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Ljungberg, Kajsa
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Publications (10 of 17) Show all publications
Smith, M. K., Moodie, S. L., Bizzotto, R., Blaudez, E., Borella, E., Carrara, L., . . . Holford, N. H. (2017). Model Description Language (MDL): A Standard for Modeling and Simulation. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, 6(10), 647-650
Open this publication in new window or tab >>Model Description Language (MDL): A Standard for Modeling and Simulation
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2017 (English)In: CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, ISSN 2163-8306, Vol. 6, no 10, p. 647-650Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
WILEY, 2017
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-340957 (URN)10.1002/psp4.12222 (DOI)000413899000001 ()28643440 (PubMedID)
Funder
EU, FP7, Seventh Framework Programme, 115156
Available from: 2018-02-12 Created: 2018-02-12 Last updated: 2018-02-12Bibliographically approved
Dosne, A.-G., Bergstrand, M., Harling, K. & Karlsson, M. O. (2016). Improving The Estimation Of Parameter Uncertainty Distributions In Nonlinear Mixed Effects Models Using Sampling Importance Resampling. Journal of Pharmacokinetics and Pharmacodynamics, 43(6), 583-596
Open this publication in new window or tab >>Improving The Estimation Of Parameter Uncertainty Distributions In Nonlinear Mixed Effects Models Using Sampling Importance Resampling
2016 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 43, no 6, p. 583-596Article in journal (Refereed) Published
Abstract [en]

Taking parameter uncertainty into account is key to make drug development decisions such as testing whether trial endpoints meet defined criteria. Currently used methods for assessing parameter uncertainty in NLMEM have limitations, and there is a lack of diagnostics for when these limitations occur. In this work, a method based on sampling importance resampling (SIR) is proposed, which has the advantage of being free of distributional assumptions and does not require repeated parameter estimation. To perform SIR, a high number of parameter vectors are simulated from a given proposal uncertainty distribution. Their likelihood given the true uncertainty is then approximated by the ratio between the likelihood of the data given each vector and the likelihood of each vector given the proposal distribution, called the importance ratio. Non-parametric uncertainty distributions are obtained by resampling parameter vectors according to probabilities proportional to their importance ratios. Two simulation examples and three real data examples were used to define how SIR should be performed with NLMEM and to investigate the performance of the method. The simulation examples showed that SIR was able to recover the true parameter uncertainty. The real data examples showed that parameter 95 % confidence intervals (CI) obtained with SIR, the covariance matrix, bootstrap and log-likelihood profiling were generally in agreement when 95 % CI were symmetric. For parameters showing asymmetric 95 % CI, SIR 95 % CI provided a close agreement with log-likelihood profiling but often differed from bootstrap 95 % CI which had been shown to be suboptimal for the chosen examples. This work also provides guidance towards the SIR workflow, i.e.,which proposal distribution to choose and how many parameter vectors to sample when performing SIR, using diagnostics developed for this purpose. SIR is a promising approach for assessing parameter uncertainty as it is applicable in many situations where other methods for assessing parameter uncertainty fail, such as in the presence of small datasets, highly nonlinear models or meta-analysis.

Keywords
Sampling importance resampling, Parameter uncertainty, Confidence intervals, Asymptotic covariance matrix, Nonlinear mixed-effects models, Bootstrap
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-303629 (URN)10.1007/s10928-016-9487-8 (DOI)000388634800003 ()27730482 (PubMedID)
Funder
EU, European Research Council, 602552 115156
Available from: 2016-10-20 Created: 2016-09-21 Last updated: 2018-01-14Bibliographically approved
Swat, M. J., Moodie, S., Wimalaratne, S. M., Kristensen, N. R., Lavielle, M., Mari, A., . . . Le Novère, N. (2015). Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development. CPT pharmacometrics & systems pharmacology, 4(6), 316-319
Open this publication in new window or tab >>Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development
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2015 (English)In: CPT pharmacometrics & systems pharmacology, ISSN 2163-8306, Vol. 4, no 6, p. 316-319Article in journal (Refereed) Published
Abstract [en]

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.

National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-276254 (URN)10.1002/psp4.57 (DOI)26225259 (PubMedID)
Available from: 2016-02-10 Created: 2016-02-10 Last updated: 2018-01-10Bibliographically approved
Khandelwal, A., Harling, K., Jonsson, N. E., Hooker, A. C. & Karlsson, M. O. (2011). A Fast Method for Testing Covariates in Population PK/PD Models. AAPS Journal, 13(3), 464-472
Open this publication in new window or tab >>A Fast Method for Testing Covariates in Population PK/PD Models
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2011 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 13, no 3, p. 464-472Article in journal (Refereed) Published
Abstract [en]

The development of covariate models within the population modeling program like NONMEM is generally a time-consuming and non-trivial task. In this study, a fast procedure to approximate the change in objective function values of covariate-parameter models is presented and evaluated. The proposed method is a first-order conditional estimation (FOCE)-based linear approximation of the influence of covariates on the model predictions. Simulated and real datasets were used to compare this method with the conventional nonlinear mixed effect model using both first-order (FO) and FOCE approximations. The methods were mainly assessed in terms of difference in objective function values (Delta OFV) between base and covariate models. The FOCE linearization was superior to the FO linearization and showed a high degree of concordance with corresponding nonlinear models in Delta OFV. The linear and nonlinear FOCE models provided similar coefficient estimates and identified the same covariate-parameter relations as statistically significant or non-significant for the real and simulated datasets. The time required to fit tesaglitazar and docetaxel datasets with 4 and 15 parameter-covariate relations using the linearization method was 5.1 and 0.5 min compared with 152 and 34 h, respectively, with the nonlinear models. The FOCE linearization method allows for a fast estimation of covariate-parameter relations models with good concordance with the nonlinear models. This allows a more efficient model building and may allow the utilization of model building techniques that would otherwise be too time-consuming.

Keywords
conditional estimation, covariate model building, NONMEM, population PK/PD
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-157007 (URN)10.1208/s12248-011-9289-2 (DOI)000293186000015 ()
Available from: 2011-08-16 Created: 2011-08-15 Last updated: 2018-01-12Bibliographically approved
Ljungberg, K., Mishchenko, K. & Holmgren, S. (2010). Efficient algorithms for multidimensional global optimization in genetic mapping of complex traits. Advances and Applications in Bioinformatics and Chemistry, 3, 75-88
Open this publication in new window or tab >>Efficient algorithms for multidimensional global optimization in genetic mapping of complex traits
2010 (English)In: Advances and Applications in Bioinformatics and Chemistry, ISSN 1178-6949, Vol. 3, p. 75-88Article in journal (Refereed) Published
National Category
Computational Mathematics Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-93884 (URN)10.2147/AABC.S9240 (DOI)
Projects
eSSENCE
Available from: 2005-12-22 Created: 2005-12-22 Last updated: 2018-01-13Bibliographically approved
Jayawardena, M., Ljungberg, K. & Holmgren, S. (2007). Using parallel computing and grid systems for genetic mapping of quantitative traits. In: Applied Parallel Computing: State of the Art in Scientific Computing (pp. 627-636). Berlin: Springer-Verlag
Open this publication in new window or tab >>Using parallel computing and grid systems for genetic mapping of quantitative traits
2007 (English)In: Applied Parallel Computing: State of the Art in Scientific Computing, Berlin: Springer-Verlag , 2007, p. 627-636Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Berlin: Springer-Verlag, 2007
Series
Lecture Notes in Computer Science ; 4699
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-11546 (URN)10.1007/978-3-540-75755-9_76 (DOI)000250904900076 ()978-3-540-75754-2 (ISBN)
Available from: 2007-09-26 Created: 2007-09-26 Last updated: 2018-01-12Bibliographically approved
Ljungberg, K., Mishchenko, K. & Holmgren, S. (2005). Efficient algorithms for multi-dimensional global optimization in genetic mapping of complex traits.
Open this publication in new window or tab >>Efficient algorithms for multi-dimensional global optimization in genetic mapping of complex traits
2005 (English)Report (Other academic)
Series
Technical report / Department of Information Technology, Uppsala University, ISSN 1404-3203 ; 2005-035
National Category
Computational Mathematics Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-76702 (URN)
Available from: 2007-02-04 Created: 2007-02-04 Last updated: 2018-01-13Bibliographically approved
Ljungberg, K. (2005). Efficient evaluation of the residual sum of squares for quantitative trait locus models in the case of complete marker genotype information.
Open this publication in new window or tab >>Efficient evaluation of the residual sum of squares for quantitative trait locus models in the case of complete marker genotype information
2005 (English)Report (Other academic)
Series
Technical report / Department of Information Technology, Uppsala University, ISSN 1404-3203 ; 2005-033
National Category
Computational Mathematics Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-76701 (URN)
Available from: 2007-02-04 Created: 2007-02-04 Last updated: 2018-01-13Bibliographically approved
Ljungberg, K. (2005). Numerical Algorithms for Mapping of Multiple Quantitative Trait Loci in Experimental Populations. (Doctoral dissertation). Uppsala: Acta Universitatis Upsaliensis
Open this publication in new window or tab >>Numerical Algorithms for Mapping of Multiple Quantitative Trait Loci in Experimental Populations
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Most traits of medical or economic importance are quantitative, i.e. they can be measured on a continuous scale. Strong biological evidence indicates that quantitative traits are governed by a complex interplay between the environment and multiple quantitative trait loci, QTL, in the genome. Nonlinear interactions make it necessary to search for several QTL simultaneously. This thesis concerns numerical methods for QTL search in experimental populations. The core computational problem of a statistical analysis of such a population is a multidimensional global optimization problem with many local optima. Simultaneous search for d QTL involves solving a d-dimensional problem, where each evaluation of the objective function involves solving one or several least squares problems with special structure. Using standard software, already a two-dimensional search is costly, and searches in higher dimensions are prohibitively slow.

Three efficient algorithms for evaluation of the most common forms of the objective function are presented. The computing time for the linear regression method is reduced by up to one order of magnitude for real data examples by using a new scheme based on updated QR factorizations. Secondly, the objective function for the interval mapping method is evaluated using an updating technique and an efficient iterative method, which results in a 50 percent reduction in computing time. Finally, a third algorithm, applicable to the imputation and weighted linear mixture model methods, is presented. It reduces the computing time by between one and two orders of magnitude.

The global search problem is also investigated. Standard software techniques for finding the global optimum of the objective function are compared with a new approach based on the DIRECT algorithm. The new method is more accurate than the previously fastest scheme and locates the optimum in 1-2 orders of magnitude less time. The method is further developed by coupling DIRECT to a local optimization algorithm for accelerated convergence, leading to additional time savings of up to eight times. A parallel grid computing implementation of exhaustive search is also presented, and is suitable e.g for verifying global optima when developing efficient optimization algorithms tailored for the QTL mapping problem.

Using the algorithms presented in this thesis, simultaneous search for at least six QTL can be performed routinely. The decrease in overall computing time is several orders of magnitude. The results imply that computations which were earlier considered impossible are no longer difficult, and that genetic researchers thus are free to focus on model selection and other central genetical issues.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2005. p. 61
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 133
Keywords
Scientific computing
National Category
Bioinformatics (Computational Biology)
Research subject
Scientific Computing
Identifiers
urn:nbn:se:uu:diva-6248 (URN)91-554-6427-0 (ISBN)
Public defence
2006-01-13, Room 2446, Polacksbacken, Lägerhyddsvägen 2D, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2005-12-22 Created: 2005-12-22 Last updated: 2018-01-13Bibliographically approved
Jayawardena, M., Ljungberg, K. & Holmgren, S. (2005). Using parallel computing and grid systems for genetic mapping of multifactorial traits.
Open this publication in new window or tab >>Using parallel computing and grid systems for genetic mapping of multifactorial traits
2005 (English)Report (Other academic)
Series
Technical report / Department of Information Technology, Uppsala University, ISSN 1404-3203 ; 2005-036
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-76703 (URN)
Available from: 2007-02-05 Created: 2007-02-05 Last updated: 2018-01-13Bibliographically approved
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