uu.seUppsala University Publications
Change search
Link to record
Permanent link

Direct link
BETA
Publications (7 of 7) Show all publications
Aoki, Y., Roshammar, D., Hamren, B. & Hooker, A. (2017). Model selection and averaging of nonlinear mixed-effect models for robust phase III dose selection. Journal of Pharmacokinetics and Pharmacodynamics, 44(6), 581-597
Open this publication in new window or tab >>Model selection and averaging of nonlinear mixed-effect models for robust phase III dose selection
2017 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 44, no 6, p. 581-597Article in journal (Refereed) Published
Abstract [en]

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.

Keywords
Model averaging, Model selection, Pharmacometrics, Phase IIb clinical trial, Dose finding study, Mathematical modelling, Dose-effect relationship
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:uu:diva-342205 (URN)10.1007/s10928-017-9550-0 (DOI)000415375800006 ()29103208 (PubMedID)
Available from: 2018-02-20 Created: 2018-02-20 Last updated: 2018-02-20Bibliographically approved
Aoki, Y., Monika, S., Hooker, A. C. & Gennemark, P. (2016). PopED lite: an optimal design software for preclinical pharmacokinetic and pharmacodynamic studies. Computer Methods and Programs in Biomedicine, 127, 126-143
Open this publication in new window or tab >>PopED lite: an optimal design software for preclinical pharmacokinetic and pharmacodynamic studies
2016 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 127, p. 126-143Article in journal (Refereed) Published
Abstract [en]

Background and Objective

Optimal 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.

Methods

Several 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.

Results

The 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.

Conclusions

PopED 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.

National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-276491 (URN)10.1016/j.cmpb.2016.02.001 (DOI)000372521500012 ()27000295 (PubMedID)
Funder
AstraZeneca
Available from: 2016-02-15 Created: 2016-02-15 Last updated: 2018-01-10Bibliographically approved
Aoki, Y., Nordgren, R. & Hooker, A. C. (2016). Preconditioning of Nonlinear Mixed Effects Models for Stabilisation of Variance-Covariance Matrix Computations. AAPS Journal, 18(2), 505-518
Open this publication in new window or tab >>Preconditioning of Nonlinear Mixed Effects Models for Stabilisation of Variance-Covariance Matrix Computations
2016 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 18, no 2, p. 505-518Article in journal (Refereed) Published
Abstract [en]

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.

Keywords
computational stability; identifiability; non-linear mixed effects model; parameter estimation uncertainty; preconditioning
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-276488 (URN)10.1208/s12248-016-9866-5 (DOI)000375460900003 ()26857397 (PubMedID)
Available from: 2016-02-15 Created: 2016-02-15 Last updated: 2018-01-10Bibliographically approved
Gaudreau, P., Hayami, K., Aoki, Y., Safouhi, H. & Konagaya, A. (2015). Improvements to the cluster Newton method for underdetermined inverse problems. Journal of Computational and Applied Mathematics, 283, 122-141
Open this publication in new window or tab >>Improvements to the cluster Newton method for underdetermined inverse problems
Show others...
2015 (English)In: Journal of Computational and Applied Mathematics, ISSN 0377-0427, E-ISSN 1879-1778, Vol. 283, p. 122-141Article in journal (Refereed) Published
Abstract [en]

The Cluster Newton method (CN method) has proved to be very efficient at finding multiple solutions to underdetermined inverse problems. In the case of pharmacokinetics, underdetermined inverse problems are often given extra constraints to restrain the variety of solutions. In this paper, we propose a new algorithm based on the two parameters of the Beta distribution for finding a family of solutions which best fit the extra constraints. This allows for a much greater control on the variety of solutions that can be obtained with the CN method. In addition, this algorithm facilitates the task of obtaining pharmacologically feasible parameters. Moreover, we also make some improvements to the original CN method including an adaptive margin of error for the perturbation of the target values and the use of an analytical Jacobian in the resolution of the forward problem.

Keywords
Cluster Newton method, Underdetermined inverse problem, Beta distribution, Pharmacokinetics
National Category
Computational Mathematics
Research subject
Mathematics with specialization in Applied Mathematics
Identifiers
urn:nbn:se:uu:diva-244413 (URN)10.1016/j.cam.2015.01.014 (DOI)000351645000010 ()
Available from: 2015-02-16 Created: 2015-02-16 Last updated: 2017-12-04Bibliographically approved
Aoki, Y., Hayami, K., De Sterck, H. & Konagaya, A. (2014). Cluster Newton Method for Sampling Multiple Solutions of Underdetermined Inverse Problems: Application to a Parameter Identification Problem in Pharmacokinetics. SIAM Journal on Scientific Computing, 36(1), B14-B44
Open this publication in new window or tab >>Cluster Newton Method for Sampling Multiple Solutions of Underdetermined Inverse Problems: Application to a Parameter Identification Problem in Pharmacokinetics
2014 (English)In: SIAM Journal on Scientific Computing, ISSN 1064-8275, E-ISSN 1095-7197, Vol. 36, no 1, p. B14-B44Article in journal (Refereed) Published
Abstract [en]

A new algorithm is proposed for simultaneously finding multiple solutions of an underdetermined inverse problem. The algorithm was developed for an ODE parameter identification problem in pharmacokinetics for which multiple solutions are of interest. The algorithm proceeds by computing a cluster of solutions simultaneously, and is more efficient than algorithms that compute multiple solutions one-by-one because it fits the Jacobian in a collective way using a least squares approach. It is demonstrated numerically that the algorithm finds accurate solutions that are suitably distributed, guided by a priori information on which part of the solution set is of interest, and that it does so much more efficiently than a baseline Levenberg-Marquardt method that computes solutions one-by-one. It is also demonstrated that the algorithm benefits from improved robustness due to an inherent smoothing provided by the least-squares fitting.

Keywords
inverse problems, method of least squares, pharmacokinetics, underdetermined problems
National Category
Computational Mathematics
Identifiers
urn:nbn:se:uu:diva-244411 (URN)10.1137/120885462 (DOI)000333415500016 ()
Available from: 2015-02-16 Created: 2015-02-16 Last updated: 2017-12-04Bibliographically approved
Aoki, Y. & De Sterck, H. (2014). Numerical study of unbounded capillary surfaces. Pacific Journal of Mathematics, 267(1), 1-34
Open this publication in new window or tab >>Numerical study of unbounded capillary surfaces
2014 (English)In: Pacific Journal of Mathematics, ISSN 0030-8730, E-ISSN 1945-5844, Vol. 267, no 1, p. 1-34Article in journal (Refereed) Published
Abstract [en]

Unbounded capillary surfaces in domains with a sharp corner or a cusp are studied. It is shown how numerical study using a proposed computational methodology leads to two new conjectures for open problems on the asymptotic behavior of capillary surfaces in domains with a cusp. The numerical methodology contains two simple but important ingredients, a change of variable and a change of coordinates, which are inspired by known asymptotic approximations for unbounded capillary surfaces. These ingredients are combined with the finite volume element or Galerkin finite element methods. Extensive numerical tests show that the proposed computational methodology leads to a global approximation method for singular solutions of the Laplace–Young equation that recovers the proper asymptotic behavior at the singular point, is more accurate and has better convergence properties than numerical methods considered for singular capillary surfaces before. Using this computational methodology, two open problems on the asymptotic behavior of capillary surfaces in domains with a cusp are studied numerically, leading to two conjectures that may guide future analytical work on these open problems.

Keywords
singularity, asymptotic analysis, nonlinear elliptic PDE, Laplace–Young equation, finite element method
National Category
Computational Mathematics
Research subject
Mathematics with specialization in Applied Mathematics
Identifiers
urn:nbn:se:uu:diva-244410 (URN)10.2140/pjm.2014.267.1 (DOI)
Available from: 2015-02-16 Created: 2015-02-16 Last updated: 2017-12-04Bibliographically approved
Aoki, Y., Sundqvist, M., Hooker, A. C. & Gennemark, P.PopED lite: an optimal design software for preclinical pharmacokinetic and pharmacodynamic studies.
Open this publication in new window or tab >>PopED lite: an optimal design software for preclinical pharmacokinetic and pharmacodynamic studies
(English)Manuscript (preprint) (Other academic)
Abstract [en]

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. 

Keywords
optimal experimental design, pre-clinical drug discovery, model-based drug discovery
National Category
Pharmaceutical Sciences Computational Mathematics
Research subject
Mathematics with specialization in Applied Mathematics; Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-253304 (URN)
Available from: 2015-05-26 Created: 2015-05-26 Last updated: 2018-01-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-5881-2023

Search in DiVA

Show all publications