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  • 1.
    Alskar, Oskar
    et al.
    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.
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Interspecies scaling of dynamic glucose and insulin using a mathematical model approach2015In: Diabetologia, ISSN 0012-186X, E-ISSN 1432-0428, Vol. 58, no Suppl. 1, p. S306-S307Article in journal (Other academic)
  • 2.
    Alskär, Oskar
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Bagger, Jonatan I
    Røge, Rikke M
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Knop, Filip K
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Vilsbøll, Tina
    Kjellsson, Maria C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Semi-mechanistic model describing gastric emptying and glucose absorption in healthy subjects and patients with type 2 diabetes2016In: Journal of clinical pharmacology, ISSN 0091-2700, E-ISSN 1552-4604, Vol. 56, no 3, p. 340-348Article in journal (Refereed)
    Abstract [en]

    The integrated glucose-insulin (IGI) model is a previously published semi-mechanistic model, which describes plasma glucose and insulin concentrations after glucose challenges. The aim of this work was to use knowledge of physiology to improve the IGI model's description of glucose absorption and gastric emptying after tests with varying glucose doses. The developed model's performance was compared to empirical models. To develop our model, data from oral and intravenous glucose challenges in patients with type 2 diabetes and healthy control subjects were used together with present knowledge of small intestinal transit time, glucose inhibition of gastric emptying and saturable absorption of glucose over the epithelium to improve the description of gastric emptying and glucose absorption in the IGI model. Duodenal glucose was found to inhibit gastric emptying. The performance of the saturable glucose absorption was superior to linear absorption regardless of the gastric emptying model applied. The semi-physiological model developed performed better than previously published empirical models and allows for better understanding of the mechanisms underlying glucose absorption. In conclusion, our new model provides a better description and improves the understanding of dynamic glucose tests involving oral glucose.

  • 3.
    Alskär, Oskar
    et al.
    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.
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Model-Based Interspecies Scaling of Glucose Homeostasis2017In: CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, ISSN 2163-8306, Vol. 6, no 11, p. 778-786Article in journal (Refereed)
    Abstract [en]

    Being able to scale preclinical pharmacodynamic response to clinical would be beneficial in drug development. In this work, the integrated glucose insulin (IGI) model, developed on clinical intravenous glucose tolerance test (IVGTT) data, describing dynamic glucose and insulin concentrations during glucose tolerance tests, was scaled to describe data from similar tests performed in healthy rats, mice, dogs, pigs, and humans. Several approaches to scaling the dynamic glucose and insulin were investigated. The theoretical allometric exponents of 0.75 and 1, for clearances and volumes, respectively, could describe the data well with some species-specific adaptations: dogs and pigs showed slower first phase insulin secretion than expected from the scaling, pigs also showed more rapid insulin dependent glucose elimination, and rodents showed differences in glucose effectiveness. The resulting scaled IGI model was shown to accurately predict external preclinical IVGTT data and may be useful in facilitating translations of preclinical research into the clinic.

  • 4.
    Choy, Steve
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Henin, Emilie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    van der Walt, Jan-Stefan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kjellsson, Maria
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Identification of the primary mechanism of action of an insulin secretagogue from meal test data in healthy volunteers based on an integrated glucose-insulin model2013In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 40, no 1, p. 1-10Article in journal (Refereed)
    Abstract [en]

    The integrated glucose–insulin (IGI) model is a previously developed semi-mechanistic model that incorporates control mechanisms for the regulation of glucose production, insulin secretion, and glucose uptake. It has been shown to adequately describe insulin and glucose profiles in both type 2 diabetics and healthy volunteers following various glucose tolerance tests. The aim of this study was to investigate the ability of the IGI model to correctly identify the primary mechanism of action of glibenclamide (Gb), based on meal tolerance test (MTT) data in healthy volunteers. IGI models with different mechanism of drug action were applied to data from eight healthy volunteers participating in a randomized crossover study with five single-dose tests (placebo and four drug arms). The study participants were given 3.5 mg of Gb, intravenously or orally, or 3.5 mg of the two main metabolites M1 and M2 intravenously, 0.5 h prior to a standardized breakfast with energy content of 1800 kJ. Simultaneous analysis of all data by nonlinear mixed effect modeling was performed using NONMEM®. Drug effects that increased insulin secretion resulted in the best model fit, thus identifying the primary mechanism of action of Gb and metabolites as insulin secretagogues. The model also quantified the combined effect of Gb, M1 and M2 to have a fourfold maximal increase on endogenous insulin secretion, with an EC50 of 169.1 ng mL−1 for Gb, 151.4 ng mL−1 for M1 and 267.1 ng mL−1 for M2. The semi-mechanistic IGI model was successfully applied to MTT data and identified the primary mechanism of action for Gb, quantifying its effects on glucose and insulin time profiles.

  • 5.
    Choy, Steve
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kjellsson, Maria
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    de Winter, Willem
    Janssen Prevention Center, Janssen Pharmaceutical Companies of Johnson & Johnson, Leiden, The Netherlands.
    Weight-HbA1c-Insulin-Glucose Model for Describing Disease Progression of Type 2 Diabetes2016In: CPT: Pharmacometrics & Systems Pharmacology, ISSN 2163-8306, Vol. 5, no 1, p. 11-19Article in journal (Refereed)
    Abstract [en]

    A previous semi-mechanistic model described changes in fasting serum insulin (FSI), fasting plasma glucose (FPG), and glycated hemoglobin (HbA1c) in patients with type 2 diabetic mellitus (T2DM) by modeling insulin sensitivity and β-cell function. It was later suggested that change in body weight could affect insulin sensitivity, which this study evaluated in a population model to describe the disease progression of T2DM. Nonlinear mixed effects modeling was performed on data from 181 obese patients with newly diagnosed T2DM managed with diet and exercise for 67 weeks. Baseline β-cell function and insulin sensitivity were 61% and 25% of normal, respectively. Management with diet and exercise (mean change in body weight = -4.1 kg) was associated with an increase of insulin sensitivity (30.1%) at the end of the study. Changes in insulin sensitivity were associated with a decrease of FPG (range, 7.8–7.3 mmol/L) and HbA1c (6.7–6.4%). Weight change as an effector on insulin sensitivity was successfully evaluated in a semi-mechanistic population model.

  • 6.
    Claussen, Anetta
    et al.
    Certara Strateg Consulting, Basel, Switzerland.;Novo Nordisk AS, Quantitat Clin Pharmacol, Vandtarnvej 108-110, Soborg, Denmark..
    Möller, Jonas B.
    Novo Nordisk AS, New Prod Planning, Soborg, Denmark..
    Kristensen, Niels R.
    Novo Nordisk AS, Quantitat Clin Pharmacol, Vandtarnvej 108-110, Soborg, Denmark..
    Klim, Sören
    Novo Nordisk AS, Biostat, Soborg, Denmark..
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ingwersen, Steen H.
    Novo Nordisk AS, Quantitat Clin Pharmacol, Vandtarnvej 108-110, Soborg, Denmark..
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Impact of demographics and disease progression on the relationship between glucose and HbA1c2017In: European Journal of Pharmaceutical Sciences, ISSN 0928-0987, E-ISSN 1879-0720, Vol. 104, p. 417-423Article in journal (Refereed)
    Abstract [en]

    Context: Several studies have shown that the relationship between mean plasma glucose (MPG) and glycated haemoglobin (HbA1c) may vary across populations. Especially race has previously been referred to shift the regression line that links MPG to HbA1c at steady-state (Herman & Cohen, 2012).

    Objective: To assess the influence of demographic and disease progression-related covariates on the intercept of the estimated linear MPG-HbA1c relationship in a longitudinal model.

    Data: Longitudinal patient-level data from 16 late-phase trials in type 2 diabetes with a total of 8927 subjects was used to study covariates for the relationship between MPG and HbA1c. The analysed covariates included age group, HMI, gender, race, diabetes duration, and pre-trial treatment. Differences between trials were taken into account by estimating a trial-to-trial variability component.

    Participants: Participants included 47% females and 20% above 65 years. 77% were Caucasian, 9% were Asian, 5% were Black and the remaining 9% were analysed together as other races.

    Analysis: Estimates of the change in the intercept of the MPG-HbA1c relationship due to the mentioned covariates were determined using a longitudinal model.

    Results: The analysis showed that pre-trial treatment with insulin had the most pronounced impact associated with a 0.34% higher HbA1c at a given MPG. However, race, diabetes duration and age group also had an impact on the MPG-HbA1c relationship.

    Conclusion: Our analysis shows that the relationship between MPG and HbA1c is relatively insensitive to covariates, but shows small variations across populations, which may be relevant to take into account when predicting HbA1c response based on MPG measurements in clinical trials.

  • 7.
    Germovsek, Eva
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hansson, Anna
    McNeil AB, Helsingborg, Sweden..
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ruixo, Juan Jose Perez
    Janssen R&D, Beerse, Belgium..
    Westin, Ake
    McNeil AB, Helsingborg, Sweden..
    Soons, Paul A.
    Janssen R&D, Beerse, Belgium..
    Vermeulen, An
    Janssen R&D, Beerse, Belgium..
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    An exposure-response (ER) model relating nicotine plasma concentration to momentary craving across different nicotine replacement therapy (NRT) formulations2018In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 45, no suppl. 1, p. S28-S29Article in journal (Other academic)
  • 8.
    Ghadzi, Siti Maisharah Sheikh
    et al.
    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.
    de Mello, V. D.
    Univ Eastern Finland, Inst Publ Hlth & Clin Nutr, Joensuu, Finland..
    Uusitupa, M.
    Univ Eastern Finland, Inst Publ Hlth & Clin Nutr, Joensuu, Finland..
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A mathematical disease progression model for the effect of diet and exercise in subjects with impaired glucose tolerance in the Finnish Diabetes Prevention Study (FDPS)2015In: Diabetologia, ISSN 0012-186X, E-ISSN 1432-0428, Vol. 58, no Suppl. 1, p. S192-S192Article in journal (Other academic)
  • 9.
    Ghadzi, Siti Maisharah Sheikh
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Univ Sains Malaysia, George Town, Malaysia.
    Karlsson, Mats O
    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.
    Implications for Drug Characterization in Glucose Tolerance Tests Without Insulin: Simulation Study of Power and Predictions Using Model-Based Analysis2017In: CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, ISSN 2163-8306, Vol. 6, no 10, p. 686-694Article in journal (Refereed)
    Abstract [en]

    In antihyperglycemic drug development, drug effects are usually characterized using glucose provocations. Analyzing provocation data using pharmacometrics has shown powerful, enabling small studies. In preclinical drug development, high power is attractive due to the experiment sizes; however, insulin is not always available, which potentially impacts power and predictive performance. This simulation study was performed to investigate the implications of performing model-based drug characterization without insulin. The integrated glucose-insulin model was used to simulate and re-estimated oral glucose tolerance tests using a crossover design of placebo and study compound. Drug effects were implemented on seven different mechanisms of action (MOA); one by one or in two-drug combinations. This study showed that exclusion of insulin may severely reduce the power to distinguish the correct from competing drug effect, and to detect a primary or secondary drug effect, however, it did not affect the predictive performance of the model.

  • 10.
    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.
    Ghadzi, Siti Maisharah Sheikh
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Univ Sains Malaysia, Sch Pharmaceut Sci, Gelugor, Malaysia.
    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.
    Study Design Selection in Early Clinical Anti-Hyperglycemic Drug Development: A Simulation Study of Glucose Tolerance Tests2018In: CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, ISSN 2163-8306, Vol. 7, no 7, p. 432-441Article in journal (Refereed)
    Abstract [en]

    In antidiabetic drug development, phase I studies usually involve short-term glucose provocations. Multiple designs are available for these provocations (e.g., meal tolerance tests (MTTs) and graded glucose infusions (GGIs)). With a highly nonlinear, complex system as the glucose homeostasis, the various provocations will contribute with different information offering a rich choice. Here, we investigate the most appropriate study design in phase I for several hypothetical mechanisms of action of a study drug. Five drug effects in diabetes therapeutic areas were investigated using six study designs. Power to detect drug effect was assessed using the likelihood ratio test, whereas precision and accuracy of the quantification of drug effect was assessed using stochastic simulation and estimations. An overall summary was developed to aid designing the studies of antihyperglycemic drug development using model-based analysis. This guidance is to be used when the integrated glucose insulin model is used, involving the investigated drug mechanisms of action.

  • 11.
    Ibrahim, Moustafa M. A.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Largajolli, Anna
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats
    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.
    The Integrated Glucose Insulin Minimal Model: An improved version2019In: European Journal of Pharmaceutical Sciences, ISSN 0928-0987, E-ISSN 1879-0720, Vol. 134, p. 7-19Article in journal (Refereed)
    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.

  • 12.
    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.
    Largajolli, Anna
    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
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Translation between two models; Application with integrated glucose homeostasis models2019In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 36, article id 86Article in journal (Refereed)
    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.

  • 13.
    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)
  • 14.
    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.

  • 15.
    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.

  • 16.
    Ibrahim, Moustafa M. A.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ueckert, Sebastian
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Freiberga, Svetlana
    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 Conditional Weighted Residuals Analysis for Structural Model Assessment2019In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 21, no 3, article id UNSP 34Article in journal (Refereed)
    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.

  • 17.
    Jönsson, Siv
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Estimating bias in population parameters for some models for repeated measures ordinal data using NONMEM or NLMIXED2004In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 31, no 4, p. 299-320Article in journal (Refereed)
    Abstract [en]

    The application of proportional odds models to ordered categorical data using the mixed-effects modeling approach has become more frequently reported within the pharmacokinetic/pharmacodynamic area during the last decade. The aim of this paper was to investigate the bias in parameter estimates, when models for ordered categorical data were estimated using methods employing different approximations of the likelihood integral; the Laplacian approximation in NONMEM (without and with the centering option) and NLMIXED, and the Gaussian quadrature approximations in NLMIXED. In particular, we have focused on situations with non-even distributions of the response categories and the impact of interpatient variability. This is a Monte Carlo simulation study where original data sets were derived from a known model and fixed study design. The simulated response was a four-category variable on the ordinal scale with categories 0, 1, 2 and 3. The model used for simulation was fitted to each data set for assessment of bias. Also, simulations of new data based on estimated population parameters were performed to evaluate the usefulness of the estimated model. For the conditions tested, Gaussian quadrature performed without appreciable bias in parameter estimates. However, markedly biased parameter estimates were obtained using the Laplacian estimation method without the centering option, in particular when distributions of observations between response categories were skewed and when the interpatient variability was moderate to large. Simulations under the model could not mimic the original data when bias was present, but resulted in overestimation of rare events. The bias was considerably reduced when the centering option in NONMEM was used. The cause for the biased estimates appears to be related to the conditioning on uninformative and uncertain empirical Bayes estimate of interindividual random effects during the estimation, in conjunction with the normality assumption.

  • 18.
    Karlsson, Markus
    et al.
    Linkoping Univ, Dept Biomed Engn, SE-58185 Linkoping, Sweden..
    Janzen, David L. I.
    Linkoping Univ, Dept Biomed Engn, SE-58185 Linkoping, Sweden.;Linkoping Univ, Dept Clin & Expt Med, SE-58185 Uppsala, Sweden.;AstraZeneca, Modeling & Simulat, Molndal, Sweden.;Fraunhofer Chalmers Ctr, Dept Syst & Data Anal, SE-41288 Gothenburg, Sweden..
    Durrieu, Lucia
    Univ Buenos Aires, Inst Fisiol Biol Mol & Neurociencias, Consejo Nacl Invest Cient & Tecn, Buenos Aires, DF, Argentina.;Univ Buenos Aires, Fac Ciencias Exactas & Nat, Buenos Aires, DF, Argentina..
    Colman-Lerner, Alejandro
    Univ Buenos Aires, Inst Fisiol Biol Mol & Neurociencias, Consejo Nacl Invest Cient & Tecn, Buenos Aires, DF, Argentina.;Univ Buenos Aires, Fac Ciencias Exactas & Nat, Buenos Aires, DF, Argentina..
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Cedersund, Gunnar
    Linkoping Univ, Dept Biomed Engn, SE-58185 Linkoping, Sweden.;Linkoping Univ, Dept Clin & Expt Med, SE-58185 Uppsala, Sweden.;Linkoping Univ, IKE, S-58185 Linkoping, Sweden..
    Nonlinear mixed-effects modelling for single cell estimation: when, why, and how to use it2015In: BMC Systems Biology, ISSN 1752-0509, E-ISSN 1752-0509, Vol. 9, article id 52Article in journal (Refereed)
    Abstract [en]

    Background: Studies of cell-to-cell variation have in recent years grown in interest, due to improved bioanalytical techniques which facilitates determination of small changes with high uncertainty. Like much high-quality data, single-cell data is best analysed using a systems biology approach. The most common systems biology approach to single-cell data is the standard two-stage (STS) approach. In STS, data from each cell is analysed in a separate sub-problem, meaning that only data from the same cell is used to calculate the parameter values within that cell. Because only parts of the data are considered, problems with parameter unidentifiability are exaggerated in STS. In contrast, a related approach to data analysis has been developed for the studies of patient-to-patient variations. This approach, called nonlinear mixed-effects modelling (NLME), makes use of all data, when estimating the patient-specific parameters. NLME would therefore be advantageous compared to STS also for the study of cell-to-cell variation. However, no such systematic evaluation of the two approaches exists. Results: Herein, such a systematic comparison between STS and NLME has been performed. Different examples, both linear and nonlinear, and both simulated and real experimental data, have been examined. With informative data, there is no significant difference in the results for either parameter or noise estimation. However, when data becomes uninformative, NLME is significantly superior to STS. These results hold independently of whether the loss of information is due to a low signal-to-noise ratio, too few data points, or a bad input signal. The improvement is shown to come from both the consideration of a joint likelihood (JLH) function, describing all parameters and data, and from an a priori postulated form of the population parameters. Finally, we provide a small tutorial that shows how to use NLME for single-cell analysis, using the free and user-friendly software Monolix. Conclusions: When considering uninformative single-cell data, NLME yields more accurate parameter and noise estimates, compared to more traditional approaches, such as STS and JLH.

  • 19.
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Methodological Studies on Models and Methods for Mixed-Effects Categorical Data Analysis2008Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Effects of drugs are in clinical trials often measured on categorical scales. These measurements are increasingly being analyzed using mixed-effects logistic regression. However, the experience with such analyzes is limited and only a few models are used.

    The aim of this thesis was to investigate the performance and improve the use of models and methods for mixed-effects categorical data analysis. The Laplacian method was shown to produce biased parameter estimates if (i) the data variability is large or (ii) the distribution of the responses is skewed. Two solutions are suggested; the Gaussian quadrature method and the back-step method. Two assumptions made with the proportional odds model have also been investigated. The assumption with proportional odds for all categories was shown to be unsuitable for analysis of data arising from a ranking scale of effects with several underlying causes. An alternative model, the differential odds model, was developed and shown to be an improvement, in regard to statistical significance as well as predictive performance, over the proportional odds model for such data. The appropriateness of the likelihood ratio test was investigated for an analysis where dependence between observations is ignored, i.e. performing the analysis using the proportional odds model. The type I error was found to be affected; thus assessing the actual critical value is prudent in order to verify the statistical significance level. An alternative approach is to use a Markov model, in which dependence between observations is incorporated. In the case of polychotomous data such model may involve considerable complexity and thus, a strategy for the reduction of the time-consuming model building with the Markov model and sleep data is presented.

    This thesis will hopefully contribute to a more confident use of models for categorical data analysis within the area of pharmacokinetic and pharmacodynamic modelling in the future.

    List of papers
    1. Estimating bias in population parameters for some models for repeated measures ordinal data using NONMEM or NLMIXED
    Open this publication in new window or tab >>Estimating bias in population parameters for some models for repeated measures ordinal data using NONMEM or NLMIXED
    2004 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 31, no 4, p. 299-320Article in journal (Refereed) Published
    Abstract [en]

    The application of proportional odds models to ordered categorical data using the mixed-effects modeling approach has become more frequently reported within the pharmacokinetic/pharmacodynamic area during the last decade. The aim of this paper was to investigate the bias in parameter estimates, when models for ordered categorical data were estimated using methods employing different approximations of the likelihood integral; the Laplacian approximation in NONMEM (without and with the centering option) and NLMIXED, and the Gaussian quadrature approximations in NLMIXED. In particular, we have focused on situations with non-even distributions of the response categories and the impact of interpatient variability. This is a Monte Carlo simulation study where original data sets were derived from a known model and fixed study design. The simulated response was a four-category variable on the ordinal scale with categories 0, 1, 2 and 3. The model used for simulation was fitted to each data set for assessment of bias. Also, simulations of new data based on estimated population parameters were performed to evaluate the usefulness of the estimated model. For the conditions tested, Gaussian quadrature performed without appreciable bias in parameter estimates. However, markedly biased parameter estimates were obtained using the Laplacian estimation method without the centering option, in particular when distributions of observations between response categories were skewed and when the interpatient variability was moderate to large. Simulations under the model could not mimic the original data when bias was present, but resulted in overestimation of rare events. The bias was considerably reduced when the centering option in NONMEM was used. The cause for the biased estimates appears to be related to the conditioning on uninformative and uncertain empirical Bayes estimate of interindividual random effects during the estimation, in conjunction with the normality assumption.

    Keywords
    NONMEM, NLMIXED, SAS, Laplacian, Gaussian quadrature, maximum likelihood estimation, ordered categorical, proportional odds model, bias in parameter estimates
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:uu:diva-91736 (URN)10.1023/B:JOPA.0000042738.06821.61 (DOI)15563005 (PubMedID)
    Available from: 2004-04-26 Created: 2004-04-26 Last updated: 2017-12-14
    2. The back-step method: method for obtaining unbiased population parameter estimates for ordered categorical data
    Open this publication in new window or tab >>The back-step method: method for obtaining unbiased population parameter estimates for ordered categorical data
    2004 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 6, no 3, p. 13-22Article in journal (Refereed) Published
    Abstract [en]

    A significant bias in parameters, estimated with the proportional odds model using the software NONMEM, has been reported. Typically, this bias occurs with ordered categorical data, when most of the observations are found at one extreme of the possible outcomes. The aim of this study was to assess, through simulations, the performance of the Back-Step Method (BSM), a novel approach for obtaining unbiased estimates when the standard approach provides biased estimates. BSM is an iterative method involving sequential simulation-estimation steps. BSM was compared with the standard approach in the analysis of a 4-category ordered variable using the Laplacian method in NONMEM. The bias in parameter estimates and the accuracy of model predictions were determined for the 2 methods on 3 conditions: (1) a nonskewed distribution of the response with low interindividual variability (IIV), (2) a skewed distribution with low IIV, and (3) a skewed distribution with high IIV. An increase in bias with increasing skewness and IIV was shown in parameters estimated using the standard approach in NONMEM. BSM performed without appreciable bias in the estimates under the 3 conditions, and the model predictions were in good agreement with the original data. Each BSM estimation represents a random sample of the population; hence, repeating the BSM estimation reduces the imprecision of the parameter estimates. The BSM is an accurate estimation method when the standard modeling approach in NONMEM gives biased estimates.

    Keywords
    ordered categorical, proportional odds model, bias in parameter estimates, NONMEM, Laplacian, pharmacodynamics
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:uu:diva-97669 (URN)10.1208/aapsj060319 (DOI)15760104 (PubMedID)
    Available from: 2008-10-30 Created: 2008-10-30 Last updated: 2017-12-14
    3. Comparison of proportional odds and differential odds models for mixed-effects analysis of categorical data
    Open this publication in new window or tab >>Comparison of proportional odds and differential odds models for mixed-effects analysis of categorical data
    2008 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 35, no 5, p. 483-501Article in journal (Refereed) Published
    Abstract [en]

    In this work a model for analyzing categorical data is presented; the differential odds model. Unlike the commonly used proportional odds model, this model does not assume that a covariate affects all categories equally on the log odds scale. The differential odds model was compared to the proportional odds model, by assessing statistical significance and improvement of predictive performance when applying the differential odds model to data previously analyzed using the proportional odds model. Three clinical studies; 3-category T-cell receptor density data, 5-category diarrhea data and 6-category sedation data, were re-analyzed with the differential odds model. As expected, no improvements were seen with T-cell receptor density and diarrhea data. However, for the more complex measurement sedation, the differential odds model provided both statistical improvements and improvements in simulation properties. The estimated actual critical value was for all data lower than the nominal value, using the number of added parameters as the degree of freedom, i.e. the differential odds model is statistically indicated to a less extent than expected. The differential odds model had the desired property of not being indicated when not necessary, but it may provide improvements when the data does not represent a categorization of continuous data.

    Keywords
    NONMEM, Mixed-effects models, Pharmacodynamics, Categorical data, Proportional odds model, Differential odds model
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:uu:diva-97670 (URN)10.1007/s10928-008-9098-0 (DOI)000262699200001 ()
    Available from: 2008-10-30 Created: 2008-10-30 Last updated: 2017-12-14
    4. The impact of misspecification of residual error or correlation structure on the type I error rate for covariate inclusion
    Open this publication in new window or tab >>The impact of misspecification of residual error or correlation structure on the type I error rate for covariate inclusion
    2009 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 36, no 1, p. 81-99Article in journal (Refereed) Published
    Abstract [en]

    It has been shown that when using the FOCE method in NONMEM, the likelihood ratio test (LRT) can be sensitive to the use of an inappropriate estimation method in that ignoring an existing eta-epsilon interaction leads to actual significance levels for type I errors being higher than the nominal levels. The objective of this study was to assess through simulations the LRT sensitivity to various types of residual error model misspecifications in both continuous and categorical data. The study contained two parts, simulations based on continuous and categorical data. Data sets containing 250 individuals with up to 24 observations per individual were simulated multiple times (1000) with different types of residual error models for the continuous data and different strength of correlation between observations for the categorical data. The data sets were analyzed using either the correct or a simpler (incorrect) model with or without addition of a covariate. The type I error rate of inclusion of the non-informative covariate on the 5% level was calculated as the number of runs where the drop in the objective function value (OFV) was larger than 3.84 when the covariate relationship was included in the model using the correct or the incorrect model. The difference in OFV between the model with the correct and the incorrect structure was also calculated as a measure of the residual error model misspecification. For continuous data the FOCE method was used in most cases (with interaction when appropriate). The Laplacian estimation method was used for one of the continuous models and for categorical data. The results showed that the residual error model misspecifications when the erroneous model was used were pronounced, as indicated by the OFV being substantially higher than for the corresponding correct models. The significance levels of the LRT with the incorrect model were appropriate in all cases but ignoring (serial) correlations between observations (continuous and categorical data) as well as when the eta-epsilon interaction was ignored (which has previously been shown, continuous data). When ignoring correlation, the type I error rates were shown to be sensitive to the correlation strength, the number of observations per individual and the magnitude of the inter-individual variability on clearance. We conclude that the LRT appears robust towards all tested cases, but ignoring (serial) correlations between observations and eta-epsilon interaction.

    Keywords
    Residual error, Model misspecification, Continuous data, Categorical data, NONMEM, Markov model, Proportional odds model, Simulation, Likelihood ratio test
    National Category
    Pharmaceutical Sciences
    Research subject
    Pharmacokinetics and Drug Therapy
    Identifiers
    urn:nbn:se:uu:diva-99064 (URN)10.1007/s10928-009-9112-1 (DOI)000263797800005 ()19219538 (PubMedID)
    Available from: 2009-03-09 Created: 2009-03-06 Last updated: 2018-01-13
    5. Modeling sleep data for a new drug in development using Markov mixed-effects models
    Open this publication in new window or tab >>Modeling sleep data for a new drug in development using Markov mixed-effects models
    2011 (English)In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 28, no 10, p. 2610-2627Article in journal (Refereed) Published
    Abstract [en]

    To characterize the time-course of sleep in insomnia patients as well as placebo and concentration-effect relationships of two hypnotic compounds, PD 0200390 and zolpidem, using an accelerated model-building strategy based on mixed-effects Markov models. Data were obtained in a phase II study with the drugs. Sleep stages were recorded during eight hours of sleep for two nights per treatment for the five treatments. First-order Markov models were developed for one transition at a time in a sequential manner; first a baseline model, followed by placebo and lastly the drug models. To accelerate the process, predefined models were selected based on a priori knowledge of sleep, including inter-subject and inter-occasion variability. Baseline sleep was described using piece-wise linear models, depending on time of night and duration of sleep stage. Placebo affected light sleep stages; drugs also affected slow-wave sleep. Administering PD 0200390 30 min earlier than standard dosing was shown through simulations to reduce latency to persistent sleep by 40%. The proposed accelerated model-building strategy resulted in a model well describing sleep patterns of insomnia patients with and without treatments.

    Keywords
    Markov model, NONMEM, pharmacodynamics, polysomnography, population analysis, sleep, transition model, zolpidem
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:uu:diva-97672 (URN)10.1007/s11095-011-0490-x (DOI)000294811700023 ()
    Available from: 2008-10-30 Created: 2008-10-30 Last updated: 2017-12-14Bibliographically approved
  • 20.
    Kjellsson, Maria C.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Cosson, Valerie F.
    Mazer, Norman A.
    Frey, Nicolas
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A Model-Based Approach to Predict Longitudinal HbA1c, Using Early Phase Glucose Data From Type 2 Diabetes Mellitus Patients After Anti-Diabetic Treatment2013In: Journal of clinical pharmacology, ISSN 0091-2700, E-ISSN 1552-4604, Vol. 53, no 6, p. 589-600Article in journal (Refereed)
    Abstract [en]

    Predicting late phase outcomes from early-phase findings can help inform decisions in drug development. If the measurements in early-phase differ from those in late phase, forecasting is more challenging. In this paper, we present a model-based approach for predicting glycosylated hemoglobin (HbA1c) in late phase using glucose and insulin concentrations from an early-phase study, investigating an anti-diabetic treatment. Two previously published models were used; an integrated glucose and insulin (IGI) model for meal tolerance tests and an integrated glucose-red blood cell-HbA1c (IGRH) model predicting the formation of HbA1c from the average glucose concentration (Cg,av). Output from the IGI model was used as input to the IGRH model. Parameters of the IGI model and drug effects were estimated using data from a phase1 study in 59 diabetic patients receiving various doses of a glucokinase activator. Cg,av values were simulated according to a Phase 2 study design and used in the IGRH model for predictions of HbA1c. The performance of the model-based approach was assessed by comparing the predicted to the actual outcome of the Phase 2 study. We have shown that this approach well predicts the longitudinal HbA1c response in a 12-week study using only information from a 1-week study where glucose and insulin concentrations were measured.

  • 21.
    Kjellsson, Maria C.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Jönsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    The back-step method: method for obtaining unbiased population parameter estimates for ordered categorical data2004In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 6, no 3, p. 13-22Article in journal (Refereed)
    Abstract [en]

    A significant bias in parameters, estimated with the proportional odds model using the software NONMEM, has been reported. Typically, this bias occurs with ordered categorical data, when most of the observations are found at one extreme of the possible outcomes. The aim of this study was to assess, through simulations, the performance of the Back-Step Method (BSM), a novel approach for obtaining unbiased estimates when the standard approach provides biased estimates. BSM is an iterative method involving sequential simulation-estimation steps. BSM was compared with the standard approach in the analysis of a 4-category ordered variable using the Laplacian method in NONMEM. The bias in parameter estimates and the accuracy of model predictions were determined for the 2 methods on 3 conditions: (1) a nonskewed distribution of the response with low interindividual variability (IIV), (2) a skewed distribution with low IIV, and (3) a skewed distribution with high IIV. An increase in bias with increasing skewness and IIV was shown in parameters estimated using the standard approach in NONMEM. BSM performed without appreciable bias in the estimates under the 3 conditions, and the model predictions were in good agreement with the original data. Each BSM estimation represents a random sample of the population; hence, repeating the BSM estimation reduces the imprecision of the parameter estimates. The BSM is an accurate estimation method when the standard modeling approach in NONMEM gives biased estimates.

  • 22.
    Kjellsson, Maria C.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Ouellet, Daniele
    Corrigan, Brian
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Modeling sleep data for a new drug in development using Markov mixed-effects models2011In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 28, no 10, p. 2610-2627Article in journal (Refereed)
    Abstract [en]

    To characterize the time-course of sleep in insomnia patients as well as placebo and concentration-effect relationships of two hypnotic compounds, PD 0200390 and zolpidem, using an accelerated model-building strategy based on mixed-effects Markov models. Data were obtained in a phase II study with the drugs. Sleep stages were recorded during eight hours of sleep for two nights per treatment for the five treatments. First-order Markov models were developed for one transition at a time in a sequential manner; first a baseline model, followed by placebo and lastly the drug models. To accelerate the process, predefined models were selected based on a priori knowledge of sleep, including inter-subject and inter-occasion variability. Baseline sleep was described using piece-wise linear models, depending on time of night and duration of sleep stage. Placebo affected light sleep stages; drugs also affected slow-wave sleep. Administering PD 0200390 30 min earlier than standard dosing was shown through simulations to reduce latency to persistent sleep by 40%. The proposed accelerated model-building strategy resulted in a model well describing sleep patterns of insomnia patients with and without treatments.

  • 23.
    Kjellsson, Maria C.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Via, Laura E.
    Goh, Anne
    Weiner, Danielle
    Low, Kang Min
    Kern, Steven
    Pillai, Goonaseelan
    Barry, Clifton E., III
    Dartois, Veronique
    Pharmacokinetic Evaluation of the Penetration of Antituberculosis Agents in Rabbit Pulmonary Lesions2012In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 56, no 1, p. 446-457Article in journal (Refereed)
    Abstract [en]

    Standard antituberculosis (anti-TB) therapy requires the use of multiple drugs for a minimum of 6 months, with variable outcomes that are influenced by a number of microbiological, pathological, and clinical factors. This is despite the availability of antibiotics that have good activity against Mycobacterium tuberculosis in vitro and favorable pharmacokinetic profiles in plasma. However, little is known about the distribution of widely used antituberculous agents in the pulmonary lesions where the pathogen resides. The rabbit model of TB infection was used to explore the hypothesis that standard drugs have various abilities to penetrate lung tissue and lesions and that adequate drug levels are not consistently reached at the site of infection. Using noncompartmental and population pharmacokinetic approaches, we modeled the rate and extent of distribution of isoniazid, rifampin, pyrazinamide, and moxifloxacin in rabbit lung and lesions. Moxifloxacin reproducibly showed favorable partitioning into lung and granulomas, while the exposure of isoniazid, rifampin, and pyrazinamide in lesions was markedly lower than in plasma. The extent of penetration in lung and lesions followed different trends for each drug. All four agents distributed rapidly from plasma to tissue with equilibration half-lives of less than 1 min to an hour. The models adequately described the plasma concentrations and reasonably captured actual lesion concentrations. Though further refinement is needed to accurately predict the behavior of these drugs in human subjects, our results enable the integration of lesion-specific pharmacokinetic-pharmacodynamic (PK-PD) indices in clinical trial simulations and in in vitro PK-PD studies with M. tuberculosis.

  • 24.
    Kjellsson, Maria C.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Zingmark, Per-Henrik
    Jonsson, E. Niclas
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Comparison of proportional odds and differential odds models for mixed-effects analysis of categorical data2008In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 35, no 5, p. 483-501Article in journal (Refereed)
    Abstract [en]

    In this work a model for analyzing categorical data is presented; the differential odds model. Unlike the commonly used proportional odds model, this model does not assume that a covariate affects all categories equally on the log odds scale. The differential odds model was compared to the proportional odds model, by assessing statistical significance and improvement of predictive performance when applying the differential odds model to data previously analyzed using the proportional odds model. Three clinical studies; 3-category T-cell receptor density data, 5-category diarrhea data and 6-category sedation data, were re-analyzed with the differential odds model. As expected, no improvements were seen with T-cell receptor density and diarrhea data. However, for the more complex measurement sedation, the differential odds model provided both statistical improvements and improvements in simulation properties. The estimated actual critical value was for all data lower than the nominal value, using the number of added parameters as the degree of freedom, i.e. the differential odds model is statistically indicated to a less extent than expected. The differential odds model had the desired property of not being indicated when not necessary, but it may provide improvements when the data does not represent a categorization of continuous data.

  • 25.
    Leohr, Jennifer
    et al.
    Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana, USA.
    Heathman, Michael
    Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana, USA.
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Semi-physiological model of postprandial triglyceride response in lean, obese and very obese individuals after a high-fat meal2018In: Diabetes, obesity and metabolism, ISSN 1462-8902, E-ISSN 1463-1326, Vol. 20, no 3, p. 660-666Article in journal (Refereed)
    Abstract [en]

    AIMS: To quantify the postprandial triglyceride (TG) response of chylomicrons and very-low-density lipoprotein-V6 (VLDL-V6) after a high-fat meal in lean, obese and very obese healthy individuals, using a mechanistic population lipokinetic modelling approach.

    METHODS: ) were enrolled in a clinical study to assess the TG response after a high-fat meal, containing 60% fat. Non-linear mixed-effect modelling was used to analyse the TG concentrations of chylomicrons and large VLDL-V6 particles.

    RESULTS: The TGs in chylomicrons and VLDL-V6 particles had a prominent postprandial peak and represented the majority of the postprandial response; only the VLDL-V6 showed a difference across the populations. A turn-over model successfully described the TG concentration-time profiles of both chylomicrons and large VLDL-V6 particles after the high-fat meal. This model consisted of four compartments: two transit compartments for the lag between meal consumption and appearance of TGs in the blood, and one compartment each for the chylomicrons and large VLDL-V6 particles. The rate constants for the production of chylomicrons and elimination of large VLDL-V6 particles, along with the conversion rate of chylomicrons to large VLDL-V6 particles were well defined.

    CONCLUSIONS: This is the first lipokinetic model to describe the absorption of TGs from dietary fats into the blood stream and compares the dynamics of TGs in chylomicrons and large VLDL-V6 particles among lean, obese and very obese people. Such a model can be used to identify where pharmacological therapies act, thereby improving the determination of efficacy, and identifying complementary mechanisms for combinational drug therapies.

  • 26. Moller, Jonas B.
    et al.
    Overgaard, Rune V.
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kristensen, Niels R.
    Klim, Soren
    Ingwersen, Steen H.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    ADOPT (A Dynamic HbA(1c) EndpOint Prediction Tool): A framework for Predicting Primary Endpoint in Phase 3 Diabetes Trials2013In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 40, p. S57-S58Article in journal (Other academic)
  • 27. Møller, J B
    et al.
    Kristensen, N R
    Klim, S
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ingwersen, S H
    Kjellsson, M C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Methods for Predicting Diabetes Phase III Efficacy Outcome From Early Data: Superior Performance Obtained Using Longitudinal Approaches2014In: CPT: pharmacometrics & systems pharmacology, ISSN 2163-8306, Vol. 3, no 7, p. e122-Article in journal (Refereed)
    Abstract [en]

    The link between glucose and HbA1c at steady state has previously been described using steady-state or longitudinal relationships. We evaluated five published methods for prediction of HbA1c after 26/28 weeks using data from four clinical trials. Methods (1) and (2): steady-state regression of HbA1c on fasting plasma glucose and mean plasma glucose, respectively, (3) an indirect response model of fasting plasma glucose effects on HbA1c, (4) model of glycosylation of red blood cells, and (5) coupled indirect response model for mean plasma glucose and HbA1c. Absolute mean prediction errors were 0.61, 0.38, 0.55, 0.37, and 0.15% points, respectively, for Methods 1 through 5. This indicates that predictions improved by using mean plasma glucose instead of fasting plasma glucose, by inclusion of longitudinal glucose data and further by inclusion of longitudinal HbA1c data until 12 weeks. For prediction of trial outcome, the longitudinal models based on mean plasma glucose (Methods 4 and 5) had substantially better performance compared with the other methods.

  • 28. Møller, J B
    et al.
    Overgaard, R V
    Kjellsson, Maria C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kristensen, N R
    Klim, S
    Ingwersen, S H
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Longitudinal Modeling of the Relationship Between Mean Plasma Glucose and HbA1c Following Antidiabetic Treatments2013In: CPT: pharmacometrics & systems pharmacology, ISSN 2163-8306, Vol. 2, p. e82-Article in journal (Refereed)
    Abstract [en]

    Late-phase clinical trials within diabetes generally have a duration of 12-24 weeks, where 12 weeks may be too short to reach steady-state glycated hemoglobin (HbA1c). The main determinant for HbA1c is blood glucose, which reaches steady state much sooner. In spite of this, few publications have used individual data to assess the time course of both glucose and HbA1c, for predicting HbA1c. In this paper, we present an approach for predicting HbA1c at end-of-trial (24-28 weeks) using glucose and HbA1c measurements up to 12 weeks. The approach was evaluated using data from 4 trials covering 12 treatment arms (oral antidiabetic drug, glucagon-like peptide-1, and insulin treatment) with measurements at 24-28 weeks to evaluate predictions vs. observations. HbA1c percentage was predicted for each arm at end-of-trial with a mean prediction error of 0.14% [0.01;0.24]. Furthermore, end points in terms of HbA1c reductions relative to comparator were accurately predicted. The proposed model provides a good basis to optimize late-stage clinical development within diabetes.

  • 29.
    Nyman, Elin
    et al.
    Linkoping Univ, Dept Biomed Engn, Linkoping, Sweden.;CVMD iMed DMPK AstraZeneca R&D, Gothenburg, Sweden..
    Rozendaal, Yvonne J. W.
    Eindhoven Univ Technol, Dept Biomed Engn, POB 513, NL-5600 MB Eindhoven, Netherlands..
    Helmlinger, Gabriel
    Pharmaceut LP, AstraZeneca, Quantitat Clin Pharmacol, Waltham, MA USA..
    Hamren, Bengt
    AstraZeneca, Quantitat Clin Pharmacol, Gothenburg, Sweden..
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Strålfors, Peter
    Linkoping Univ, Dept Clin & Expt Med, Linkoping, Sweden..
    van Riel, Natal A. W.
    Eindhoven Univ Technol, Dept Biomed Engn, POB 513, NL-5600 MB Eindhoven, Netherlands..
    Gennemark, Peter
    CVMD iMed DMPK AstraZeneca R&D, Gothenburg, Sweden..
    Cedersund, Gunnar
    Linkoping Univ, Dept Biomed Engn, Linkoping, Sweden.;Linkoping Univ, Dept Clin & Expt Med, Linkoping, Sweden..
    Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes2016In: Interface Focus, ISSN 2042-8898, E-ISSN 2042-8901, Vol. 6, no 2, article id 20150075Article, review/survey (Refereed)
    Abstract [en]

    We are currently in the middle of a major shift in biomedical research: unprecedented and rapidly growing amounts of data may be obtained today, from in vitro, in vivo and clinical studies, at molecular, physiological and clinical levels. To make use of these large-scale, multi-level datasets, corresponding multi-level mathematical models are needed, i.e. models that simultaneously capture multiple layers of the biological, physiological and disease-level organization (also referred to as quantitative systems pharmacology-QSP-models). However, today's multi-level models are not yet embedded in end-usage applications, neither in drug research and development nor in the clinic. Given the expectations and claims made historically, this seemingly slow adoption may seem surprising. Therefore, we herein consider a specific example-type 2 diabetes-and critically review the current status and identify key remaining steps for these models to become mainstream in the future. This overview reveals how, today, we may use models to ask scientific questions concerning, e.g., the cellular origin of insulin resistance, and how this translates to the whole-body level and short-term meal responses. However, before these multi-level models can become truly useful, they need to be linked with the capabilities of other important existing models, in order to make them 'personalized' (e.g. specific to certain patient phenotypes) and capable of describing long-term disease progression. To be useful in drug development, it is also critical that the developed models and their underlying data and assumptions are easily accessible. For clinical end-usage, in addition, model links to decisionsupport systems combined with the engagement of other disciplines are needed to create user-friendly and cost-efficient software packages.

  • 30.
    Parkinson, Joanna
    et al.
    AstraZeneca, Cardiovasc & Metab Dis, Innovat Med & Early Dev Biotech Unit, Pepparedsleden 1, S-43183 Molndal, Sweden..
    Hamren, Bengt
    AstraZeneca, Cardiovasc & Metab Dis, Innovat Med & Early Dev Biotech Unit, Pepparedsleden 1, S-43183 Molndal, Sweden..
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Skrtic, Stanko
    AstraZeneca, Cardiovasc & Metab Dis, Innovat Med & Early Dev Biotech Unit, Pepparedsleden 1, S-43183 Molndal, Sweden.;Univ Gothenburg, Dept Endocrinol, Sahlgrenska Univ Hosp, Gothenburg, Sweden.;Univ Gothenburg, Inst Med, Sahlgrenska Acad, Gothenburg, Sweden..
    Application of the integrated glucose-insulin model for cross-study characterization of T2DM patients on metformin background treatment2016In: British Journal of Clinical Pharmacology, ISSN 0306-5251, E-ISSN 1365-2125, Vol. 82, no 6, p. 1613-1624Article in journal (Refereed)
    Abstract [en]

    AimThe integrated glucose-insulin (IGI) model is a semi-mechanistic physiological model which can describe the glucose-insulin homeostasis system following various glucose challenge settings. The aim of the present work was to apply the model to a large and diverse population of metformin-only-treated type 2 diabetes mellitus (T2DM) patients and identify patient-specific covariates. MethodsData from four clinical studies were pooled, including glucose and insulin concentration-time profiles from T2DM patients on stable treatment with metformin alone following mixed-meal tolerance tests. The data were collected from a wide range of patients with respect to the duration of diabetes and level of glycaemic control. ResultsThe IGI model was expanded by four patient-specific covariates. The level of glycaemic control, represented by baseline glycosylated haemoglobin was identified as a significant covariate for steady-state glucose, insulin-dependent glucose clearance and the magnitude of the incretin effect, while baseline body mass index was a significant covariate for steady-state insulin levels. In addition, glucose dose was found to have an impact on glucose absorption rate. The developed model was used to simulate glucose and insulin profiles in different groups of T2DM patients, across a range of glycaemic control, and it was found accurately to characterize their response to the standard oral glucose challenge. ConclusionsThe IGI model was successfully applied to characterize differences between T2DM patients across a wide range of glycaemic control. The addition of patient-specific covariates in the IGI model might be valuable for the future development of antidiabetic treatment and for the design and simulation of clinical studies.

  • 31.
    Røge, Rikke M
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Klim, Søren
    Ingwersen, Steen H
    Kjellsson, Maria C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kristensen, Niels R
    The Effects of a GLP-1 Analog on Glucose Homeostasis in Type 2 Diabetes Mellitus Quantified by an Integrated Glucose Insulin Model2015In: CPT: Pharmacometrics & Systems Pharmacology, ISSN 2163-8306, Vol. 4, no 1, p. 1-9Article in journal (Refereed)
    Abstract [en]

    In recent years, several glucagon-like peptide-1 (GLP-1)-based therapies for the treatment of type 2 diabetes mellitus (T2DM) have been developed. The aim of this work was to extend the semimechanistic integrated glucose-insulin model to include the effects of a GLP-1 analog on glucose homeostasis in T2DM patients. Data from two trials comparing the effect of steady-state liraglutide vs. placebo on the responses of postprandial glucose and insulin in T2DM patients were used for model development. The effect of liraglutide was incorporated in the model by including a stimulatory effect on insulin secretion. Furthermore, for one of the trials an inhibitory effect on glucose absorption was included to account for a delay in gastric emptying. As other GLP-1 receptor agonists have similar modes of action, it is believed that the model can also be used to describe the effect of other receptor agonists on glucose homeostasis.

  • 32.
    Røge, Rikke M
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Klim, Søren
    Kristensen, Niels R
    Ingwersen, Steen H
    Kjellsson, Maria C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Modeling of 24-Hour Glucose and Insulin Profiles in Patients With Type 2 Diabetes Mellitus Treated With Biphasic Insulin Aspart2014In: Journal of clinical pharmacology, ISSN 0091-2700, E-ISSN 1552-4604, Vol. 54, no 7, p. 809-817Article in journal (Refereed)
    Abstract [en]

    Insulin therapy for diabetes patients is designed to mimic the endogenous insulin response of healthy subjects and thereby generate normal blood glucose levels. In order to control the blood glucose in insulin-treated diabetes patients, it is important to be able to predict the effect of exogenous insulin on blood glucose. A pharmacokinetic/pharmacodynamic model for glucose homoeostasis describing the effect of exogenous insulin would facilitate such prediction. Thus the aim of this work was to extend the previously developed integrated glucose-insulin (IGI) model to predict 24-hour glucose profiles for patients with Type 2 diabetes following exogenous insulin administration. Clinical data from two trials were included in the analysis. In both trials, 24-hour meal tolerance tests were used as the experimental setup, where exogenous insulin (biphasic insulin aspart) was administered in relation to meals. The IGI model was successfully extended to include the effect of exogenous insulin. Circadian variations in glucose homeostasis were assessed on relevant parameters, and a significant improvement was achieved by including a circadian rhythm on the endogenous glucose production in the model. The extended model is a useful tool for clinical trial simulation and for elucidating the effect profile of new insulin products.

  • 33. Sauermann, Robert
    et al.
    Feurstein, Thomas
    Karch, Rudolf
    Kjellsson, Maria C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Jäger, Walter
    Böhmdorfer, Michaela
    Püspök, Andreas
    Langenberger, Herbert
    Wild, Thomas
    Winkler, Stefan
    Zeitlinger, Markus
    Abscess penetration of cefpirome: concentrations and simulated pharmacokinetic profiles in pus2012In: European Journal of Clinical Pharmacology, ISSN 0031-6970, E-ISSN 1432-1041, Vol. 68, no 10, p. 1419-1423Article in journal (Refereed)
    Abstract [en]

    PURPOSE

    Abscess patients frequently receive antibiotic therapy when incision cannot be performed or in addition to incision. However, antibiotic concentrations in human abscesses are widely unknown.

    METHODS

    Pharmacokinetics of cefpirome in 12 human abscesses located in different body regions was studied. Cefpirome (2 g) was administered as an intravenous short infusion, and concentrations were measured in plasma over an 8-h period and in abscesses at incision. A pharmacokinetic two-stage model was applied.

    RESULTS

    At abscess incision performed 158 ± 112 min after the start of the infusion, the cefpirome concentrations in the abscess fluid varied markedly, ranging from ≤0.1 (limit of quantification) to 47 (mean 8.4 ± 14.1 ) mg/L. Cefpirome was detectable in nine of 12 abscesses. Maximum concentrations were calculated to be 183 ± 106 mg/L in plasma and 12 ± 16 mg/L in the abscess. A cefpirome concentration of 2 mg/L, which is the minimum concentration inhibiting growth of 90% of the most relevant bacterial pathogens, was exceeded spontaneously in six of 12 abscesses after a single dose. Cefpirome concentrations in the abscess did not correlate with either the pH or the ratio of surface area to volume of the abscesses, nor with plasma pharmacokinetics.

    CONCLUSIONS

    Cefpirome may be useful to treat abscess patients because it was detectable in most abscesses after a single dose. However, the penetration of cefpirome into abscesses is extremely variable and cannot be predicted by measuring other available covariates.

  • 34. Sauermann, Robert
    et al.
    Karch, Rudolf
    Kjellsson, Maria C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Feurstein, Thomas
    Püspök, Andreas
    Langenberger, Herbert
    Böhmdorfer, Michaela
    Jäger, Walter
    Zeitlinger, Markus
    Good penetration of moxifloxacin into human abscesses2012In: Pharmacology, ISSN 0031-7012, E-ISSN 1423-0313, Vol. 90, no 3-4, p. 146-150Article in journal (Refereed)
    Abstract [en]

    Abscesses are often treated with antibiotics in addition to incision or when incision is unfeasible, but accurate information about antibiotic abscess penetration in humans is missing. This study aimed at evaluating the penetration of moxifloxacin into human abscesses. After administration of a single dose of 400 mg moxifloxacin, drug concentrations were measured in 10 differently located abscesses at incision, and in plasma over 8 h. At incision performed 0.9-4.8 h after administration, moxifloxacin concentrations in abscesses ranged from ≤0.01 to 9.2 mg/l (1.9 ± 3.4 mg/l), indicating pronounced drug accumulation in some abscesses. The degree of abscess penetration could not be explained by covariates like the ratio of surface area to volume or pH of abscesses, or by moxifloxacin plasma concentrations. Concluding, moxifloxacin was detectable in most abscesses and may be a useful antibiotic for this indication. However, antibiotic abscess penetration was highly variable and unpredictable, suggesting surgical abscess incision whenever possible.

  • 35.
    Savic, Radojka M.
    et al.
    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.
    Evaluation of the Nonparametric Estimation Method in NONMEM VI2009In: European Journal of Pharmaceutical Sciences, ISSN 0928-0987, E-ISSN 1879-0720, Vol. 37, no 1, p. 27-35Article in journal (Refereed)
    Abstract [en]

    PURPOSE: In NONMEM VI, a novel method exists for estimation of a nonparametric parameter distribution. The parameter distributions are approximated by discrete probability density functions at a number of parameter values (support points). The support points are obtained from the empirical Bayes estimates from a preceding parametric estimation step, run with the First Order (FO) or First Order Conditional Estimation (FOCE) methods. The purpose of this work is to evaluate this new method with respect to parameter distribution estimation. METHODS: The performance of the method, with special emphasis on the analysis of data with non-normal distribution of random effects, was studied using Monte Carlo (MC) simulations. RESULTS: The mean value (and ranges) of absolute relative biases (ARBs, %) in parameter distribution estimates with nonparametric methods preceded with FO and FOCE were 0.80 (0.1-3.7) and 0.70 (0-3), respectively, while for parametric methods, these values were 23.74 (3.3-97.5) and 4.38 (0.1-17.9), for FO and FOCE, respectively. The nonparametric estimation method in NONMEM could identify non-normal parameter distributions and correct bias in parameter estimates seen when applying the FO estimation method. CONCLUSIONS: The method shows promising properties when analyzing different types of pharmacokinetic (PK) data with both the FO and FOCE methods as preceding steps.

  • 36.
    Silber, Hanna E
    et al.
    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.
    The impact of misspecification of residual error or correlation structure on the type I error rate for covariate inclusion2009In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 36, no 1, p. 81-99Article in journal (Refereed)
    Abstract [en]

    It has been shown that when using the FOCE method in NONMEM, the likelihood ratio test (LRT) can be sensitive to the use of an inappropriate estimation method in that ignoring an existing eta-epsilon interaction leads to actual significance levels for type I errors being higher than the nominal levels. The objective of this study was to assess through simulations the LRT sensitivity to various types of residual error model misspecifications in both continuous and categorical data. The study contained two parts, simulations based on continuous and categorical data. Data sets containing 250 individuals with up to 24 observations per individual were simulated multiple times (1000) with different types of residual error models for the continuous data and different strength of correlation between observations for the categorical data. The data sets were analyzed using either the correct or a simpler (incorrect) model with or without addition of a covariate. The type I error rate of inclusion of the non-informative covariate on the 5% level was calculated as the number of runs where the drop in the objective function value (OFV) was larger than 3.84 when the covariate relationship was included in the model using the correct or the incorrect model. The difference in OFV between the model with the correct and the incorrect structure was also calculated as a measure of the residual error model misspecification. For continuous data the FOCE method was used in most cases (with interaction when appropriate). The Laplacian estimation method was used for one of the continuous models and for categorical data. The results showed that the residual error model misspecifications when the erroneous model was used were pronounced, as indicated by the OFV being substantially higher than for the corresponding correct models. The significance levels of the LRT with the incorrect model were appropriate in all cases but ignoring (serial) correlations between observations (continuous and categorical data) as well as when the eta-epsilon interaction was ignored (which has previously been shown, continuous data). When ignoring correlation, the type I error rates were shown to be sensitive to the correlation strength, the number of observations per individual and the magnitude of the inter-individual variability on clearance. We conclude that the LRT appears robust towards all tested cases, but ignoring (serial) correlations between observations and eta-epsilon interaction.

  • 37.
    Stage, Tore Bjerregaard
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Univ Southern Denmark, Dept Publ Hlth, Clin Pharmacol & Pharm, Odense, Denmark.
    Wellhagen, Gustaf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Christensen, Mette Marie Hougaard
    Odense Univ Hosp, Dept Clin Biochem & Pharmacol, Odense, Denmark.
    Guiastrennec, Benjamin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Brosen, Kim
    Univ Southern Denmark, Dept Publ Hlth, Clin Pharmacol & Pharm, Odense, Denmark.
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Using a semi-mechanistic model to identify the main sources of variability of metformin pharmacokinetics2019In: Basic & Clinical Pharmacology & Toxicology, ISSN 1742-7835, E-ISSN 1742-7843, Vol. 124, no 1, p. 105-114Article in journal (Refereed)
    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.

  • 38.
    Svensson, Elin M
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Radboud Univ Nijmegen, Med Ctr, Dept Pharm, Nijmegen, Netherlands..
    Yngman, Gunnar
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Denti, Paolo
    Univ Cape Town, Dept Med, Div Clin Pharmacol, Cape Town, South Africa..
    McIlleron, Helen
    Univ Cape Town, Dept Med, Div Clin Pharmacol, Cape Town, South Africa..
    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.
    Evidence-based design of fixed-dose combinations - principles and application to pediatric anti-tuberculosis therapy2017In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 44, p. S95-S96Article in journal (Other academic)
  • 39.
    Svensson, Elin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Yngman, Gunnar
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Denti, Paolo
    Univ Cape Town, Dept Med, Div Clin Pharmacol, Cape Town, South Africa.
    McIlleron, Helen
    Univ Cape Town, Dept Med, Div Clin Pharmacol, Cape Town, South Africa.
    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.
    Evidence-Based Design of Fixed-Dose Combinations: Principles and Application to Pediatric Anti-Tuberculosis Therapy2018In: Clinical Pharmacokinetics, ISSN 0312-5963, E-ISSN 1179-1926, Vol. 57, no 5, p. 591-599Article in journal (Refereed)
    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.

  • 40.
    Vildhede, Anna
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Mateus, André
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Khan, Elin K.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Lai, Yurong
    Bristol Myers Squibb, Dept Metab & Pharmacokinet, Princeton, NJ USA..
    Karlgren, Maria
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Artursson, Per
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Mechanistic modeling of hepatic pitavastatin disposition: a proteomics-informed bottom-up approach2016In: Drug metabolism reviews (Softcover ed.), ISSN 0360-2532, E-ISSN 1097-9883, Vol. 48, p. 56-57Article in journal (Other academic)
  • 41.
    Vildhede, Anna
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Mateus, André
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Khan, Elin K.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Lai, Yurong
    Bristol Myers Squibb Co, Princeton, NJ USA.
    Karlgren, Maria
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Artursson, Per
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Mechanistic Modeling of Pitavastatin Disposition in Sandwich-Cultured Human Hepatocytes: A Proteomics-Informed Bottom-Up Approach2016In: Drug Metabolism And Disposition, ISSN 0090-9556, E-ISSN 1521-009X, Vol. 44, no 4, p. 505-516Article in journal (Refereed)
    Abstract [en]

    Isolated human hepatocytes are commonly used to predict transporter-mediated clearance in vivo. Such predictions, however, do not provide estimations of transporter contributions and consequently do not allow predictions of the outcome resulting from a change in the activity of a certain transporter, e.g., by inhibition or a genetic variant with reduced function. Pitavastatin is a drug that is heavily dependent on hepatic transporters for its elimination and it is mainly excreted as unchanged drug in the bile. For this reason, pitavastatin was used as a model drug to demonstrate the applicability of a bottom-up approach to predict transporter-mediated disposition in sandwich-cultured human hepatocytes (SCHH), allowing for the estimation of transporter contributions. Transport experiments in transfected HEK293 cells and inverted membrane vesicles overexpressing each of the relevant transport proteins were used to generate parameter estimates for the mechanistic model. By adjusting for differences in transporter abundance between the in vitro systems and individual SCHH batches, the model successfully predicted time profiles of medium and intracellular accumulation. Our predictions of pitavastatin bile accumulation could, however, not be confirmed due to a very low biliary excretion of pitavastatin in relation to the hepatic uptake in our SCHH. This study is, to our knowledge, the first to successfully simulate transporter-mediated processes in a complex system such as SCHH at the level of individual transport proteins using a bottom-up approach.

  • 42.
    Wang, Shijun
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Kristin E.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kjellsson, Maria
    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.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A proof-of-principle example for identifying drug effect from a mechanistic model with a more parsimonious model2016In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 43, p. S35-S35Article in journal (Refereed)
  • 43.
    Wellhagen, Gustaf J.
    et al.
    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.
    A Bounded Integer Model for Rating and Composite Scale Data2019In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 21, no 4, article id 74Article in journal (Refereed)
    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.

  • 44.
    Wellhagen, Gustaf
    et al.
    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.
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Comparison of Power, Prognosis, and Extrapolation Properties of Four Population Pharmacodynamic Models of HbA1c for Type 2 Diabetes2018In: CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, ISSN 2163-8306, Vol. 7, no 5, p. 331-341Article in journal (Refereed)
    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.

  • 45.
    Yang, Jiansong
    et al.
    Academic Unit of Molecular Pharmacology & Pharmacogenetics, Division of Clinical Sciences (South), University of Sheffield.
    Kjellsson, Maria
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Rostami-Hodjegan, Amin
    Academic Unit of Molecular Pharmacology & Pharmacogenetics, Division of Clinical Sciences (South), University of Sheffield.
    Tucker, Geoffrey T
    Academic Unit of Molecular Pharmacology & Pharmacogenetics, Division of Clinical Sciences (South), University of Sheffield.
    The effects of dose staggering on metabolic drug-drug interactions2003In: European Journal of Pharmaceutical Sciences, ISSN 0928-0987, E-ISSN 1879-0720, Vol. 20, no 2, p. 223-232Article in journal (Refereed)
    Abstract [en]

    PURPOSE

    To investigate the effect of dose staggering on metabolic drug-drug interactions (MDDI).

    METHODS

    Using Matlab, anatomical, physiological and biochemical data relating to human pharmacokinetics were integrated to create a representative virtual healthy subject relevant to in vivo studies. The effects of dose staggering on AUC and C(max) were investigated under various scenarios with respect to pharmacokinetic characteristics of the inhibitor and substrate drugs (e.g. hepatic extraction ratio). Specific cases were also simulated where MDDI had been studied experimentally for combinations of drugs (budesonide and ketoconazole; triazolam and itraconazole).

    RESULTS

    The decrease in the magnitude of the inhibitory effect of the 'perpetrator' drug (inhibitor) on the 'victim' drug (substrate) as a result of 'dose staggering' was greater when the 'perpetrator' was given after the 'victim'. There was reasonable agreement between the predicted extent of the interactions and the observed in vivo data (mean prediction errors of 25 and -14% for AUC and C(max) values, respectively (n=7)). The impact of dose staggering was minimal during continuous dosage of inhibitors with long elimination half-lives (e.g. itraconazole, >20 h).

    CONCLUSIONS

    Clinical trial simulations using physiological information may provide useful guidelines for optimal dose staggering when poly-pharmacy is inevitable.

  • 46.
    Zingmark, Per-Henrik
    et al.
    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.
    Jonsson, E. Niclas
    Karlsson, Mats O.
    Comparing the differential drug effect model to the proportional odds modelManuscript (Other academic)
1 - 46 of 46
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