixed Effects Models
2020
Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 22:4
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.
8.
Ernest, C. Steven, II, et al.
(författare)
Methodological Comparison of In Vitro Binding Parameter Estimation : Sequential vs. Simultaneous Non-linear Regression
2010
Ingår i: Pharmaceutical research. - : Springer Science and Business Media LLC. - 0724-8741 .- 1573-904X. ; 27:5, s. 866-877
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.
9.
10.
Ernest II, Charles, et al.
(författare)
Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model
2014
Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 41:6, s. 639-654
Tidskriftsartikel (refereegranskat) abstract
D-optimal designs for discrete-type responses have been derived using generalized linear mixed models, simulation based methods and analytical approximations for computing the fisher information matrix (FIM) of non-linear mixed effect models with homogeneous probabilities over time. In this work, D-optimal designs using an analytical approximation of the FIM for a dichotomous, non-homogeneous, Markov-chain phase advanced sleep non-linear mixed effect model was investigated. The non-linear mixed effect model consisted of transition probabilities of dichotomous sleep data estimated as logistic functions using piecewise linear functions. Theoretical linear and nonlinear dose effects were added to the transition probabilities to modify the probability of being in either sleep stage. D-optimal designs were computed by determining an analytical approximation the FIM for each Markov component (one where the previous state was awake and another where the previous state was asleep). Each Markov component FIM was weighted either equally or by the average probability of response being awake or asleep over the night and summed to derive the total FIM (FIMtotal). The reference designs were placebo, 0.1, 1-, 6-, 10- and 20-mg dosing for a 2- to 6-way crossover study in six dosing groups. Optimized design variables were dose and number of subjects in each dose group. The designs were validated using stochastic simulation/re-estimation (SSE). Contrary to expectations, the predicted parameter uncertainty obtained via FIMtotal was larger than the uncertainty in parameter estimates computed by SSE. Nevertheless, the D-optimal designs decreased the uncertainty of parameter estimates relative to the reference designs. Additionally, the improvement for the D-optimal designs were more pronounced using SSE than predicted via FIMtotal. Through the use of an approximate analytic solution and weighting schemes, the FIMtotal for a non-homogeneous, dichotomous Markov-chain phase advanced sleep model was computed and provided more efficient trial designs and increased nonlinear mixed-effects modeling parameter precision.
