uu.seUppsala University Publications
Change search
Refine search result
1 - 9 of 9
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Aoki, Yasunori
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Applied Mathematics and Statistics. Department of Mathematics, Uppsala University.
    Nordgren, Rikard
    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.
    Preconditioning of Nonlinear Mixed Effects Models for Stabilisation of Variance-Covariance Matrix Computations2016In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 18, no 2, p. 505-518Article in journal (Refereed)
    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.

  • 2.
    Arshad, Usman
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Univ Cologne, Fac Med, Gleueler Str 24, D-50931 Cologne, Germany;Univ Cologne, Univ Hosp Cologne, Ctr Pharmacol, Dept Pharmacol 1, Gleueler Str 24, D-50931 Cologne, Germany.
    Chasseloup, Estelle
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nordgren, Rikard
    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.
    Development of visual predictive checks accounting for multimodal parameter distributions in mixture models2019In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 46, no 3, p. 241-250Article in journal (Refereed)
    Abstract [en]

    The assumption of interindividual variability being unimodally distributed in nonlinear mixed effects models does not hold when the population under study displays multimodal parameter distributions. Mixture models allow the identification of parameters characteristic to a subpopulation by describing these multimodalities. Visual predictive check (VPC) is a standard simulation based diagnostic tool, but not yet adapted to account for multimodal parameter distributions. Mixture model analysis provides the probability for an individual to belong to a subpopulation (IPmix) and the most likely subpopulation for an individual to belong to (MIXEST). Using simulated data examples, two implementation strategies were followed to split the data into subpopulations for the development of mixture model specific VPCs. The first strategy splits the observed and simulated data according to the MIXEST assignment. A shortcoming of the MIXEST-based allocation strategy was a biased allocation towards the dominating subpopulation. This shortcoming was avoided by splitting observed and simulated data according to the IPmix assignment. For illustration purpose, the approaches were also applied to an irinotecan mixture model demonstrating 36% lower clearance of irinotecan metabolite (SN-38) in individuals with UGT1A1 homo/heterozygote versus wild-type genotype. VPCs with segregated subpopulations were helpful in identifying model misspecifications which were not evident with standard VPCs. The new tool provides an enhanced power of evaluation of mixture models.

  • 3.
    Chen, Chunli
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wicha, Sebastian G.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nordgren, Rikard
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Simonsson, Ulrika S H
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Comparisons of analysis methods for assessment of pharmacodynamic interactions including design recommendations2018In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 20, article id 77Article in journal (Refereed)
    Abstract [en]

    Quantitative evaluation of potential pharmacodynamic (PD) interactions is important in tuberculosis drug development in order to optimize Phase 2b drug selection and ultimately to define clinical combination regimens. In this work, we used simulations to (1) evaluate different analysis methods for detecting PD interactions between two hypothetical anti-tubercular drugs in in vitro time-kill experiments, and (2) provide design recommendations for evaluation of PD interactions. The model used for all simulations was the Multistate Tuberculosis Pharmacometric (MTP) model linked to the General Pharmacodynamic Interaction (GPDI) model. Simulated data were re-estimated using the MTP–GPDI model implemented in Bliss Independence or Loewe Additivity, or using a conventional model such as an Empirical Bliss Independence-based model or the Greco model based on Loewe Additivity. The GPDI model correctly characterized different PD interactions (antagonism, synergism, or asymmetric interaction), regardless of the underlying additivity criterion. The commonly used conventional models were not able to characterize asymmetric PD interactions, i.e., concentration-dependent synergism and antagonism. An optimized experimental design was developed that correctly identified interactions in ≥ 94% of the evaluated scenarios using the MTP–GPDI model approach. The MTP–GPDI model approach was proved to provide advantages to other conventional models for assessing PD interactions of anti-tubercular drugs and provides key information for selection of drug combinations for Phase 2b evaluation.

  • 4.
    Ibrahim, Moustafa M. A.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nordgren, Rikard
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kjellsson, Maria 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.
    Comparison of diagnostics using model-based post-processing for fast automated model building2017In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 44, p. S60-S60Article in journal (Other academic)
  • 5.
    Ibrahim, Moustafa M. A.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Helwan Univ, Dept Pharm Practice, Cairo, Egypt.
    Nordgren, Rikard
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kjellsson, Maria 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.
    Model-Based Residual Post-Processing for Residual Model Identification2018In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 20, no 5, article id 81Article in journal (Refereed)
    Abstract [en]

    The purpose of this study was to investigate if model-based post-processing of common diagnostics can be used as a diagnostic tool to quantitatively identify model misspecifications and rectifying actions. The main investigated diagnostic is conditional weighted residuals (CWRES). We have selected to showcase this principle with residual unexplained variability (RUV) models, where the new diagnostic tool is used to scan extended RUV models and assess in a fast and robust way whether, and what, extensions are expected to provide a superior description of data. The extended RUV models evaluated were autocorrelated errors, dynamic transform both sides, inter-individual variability on RUV, power error model, t-distributed errors, and time-varying error magnitude. The agreement in improvement in goodness-of-fit between implementing these extended RUV models on the original model and implementing these extended RUV models on CWRES was evaluated in real and simulated data examples. Real data exercise was applied to three other diagnostics: conditional weighted residuals with interaction (CWRESI), individual weighted residuals (IWRES), and normalized prediction distribution errors (NPDE). CWRES modeling typically predicted (i) the nature of model misspecifications, (ii) the magnitude of the expected improvement in fit in terms of difference in objective function value (Delta OFV), and (iii) the parameter estimates associated with the model extension. Alternative metrics (CWRESI, IWRES, and NPDE) also provided valuable information, but with a lower predictive performance of Delta OFV compared to CWRES. This method is a fast and easily automated diagnostic tool for RUV model development/evaluation process; it is already implemented in the software package PsN.

  • 6.
    Ibrahim, Moustafa M. A.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nordgren, Rikard
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kjellsson, Maria 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.
    Variability Attribution for Automated Model Building2019In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 21, no 3, article id UNSP 37Article in journal (Refereed)
    Abstract [en]

    We investigated the possible advantages of using linearization to evaluate models of residual unexplained variability (RUV) for automated model building in a similar fashion to the recently developed method “residual modeling.” Residual modeling, although fast and easy to automate, cannot identify the impact of implementing the needed RUV model on the imprecision of the rest of model parameters. We used six RUV models to be tested with 12 real data examples. Each example was first linearized; then, we assessed the agreement in improvement of fit between the base model and its extended models for linearization and conventional analysis, in comparison to residual modeling performance. Afterward, we compared the estimates of parameters’ variabilities and their uncertainties obtained by linearization to conventional analysis. Linearization accurately identified and quantified the nature and magnitude of RUV model misspecification similar to residual modeling. In addition, linearization identified the direction of change and quantified the magnitude of this change in variability parameters and their uncertainties. This method is implemented in the software package PsN for automated model building/evaluation with continuous data.

  • 7.
    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.

  • 8. 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.

  • 9.
    Terranova, Nadia
    et al.
    Merck Serono SA, Merck Inst Pharmacometr, Zurich, Switzerland.
    Smith, Mike K.
    Pfizer, Global Clin Pharmacol, Sandwich, Kent, England.
    Nordgren, Rikard
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Comets, Emmanuelle
    INSERM, UMR 1137, IAME, Paris, France; Univ Paris Diderot, Sorbonne Paris Cite, IAME, UMR 1137, Paris, France.
    Lavielle, Marc
    Inria Ile De France, Paris, France;Ecole Polytech, Paris, France.
    Harling, Kajsa
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Sarr, Celine
    Pharmetheus, Uppsala, Sweden.
    Mentre, France
    INSERM, UMR 1137, IAME, Paris, France; Univ Paris Diderot, Sorbonne Paris Cite, IAME, UMR 1137, Paris, France.
    Yvon, Florent
    EMBL European Bioinformat Inst, Cambridge, England; Barcelona Supercomp Ctr, Barcelona, Spain.
    Swat, Maciej J.
    EMBL European Bioinformat Inst, Cambridge, England; Simcyp, Sheffield, S Yorkshire, England.
    The Standard Output: A Tool-Agnostic Modeling Storage Format2018In: CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, ISSN 2163-8306, Vol. 7, no 9, p. 543-546Article in journal (Other academic)
1 - 9 of 9
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf