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  • 1.
    Abrantes, João A.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Jönsson, Siv
    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.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Handling interoccasion variability in model-based dose individualization using therapeutic drug monitoring data2019In: British Journal of Clinical Pharmacology, ISSN 0306-5251, E-ISSN 1365-2125, Vol. 85, no 6, p. 1326-1336Article in journal (Refereed)
    Abstract [en]

    AIMS: This study aims to assess approaches to handle interoccasion variability (IOV) in a model-based therapeutic drug monitoring (TDM) context, using a population pharmacokinetic model of coagulation factor VIII as example.

    METHODS: We assessed five model-based TDM approaches: empirical Bayes estimates (EBEs) from a model including IOV, with individualized doses calculated based on individual parameters either (i) including or (ii) excluding variability related to IOV; and EBEs from a model excluding IOV by (iii) setting IOV to zero, (iv) summing variances of interindividual variability (IIV) and IOV into a single IIV term, or (v) re-estimating the model without IOV. The impact of varying IOV magnitudes (0-50%) and number of occasions/observations was explored. The approaches were compared with conventional weight-based dosing. Predictive performance was assessed with the prediction error (PE) percentiles.

    RESULTS: When IOV was lower than IIV, the accuracy was good for all approaches (50th percentile of the PE [P50] <7.4%), but the precision varied substantially between IOV magnitudes (P97.5 61-528%). Approach (ii) was the most precise forecasting method across a wide range of scenarios, particularly in case of sparse sampling or high magnitudes of IOV. Weight-based dosing led to less precise predictions than the model-based TDM approaches in most scenarios.

    CONCLUSIONS: Based on the studied scenarios and theoretical expectations, the best approach to handle IOV in model-based dose individualisation is to include IOV in the generation of the EBEs, but exclude the portion of unexplained variability related to IOV in the individual parameters used to calculate the future dose.

  • 2.
    Abrantes, João A.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Solms, Alexander
    Bayer, Berlin, Germany.
    Garmann, Dirk
    Bayer, Wuppertal, Germany.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Jönsson, Siv
    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.
    Bayesian Forecasting Utilizing Bleeding Information to Support Dose Individualization of Factor VIII2019In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 8, no 12, p. 894-903Article in journal (Refereed)
    Abstract [en]

    Bayesian forecasting for dose individualization of prophylactic factor VIII replacement therapy using pharmacokinetic samples is challenged by large interindividual variability in the bleeding risk. A pharmacokinetic‐repeated time‐to‐event model‐based forecasting approach was developed to contrast the ability to predict the future occurrence of bleeds based on individual (i) pharmacokinetic, (ii) bleeding, and (iii) pharmacokinetic, bleeding and covariate information using observed data from the Long‐Term Efficacy Open‐Label Program in Severe Hemophilia A Disease (LEOPOLD) clinical trials (172 severe hemophilia A patients taking prophylactic treatment). The predictive performance assessed by the area under receiver operating characteristic (ROC) curves was 0.67 (95% confidence interval (CI), 0.65–0.69), 0.78 (95% CI, 0.76–0.80), and 0.79 (95% CI, 0.77–0.81) for patients ≥ 12 years when using pharmacokinetics, bleeds, and all data, respectively, suggesting that individual bleed information adds value to the optimization of prophylactic dosing regimens in severe hemophilia A. Further steps to optimize the proposed tool for factor VIII dose adaptation in the clinic are required.

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  • 3.
    Abrantes, João A.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Solms, Alexander
    Garmann, Dirk
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Jönsson, Siv
    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.
    Relationship between factor VIII activity, bleeds and individual characteristics in severe hemophilia A patients2020In: Haematologica, ISSN 0390-6078, E-ISSN 1592-8721, Vol. 105, no 5, p. 1443-1453Article in journal (Refereed)
    Abstract [en]

    Pharmacokinetic-based prophylaxis of replacement factor VIII products has been encouraged in the past years, but the exposure (factor VIII activity)-response (bleeding frequency) relationship remains unclear. The aim of this study was to characterize the relationship between factor VIII dose, plasma factor VIII activity, bleeding patterns and individual characteristics in severe hemophilia A patients. Pooled pharmacokinetic and bleeding data during prophylactic treatment with BAY 81-8973 (octocog alfa) were obtained from the three LEOPOLD trials. The population pharmacokinetics of factor VIII activity and longitudinal bleeding frequency, as well as bleeding severity, were described using nonlinear mixed effects modelling in NONMEM. In total, 183 patients (median age 22 years [range, 1-61]; weight 60 kg [11-124]) contributed with 1535 plasma factor VIII activity observations, 633 bleeds and 11 patient/study characteristics (median observation period 12 months [3.1-13.1]). A parametric repeated time-to-categorical bleed model, guided by plasma factor VIII activity from a 2-compartment population pharmacokinetic model, described the time to the occurrence of bleeds and their severity. Bleeding probability decreased with time of study, and a bleed was not found to affect the time of the next bleed. Several covariate effects were identified, including the bleeding history in the 12-month pre-study period increasing the bleeding hazard. However, unexplained inter-patient variability for the phenotypic bleeding pattern remained large (111%CV). Further studies to translate the model into a tool for dose individualization that considers the individual bleeding risk are required. Research based on a post-hoc analysis of the LEOPOLD studies (ClinicalTrials.gov identifiers NCT01029340, NCT01233258 and NCT01311648).

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  • 4.
    Abrantes, João
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Solms, Alexander
    Bayer, Berlin, Germany.
    Garmann, Dirk
    Bayer, Wuppertal, Germany.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Jönsson, Siv
    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. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Relationship between factor VIII activity, bleeds and individual characteristics in severe hemophilia A patientsIn: Article in journal (Refereed)
  • 5.
    Acharya, Chayan
    et al.
    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.
    Turkyilmaz, Gulbeyaz Yildiz
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Ege Univ, Fac Pharm, Dept Biopharmaceut & Pharmacokinet, TR-35100 Izmir, Turkey..
    Jönsson, Siv
    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 diagnostic tool for population models using non-compartmental analysis: The ncappc package for R2016In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 127, p. 83-93Article in journal (Refereed)
    Abstract [en]

    Background and objective: Non-compartmental analysis (NCA) calculates pharmacokinetic (PK) metrics related to the systemic exposure to a drug following administration, e.g. area under the concentration time curve and peak concentration. We developed a new package in R, called ncappc, to perform (i) a NCA and (ii) simulation-based posterior predictive checks (ppc) for a population PK (PopPK) model using NCA metrics. Methods: The nca feature of ncappc package estimates the NCA metrics by NCA. The ppc feature of ncappc estimates the NCA metrics from multiple sets of simulated concentration time data and compares them with those estimated from the observed data. The diagnostic analysis is performed at the population as well as the individual level. The distribution of the simulated population means of each NCA metric is compared with the corresponding observed population mean. The individual level comparison is performed based on the deviation of the mean of any NCA metric based on simulations for an individual from the corresponding NCA metric obtained from the observed data. The ncappc package also reports the normalized prediction distribution error (NPDE) of the simulated NCA metrics for each individual and their distribution within a population. Results: The ncappc produces two default outputs depending on the type of analysis performed, i.e., NCA and PopPK diagnosis. The PopPK diagnosis feature of ncappc produces 8 sets of graphical outputs to assess the ability of a population model to simulate the concentration time profile of a drug and thereby evaluate model adequacy. In addition, tabular outputs are generated showing the values of the NCA metrics estimated from the observed and the simulated data, along with the deviation, NPDE, regression parameters used to estimate the elimination rate constant and the related population statistics. Conclusions: The ncappc package is a versatile and flexible tool-set written in R that successfully estimates NCA metrics from concentration time data and produces a comprehensive set of graphical and tabular output to summarize the diagnostic results including the model specific outliers. The output is easy to interpret and to use in evaluation of a population PK model. ncappc is freely available on CRAN (http://crantoprojectorg/web/packages/ncappc/index.html/) and GitHub (https://github.comicacha0227/ncappc/). 

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  • 6. Agerso¸, Henrik
    et al.
    Koechling, Wolfgang
    Knutsson, Magnus
    Hjortkjaer, Rolf
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    The dosing solution influence on the pharmacokinetics of degarelix, a new GnRH antagonist, after s.c. administration to beagle dogs.2003In: European Journal of Pharmaceutical Sciences, ISSN 0928-0987, E-ISSN 1879-0720, Vol. 20, no 3, p. 335-340Article in journal (Refereed)
    Abstract [en]

    Objective

    Degarelix (FE200486) is a new GnRH-receptor antagonist intended for the treatment of prostate cancer. The objective of the present analysis was to evaluate the pharmacokinetics of degarelix after subcutaneous (s.c.) and intra-muscular (i.m.) administration to male beagle dogs, and to determine the influence of the different dosing conditions on the absorption profile of degarelix.

    Methods

    Degarelix was administered to 27 dogs and plasma concentrations were measured. The dosing conditions varied with respect to route (s.c. or i.m.), dose (0.25–1.5 mg/kg), solution strength (1.25–40 mg/ml) and volume administered (0.15–2.9 ml). Data were analysed by use of non-linear mixed effect modelling to characterize the pharmacokinetics, in particular the relationship between dosing conditions and rate, and extent of absorption.

    Results

    After s.c. and i.m. administration of degarelix, the plasma concentration versus time profile was best described by applying a two-compartment model, with two input functions: a fast first-order input function to describe the rapid initial increase in the plasma concentration levels, and a slow first-order input function to describe the prolonged absorption profile of degarelix. Intra-muscular as opposed to s.c. administration led to a more rapid absorption of degarelix, reaching a mean maximum concentration of 64 and 31 ng/ml roughly 2.0 and 3.7 h after administration, respectively. The slow absorption half-life was found to be 268 h (∼11 days). The relative fraction absorbedwas found to vary with the concentration of the dosing solution. The present analysis suggested that the absorbed fractionwas reduced by approximately 50% when the concentration in dosing solution was increased from 1.25 to 40 mg/ml. The rate of the initial absorption component was also dependent on the concentration in the dosing solution, with slower absorption at higher concentrations.

    Conclusion

    Through varying the dosing conditions and by applying a joint analysis of all data, the important factors determining the complex absorption of degarelix could be described.

  • 7. Ahn, Jae Eun
    et al.
    Plan, Elodie L
    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.
    Miller, Raymond
    Modeling longitudinal daily seizure frequency data from pregabalin add-on treatment2012In: Journal of clinical pharmacology, ISSN 0091-2700, E-ISSN 1552-4604, Vol. 52, no 6, p. 880-892Article in journal (Refereed)
    Abstract [en]

    The purpose of this study was to describe longitudinal daily seizure count data with respect to the effects of time and pregabalin add-on therapy. Models were developed in step-wise manner: base model, time effect model, and time and drug effect (final) model, using a negative binomial distribution with Markovian features. Mean daily seizure count (λ) was estimated to be 0.385 (RSE 3.09%) and was further increased depending on the seizure count on the previous day. An overdispersion parameter (OVDP), representing extra-Poisson variation, was estimated to be 0.330 (RSE 11.7%). Inter-individual variances on λ and OVDP were 84.7% and 210%, respectively. Over time, λ tended to increase exponentially with a rate constant of 0.272 year-1 (RSE 26.8%). A mixture model was applied to classify responders/non-responders to pregabalin treatment. Within the responders, λ decreased exponentially with respect to dose with a constant of 0.00108 mg-1 (RSE 11.9%). The estimated responder rate was 66% (RSE 27.6%). Simulation-based diagnostics showed the model reasonably reproduced the characteristics of observed data. Highly variable daily seizure frequency was successfully characterized incorporating baseline characteristics, time effect, and the effect of pregabalin with classification of responders/non-responders, all of which are necessary to adequately assess the efficacy of antiepileptic drugs.

     

  • 8.
    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)
  • 9.
    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.

  • 10.
    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 and Systems Pharmacology (PSP), E-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.

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  • 11.
    Aoki, Yasunori
    et al.
    Natl Inst Informat, Tokyo, Japan..
    Nyberg, Joakim
    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.
    Second order Taylor expansion of likelihood-based models2017In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 44, p. S72-S72Article in journal (Other academic)
  • 12.
    Arrington, Leticia
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Comparison of Two Methods for Determining Item Characteristic Functions and Latent Variable Time-Course for Pharmacometric Item Response Models2024In: AAPS Journal, E-ISSN 1550-7416, Vol. 26, article id 21Article in journal (Refereed)
    Abstract [en]

    There are examples in the literature demonstrating different approaches to defining the item characteristic functions (ICF) and characterizing the latent variable time-course within a pharmacometrics item response theory (IRT) framework. One such method estimates both the ICF and latent variable time-course simultaneously, and another method establishes the ICF first then models the latent variable directly. To date, a direct comparison of the "simultaneous" and "sequential" methodologies described in this work has not yet been systematically investigated. Item parameters from a graded response IRT model developed from Parkinson's Progression Marker Initiative (PPMI) study data were used as simulation parameters. Each method was evaluated under the following conditions: (i) with and without drug effect and (ii) slow progression rate with smaller sample size and rapid progression rate with larger sample size. Overall, the methods performed similarly, with low bias and good precision for key parameters and hypothesis testing for drug effect. The ICF parameters were well determined when the model was correctly specified, with an increase in precision in the scenario with rapid progression. In terms of drug effect, both methods had large estimation bias for the slow progression rate; however, this bias can be considered small relative to overall progression rate. Both methods demonstrated type 1 error control and similar discrimination between model with and without drug effect. The simultaneous method was slightly more precise than the sequential method while the sequential method was more robust towards longitudinal model misspecification and offers practical advantages in model building.

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  • 13.
    Arrington, Leticia
    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.
    Ahamadi, Malidi
    Macha, Sreeraj
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Performance of longitudinal item response theory models in shortened or partial assessments.2020In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 47, no 5, p. 461-471Article in journal (Refereed)
    Abstract [en]

    This work evaluates the performance of longitudinal item response (IR) theory models in shortened assessments using an existing model for part II and III of the MDS-UPDRS score. Based on the item information content, the assessment was reduced by removal of items in multiple increments and the models' ability to recover the item characteristics of the remaining items at each level was evaluated. This evaluation was done for both simulated and real data. The metric of comparison in both cases was the item information function. For real data, the impact of shortening on the estimated disease progression and drug effect was also studied. In the simulated data setting, the item characteristics did not differ between the full and the shortened assessments down to the lowest level of information remaining; indicating a considerable independence between items. In contrast when reducing the assessment in a real data setting, a substantial change in item information was observed for some of the items. Disease progression and drug effect estimates also decreased in the reduced assessments. These changes indicate a shift in the measured construct of the shortened assessment and warrant caution when comparing results from a partial assessment with results from the full assessment.

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  • 14.
    Arshad, Usman
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Univ Cologne, Fac Med, Gleueler Str 24, D-50931 Cologne, Germany;Univ Cologne, Univ Hosp Cologne, Ctr Pharmacol, Dept Pharmacol 1, Gleueler Str 24, D-50931 Cologne, Germany.
    Chasseloup, Estelle
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nordgren, Rikard
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Development of visual predictive checks accounting for multimodal parameter distributions in mixture models2019In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 46, no 3, p. 241-250Article in journal (Refereed)
    Abstract [en]

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

  • 15.
    Arshad, Usman
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Univ Cologne, Fac Med, Ctr Pharmacol, Dept Pharmacol 1, Cologne, Germany;Univ Cologne, Univ Hosp Cologne, Ctr Pharmacol, Dept Pharmacol 1, Cologne, Germany.
    Ploylearmsaeng, Su-arpa
    Univ Cologne, Fac Med, Ctr Pharmacol, Dept Pharmacol 1, Cologne, Germany;Univ Cologne, Univ Hosp Cologne, Ctr Pharmacol, Dept Pharmacol 1, Cologne, Germany.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Doroshyenko, Oxana
    Univ Cologne, Fac Med, Ctr Pharmacol, Dept Pharmacol 1, Cologne, Germany;Univ Cologne, Univ Hosp Cologne, Ctr Pharmacol, Dept Pharmacol 1, Cologne, Germany.
    Langer, Dorothee
    Univ Cologne, Fac Med, Ctr Pharmacol, Dept Pharmacol 1, Cologne, Germany;Univ Cologne, Univ Hosp Cologne, Ctr Pharmacol, Dept Pharmacol 1, Cologne, Germany.
    Schömig, Edgar
    Univ Cologne, Fac Med, Ctr Pharmacol, Dept Pharmacol 1, Cologne, Germany;Univ Cologne, Univ Hosp Cologne, Ctr Pharmacol, Dept Pharmacol 1, Cologne, Germany.
    Kunze, Sabine
    Univ Hosp Cologne, Dept Radiotherapy, Cologne, Germany.
    Güner, Semih A.
    Univ Hosp Cologne, Dept Radiotherapy, Cologne, Germany.
    Skripnichenko, Roman
    Univ Hosp Cologne, Dept Radiotherapy, Cologne, Germany.
    Ullah, Sami
    Univ Cologne, Fac Med, Ctr Pharmacol, Dept Pharmacol 1, Cologne, Germany;Univ Cologne, Univ Hosp Cologne, Ctr Pharmacol, Dept Pharmacol 1, Cologne, Germany;Univ Bonn, Inst Pharm, Clin Pharm, Bonn, Germany.
    Jaehde, Ulrich
    Univ Bonn, Inst Pharm, Clin Pharm, Bonn, Germany.
    Fuhr, Uwe
    Univ Cologne, Fac Med, Ctr Pharmacol, Dept Pharmacol 1, Cologne, Germany;Univ Cologne, Univ Hosp Cologne, Ctr Pharmacol, Dept Pharmacol 1, Cologne, Germany.
    Jetter, Alexander
    Univ Zurich Hosp, Univ Zurich, Dept Clin Pharmacol & Toxicol, Zurich, Switzerland.
    Taubert, Max
    Univ Cologne, Fac Med, Ctr Pharmacol, Dept Pharmacol 1, Cologne, Germany;Univ Cologne, Univ Hosp Cologne, Ctr Pharmacol, Dept Pharmacol 1, Cologne, Germany.
    Prediction of exposure-driven myelotoxicity of continuous infusion 5-fluorouracil by a semi-physiological pharmacokinetic-pharmacodynamic model in gastrointestinal cancer patients2020In: Cancer Chemotherapy and Pharmacology, ISSN 0344-5704, E-ISSN 1432-0843, Vol. 85, no 4, p. 711-722Article in journal (Refereed)
    Abstract [en]

    Purpose

    To describe 5-fluorouracil (5FU) pharmacokinetics, myelotoxicity and respective covariates using a simultaneous nonlinear mixed effect modelling approach.

    Methods

    Thirty patients with gastrointestinal cancer received 5FU 650 or 1000 mg/m2/day as 5-day continuous venous infusion (14 of whom also received cisplatin 20 mg/m2/day). 5FU and 5-fluoro-5,6-dihydrouracil (5FUH2) plasma concentrations were described by a pharmacokinetic model using NONMEM. Absolute leukocyte counts were described by a semi-mechanistic myelosuppression model. Covariate relationships were evaluated to explain the possible sources of variability in 5FU pharmacokinetics and pharmacodynamics.

    Results

    Total clearance of 5FU correlated with body surface area (BSA). Population estimate for total clearance was 249 L/h. Clearances of 5FU and 5FUH2 fractionally changed by 77%/m2 difference from the median BSA. 5FU central and peripheral volumes of distribution were 5.56 L and 28.5 L, respectively. Estimated 5FUH2 clearance and volume of distribution were 121 L/h and 96.7 L, respectively. Baseline leukocyte count of 6.86 × 109/L, as well as mean leukocyte transit time of 281 h accounting for time delay between proliferating and circulating cells, was estimated. The relationship between 5FU plasma concentrations and absolute leukocyte count was found to be linear. A higher degree of myelosuppression was attributed to combination therapy (slope = 2.82 L/mg) with cisplatin as compared to 5FU monotherapy (slope = 1.17 L/mg).

    Conclusions

    BSA should be taken into account for predicting 5FU exposure. Myelosuppression was influenced by 5FU exposure and concomitant administration of cisplatin.

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  • 16.
    Back, Hyun-moon
    et al.
    Chungnam Natl Univ, Coll Pharm, 99 Daehak Ro, Daejeon 34134, South Korea..
    Song, Byungjeong
    JW Pharmaceut, Drug Discovery Ctr, Seoul 06725, South Korea..
    Pradhan, Sudeep
    Univ Otago, Sch Pharm, Dunedin 9054, New Zealand..
    Chae, Jung-woo
    Chungnam Natl Univ, Coll Pharm, 99 Daehak Ro, Daejeon 34134, South Korea..
    Han, Nayoung
    Seoul Natl Univ, Coll Pharm, Seoul 03080, South Korea..
    Kang, Wonku
    Chung Ang Univ, Coll Pharm, Seoul 06974, South Korea..
    Chang, Min Jung
    Yonsei Univ, Coll Pharm, Incheon 21983, South Korea.;Yonsei Univ, Yonsei Inst Pharmaceut Sci, Incheon 21983, South Korea.;Yonsei Univ, Coll Med & Pharm, Dept Pharmaceut Med & Regulatory Sci, Incheon 21983, South Korea..
    Zheng, Jiao
    Fudan Univ, Huashan Hosp, Dept Pharm, Shanghai 200040, Peoples R China..
    Kwon, Kwang-il
    Chungnam Natl Univ, Coll Pharm, 99 Daehak Ro, Daejeon 34134, South Korea..
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Yun, Hwi-yeol
    Chungnam Natl Univ, Coll Pharm, 99 Daehak Ro, Daejeon 34134, South Korea..
    A mechanism-based pharmacokinetic model of fenofibrate for explaining increased drug absorption after food consumption2018In: BMC Pharmacology & Toxicology, E-ISSN 2050-6511, Vol. 19, article id 4Article in journal (Refereed)
    Abstract [en]

    Background: Oral administration of drugs is convenient and shows good compliance but it can be affected by many factors in the gastrointestinal (GI) system. Consumption of food is one of the major factors affecting the GI system and consequently the absorption of drugs. The aim of this study was to develop a mechanistic GI absorption model for explaining the effect of food on fenofibrate pharmacokinetics (PK), focusing on the food type and calorie content.

    Methods: Clinical data from a fenofibrate PK study involving three different conditions (fasting, standard meals and high-fat meals) were used. The model was developed by nonlinear mixed effect modeling method. Both linear and nonlinear effects were evaluated to explain the impact of food intake on drug absorption. Similarly, to explain changes in gastric emptying time for the drug due to food effects was evaluated.

    Results: The gastric emptying rate increased by 61.7% during the first 6.94 h after food consumption. Increased calories in the duodenum increased the absorption rate constant of the drug in fed conditions (standard meal = 16.5%, high-fat meal = 21.8%) compared with fasted condition. The final model displayed good prediction power and precision.

    Conclusions: A mechanistic GI absorption model for quantitatively evaluating the effects of food on fenofibrate absorption was successfully developed, and acceptable parameters were obtained. The mechanism-based PK model of fenofibrate can quantify the effects of food on drug absorption by food type and calorie content.

  • 17.
    Baverel, P.
    et al.
    MedImmune, Clin Pharmacol, Cambridge, England..
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Agoram, B.
    MedImmune, Clin Pharmacol, Cambridge, England..
    Kantaria, R.
    AstraZeneca, Global Med Affairs, London, England..
    Characterization of dose-FEV1 response of tralokinumab, an investigational anti-IL13 monoclonal antibody in patients with uncontrolled asthma: a population pharmacokinetic/pharmacodynamic modeling analysis2015In: Allergy. European Journal of Allergy and Clinical Immunology, ISSN 0105-4538, E-ISSN 1398-9995, Vol. 70, no S101, p. 35-35, article id 70Article in journal (Other academic)
  • 18.
    Baverel, Paul G
    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.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Predictive performance of internal and external validation proceduresArticle in journal (Other academic)
    Abstract [en]

    Purpose: To compare estimates of predictive performance between internal (IV) and external data-splitting (EV) validation procedures. Methods: Datasets of different study size (n=6, 12, 24, 48, 96, 192, or 384 individuals) were simulated from a one compartment, first-order absorption, pharmacokinetic model and both parametric (FOCE), and nonparametric (NONP) parameter estimates were obtained in NONMEM. From these, three different validation procedures (IV, EV, and a population validation (PV)) were undertaken by means of numerical predictive checks (NPCs) to provide estimates of predictive performance, the PV procedure serving as a reference to assess performance of IV and EV. The predictive performance of NONP versus FOCE estimates was further assessed. Results: Estimates of predictive performance for predicting the median of the population distribution had in general significantly lower imprecision for IV than EV, with little bias for both procedures. For small study sizes, n=6-12 (FOCE) or n=6-24 (NONP), the tails of the population distribution were significantly more biased with IV than EV, but similar imprecision was obtained. The predictive performance for FOCE was similar or superior to that of NONP. Conclusions: Data-splitting is inferior to IV when evaluating predictive models to retain sufficient precision both in predictions and in estimates of predictive performance.

  • 19.
    Baverel, Paul G.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Savic, Radojka M.
    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.
    Two bootstrapping routines for obtaining imprecision estimates for nonparametric parameter distributions in nonlinear mixed effects models2011In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 38, no 1, p. 63-82Article in journal (Refereed)
    Abstract [en]

    When parameter estimates are used in predictions or decisions, it is important to consider the magnitude of imprecision associated with the estimation. Such imprecision estimates are, however, presently lacking for nonparametric algorithms intended for nonlinear mixed effects models. The objective of this study was to develop resampling-based methods for estimating imprecision in nonparametric distribution (NPD) estimates obtained in NONMEM. A one-compartment PK model was used to simulate datasets for which the random effect of clearance conformed to a (i) normal (ii) bimodal and (iii) heavy-tailed underlying distributional shapes. Re-estimation was conducted assuming normality under FOCE, and NPDs were estimated sequential to this step. Imprecision in the NPD was then estimated by means of two different resampling procedures. The first (full) method relies on bootstrap sampling from the raw data and a re-estimation of both the preceding parametric (FOCE) and the nonparametric step. The second (simplified) method relies on bootstrap sampling of individual nonparametric probability distributions. Nonparametric 95% confidence intervals (95% CIs) were obtained and mean errors (MEs) of the 95% CI width were computed. Standard errors (SEs) of nonparametric population estimates were obtained using the simplified method and evaluated through 100 stochastic simulations followed by estimations (SSEs). Both methods were successfully implemented to provide imprecision estimates for NPDs. The imprecision estimates adequately reflected the reference imprecision in all distributional cases and regardless of the numbers of individuals in the original data. Relative MEs of the 95% CI width of CL marginal density when original data contained 200 individuals were equal to: (i) -22 and -12%, (ii) -22 and -9%, (iii) -13 and -5% for the full and simplified (n = 100), respectively. SEs derived from the simplified method were consistent with the ones obtained from 100 SSEs. In conclusion, two novel bootstrapping methods intended for nonparametric estimation methods are proposed. In addition of providing information about the precision of nonparametric parameter estimates, they can serve as diagnostic tools for the detection of misspecified parameter distributions.

  • 20.
    Baverel, Paul G.
    et al.
    MedImmune, Clin Pharmacol Drug Metab & Pharmacokinet, Cambridge, England.
    White, Nicholas
    MedImmune, Clin Pharmacol Drug Metab & Pharmacokinet, Cambridge, England.
    Vicini, Paolo
    MedImmune, Clin Pharmacol Drug Metab & Pharmacokinet, Cambridge, England.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Agoram, Balaji
    MedImmune, Clin Pharmacol Drug Metab & Pharmacokinet, Cambridge, England;FortySeven Inc, Clin Pharmacol & DMPK, Menlo Pk, CA USA.
    Dose-Exposure-Response Relationship of the Investigational Anti-Interleukin-13 Monoclonal Antibody Tralokinumab in Patients With Severe, Uncontrolled Asthma2018In: Clinical Pharmacology and Therapeutics, ISSN 0009-9236, E-ISSN 1532-6535, Vol. 103, no 5, p. 826-835Article in journal (Refereed)
    Abstract [en]

    Interleukin (IL)-13 is involved in the pathogenesis of some types of asthma. Tralokinumab is a human immunoglobulin G(4) monoclonal antibody that specifically binds to IL-13. Two placebo-controlled phase II studies (phase IIa, NCT00873860 and phase IIb, NCT01402986) have been conducted in which tralokinumab was administered subcutaneously. This investigation aimed to characterize tralokinumab's dose-exposure-response (forced expiratory volume in 1 s (FEV1)) relationship in patients with asthma and to predict the most appropriate dose for phase III. An integrated population pharmacokinetic-pharmacodynamic (PK/PD) modeling analysis was required for phase III dose selection, due to differing phase II patient populations, designs, and regimens. Analysis of combined datasets enabled the identification of tralokinumab's dose-exposure-FEV1 response relationship in patients with asthma. Near-maximal FEV1 increase was predicted at a dose of 300 mg SC once every 2 weeks (Q2W). This dose was chosen for tralokinumab in the phase III clinical development program for treatment of severe, uncontrolled asthma.

  • 21.
    Baverel, Paul
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Savic, Rada
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Marshall, Scott
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A novel covariate search method intended for PKPD models with nonparametric parameter distributionsManuscript (preprint) (Other academic)
    Abstract [en]

    Objective. To develop a new covariate modeling approach adapted for nonparametric parameter distributions and to evaluate its statistical properties in terms of power and type-I error rate of covariate inclusion.

    Methods. The proposed methodology is articulated around the decomposition of the nonparametric joint density obtained in NONMEM into a set of unique individual probability density distributions. These individual probabilities are then exported into R and used as weighting factors of a generalized additive model (GAM) regressing support points on covariate distributions. A calibration of the method is undertaken by means of 1000 randomization tests automated with GAM analyses to derive a decision criterion based on the Akaike’s information criterion (AIC) given the null hypothesis and a user-defined confidence level α. Statistical properties of the proposed methodology were then evaluated through Monte-Carlo simulations with α=5%. Eight scenarios of 1000 stochastic simulations followed by estimations (SSEs) were performed under FOCE-NONP given a 1-compartment pharmacokinetic model and an informative design. Estimates of the statistical power of inclusion of both a continuous and a categorical covariates with varying correlation strengths on CL were obtained with associated estimates of type-I error rate. A comparison was then intended with likelihood ratio test statistics (LRTs) given FOCE parameter distributions. Errors in estimates of correlation coefficients were further assessed.

    Results. The methodology was successfully implemented by means of a Perl script calling PsN, NONMEM and R. Estimates of statistical power and type-I error rate of the proposed method were in close agreement with LRT statistics under ideal conditions of hypothesis-testing for the latter, and this, regardless of the correlation strengths and of the attributes of the covariate distribution investigated. Estimates of regression coefficients presented negligible bias and were as precise as the ones obtained with parametric models.

    Conclusions. The set of covariate analysis tools is extended with a new, calibrated, covariate identification technique intended for nonparametric population models.

  • 22.
    Baverel, Paul
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Savic, Radojka
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wilkins, Justin
    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.
    Evaluation of the Nonparametric Estimation Method in NONMEM VI: Application to Real Data2009In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 36, no 4, p. 297-315Article in journal (Refereed)
    Abstract [en]

    The aim of the study was to evaluate the nonparametric estimation methods available in NONMEM VI in comparison with the parametric first-order method (FO) and the first-order conditional estimation method (FOCE) when applied to real datasets. Four methods for estimating model parameters and parameter distributions (FO, FOCE, nonparametric preceded by FO (FO-NONP) and nonparametric preceded by FOCE (FOCE-NONP)) were compared for 25 models previously developed using real data and a parametric method. Numerical predictive checks were used to test the appropriateness of each model. Up to 1000 new datasets were simulated from each model and with each method to construct 90% and 50% prediction intervals. The mean absolute error and the mean error of the different outcomes investigated were computed as indicators of imprecision and bias respectively and formal statistical tests were performed. Overall, less imprecision and less bias were observed with nonparametric methods than with parametric methods. Across the 25 models, t-tests revealed that imprecision and bias were significantly lower (P < 0.05) with FOCE-NONP than with FOCE for half of the NPC outcomes investigated. Improvements were even more pronounced with FO-NONP in comparison with FO. In conclusion, when applied to real datasets and evaluated by numerical predictive checks, the nonparametric estimation methods in NONMEM VI performed better than the corresponding parametric methods (FO or FOCE).

  • 23.
    Bergstrand, Martin
    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.
    Handling data below the limit of quantification in mixed effect models.2009In: AAPS Journal, E-ISSN 1550-7416, Vol. 11, no 2, p. 371-380Article in journal (Refereed)
    Abstract [en]

    The purpose of this study is to investigate the impact of observations below the limit of quantification (BQL) occurring in three distinctly different ways and assess the best method for prevention of bias in parameter estimates and for illustrating model fit using visual predictive checks (VPCs). Three typical ways in which BQL can occur in a model was investigated with simulations from three different models and different levels of the limit of quantification (LOQ). Model A was used to represent a case with BQL observations in an absorption phase of a PK model whereas model B represented a case with BQL observations in the elimination phase. The third model, C, an indirect response model illustrated a case where the variable of interest in some cases decreases below the LOQ before returning towards baseline. Different approaches for handling of BQL data were compared with estimation of the full dataset for 100 simulated datasets following models A, B, and C. An improved standard for VPCs was suggested to better evaluate simulation properties both for data above and below LOQ. Omission of BQL data was associated with substantial bias in parameter estimates for all tested models even for seemingly small amounts of censored data. Best performance was seen when the likelihood of being below LOQ was incorporated into the model. In the tested examples this method generated overall unbiased parameter estimates. Results following substitution of BQL observations with LOQ/2 were in some cases shown to introduce bias and were always suboptimal to the best method. The new standard VPCs was found to identify model misfit more clearly than VPCs of data above LOQ only.

  • 24.
    Bergstrand, Martin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nosten, Francois
    Lwin, Khin Maung
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    White, Nicholas J.
    Tarning, Joel
    Characterization of an in vivo concentration-effect relationship for piperaquine in malaria chemoprevention2014In: Science Translational Medicine, ISSN 1946-6234, E-ISSN 1946-6242, Vol. 6, no 260, p. 260ra147-Article in journal (Refereed)
    Abstract [en]

    A randomized, placebo-controlled trial conducted on the northwest border of Thailand compared malaria chemoprevention with monthly or bimonthly standard 3-day treatment regimens of dihydroartemisinin-piperaquine. Healthy adult male subjects (N = 1000) were followed weekly during 9 months of treatment. Using nonlinear mixed-effects modeling, the concentration-effect relationship for the malaria-preventive effect of piperaquine was best characterized with a sigmoidal E-max relationship, where plasma concentrations of 6.7 ng/ml [relative standard error (RSE), 23%] and 20 ng/ml were found to reduce the hazard of acquiring a malaria infection by 50% [that is, median inhibitory concentration (IC50)] and 95% (IC95), respectively. Simulations of monthly dosing, based on the final model and published pharmacokinetic data, suggested that the incidence of malaria infections over 1 year could be reduced by 70% with a recently suggested dosing regimen compared to the current manufacturer's recommendations for small children (8 to 12 kg). This model provides a rational framework for piperaquine dose optimization in different patient groups.

  • 25.
    Bergstrand, Martin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Söderlind, E.
    Weitschies, W.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Mechanistic modeling of a magnetic marker monitoring study linking gastrointestinal tablet transit, in vivo drug release, and pharmacokinetics2009In: Clinical Pharmacology and Therapeutics, ISSN 0009-9236, E-ISSN 1532-6535, Vol. 86, no 1, p. 77-83Article in journal (Refereed)
    Abstract [en]

    Magnetic marker monitoring (MMM) is a new technique for visualizing transit and disintegration of solid oral dosage forms through the gastrointestinal (GI) tract. The aim of this work was to develop a modeling approach for gaining information from MMM studies using data from a food interaction study with felodipine extended-release (ER) formulation. The interrelationship between tablet location in the GI tract, in vivo drug release, and felodipine disposition was modeled. A Markov model was developed to describe the tablet's movement through the GI tract. Tablet location within the GI tract significantly affected drug release and absorption through the gut wall. Food intake decreased the probability of tablet transition from the stomach, decreased the rate with which released felodipine left the stomach, and increased the fraction absorbed across the gut wall. In conclusion, the combined information of tablet location in the GI tract, in vivo drug release, and plasma concentration can be utilized in a mechanistically informative way with integrated modeling of data from MMM studies.

  • 26.
    Bergstrand, Martin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Söderlind, Erik
    AstraZeneca R&D, Mölndal, Sweden.
    Eriksson, Ulf G
    AstraZeneca R&D, Mölndal, Sweden.
    Weitschies, Werner
    Institute of Pharmacy, University of Greifswald, Greifswald, Germany.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A semi-mechanistic modeling strategy for characterization of regional absorption properties and prospective prediction of plasma concentrations following administration of new modified release formulations2012In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 29, no 2, p. 574-584Article in journal (Refereed)
    Abstract [en]

    PURPOSE

    To outline and test a new modeling approach for prospective predictions of absorption from newly developed modified release formulations based on in vivo studies of gastro intestinal (GI) transit, drug release and regional absorption for the investigational drug AZD0837.

    METHODS

    This work was a natural extension to the companion article "A semi-mechanistic model to link in vitro and in vivo drug release for modified release formulations". The drug release model governed the amount of substance released in distinct GI regions over time. GI distribution of released drug substance, region specific rate and extent of absorption and the influence of food intake were estimated. The model was informed by magnetic marker monitoring data and data from an intubation study with local administration in colon.

    RESULTS

    Distinctly different absorption properties were characterized for different GI regions. Bioavailability over the gut-wall was estimated to be high in duodenum (70%) compared to the small intestine (25%). Colon was primarily characterized by a very slow rate of absorption.

    CONCLUSIONS

    The established model was largely successful in predicting plasma concentration following administration of three newly developed formulations for which no clinical data had been applied during model building.

  • 27.
    Bergstrand, Martin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Söderlind, Erik
    AstraZeneca R&D, Mölndal, Sweden.
    Eriksson, Ulf G
    AstraZeneca R&D, Mölndal, Sweden.
    Weitschies, Werner
    University of Greifswald, Greifswald, Germany.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A Semi-mechanistic Modeling Strategy to Link In Vitro and In Vivo Drug Release for Modified Release Formulations2012In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 29, no 3, p. 695-706Article in journal (Refereed)
    Abstract [en]

    PURPOSE:

    To develop a semi-mechanistic model linking in vitro to in vivo drug release.

    METHODS:

    A nonlinear mixed-effects model describing the in vitro drug release for 6 hydrophilic matrix based modified release formulations across different experimental conditions (pH, rotation speed and ionic strength) was developed. It was applied to in vivo observations of drug release and tablet gastro intestinal (GI) position assessed with magnetic marker monitoring (MMM). By combining the MMM observations with literature information on pH and ionic strength along the GI tract, the mechanical stress in different parts of the GI tract could be estimated in units equivalent to rotation speed in the in vitro USP 2 apparatus.

    RESULTS:

    The mechanical stress in the upper and lower stomach was estimated to 94 and 134 rpm, respectively. For the small intestine and colon the estimates of mechanical stress was 93 and 38 rpm. Predictions of in vivo drug release including between subject/tablet variability was made for other newly developed formulations based on the drug release model and a model describing tablet GI transit.

    CONCLUSION:

    The paper outlines a modeling approach for predicting in vivo behavior from standard in vitro experiments and support formulation development and quality control.

  • 28. Bizzotto, Roberto
    et al.
    Zamuner, Stefano
    De Nicolao, Giuseppe
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Gomeni, Roberto
    Multinomial logistic estimation of Markov-chain models for modeling sleep architecture in primary insomnia patients2010In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 37, no 2, p. 137-155Article in journal (Refereed)
    Abstract [en]

    Hypnotic drug development calls for a better understanding of sleep physiology in order to improve and differentiate novel medicines for the treatment of sleep disorders. On this basis, a proper evaluation of polysomnographic data collected in clinical trials conducted to explore clinical efficacy of novel hypnotic compounds should include the assessment of sleep architecture and its drug-induced changes. This work presents a non-linear mixed-effect Markov-chain model based on multinomial logistic functions which characterize the time course of transition probabilities between sleep stages in insomniac patients treated with placebo. Polysomnography measurements were obtained from patients during one night treatment. A population approach was used to describe the time course of sleep stages (awake stage, stage 1, stage 2, slow-wave sleep and REM sleep) using a Markov-chain model. The relationship between time and individual transition probabilities between sleep stages was modelled through piecewise linear multinomial logistic functions. The identification of the model produced a good adherence of mean post-hoc estimates to the observed transition frequencies. Parameters were generally well estimated in terms of CV, shrinkage and distribution of empirical Bayes estimates around the typical values. The posterior predictive check analysis showed good consistency between model-predicted and observed sleep parameters. In conclusion, the Markov-chain model based on multinomial logistic functions provided an accurate description of the time course of sleep stages together with an assessment of the probabilities of transition between different stages.

  • 29.
    Bjugård Nyberg, Henrik
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Chen, Xiaomei
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Donnelly, Mark
    Division of Quantitative Methods and Modelling, Office of Research and Standards, Office of Generic Drugs, Food and Drug Administration.
    Fang, Lanyan
    Division of Quantitative Methods and Modelling, Office of Research and Standards, Office of Generic Drugs, Food and Drug Administration.
    Zhao, Liang
    Division of Quantitative Methods and Modelling, Office of Research and Standards, Office of Generic Drugs, Food and Drug Administration.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Hooker, Andrew
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Evaluation of model-integrated evidence approaches for pharmacokinetic bioequivalence studies using model averaging methodsManuscript (preprint) (Other academic)
  • 30.
    Björnsson, Marcus A.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Norberg, Ake
    Kalman, Sigridur
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Simonsson, Ulrika S. H.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A two-compartment effect site model describes the bispectral index after different rates of propofol infusion2010In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 37, no 3, p. 243-255Article in journal (Refereed)
    Abstract [en]

    Different estimates of the rate constant for the effect site distribution (k(e0)) of propofol, depending on the rate and duration of administration, have been reported. This analysis aimed at finding a more general pharmacodynamic model that could be used when the rate of administration is changed during the treatment. In a cross-over study, 21 healthy volunteers were randomised to receive a 1 min infusion of 2 mg/kg of propofol at one occasion, and a 1 min infusion of 2 mg/kg of propofol immediately followed by a 29 min infusion of 12 mg kg(-1) h(-1) of propofol at another occasion. Arterial plasma concentrations of propofol were collected up to 4 h after dosing, and BIS was collected before start of infusion and until the subjects were fully awake. The population pharmacokinetic-pharmacodynamic analysis was performed using NONMEM VI. A four-compartment PK model with time-dependent elimination and distribution described the arterial propofol concentrations, and was used as input to the pharmacodynamic model. A standard effect compartment model could not accurately describe the delay in the effects of propofol for both regimens, whereas a two-compartment effect site model significantly improved the predictions. The two-compartment effect site model included a central and a peripheral effect site compartment, possibly representing a distribution within the brain, where the decrease in BIS was linked to the central effect site compartment concentrations through a sigmoidal E-max model.

  • 31.
    Bonate, Peter L.
    et al.
    Astellas, 1 Astellas Way, Northbrook, IL 60062 USA..
    Ahamadi, Malidi
    Merck & Co Inc, 351 N Sumneytown Pike, N Wales, PA 19454 USA..
    Budha, Nageshwar
    Genentech Inc, 1 DNA Way, San Francisco, CA 94080 USA..
    de la Pena, Amparo
    Eli Lilly & Co Chorus, Lilly Corp Ctr, Indianapolis, IN 46285 USA..
    Earp, Justin C.
    US FDA, 10903 New Hampshire Ave,Bldg 51,Room 3154, Silver Spring, MD 20993 USA..
    Hong, Ying
    Novartis Pharmaceut, One Hlth Plaza, E Hanover, NJ 07936 USA..
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ravva, Patanjali
    Boehringer Ingelheim Pharmaceut Inc, 900 Ridgebury Rd, Ridgefield, CT 06877 USA..
    Ruiz-Garcia, Ana
    Pfizer, 10646 Sci Ctr Dr CB10 Off 2448, San Diego, CA 92121 USA..
    Struemper, Herbert
    Parexel Int Inc, 2520 Meridian Pkwy, Durham, NC 27713 USA..
    Wade, Janet R.
    Occams Cooperatie UA, Malandolaan 10, NL-1187 HE Amstelveen, Netherlands..
    Methods and strategies for assessing uncontrolled drug-drug interactions in population pharmacokinetic analyses: results from the International Society of Pharmacometrics (ISOP) Working Group2016In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 43, no 2, p. 123-135Article in journal (Other academic)
    Abstract [en]

    The purpose of this work was to present a consolidated set of guidelines for the analysis of uncontrolled concomitant medications (ConMed) as a covariate and potential perpetrator in population pharmacokinetic (PopPK) analyses. This white paper is the result of an industry-academia-regulatory collaboration. It is the recommendation of the working group that greater focus be given to the analysis of uncontrolled ConMeds as part of a PopPK analysis of Phase 2/3 data to ensure that the resulting outcome in the PopPK analysis can be viewed as reliable. Other recommendations include: (1) collection of start and stop date and clock time, as well as dose and frequency, in Case Report Forms regarding ConMed administration schedule; (2) prespecification of goals and the methods of analysis, (3) consideration of alternate models, other than the binary covariate model, that might more fully characterize the interaction between perpetrator and victim drug, (4) analysts should consider whether the sample size, not the percent of subjects taking a ConMed, is sufficient to detect a ConMed effect if one is present and to consider the correlation with other covariates when the analysis is conducted, (5) grouping of ConMeds should be based on mechanism (e.g., PGP-inhibitor) and not drug class (e.g., beta-blocker), and (6) when reporting the results in a publication, all details related to the ConMed analysis should be presented allowing the reader to understand the methods and be able to appropriately interpret the results.

  • 32. Bonate, Peter L.
    et al.
    Barrett, Jeffrey S.
    Ait-Oudhia, Sihem
    Brundage, Richard
    Corrigan, Brian
    Duffull, Stephen
    Gastonguay, Marc
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Kijima, Shinichi
    Krause, Andreas
    Lovern, Mark
    Riggs, Matthew M.
    Neely, Michael
    Ouellet, Daniele
    Plan, Elodie L.
    Rao, Gauri G.
    Standing, Joseph
    Wilkins, Justin
    Zhu, Hao
    Training the next generation of pharmacometric modelers: a multisector perspective2023In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 51, no 1, p. 5-31Article in journal (Refereed)
    Abstract [en]

    The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970’s and early 1980’s and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations.

  • 33.
    Bouchene, Salim
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Marchand, Sandrine
    INSERM, U 1070, Pole Biol Sante, Poitiers, France;CHU Poitiers, Lab Toxicol & Pharmacocinet, Poitiers, France.
    Couet, William
    INSERM, U 1070, Pole Biol Sante, Poitiers, France;CHU Poitiers, Lab Toxicol & Pharmacocinet, Poitiers, France.
    Friberg, Lena E
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Gobin, Patrice
    INSERM, U 1070, Pole Biol Sante, Poitiers, France.
    Lamarche, Isabelle
    INSERM, U 1070, Pole Biol Sante, Poitiers, France.
    Gregoire, Nicolas
    INSERM, U 1070, Pole Biol Sante, Poitiers, France;CHU Poitiers, Lab Toxicol & Pharmacocinet, Poitiers, France.
    Björkman, Sven
    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 Whole-Body Physiologically Based Pharmacokinetic Model for Colistin and Colistin Methanesulfonate in Rat2018In: Basic & Clinical Pharmacology & Toxicology, ISSN 1742-7835, E-ISSN 1742-7843, Vol. 123, no 4, p. 407-422Article in journal (Refereed)
    Abstract [en]

    Colistin is a polymyxin antibiotic used to treat patients infected with multidrug-resistant Gram-negative bacteria (MDR-GNB). The objective of this work was to develop a whole-body physiologically based pharmacokinetic (WB-PBPK) model to predict tissue distribution of colistin in rat. The distribution of a drug in a tissue is commonly characterized by its tissue-to-plasma partition coefficient, K-p. Colistin and its prodrug, colistin methanesulfonate (CMS) K-p priors, were measured experimentally from rat tissue homogenates or predicted in silico. The PK parameters of both compounds were estimated fitting invivo their plasma concentration-time profiles from six rats receiving an i.v. bolus of CMS. The variability in the data was quantified by applying a nonlinear mixed effect (NLME) modelling approach. A WB-PBPK model was developed assuming a well-stirred and perfusion-limited distribution in tissue compartments. Prior information on tissue distribution of colistin and CMS was investigated following three scenarios: K-p was estimated using in silico K-p priors (I) or K-p was estimated using experimental K-p priors (II) or K-p was fixed to the experimental values (III). The WB-PBPK model best described colistin and CMS plasma concentration-time profiles in scenario II. Colistin-predicted concentrations in kidneys in scenario II were higher than in other tissues, which was consistent with its large experimental K-p prior. This might be explained by a high affinity of colistin for renal parenchyma and active reabsorption into the proximal tubular cells. In contrast, renal accumulation of colistin was not predicted in scenario I. Colistin and CMS clearance estimates were in agreement with published values. The developed model suggests using experimental priors over in silico K-p priors for kidneys to provide a better prediction of colistin renal distribution. Such models might serve in drug development for interspecies scaling and investigate the impact of disease state on colistin disposition.

  • 34.
    Bouchene, Salim
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Marchand, Sandrine
    Friberg, Lena E.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Björkman, Sven
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Couet, William
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Whole Body Physiologically-Based Pharmacokinetic Model for Colistin and Colistimethate Sodium (CMS) in Six Different Species: Mouse, Rat, Rabbit, Baboon, Pig and Human2013In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 40, no S1, p. S115-S116Article in journal (Other academic)
  • 35.
    Brekkan, Ari
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Jönsson, Siv
    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
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Reduced and optimized trial designs for drugs described by a target mediated drug disposition model2018In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 45, no 4, p. 637-647Article in journal (Refereed)
    Abstract [en]

    Monoclonal antibodies against soluble targets are often rich and include the sampling of multiple analytes over a lengthy period of time. Predictive models built on data obtained in such studies can be useful in all drug development phases. If adequate model predictions can be maintained with a reduced design (e.g. fewer samples or shorter duration) the use of such designs may be advocated. The effect of reducing and optimizing a rich design based on a published study for Omalizumab (OMA) was evaluated as an example. OMA pharmacokinetics were characterized using a target-mediated drug disposition model considering the binding of OMA to free IgE and the subsequent formation of an OMA-IgE complex. The performance of the reduced and optimized designs was evaluated with respect to: efficiency, parameter uncertainty and predictions of free target. It was possible to reduce the number of samples in the study by 30% while still maintaining an efficiency of almost 90%. A reduction in sampling duration by two-thirds resulted in an efficiency of 75%. Omission of any analyte measurement or a reduction of the number of dose levels was detrimental to the efficiency of the designs (efficiency ae<currency> 51%). However, other metrics were, in some cases, relatively unaffected, showing that multiple metrics may be needed to obtain balanced assessments of design performance.

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  • 36.
    Brekkan, Ari
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Jönsson, Siv
    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.
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Handling underlying discrete variables with bivariate mixed hidden Markov models in NONMEM2019In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 46, no 6, p. 591-604Article in journal (Refereed)
    Abstract [en]

    Non-linear mixed effects models typically deal with stochasticity in observed processes but models accounting for only observed processes may not be the most appropriate for all data. Hidden Markov models (HMMs) characterize the relationship between observed and hidden variables where the hidden variables can represent an underlying and unmeasurable disease status for example. Adding stochasticity to HMMs results in mixed HMMs (MHMMs) which potentially allow for the characterization of variability in unobservable processes. Further, HMMs can be extended to include more than one observation source and are then multivariate HMMs. In this work MHMMs were developed and applied in a chronic obstructive pulmonary disease example. The two hidden states included in the model were remission and exacerbation and two observation sources were considered, patient reported outcomes (PROs) and forced expiratory volume (FEV1). Estimation properties in the software NONMEM of model parameters were investigated with and without random and covariate effect parameters. The influence of including random and covariate effects of varying magnitudes on the parameters in the model was quantified and a power analysis was performed to compare the power of a single bivariate MHMM with two separate univariate MHMMs. A bivariate MHMM was developed for simulating and analysing hypothetical COPD data consisting of PRO and FEV1 measurements collected every week for 60 weeks. Parameter precision was high for all parameters with the exception of the variance of the transition rate dictating the transition from remission to exacerbation (relative root mean squared error [RRMSE] > 150%). Parameter precision was better with higher magnitudes of the transition probability parameters. A drug effect was included on the transition rate probability and the precision of the drug effect parameter improved with increasing magnitude of the parameter. The power to detect the drug effect was improved by utilizing a bivariate MHMM model over the univariate MHMM models where the number of subject required for 80% power was 25 with the bivariate MHMM model versus 63 in the univariate MHMM FEV1 model and > 100 in the univariate MHMM PRO model. The results advocates for the use of bivariate MHMM models when implementation is possible.

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  • 37.
    Brekkan, Ari
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lledo-Garcia, Rocio
    UCB Pharm, Slough, Buckinghamshire, England..
    Lacroix, Brigitte
    UCB Pharm, Braine Lalleud, Belgium..
    Jonsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Characterization of anti-drug antibody dynamics using a bivariate mixed hidden-markov model by nonlinear-mixed effects approach2024In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 51, no 1, p. 65-75Article in journal (Refereed)
    Abstract [en]

    Biological therapies may act as immunogenic triggers leading to the formation of anti-drug antibodies (ADAs). Population pharmacokinetic (PK) models can be used to characterize the relationship between ADA and drug disposition but often rely on the ADA bioassay results, which may not be sufficiently sensitive to inform on this characterization.In this work, a methodology that could help to further elucidate the underlying ADA production and impact on the drug disposition was explored. A mixed hidden-Markov model (MHMM) was developed to characterize the underlying (hidden) formation of ADA against the biologic, using certolizumab pegol (CZP), as a test drug. CZP is a PEGylated Fc free TNF-inhibitor used in the treatment of rheumatoid arthritis and other chronic inflammatory diseases.The bivariate MHMM used information from plasma drug concentrations and ADA measurements, from six clinical studies (n = 845), that were correlated through a bivariate Gaussian function to infer about two hidden states; production and no-production of ADA influencing PK. Estimation of inter-individual variability was not supported in this case. Parameters associated with the observed part of the model were reasonably well estimated while parameters associated with the hidden part were less precise. Individual state sequences obtained using a Viterbi algorithm suggested that the model was able to determine the start of ADA production for each individual, being a more assay-independent methodology than traditional population PK. The model serves as a basis for identification of covariates influencing the ADA formation, and thus has the potential to identify aspects that minimize its impact on PK and/or efficacy.

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  • 38.
    Brekkan, Ari
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lledo-Garcia, Rocio
    Lacroix, Brigitte
    Jönsson, Siv
    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. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Characterization of Anti-Drug Antibody Dynamics Using a Bivariate Mixed Hidden-Markov ModelManuscript (preprint) (Other academic)
  • 39.
    Brekkan, Ari
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Pharmetheus, Uppsala, Sweden.
    Lopez-Lazaro, Luis
    Dr Reddys Labs, Basel, Switzerland.
    Plan, Elodie L.
    Pharmetheus, Uppsala, Sweden.
    Nyberg, Joakim
    Pharmetheus, Uppsala, Sweden.
    Kankanwadi, Suresh
    Dr Reddys Labs, Basel, Switzerland.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Pharmetheus, Uppsala, Sweden.
    Sensitivity of Pegfilgrastim Pharmacokinetic and Pharmacodynamic Parameters to Product Differences in Similarity Studies2019In: AAPS Journal, E-ISSN 1550-7416, Vol. 21, no 5, article id 85Article in journal (Refereed)
    Abstract [en]

    In this work, a previously developed pegfilgrastim (PG) population pharmacokinetic-pharmacodynamic (PKPD) model was used to evaluate potential factors of importance in the assessment of PG PK and PD similarity. Absolute neutrophil count (ANC) was the modelled PD variable. A two-way cross-over study was simulated where a reference PG and a potentially biosimilar test product were administered to healthy volunteers. Differences in delivered dose amounts or potency between the products were simulated. A different baseline absolute neutrophil count (ANC) was also considered. Additionally, the power to conclude PK or PD similarity based on areas under the PG concentration-time curve (AUC) and ANC-time curve (AUEC) were calculated. Delivered dose differences between the products led to a greater than dose proportional differences in AUC but not in AUEC, respectively. A 10% dose difference from a 6mg dose resulted in 51% and 7% differences in AUC and AUEC, respectively. These differences were more pronounced with low baseline ANC. Potency differences up to 50% were not associated with large differences in either AUCs or AUECs. The power to conclude PK similarity was affected by the simulated dose difference; with a 4% dose difference from 6mg the power was approximately 29% with 250 subjects. The power to conclude PD similarity was high for all delivered dose differences and sample sizes.

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  • 40.
    Brekkan, Ari
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lopez-Lazaro, Luis
    Dr. Reddy’s Laboratories, Basel, Switzerland..
    Plan, Elodie L.
    Pharmetheus, Uppsala, Sweden.
    Nyberg, Joakim
    Pharmetheus, Uppsala, Sweden..
    Kankanwadi, Suresh
    Dr. Reddy’s Laboratories, Basel, Switzerland..
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Sensitivity of Pegfilgrastim Pharmacokinetic and Pharmacodynamic Parameters to Product Differences in Similarity Studies2019In: AAPS Journal, E-ISSN 1550-7416, Vol. 21, no 85Article in journal (Refereed)
    Abstract [en]

    In this work, a previously developed pegfilgrastim (PG) population pharmacokinetic-pharmacodynamic (PKPD) model was used to evaluate potential factors of importance in the assessment of PG PK and PD similarity. Absolute neutrophil count (ANC) was the modelled PD variable. A two-way cross-over study was simulated where a reference PG and a potentially biosimilar test product were administered to healthy volunteers. Differences in delivered dose amounts or potency between the products were simulated. A different baseline absolute neutrophil count (ANC) was also considered. Additionally, the power to conclude PK or PD similarity based on areas under the PG concentration-time curve (AUC) and ANC-time curve (AUEC) were calculated. Delivered dose differences between the products led to a greater than dose proportional differences in AUC but not in AUEC, respectively. A 10% dose difference from a 6 mg dose resulted in 51% and 7% differences in AUC and AUEC, respectively. These differences were more pronounced with low baseline ANC. Potency differences up to 50% were not associated with large differences in either AUCs or AUECs. The power to conclude PK similarity was affected by the simulated dose difference; with a 4% dose difference from 6 mg the power was approximately 29% with 250 subjects. The power to conclude PD similarity was high for all delivered dose differences and sample sizes.

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  • 41.
    Brekkan, Ari
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Pharmetheus, Uppsala, Sweden.
    Lopez-Lazaro, Luis
    Dr Reddys Labs, Basel, Switzerland.
    Yngman, Gunnar
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Pharmetheus, Uppsala, Sweden;Uppsala Univ, Dept Pharmaceut Biosci, Pharmacometr Res Grp, Uppsala, Sweden.
    Plan, Elodie L.
    Pharmetheus, Uppsala, Sweden.
    Acharya, Chayan
    Pharmetheus, Uppsala, Sweden.
    Hooker, Andrew
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Pharmetheus, Uppsala, Sweden.
    Kankanwadi, Suresh
    Dr Reddys Labs, Basel, Switzerland.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Pharmetheus, Uppsala, Sweden.
    A Population Pharmacokinetic-Pharmacodynamic Model of Pegfilgrastim2018In: AAPS Journal, E-ISSN 1550-7416, Vol. 20, no 5, article id 91Article in journal (Refereed)
    Abstract [en]

    Neutropenia and febrile neutropenia (FN) are serious side effects of cytotoxic chemotherapy which may be alleviated with the administration of recombinant granulocyte colony-stimulating factor (GCSF) derivatives, such as pegfilgrastim (PG) which increases absolute neutrophil count (ANC). In this work, a population pharmacokinetic-pharmacodynamic (PKPD) model was developed based on data obtained from healthy volunteers receiving multiple administrations of PG. The developed model was a bidirectional PKPD model, where PG stimulated the proliferation, maturation, and margination of neutrophils and where circulating neutrophils in turn increased the elimination of PG. Simulations from the developed model show disproportionate changes in response with changes in dose. A dose increase of 10% from the 6 mg therapeutic dose taken as a reference leads to area under the curve (AUC) increases of similar to 50 and similar to 5% for PK and PD, respectively. A full random effects covariate model showed that little of the parameter variability could be explained by sex, age, body size, and race. As a consequence, little of the secondary parameter variability (C-max and AUC of PG and ANC) could be explained by these covariates.

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  • 42.
    Brill, Margreke JE
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Svensson, Elin M
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Pandie, Mishal
    Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa.
    Maartens, Gary
    Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Confirming model-predicted pharmacokinetic interactions between bedaquiline and lopinavir/ritonavir or nevirapine in patients with HIV and drug resistant tuberculosis2017In: International Journal of Antimicrobial Agents, ISSN 0924-8579, E-ISSN 1872-7913, Vol. 49, p. 212-217Article in journal (Refereed)
    Abstract [en]

    Bedaquiline and its metabolite M2 are metabolised by CYP3A4. The antiretrovirals ritonavir-boosted lopinavir (LPV/r) and nevirapine inhibit and induce CYP3A4, respectively. Here we aimed to quantify nevirapine and LPV/r drug–drug interaction effects on bedaquiline and M2 in patients co-infected with HIV and multidrug-resistant tuberculosis (MDR-TB) using population pharmacokinetic (PK) analysis and compare these with model-based predictions from single-dose studies in subjects without TB. An observational PK study was performed in three groups of MDR-TB patients during bedaquiline maintenance dosing: HIV-seronegative patients (n = 17); and HIV-infected patients using antiretroviral therapy including nevirapine (n = 17) or LPV/r (n = 14). Bedaquiline and M2 samples were collected over 48 h post-dose. A previously developed PK model of MDR-TB patients was used as prior information to inform parameter estimation using NONMEM. The model was able to describe bedaquiline and M2 concentrations well, with estimates close to their priors and earlier model-based interaction effects from single-dose studies. Nevirapine changed bedaquiline clearance to 82% (95% CI 67–99%) and M2 clearance to 119% (92–156%) of their original values, indicating no clinically significant interaction. LPV/r substantially reduced bedaquiline clearance to 25% (17–35%) and M2 clearance to 59% (44–69%) of original values. This work confirms earlier model-based predictions of nevirapine and LPV/r interaction effects on bedaquiline and M2 clearance from subjects without TB in single-dose studies, in MDR-TB/HIV co-infected patients studied here. To normalise bedaquiline exposure in patients with concomitant LPV/r therapy, an adjusted bedaquiline dosing regimen is proposed for further study.

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  • 43.
    Bukkems, Laura H.
    et al.
    Univ Amsterdam, Med Ctr, Hosp Pharm Clin Pharmacol, Noord Holland, Netherlands..
    Versloot, Olav
    Univ Utrecht, Univ Med Ctr Utrecht, Ctr Benign Haematol Thrombosis & Haemostasis, Van Creveldklin, Utrecht, Netherlands.;Univ Appl Sci, Inst Movement Studies, Dept Physiotherapy, Utrecht, Netherlands..
    Cnossen, Marjon H.
    Erasmus MC, Sophia Childrens Hosp Rotterdam, Dept Pediat Hematol & Oncol, Rotterdam, Netherlands..
    Jönsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Mathot, Ron A. A.
    Univ Amsterdam, Med Ctr, Hosp Pharm Clin Pharmacol, Noord Holland, Netherlands..
    Fischer, Kathelijn
    Univ Utrecht, Univ Med Ctr Utrecht, Ctr Benign Haematol Thrombosis & Haemostasis, Van Creveldklin, Utrecht, Netherlands..
    Association between Sports Participation, Factor VIII Levels and Bleeding in Hemophilia A2023In: Thrombosis and Haemostasis, ISSN 0340-6245, E-ISSN 2567-689X, Vol. 123, no 03, p. 317-325Article in journal (Refereed)
    Abstract [en]

    Background

    Little is known on how sports participation affects bleeding risk in hemophilia. This study aimed to examine associations between sports participation, factor VIII (FVIII) levels and bleeding in persons with hemophilia A.

    Methods

    In this observational, prospective, single-center study, persons with hemophilia A who regularly participated in sports were followed for 12 months. The associations of patient characteristics, FVIII levels, and type/frequency of sports participation with bleeding were analyzed by repeated time-to-event modelling.

    Results

    One hundred and twelve persons (median age: 24 years [interquartile range:16-34], 49% severe, 49% on prophylaxis) were included. During follow-up, 70 bleeds of which 20 sports-induced were observed. FVIII levels were inversely correlated with the bleeding hazard; a 50% reduction of the baseline bleeding hazard was observed at FVIII levels of 3.1 and a 90% reduction at 28.0 IU/dL. The bleeding hazard did not correlate with sports participation. In addition, severe hemophilia, prestudy annual bleeding rate, and presence of arthropathy showed a positive association with the bleeding hazard.

    Conclusions

    This analysis showed that FVIII levels were an important determinant of the bleeding hazard, but sports participation was not. This observation most likely reflects the presence of adequate FVIII levels during sports participation in our study. Persons with severe hemophilia A exhibited a higher bleeding hazard at a similar FVIII levels than nonsevere, suggesting that the time spent at lower FVIII levels impacts overall bleeding hazard. These data may be used to counsel persons with hemophilia regarding sports participation and the necessity of adequate prophylaxis.

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  • 44.
    Bukkems, Laura
    et al.
    Amsterdam Univ Med Ctr, Hosp Pharm Clin Pharmacol, Amsterdam, Netherlands..
    Jönsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Cnossen, Marjon O.
    Erasmus MC, Sophia Childrens Hosp Rotterdam, Dept Pediat Hematol, Rotterdam, Netherlands..
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Mathot, Ron A. A.
    Amsterdam Univ Med Ctr, Hosp Pharm Clin Pharmacol, Amsterdam, Netherlands.;Amsterdam Univ Med Ctr, POB 22660, NL-1100 DD Amsterdam, Netherlands..
    Relationship between factor VIII levels and bleeding for rFVIII-SingleChain in severe hemophilia A: A repeated time-to-event analysis2023In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 12, no 5, p. 706-718Article in journal (Refereed)
    Abstract [en]

    Publications on the exposure-effect relationships of factor concentrates for hemophilia treatment are limited, whereas such analyses give insight on treatment efficacy. Our objective was to examine the relationship between the dose, factor VIII (FVIII) levels and bleeding for rFVIII-SingleChain (lonoctocog alfa, Afstyla). Data from persons with severe hemophilia A on rFVIII-SingleChain prophylaxis from three clinical trials were combined. The published rFVIII-SingleChain population pharmacokinetic (PK) model was evaluated and expanded. The probability of bleeding was described with a parametric repeated time-to-event (RTTE) model. Data included 2080 bleeds, 2545 chromogenic stage assay, and 3052 one-stage assay FVIII levels from 241 persons (median age 19 years) followed for median 1090 days. The majority of the bleeds occurred in joints (65%) and the main bleeding reason was trauma (44%). The probability of bleeding decreased during follow-up and a FVIII level of 8.9 IU/dL (95% confidence interval: 6.9-10.9) decreased the bleeding hazard by 50% compared to a situation without FVIII in plasma. Variability in bleeding hazard between persons with similar FVIII levels was large, and the pre-study annual bleeding rate explained part of this variability. When a FVIII trough level of 1 or 3 IU/dL is targeted during prophylaxis, simulations predicted two (90% prediction interval [PI]: 0-17) or one (90% PI: 0-11) bleeds per year, respectively. In conclusion, the developed PK-RTTE model adequately described the relationship between dose, FVIII levels and bleeds for rFVIII-SingleChain. The obtained estimates were in agreement with those published for the FVIII concentrates BAY 81-8973 (octocog alfa) and BAY 94-9027 (damoctocog alfa pegol), indicating similar efficacy to reduce bleeding.

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  • 45. Bååthe, Sofie
    et al.
    Hamrén, Bengt
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wollbratt, Maria
    Grind, Margaretha
    Eriksson, Ulf G.
    Population pharmacokinetics of melagatran, the active form of the oral direct thrombin inhibitor ximelagatran, in atrial fibrillation patients receiving long-term anticoagulation therapy2006In: Clinical Pharmacokinetics, ISSN 0312-5963, E-ISSN 1179-1926, Vol. 45, no 8, p. 803-819Article in journal (Refereed)
    Abstract [en]

    Background: Ximelagatran is an oral direct thrombin inhibitor for the prevention of thromboembolic disease. After oral administration, ximelagatran is rapidly absorbed and bioconverted to its active form, melagatran.

    Objective: To characterise the pharmacokinetics of melagatran in patients with nonvalvular atrial fibrillation (NVAF) receiving long-term treatment for prevention of stroke and systemic embolic events.

    Methods: A population pharmacokinetic model was developed based on data from three phase 11 studies (1177 plasma concentration observations in 167 patients, treated for up to 18 months) and confirmed by including data from two phase III studies (8702 plasma concentration observations in 3188 patients, treated for up to 24 months). The impact of individualised dosing on pharmacokinetic variability was evaluated by simulations of melagatran concentrations based on the pharmacokinetic model.

    Results: Melagatran pharmacokinetics were consistent across the studied doses and duration of treatment, and were described by a one-compartment model with first-order absorption and elimination. Clearance of melagatran was correlated to creatinine clearance, which was the most important predictor of melagatran exposure (explained 54% of interpatient variance in clearance). Total variability (coefficient of variation) in exposure was 45%; intraindividual variability in exposure was 23%. Concomitant medication with the most common long-term used drugs in the study population had no relevant influence on melagatran pharmacokinetics. Simulations suggested that dose adjustment based on renal function or trough plasma concentration had a minor effect on overall pharmacokinetic variability and the number of patients with high melagatran exposure.

    Conclusion: The pharmacokinetics of melagatran in NVAF patients were predictable, and consistent with results from previously studied patient populations. Dose individualisation was predicted to have a low impact on pharmacokinetic variability, supporting the use of a fixed-dose regimen of ximelagatran for long-term anticoagulant therapy in the majority of NVAF patients.

  • 46. Centanni, Maddalena
    et al.
    Nijhuis, Janine
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Friberg, Lena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Comparative Analysis of Pharmacoeconomic and Pharmacometric Modeling in the Cost-Effectiveness Evaluation of Sunitinib Therapy with Therapeutic Drug Monitoring for Gastrointestinal Stromal TumorsManuscript (preprint) (Other academic)
    Abstract [en]

    Background: Cost-effectiveness analyses (CEAs) increasingly use models to predict long-term outcomes and translate trial data to real-world settings. Model structure uncertainty affects these predictions. This study evaluates a pharmacometric modeling approach against traditional pharmacoeconomic models for CEAs of sunitinib in gastrointestinal stromal tumors (GIST).

    Methods: A two-arm trial comparing sunitinib 37.5 mg daily to no treatment was simulated using a pharmacometric model framework. Four existing pharmacoeconomic models (time-to-event (TTE) and Markov models) were applied to the survival data and linked to logistic regression models describing the toxicity data (neutropenia, thrombocytopenia, hypertension, fatigue and hand-foot syndrome (HFS)) to create pharmacoeconomic model frameworks. All five frameworks were used to simulate clinical outcomes and sunitinib treatment costs, including a therapeutic drug monitoring (TDM) scenario.

    Results: The pharmacometric model predicted sunitinib treatment costs an additional 147,065 euro/QALY compared to no treatment, with deviations -23.2% (discrete Markov), -17.8%% (continuous Markov), +3.8% (TTE Weibull) and +27.8% (TTE exponential) from the pharmacoeconomic model frameworks. The pharmacometric models captured the change in toxicity over treatment cycles (e.g. increased HFS incidence until cycle 4 with a decrease thereafter), a pattern not observed in the pharmacoeconomic models (e.g. stable HFS incidence over all treatment cycles). Furthermore, the pharmacoeconomic models excessively forecasted the percentage of patients encountering sub-therapeutic concentrations of sunitinib over the course of time (pharmacoeconomic: 24.6% at cycle 2 to 98.7% at cycle 16, versus pharmacometric: 13.7% at cycle 2 to 34.1% at cycle 16).

    Conclusions: Model structure significantly influences CEA predictions. The pharmacometric model more closely represented real-world toxicity trends and drug exposure changes. The relevance of these findings depends on the specific question a CEA seeks to address.

  • 47.
    Centanni, Maddalena
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Thijs, Abel
    Locat VU Univ, Dept Internal Med, Amsterdam UMC, Amsterdam, Netherlands..
    Desar, Ingrid
    Radboud Univ Nijmegen Med Ctr, Dept Med Oncol, Nijmegen, Netherlands..
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Friberg, Lena E.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Optimization of blood pressure measurement practices for pharmacodynamic analyses of tyrosine-kinase inhibitors2023In: Clinical and Translational Science, ISSN 1752-8054, E-ISSN 1752-8062, Vol. 16, no 1, p. 73-84Article in journal (Refereed)
    Abstract [en]

    Blood pressure measurements form a critical component of adverse event monitoring for tyrosine kinase inhibitors, but might also serve as a biomarker for dose titrations. This study explored the impact of various sources of within-individual variation on blood pressure readings to improve measurement practices and evaluated the utility for individual- and population-level dose selection. A pharmacokinetic-pharmacodynamic modeling framework was created to describe circadian blood pressure changes, inter- and intra-day variability, changes from dipper to non-dipper profiles, and the relationship between drug exposure and blood pressure changes over time. The framework was used to quantitatively evaluate the influence of physiological and pharmacological aspects on blood pressure measurements, as well as to compare measurement techniques, including office-based, home-based, and ambulatory 24-h blood pressure readings. Circadian changes, as well as random intra-day and inter-day variability, were found to be the largest sources of within-individual variation in blood pressure. Office-based and ambulatory 24-h measurements gave rise to potential bias (>5 mmHg), which was mitigated by model-based estimations. Our findings suggest that 5-8 consecutive, home-based, measurements taken at a consistent time around noon, or alternatively within a limited time frame (e.g., 8.00 a.m. to 12.00 p.m. or 12.00 p.m. to 5.00 p.m.), will give rise to the most consistent blood pressure estimates. Blood pressure measurements likely do not represent a sufficiently accurate method for individual-level dose selection, but may be valuable for population-level dose identification. A user-friendly tool has been made available to allow for interactive blood pressure simulations and estimations for the investigated scenarios.

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  • 48.
    Chasseloup, Estelle
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Hooker, Andrew
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Generation and application of avatars in pharmacometric modelling2023In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 50, p. 411-423Article in journal (Refereed)
  • 49.
    Chasseloup, Estelle
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Comparison of Seven Non-Linear Mixed Effect Model-Based Approaches to Test for Treatment Effect2023In: Pharmaceutics, ISSN 1999-4923, E-ISSN 1999-4923, Vol. 15, no 2, article id 460Article in journal (Refereed)
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  • 50.
    Chasseloup, Estelle
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Li, Xinyi
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Extension of individual model averaging assessments to unbalanced designs and dose-response.Manuscript (preprint) (Other academic)
1234567 1 - 50 of 385
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