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  • 1.
    Dosne, Anne-Gaëlle
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Bergstrand, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Harling, Kajsa
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Improving The Estimation Of Parameter Uncertainty Distributions In Nonlinear Mixed Effects Models Using Sampling Importance Resampling2016In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 43, no 6, p. 583-596Article in journal (Refereed)
    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.

  • 2.
    Jayawardena, Mahen
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Ljungberg, Kajsa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Using parallel computing and grid systems for genetic mapping of multifactorial traits2005Report (Other academic)
  • 3.
    Jayawardena, Mahen
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Ljungberg, Kajsa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Using parallel computing and grid systems for genetic mapping of quantitative traits2007In: Applied Parallel Computing: State of the Art in Scientific Computing, Berlin: Springer-Verlag , 2007, p. 627-636Conference paper (Refereed)
  • 4.
    Khandelwal, Akash
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Harling, Kajsa
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Jonsson, Niclas E.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A Fast Method for Testing Covariates in Population PK/PD Models2011In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 13, no 3, p. 464-472Article in journal (Refereed)
    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.

  • 5.
    Ljungberg, Kajsa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Efficient evaluation of the residual sum of squares for quantitative trait locus models in the case of complete marker genotype information2005Report (Other academic)
  • 6.
    Ljungberg, Kajsa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Numerical Algorithms for Mapping of Multiple Quantitative Trait Loci in Experimental Populations2005Doctoral 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.

    List of papers
    1. Efficient algorithms for quantitative trait loci mapping problems
    Open this publication in new window or tab >>Efficient algorithms for quantitative trait loci mapping problems
    2002 (English)In: Journal of Computational Biology, ISSN 1066-5277, E-ISSN 1557-8666, Vol. 9, p. 793-804Article in journal (Refereed) Published
    National Category
    Bioinformatics (Computational Biology)
    Identifiers
    urn:nbn:se:uu:diva-67952 (URN)10.1089/10665270260518272 (DOI)12614547 (PubMedID)
    Available from: 2008-07-05 Created: 2008-07-05 Last updated: 2018-01-10Bibliographically approved
    2. Simultaneous search for multiple QTL using the global optimization algorithm DIRECT
    Open this publication in new window or tab >>Simultaneous search for multiple QTL using the global optimization algorithm DIRECT
    2004 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 20, p. 1887-1895Article in journal (Refereed) Published
    National Category
    Bioinformatics (Computational Biology)
    Identifiers
    urn:nbn:se:uu:diva-67950 (URN)10.1093/bioinformatics/bth175 (DOI)15044246 (PubMedID)
    Note

    An efficient algorithm for solving least-squares problems with a specific structure is combined with an extended algorithm for global optimization. The scheme is applied to problems in genetics, where the solution can be computed several orders of magnitude faster than with previous methods.

    Available from: 2007-02-20 Created: 2007-02-20 Last updated: 2018-01-10Bibliographically approved
    3. Efficient algorithms for multidimensional global optimization in genetic mapping of complex traits
    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
    4. 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
    5. Using parallel computing and grid systems for genetic mapping of quantitative traits
    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
  • 7.
    Ljungberg, Kajsa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Numerical methods for mapping of multiple QTL2003Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis concerns numerical methods for mapping of multiple quantitative trait loci, QTL. Interactions between multiple genetic loci influencing important traits, such as growth rate in farm animals and predisposition to cancer in humans, make it necessary to search for several QTL simultaneously. Simultaneous search for n QTL involves solving an n-dimensional global optimization problem, where each evaluation of the objective function consists of solving a generalized least squares problem. In Paper A we present efficient algorithms, mainly based on updated QR factorizations, for evaluating the objective functions of different parametric QTL mapping methods. One of these algorithms reduces the computational work required for an important function class by one order of magnitude compared with the best of the methods used by other authors. In Paper B previously utilized techniques for finding the global optimum of the objective function are compared with a new approach based on the DIRECT algorithm of Jones et al. The new method gives accurate results in one order of magnitude less time than the best of the formerly employed algorithms. Using the algorithms presented in Papers A and B, simultaneous search for at least three QTL, including computation of the relevant empirical significance thresholds, can be performed routinely.

  • 8.
    Ljungberg, Kajsa B
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Structural Molecular Biology.
    Marelius, J
    Musil, D
    Svensson, P
    Norden, B
    Åqvist, J
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Structural Molecular Biology.
    Computational Modelling of Inhibitor Binding to Human Thrombin2001In: Eur. J. Pharm. Sci., Vol. 12, no 4, p. 441-446Article in journal (Refereed)
    Abstract [en]

    Thrombin is an essential protein involved in blood clot formation and an important clinical target, since disturbances of the coagulation process cause serious cardiovascular diseases such as thrombosis. Here we evaluate the performance of a molecular dynamics based method for predicting the binding affinities of different types ofhuman thrombin inhibitors. Far a series of eight ligands the method ranks their relative affinities reasonably well. The binding free energy difference between high and low affinity representatives in the test set is quantitatively reproduced, as well as the stereospecificity for a chiral inhibitor. The original parametrisation of this linear interaction energy method requires the addition of a constant energy term in the case of thrombin. This yields a mean unsigned error of 0.68 kcal/mol for the absolute binding free energies. This type of approach is also useful for elucidating three-dimensional structure-activity relationships in terms ofmicroscopic interactions of the ligands with the solvated enzyme. 

  • 9.
    Ljungberg, Kajsa
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Carlborg, Örjan
    Efficient algorithms for quantitative trait loci mapping problems2002In: Journal of Computational Biology, ISSN 1066-5277, E-ISSN 1557-8666, Vol. 9, p. 793-804Article in journal (Refereed)
  • 10.
    Ljungberg, Kajsa
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Carlborg, Örjan
    Efficient kernel algorithms for QTL mapping problems2002Report (Other academic)
  • 11.
    Ljungberg, Kajsa
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Carlborg, Örjan
    Simultaneous search for multiple QTL using the global optimization algorithm DIRECT2004In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 20, p. 1887-1895Article in journal (Refereed)
  • 12.
    Ljungberg, Kajsa
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Carlborg, Örjan
    Simultaneous search for multiple QTL using the global optimization algorithm DIRECT2003Report (Other academic)
  • 13.
    Ljungberg, Kajsa
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Mishchenko, Kateryna
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Efficient algorithms for multi-dimensional global optimization in genetic mapping of complex traits2005Report (Other academic)
  • 14.
    Ljungberg, Kajsa
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Mishchenko, Kateryna
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Efficient algorithms for multidimensional global optimization in genetic mapping of complex traits2010In: Advances and Applications in Bioinformatics and Chemistry, ISSN 1178-6949, Vol. 3, p. 75-88Article in journal (Refereed)
  • 15.
    Marelius, J.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Structural Molecular Biology.
    Ljungberg, Kajsa
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Structural Molecular Biology.
    Åqvist, J
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Structural Molecular Biology.
    Sensitivity of an Empirical Affinity Scoring Function to Changes in Receptor-Ligand Complex Conformations2001In: European Journal of Pharmaceutical Sciences, ISSN 0928-0987, E-ISSN 1879-0720, Vol. 14, no 1, p. 87-95Article in journal (Refereed)
    Abstract [en]

    A combination of empirical scoring and conformational sampling for ligand bindingaffinity prediction is examined. The behaviour of a scoring function with respect to thesensitivity to conformational changes is investigated using ensembles of structures generated by molecular dynamics simulation. The correlation between the calculated score and the coordinate deviation from the experimental structure is clear for the complex of arabinose with arabinose-binding protein, which is dominated by hydrogen bond interactions, while the score calculated for the hydrophobic complex between retinol and retinol binding protein is rather insensitive to ligand conformational changes. For typical ensembles of stuctures generated by molecular dynamics at 300 K. the variation of the calculated score is considerably smaller than that of the underlying molecular mechanics interaction energies.

  • 16.
    Smith, Mike K.
    et al.
    Pfizer, Sandwich, Kent, England.
    Moodie, Stuart L.
    Eight Pillars Ltd, Edinburgh, Midlothian, Scotland.
    Bizzotto, Roberto
    CNR Inst Neurosci, Padua, Italy.
    Blaudez, Eric
    Lixoft, Orsay, France.
    Borella, Elisa
    Univ Pavia, Pavia, Italy.
    Carrara, Letizia
    Univ Pavia, Pavia, Italy.
    Chan, Phylinda
    Pfizer, Sandwich, Kent, England.
    Chenel, Marylore
    Servier, Paris, France.
    Comets, Emmanuelle
    INSERM, Paris, France.
    Gieschke, Ronald
    Roche, Basel, Switzerland.
    Harling, Kajsa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Harnisch, Lutz
    Pfizer, Sandwich, Kent, England.
    Hartung, Niklas
    Free Univ Berlin, Berlin, Germany.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kaye, Richard
    Mango Solut, Chippenham, England.
    Kloft, Charlotte
    Free Univ Berlin, Berlin, Germany.
    Kokash, Natallia
    Leiden Univ, Leiden, Netherlands; UCL, London, England.
    Lavielle, Marc
    Inria, Saclay, Paris, France.
    Lestini, Giulia
    INSERM, Paris, France.
    Magni, Paolo
    Univ Pavia, Pavia, Italy.
    Mari, Andrea
    CNR Inst Neurosci, Padua, Italy.
    Mentre, France
    INSERM, Paris, France.
    Muselle, Chris
    Mango Solut, Chippenham, England.
    Nordgren, Rikard
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nyberg, Henrik B.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Mango Solut, Chippenham, England.
    Parra-Guillen, Zinnia P.
    Free Univ Berlin, Berlin, Germany; Univ Navarra, Navarra, Spain.
    Pasotti, Lorenzo
    Univ Pavia, Pavia, Italy.
    Rode-Kristensen, Niels
    Novo Nordisk AS, Bagsv9rd, Denmark.
    Sardu, Maria L.
    Merck Serono SA, Lausanne, Switzerland.
    Smith, Gareth R.
    Cyprotex Discovery Ltd, Sci Comp Grp, Macclesfield, Crewe, England.
    Swat, Maciej J.
    EMBL European Bioinformat Inst, Wellcome Trust Genome Campus, Hinxton, Cambs, England.
    Terranova, Nadia
    Merck Serono SA, Lausanne, Switzerland.
    Yngman, Gunnar
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Yvon, Florent
    EMBL European Bioinformat Inst, Wellcome Trust Genome Campus, Hinxton, Cambs, England.
    Holford, Nick H
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Univ Auckland, Auckland, New Zealand.
    Model Description Language (MDL): A Standard for Modeling and Simulation2017In: CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, ISSN 2163-8306, Vol. 6, no 10, p. 647-650Article in journal (Refereed)
    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.

  • 17. Swat, M J
    et al.
    Moodie, S
    Wimalaratne, S M
    Kristensen, N R
    Lavielle, M
    Mari, A
    Magni, P
    Smith, M K
    Bizzotto, R
    Pasotti, L
    Mezzalana, E
    Comets, E
    Sarr, C
    Terranova, N
    Blaudez, E
    Chan, P
    Chard, J
    Chatel, K
    Chenel, M
    Edwards, D
    Franklin, C
    Giorgino, T
    Glont, M
    Girard, P
    Grenon, P
    Harling, Kajsa
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kaye, R
    Keizer, R
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kloft, C
    Kok, J N
    Kokash, N
    Laibe, C
    Laveille, C
    Lestini, G
    Mentré, F
    Munafo, A
    Nordgren, R
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nyberg, Henrik Bjugård
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Parra-Guillen, Z P
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ribba, B
    Smith, G
    Trocóniz, I F
    Yvon, F
    Milligan, P A
    Harnisch, L
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hermjakob, H
    Le Novère, N
    Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development2015In: CPT pharmacometrics & systems pharmacology, ISSN 2163-8306, Vol. 4, no 6, p. 316-319Article in journal (Refereed)
    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.

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