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Kjellsson, Maria C., docentORCID iD iconorcid.org/0000-0003-3531-9452
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Publications (10 of 46) Show all publications
Wellhagen, G. J., Kjellsson, M. C. & Karlsson, M. O. (2019). A Bounded Integer Model for Rating and Composite Scale Data. AAPS Journal, 21(4), Article ID 74.
Open this publication in new window or tab >>A Bounded Integer Model for Rating and Composite Scale Data
2019 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 21, no 4, article id 74Article in journal (Refereed) Published
Abstract [en]

Rating and composite scales are commonly used to assess treatment efficacy. The two main strategies for modelling such endpoints are to treat them as a continuous or an ordered categorical variable (CV or OC). Both strategies have disadvantages, including making assumptions that violate the integer nature of the data (CV) and requiring many parameters for scales with many response categories (OC). We present a method, called the bounded integer (BI) model, which utilises the probit function with fixed cut-offs to estimate the probability of a certain score through a latent variable. This method was successfully implemented to describe six data sets from four different therapeutic areas: Parkinson's disease, Alzheimer's disease, schizophrenia, and neuropathic pain. Five scales were investigated, ranging from 11 to 181 categories. The fit (likelihood) was better for the BI model than for corresponding OC or CV models (ΔAIC range 11-1555) in all cases but one (ΔAIC -63), while the number of parameters was the same or lower. Markovian elements were successfully implemented within the method. The performance in external validation, assessed through cross-validation, was also in favour of the new model (ΔOFV range 22-1694) except in one case (ΔOFV -70). A residual for diagnostic purposes is discussed. This study shows that the BI model respects the integer nature of data and is parsimonious in terms of number of estimated parameters.

Keywords
Bounded integer model, Categorical data, Composite scale, Nonlinear mixed-effects modelling, Probit regression, Rating scale
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-388766 (URN)10.1208/s12248-019-0343-9 (DOI)000470776700002 ()31172350 (PubMedID)
Funder
Swedish Research Council, 2018-03317
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-08-12Bibliographically approved
Ibrahim, M. M. A., Ueckert, S., Freiberga, S., Kjellsson, M. C. & Karlsson, M. O. (2019). Model-Based Conditional Weighted Residuals Analysis for Structural Model Assessment. AAPS Journal, 21(3), Article ID UNSP 34.
Open this publication in new window or tab >>Model-Based Conditional Weighted Residuals Analysis for Structural Model Assessment
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2019 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 21, no 3, article id UNSP 34Article in journal (Refereed) Published
Abstract [en]

Nonlinear mixed effects models are widely used to describe longitudinal data to improve the efficiency of drug development process or increase the understanding of the studied disease. In such settings, the appropriateness of the modeling assumptions is critical in order to draw correct conclusions and must be carefully assessed for any substantial violations. Here, we propose a new method for structure model assessment, based on assessment of bias in conditional weighted residuals (CWRES). We illustrate this method by assessing prediction bias in two integrated models for glucose homeostasis, the integrated glucose-insulin (IGI) model, and the integrated minimal model (IMM). One dataset was simulated from each model then analyzed with the two models. CWRES outputted from each model fitting were modeled to capture systematic trends in CWRES as well as the magnitude of structural model misspecifications in terms of difference in objective function values (ΔOFVBias). The estimates of CWRES bias were used to calculate the corresponding bias in conditional predictions by the inversion of first-order conditional estimation method’s covariance equation. Time, glucose, and insulin concentration predictions were the investigated independent variables. The new method identified correctly the bias in glucose sub-model of the integrated minimal model (IMM), when this bias occurred, and calculated the absolute and proportional magnitude of the resulting bias. CWRES bias versus the independent variables agreed well with the true trends of misspecification. This method is fast easily automated diagnostic tool for model development/evaluation process, and it is already implemented as part of the Perl-speaks-NONMEM software.

Keywords
conditional weighted residuals, diagnostics, model evaluation, nonlinear mixed effects models, prediction bias, structural model
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-367054 (URN)10.1208/s12248-019-0305-2 (DOI)000460184300001 ()30815754 (PubMedID)
Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2019-03-25Bibliographically approved
Ibrahim, M. M. A., Largajolli, A., Karlsson, M. & Kjellsson, M. C. (2019). The Integrated Glucose Insulin Minimal Model: An improved version. European Journal of Pharmaceutical Sciences, 134, 7-19
Open this publication in new window or tab >>The Integrated Glucose Insulin Minimal Model: An improved version
2019 (English)In: European Journal of Pharmaceutical Sciences, ISSN 0928-0987, E-ISSN 1879-0720, Vol. 134, p. 7-19Article in journal (Refereed) Published
Abstract [en]

This paper describes the improved integrated minimal model for healthy subjects and patients with type 2 diabetes and the work leading up to this model. The original integrated minimal model characterizes simultaneously glucose and insulin following intravenous glucose tolerance test (IVGTT) in healthy subjects and provides apart from estimates of indices for insulin sensitivity (S-i) and glucose effectiveness (S-G), also full simulation capabilities. However, this model was developed using IVGTT data of total glucose and consequently, the model cannot separate hepatic glucose production from glucose disposal. By fitting the original integrated minimal model to IVGTT data of labelled and total glucose, we show that all parameter estimates of the glucose sub-model were significantly different between the fits, in particular, S-G, which was similar to 3 fold higher with total, compared to labelled glucose. In addition, the time profiles of hepatic glucose production, obtained from the model, were unphysiological in most subjects. To correct these flaws, we developed the improved integrated minimal model based on the non-integrated, two-compartment minimal model. The improved integrated minimal model showed physiologically plausible dynamic time profiles of hepatic glucose production and all parameter estimates were compatible with those reported in original publication of the non-integrated minimal model. The integrated minimal model offers the benefits of the original integrated minimal model with simulation capabilities, in presence of endogenous insulin, combined with the benefits of the non-integrated minimal model, which accurately estimates the clinical indices of insulin sensitivity and glucose effectiveness. In addition, the improved integrated minimal model describes, apart from healthy subjects, also patients with type 2 diabetes.

National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-367052 (URN)10.1016/j.ejps.2019.04.010 (DOI)000468081200002 ()30978382 (PubMedID)
Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2019-06-14Bibliographically approved
Ibrahim, M. M. A., Largajolli, A., Kjellsson, M. C. & Karlsson, M. (2019). Translation between two models; Application with integrated glucose homeostasis models. Pharmaceutical research, 36, Article ID 86.
Open this publication in new window or tab >>Translation between two models; Application with integrated glucose homeostasis models
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.

National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-367050 (URN)10.1007/s11095-019-2592-9 (DOI)000465089800006 ()31001701 (PubMedID)
Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2019-05-13Bibliographically approved
Stage, T. B., Wellhagen, G., Christensen, M. M., Guiastrennec, B., Brosen, K. & Kjellsson, M. C. (2019). Using a semi-mechanistic model to identify the main sources of variability of metformin pharmacokinetics. Basic & Clinical Pharmacology & Toxicology, 124(1), 105-114
Open this publication in new window or tab >>Using a semi-mechanistic model to identify the main sources of variability of metformin pharmacokinetics
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2019 (English)In: Basic & Clinical Pharmacology & Toxicology, ISSN 1742-7835, E-ISSN 1742-7843, Vol. 124, no 1, p. 105-114Article in journal (Refereed) Published
Abstract [en]

Metformin pharmacokinetics (PK) is highly variable, and researchers have for years tried to shed light on determinants of inter-individual (IIV) and inter-occasion variability (IOV) of metformin PK. We set out to identify the main sources of PK variability using a semi-mechanistic model. We assessed the influence of subject characteristics, including seven genetic variants. Data from three studies of healthy individuals with PK measurements of plasma and urine after single dose or at steady-state were used in this study. In total, 87 subjects were included (16 crossover subjects). Single nucleotide polymorphisms in ATM, OCT1, OCT2, MATE1 and MATE2-K were investigated as dominant, recessive or additive. A three-compartment model with transit absorption and renal elimination with a proportional error was fitted to the data using NONMEM 7.3. Oral parameters were separated from disposition parameters as dose-dependent absolute bioavailability was determined with support from urine data. Clearance was expressed as net renal secretion and filtration, assuming full fraction unbound and fraction excreted. Mean transit time and peripheral volume of distribution were identified as the main sources of variability according to estimates, with 94% IOV and 95% IIV, respectively. Clearance contributed only with 16% IIV. Glomerular filtration rate and body-weight were the only covariates found to affect metformin net secretion, reducing IIV to 14%. None of the genetic variants were found to affect metformin PK. Based on our analysis, finding covariates explaining absorption of metformin is much more valuable in understanding variability and avoiding toxicity than elimination.

National Category
Pharmaceutical Sciences Pharmacology and Toxicology
Identifiers
urn:nbn:se:uu:diva-374110 (URN)10.1111/bcpt.13139 (DOI)000454711300012 ()30267605 (PubMedID)
Available from: 2019-01-21 Created: 2019-01-21 Last updated: 2019-01-21Bibliographically 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
Germovsek, E., Hansson, A., Kjellsson, M. C., Ruixo, J. J., Westin, A., Soons, P. A., . . . Karlsson, M. O. (2018). An exposure-response (ER) model relating nicotine plasma concentration to momentary craving across different nicotine replacement therapy (NRT) formulations. Paper presented at The Ninth American Conference on Pharmacometrics (ACoP9), Oct 7-10, 2018, San Diego, USA.. Journal of Pharmacokinetics and Pharmacodynamics, 45(suppl. 1), S28-S29
Open this publication in new window or tab >>An exposure-response (ER) model relating nicotine plasma concentration to momentary craving across different nicotine replacement therapy (NRT) formulations
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2018 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 45, no suppl. 1, p. S28-S29Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
SPRINGER/PLENUM PUBLISHERS, 2018
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:uu:diva-365109 (URN)000445374700057 ()
Conference
The Ninth American Conference on Pharmacometrics (ACoP9), Oct 7-10, 2018, San Diego, USA.
Available from: 2018-11-16 Created: 2018-11-16 Last updated: 2018-11-16Bibliographically approved
Wellhagen, G., Karlsson, M. O. & Kjellsson, M. C. (2018). Comparison of Power, Prognosis, and Extrapolation Properties of Four Population Pharmacodynamic Models of HbA1c for Type 2 Diabetes. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, 7(5), 331-341
Open this publication in new window or tab >>Comparison of Power, Prognosis, and Extrapolation Properties of Four Population Pharmacodynamic Models of HbA1c for Type 2 Diabetes
2018 (English)In: CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, ISSN 2163-8306, Vol. 7, no 5, p. 331-341Article in journal (Refereed) Published
Abstract [en]

Reusing published models saves time; time to be used for informing decisions in drug development. In antihyperglycemic drug development, several published HbA1c models are available but selecting the appropriate model for a particular purpose is challenging. This study aims at helping selection by investigating four HbA1c models, specifically the ability to identify drug effects (shape, site of action, and power) and simulation properties. All models could identify glucose effect nonlinearities, although for detecting the site of action, a mechanistic glucose model was needed. Power was highest for models using mean plasma glucose to drive HbA1c formation. Insulin contribution to power varied greatly depending on the drug target; it was beneficial only if the drug target was insulin secretion. All investigated models showed good simulation properties. However, extrapolation with the mechanistic model beyond 12 weeks resulted in drug effect overprediction. This investigation aids drug development in decisions regarding model choice if reusing published HbA1c models.

Place, publisher, year, edition, pages
WILEY, 2018
National Category
Endocrinology and Diabetes
Identifiers
urn:nbn:se:uu:diva-357755 (URN)10.1002/psp4.12290 (DOI)000434085800006 ()29575656 (PubMedID)
Funder
EU, FP7, Seventh Framework Programme
Available from: 2018-08-22 Created: 2018-08-22 Last updated: 2018-08-22Bibliographically approved
Svensson, E., Yngman, G., Denti, P., McIlleron, H., Kjellsson, M. C. & Karlsson, M. O. (2018). Evidence-Based Design of Fixed-Dose Combinations: Principles and Application to Pediatric Anti-Tuberculosis Therapy. Clinical Pharmacokinetics, 57(5), 591-599
Open this publication in new window or tab >>Evidence-Based Design of Fixed-Dose Combinations: Principles and Application to Pediatric Anti-Tuberculosis Therapy
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2018 (English)In: Clinical Pharmacokinetics, ISSN 0312-5963, E-ISSN 1179-1926, Vol. 57, no 5, p. 591-599Article in journal (Refereed) Published
Abstract [en]

BACKGROUND AND OBJECTIVES: Fixed-dose combination formulations where several drugs are included in one tablet are important for the implementation of many long-term multidrug therapies. The selection of optimal dose ratios and tablet content of a fixed-dose combination and the design of individualized dosing regimens is a complex task, requiring multiple simultaneous considerations.

METHODS: In this work, a methodology for the rational design of a fixed-dose combination was developed and applied to the case of a three-drug pediatric anti-tuberculosis formulation individualized on body weight. The optimization methodology synthesizes information about the intended use population, the pharmacokinetic properties of the drugs, therapeutic targets, and practical constraints. A utility function is included to penalize deviations from the targets; a sequential estimation procedure was developed for stable estimation of break-points for individualized dosing. The suggested optimized pediatric anti-tuberculosis fixed-dose combination was compared with the recently launched World Health Organization-endorsed formulation.

RESULTS: The optimized fixed-dose combination included 15, 36, and 16% higher amounts of rifampicin, isoniazid, and pyrazinamide, respectively. The optimized fixed-dose combination is expected to result in overall less deviation from the therapeutic targets based on adult exposure and substantially fewer children with underexposure (below half the target).

CONCLUSION: The development of this design tool can aid the implementation of evidence-based formulations, integrating available knowledge and practical considerations, to optimize drug exposures and thereby treatment outcomes.

National Category
Pharmaceutical Sciences
Research subject
Pharmacokinetics and Drug Therapy
Identifiers
urn:nbn:se:uu:diva-346132 (URN)10.1007/s40262-017-0577-6 (DOI)000430321100005 ()28779464 (PubMedID)
Funder
Swedish Research Council, 521-2011-3442
Available from: 2018-03-14 Created: 2018-03-14 Last updated: 2019-12-10Bibliographically 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
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ORCID iD: ORCID iD iconorcid.org/0000-0003-3531-9452

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