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Translation between two models; Application with integrated glucose homeostasis models
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Helwan Univ, Dept Pharm Practice, Cairo, Egypt.ORCID iD: 0000-0002-2084-1531
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.ORCID iD: 0000-0003-3531-9452
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.ORCID iD: 0000-0003-1258-8297
2019 (English)In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 36, article id 86Article in journal (Refereed) Published
Abstract [en]

PurposeFor some biological systems, there exist several models with somewhat different features and perspectives. We propose an evaluation method for NLME models by analyzing real and simulated data from the model of main interest using a structurally different, but similar, NLME model. We showcase this method using the Integrated Glucose Insulin (IGI) model and the Integrated Minimal Model (IMM). Additionally, we try to map parameters carrying similar information between the two models.MethodsA bootstrap of real data and simulated datasets from both the IMM and IGI models were analyzed with the two models. Important parameters of the IMM were mapped to IGI parameters using a large IMM simulated dataset analyzed under the IGI model.ResultsComparison of the parameters estimated from real data and data simulated with the IMM and analyzed with the IGI model demonstrated differences between real and IMM-simulated data. Comparison of the parameters estimated from real data and data simulated with the IGI model and analyzed with the IMM also demonstrated differences but to a lower extent. The strongest parameter correlations were found for: insulin-dependent glucose clearance (IGI) similar to insulin sensitivity (IMM); insulin-independent glucose clearance (IGI) similar to glucose effectiveness (IMM); and insulin effect parameter (IGI) similar to insulin action (IMM).ConclusionsWe demonstrated a new approach to investigate models' ability to simulate real-life-like data, and the information captured in each model in comparison to real data, and the IMM clinically used parameters were successfully mapped to their corresponding IGI parameters.

Place, publisher, year, edition, pages
2019. Vol. 36, article id 86
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-367050DOI: 10.1007/s11095-019-2592-9ISI: 000465089800006PubMedID: 31001701OAI: oai:DiVA.org:uu-367050DiVA, id: diva2:1266291
Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2019-05-13Bibliographically approved
In thesis
1. Pharmacometric evaluation and improvement of models and study designs - applied in diabetes
Open this publication in new window or tab >>Pharmacometric evaluation and improvement of models and study designs - applied in diabetes
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Pharmacometric models are increasingly used to improve the efficiency of the drug development process and increase our understanding of the studied underlying pathophysiological system. These models require assumptions for handling different types of data and the different model components, and the appropriateness of such assumptions must be carefully inspected for unbiased conclusions. The aim of this thesis was to develop new models, that by acknowledging the complexity of the data captures more information, and novel methodologies for model evaluation, as well as applying models to improve study designs, with practical illustrations in the therapeutic area of diabetes. Two new models were developed. An integrated minimal model was developed to enable clinical trial simulations in presence of endogenous insulin secretion while deriving the important physiological indices for clinical diagnosis. A multi-state model was developed for improved handling of survival data in presence of competing risks and interval-censored data. New methodologies for model evaluations were developed that include residual modeling and linearization for assessing possible improvements of the structural and statistical model components as well as using simulations to assess the captured information from the data between structurally different models. A mapping approach for parameters carrying similar information between different models was developed, allowing the derivation of physiological indices from the integrated glucose insulin model. Models were also successfully applied with the purpose of improving study designs, either based on anticipated drug effect or for assessment of physiological indices. In conclusion, new more informative models were developed by acknowledging the complexity of the data, novel methods were proposed and applied for model development/evaluation process, and models were used to improve study designs for clinical trials and clinical diagnosis. 

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 75
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 264
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-366715 (URN)978-91-513-0518-9 (ISBN)
Public defence
2019-01-18, B41, BMC, Husargatan 3, Uppsala, 13:15 (English)
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Supervisors
Available from: 2018-12-17 Created: 2018-11-27 Last updated: 2019-01-21

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Ibrahim, Moustafa M. A.Kjellsson, Maria C.Karlsson, Mats

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