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Nordgren, Rikard
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Publications (9 of 9) Show all publications
Arshad, U., Chasseloup, E., Nordgren, R. & Karlsson, M. O. (2019). Development of visual predictive checks accounting for multimodal parameter distributions in mixture models. Journal of Pharmacokinetics and Pharmacodynamics, 46(3), 241-250
Open this publication in new window or tab >>Development of visual predictive checks accounting for multimodal parameter distributions in mixture models
2019 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 46, no 3, p. 241-250Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
SPRINGER/PLENUM PUBLISHERS, 2019
Keywords
Visual predictive checks, Mixture models, Multimodal parameter distributions, Pharmacokinetics, Pharmacodynamics
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-385960 (URN)10.1007/s10928-019-09632-9 (DOI)000468597100003 ()30968312 (PubMedID)
Available from: 2019-06-19 Created: 2019-06-19 Last updated: 2019-06-19Bibliographically approved
Ibrahim, M. M. A., Nordgren, R., Kjellsson, M. C. & Karlsson, M. O. (2019). Variability Attribution for Automated Model Building. AAPS Journal, 21(3), Article ID UNSP 37.
Open this publication in new window or tab >>Variability Attribution for Automated Model Building
2019 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 21, no 3, article id UNSP 37Article in journal (Refereed) Published
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.

Keywords
automated model building, linearization, model evaluation, nonlinear mixed effects models, stochastic model
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-367056 (URN)10.1208/s12248-019-0310-5 (DOI)000460816600001 ()30850918 (PubMedID)
Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2019-03-25Bibliographically approved
Chen, C., Wicha, S. G., Nordgren, R. & Simonsson, U. S. (2018). Comparisons of analysis methods for assessment of pharmacodynamic interactions including design recommendations. AAPS Journal, 20, Article ID 77.
Open this publication in new window or tab >>Comparisons of analysis methods for assessment of pharmacodynamic interactions including design recommendations
2018 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 20, article id 77Article in journal (Refereed) Published
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.

Keywords
optimized design, in vitro, pharmacodynamic interactions
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-318772 (URN)10.1208/s12248-018-0239-0 (DOI)000436029800001 ()29931471 (PubMedID)
Funder
Swedish Research CouncilEU, FP7, Seventh Framework Programme, 115337
Available from: 2017-03-28 Created: 2017-03-28 Last updated: 2019-06-25Bibliographically approved
Ibrahim, M. M. A., Nordgren, R., Kjellsson, M. C. & Karlsson, M. O. (2018). Model-Based Residual Post-Processing for Residual Model Identification. AAPS Journal, 20(5), Article ID 81.
Open this publication in new window or tab >>Model-Based Residual Post-Processing for Residual Model Identification
2018 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 20, no 5, article id 81Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
SPRINGER, 2018
Keywords
conditional weighted residuals, diagnostics, model evaluation, nonlinear mixed effects models, residual error model
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-360174 (URN)10.1208/s12248-018-0240-7 (DOI)000437188800001 ()29968184 (PubMedID)
Available from: 2018-09-13 Created: 2018-09-13 Last updated: 2018-11-27Bibliographically approved
Terranova, N., Smith, M. K., Nordgren, R., Comets, E., Lavielle, M., Harling, K., . . . Swat, M. J. (2018). The Standard Output: A Tool-Agnostic Modeling Storage Format. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, 7(9), 543-546
Open this publication in new window or tab >>The Standard Output: A Tool-Agnostic Modeling Storage Format
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2018 (English)In: CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, ISSN 2163-8306, Vol. 7, no 9, p. 543-546Article in journal, Editorial material (Other academic) Published
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:uu:diva-369225 (URN)10.1002/psp4.12339 (DOI)000445602000002 ()30033588 (PubMedID)
Funder
EU, FP7, Seventh Framework Programme, 115156
Available from: 2018-12-11 Created: 2018-12-11 Last updated: 2018-12-11Bibliographically approved
Ibrahim, M. M. A., Nordgren, R., Kjellsson, M. C. & Karlsson, M. O. (2017). Comparison of diagnostics using model-based post-processing for fast automated model building. Journal of Pharmacokinetics and Pharmacodynamics, 44, S60-S60
Open this publication in new window or tab >>Comparison of diagnostics using model-based post-processing for fast automated model building
2017 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 44, p. S60-S60Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
Springer, 2017
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-350693 (URN)000425764400114 ()
Available from: 2018-05-18 Created: 2018-05-18 Last updated: 2018-05-18Bibliographically approved
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
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
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
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