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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 VIIIIn: Article in journal (Refereed)
  • 3.
    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)
  • 4.
    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/). 

  • 5. 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.

  • 6. 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.

     

  • 7.
    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)
  • 8.
    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.

  • 9.
    Alskär, Oskar
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Model-Based Interspecies Scaling of Glucose Homeostasis2017In: CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, ISSN 2163-8306, Vol. 6, no 11, p. 778-786Article in journal (Refereed)
    Abstract [en]

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

  • 10.
    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)
  • 11.
    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.

  • 12.
    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.

  • 13.
    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)
  • 14.
    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.

  • 15.
    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.

  • 16.
    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.

  • 17.
    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.

  • 18.
    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).

  • 19.
    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, ISSN 1550-7416, 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.

  • 20.
    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.

  • 21.
    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.

  • 22.
    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.

  • 23.
    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.

  • 24. 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.

  • 25.
    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.

  • 26.
    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.

  • 27.
    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.

  • 28.
    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)
  • 29.
    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.

  • 30.
    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
    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.
    Handling Underlying Discrete Variables with Bivariate Mixed Hidden-Markov Models in NONMEMManuscript (preprint) (Other academic)
  • 31.
    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)
  • 32.
    Brekkan, Ari
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lopez-Lazaro, Luis
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nyberg, Joakim
    Kankanwadi, Suresh
    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.
    Pharmacokinetic and Pharmacodynamic Sensitivity of Pegfilgrastim2019In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416Article in journal (Refereed)
  • 33.
    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, ISSN 1550-7416, 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.

  • 34.
    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, ISSN 1550-7416, 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.

  • 35.
    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.

  • 36. 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.

  • 37. Chigutsa, Emmanuel
    et al.
    Patel, Kashyap
    Denti, Paolo
    Visser, Marianne
    Maartens, Gary
    Kirkpatrick, Carl M. J.
    McIlleron, Helen
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A Time-to-Event Pharmacodynamic Model Describing Treatment Response in Patients with Pulmonary Tuberculosis Using Days to Positivity in Automated Liquid Mycobacterial Culture2013In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 57, no 2, p. 789-795Article in journal (Refereed)
    Abstract [en]

    Days to positivity in automated liquid mycobacterial culture have been shown to correlate with mycobacterial load and have been proposed as a useful biomarker for treatment responses in tuberculosis. However, there is currently no quantitative method or model to analyze the change in days to positivity with time on treatment. The objectives of this study were to describe the decline in numbers of mycobacteria in sputum collected once weekly for 8 weeks from patients on treatment for tuberculosis using days to positivity in liquid culture. One hundred forty-four patients with smear-positive pulmonary tuberculosis were recruited from a tuberculosis clinic in Cape Town, South Africa. A nonlinear mixed-effects repeated-time-to-event modeling approach was used to analyze the time-to-positivity data. A biexponential model described the decline in the estimated number of bacteria in patients' sputum samples, while a logistic model with a lag time described the growth of the bacteria in liquid culture. At baseline, the estimated number of rapidly killed bacteria is typically 41 times higher than that of those that are killed slowly. The time to kill half of the rapidly killed bacteria was about 1.8 days, while it was 39 days for slowly killed bacteria. Patients with lung cavitation had higher bacterial loads than patients without lung cavitation. The model successfully described the increase in days to positivity as treatment progressed, differentiating between bacteria that are killed rapidly and those that are killed slowly. Our model can be used to analyze similar data from studies testing new drug regimens.

  • 38.
    Choy, Steve
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Henin, Emilie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    van der Walt, Jan-Stefan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kjellsson, Maria
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Identification of the primary mechanism of action of an insulin secretagogue from meal test data in healthy volunteers based on an integrated glucose-insulin model2013In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 40, no 1, p. 1-10Article in journal (Refereed)
    Abstract [en]

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

  • 39.
    Choy, Steve
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kjellsson, Maria
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    de Winter, Willem
    Janssen Prevention Center, Janssen Pharmaceutical Companies of Johnson & Johnson, Leiden, The Netherlands.
    Weight-HbA1c-Insulin-Glucose Model for Describing Disease Progression of Type 2 Diabetes2016In: CPT: Pharmacometrics & Systems Pharmacology, ISSN 2163-8306, Vol. 5, no 1, p. 11-19Article in journal (Refereed)
    Abstract [en]

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

  • 40.
    Claussen, Anetta
    et al.
    Certara Strateg Consulting, Basel, Switzerland.;Novo Nordisk AS, Quantitat Clin Pharmacol, Vandtarnvej 108-110, Soborg, Denmark..
    Möller, Jonas B.
    Novo Nordisk AS, New Prod Planning, Soborg, Denmark..
    Kristensen, Niels R.
    Novo Nordisk AS, Quantitat Clin Pharmacol, Vandtarnvej 108-110, Soborg, Denmark..
    Klim, Sören
    Novo Nordisk AS, Biostat, Soborg, Denmark..
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ingwersen, Steen H.
    Novo Nordisk AS, Quantitat Clin Pharmacol, Vandtarnvej 108-110, Soborg, Denmark..
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Impact of demographics and disease progression on the relationship between glucose and HbA1c2017In: European Journal of Pharmaceutical Sciences, ISSN 0928-0987, E-ISSN 1879-0720, Vol. 104, p. 417-423Article in journal (Refereed)
    Abstract [en]

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

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

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

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

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

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

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

  • 41.
    Clewe, 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.
    Goutelle, Sylvain
    Conte, John E., Jr.
    Simonsson, Ulrika S. H.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A Model Predicting Penetration of Rifampicin from Plasma to Epithelial Lining Fluid and Alveolar Cells2013In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 40, no S1, p. S68-S69Article in journal (Other academic)
  • 42.
    Clewe, 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.
    Simonsson, Ulrika S. H.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Evaluation of optimized bronchoalveolar lavage sampling designs for characterization of pulmonary drug distribution2015In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 42, no 6, p. 699-708Article in journal (Refereed)
    Abstract [en]

    Bronchoalveolar lavage (BAL) is a pulmonary sampling technique for characterization of drug concentrations in epithelial lining fluid and alveolar cells. Two hypothetical drugs with different pulmonary distribution rates (fast and slow) were considered. An optimized BAL sampling design was generated assuming no previous information regarding the pulmonary distribution (rate and extent) and with a maximum of two samples per subject. Simulations were performed to evaluate the impact of the number of samples per subject (1 or 2) and the sample size on the relative bias and relative root mean square error of the parameter estimates (rate and extent of pulmonary distribution). The optimized BAL sampling design depends on a characterized plasma concentration time profile, a population plasma pharmacokinetic model, the limit of quantification (LOQ) of the BAL method and involves only two BAL sample time points, one early and one late. The early sample should be taken as early as possible, where concentrations in the BAL fluid a parts per thousand yen LOQ. The second sample should be taken at a time point in the declining part of the plasma curve, where the plasma concentration is equivalent to the plasma concentration in the early sample. Using a previously described general pulmonary distribution model linked to a plasma population pharmacokinetic model, simulated data using the final BAL sampling design enabled characterization of both the rate and extent of pulmonary distribution. The optimized BAL sampling design enables characterization of both the rate and extent of the pulmonary distribution for both fast and slowly equilibrating drugs.

  • 43.
    Conrado, Daniela J.
    et al.
    Crit Path Inst, Quantitat Med, Tucson, AZ USA..
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Romero, Klaus
    Crit Path Inst, Quantitat Med, Tucson, AZ USA..
    Sarr, Celine
    Pharmetheus, Uppsala, Sweden..
    Wilkins, Justin J.
    Occams, Amstelveen, Netherlands..
    Open innovation: Towards sharing of data, models and workflows2017In: European Journal of Pharmaceutical Sciences, ISSN 0928-0987, E-ISSN 1879-0720, Vol. 109, p. S65-S71Article, review/survey (Refereed)
    Abstract [en]

    Sharing of resources across organisations to support open innovation is an old idea, but which is being taken up by the scientific community at increasing speed, concerning public sharing in particular. The ability to address new questions or provide more precise answers to old questions through merged information is among the attractive features of sharing. Increased efficiency through reuse, and increased reliability of scientific findings through enhanced transparency, are expected outcomes from sharing. In the field of pharmacometrics, efforts to publicly share data, models and workflow have recently started. Sharing of individual-level longitudinal data for modelling requires solving legal, ethical and proprietary issues similar to many other fields, but there are also pharmacometric-specific aspects regarding data formats, exchange standards, and database properties. Several organisations (CDISC, C-Path, IMI, ISoP) are working to solve these issues and propose standards. There are also a number of initiatives aimed at collecting disease-specific databases-Alzheimer's Disease (ADNI, CAMD), malaria (WWARN), oncology (PDS), Parkinson's Disease (PPMI), tuberculosis (CPTR, TB-PACTS, ReSeqTB)-suitable for drug-disease modelling. Organized sharing of pharmacometric executable model code and associated information has in the past been sparse, but a model repository (DDMoRe Model Repository) intended for the purpose has recently been launched. In addition several other services can facilitate model sharing more generally. Pharmacometric workflows have matured over the last decades and initiatives to more fully capture those applied to analyses are ongoing. In order to maximize both the impact of pharmacometrics and the knowledge extracted from clinical data, the scientific community needs to take ownership of and create opportunities for open innovation.

  • 44.
    Cullberg, Marie
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Eriksson, Ulf G.
    Larsson, Marita
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Population modelling of the effect of inogatran, at thrombin inhibitor, on ex vivo coagulation time (APTT) in healthy subjects and patients with coronary artery disease2001In: British Journal of Clinical Pharmacology, ISSN 0306-5251, E-ISSN 1365-2125, Vol. 51, no 1, p. 71-79Article in journal (Refereed)
  • 45.
    Cullberg, Marie
    et al.
    Uppsala University, Medicinska vetenskapsområdet, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Eriksson, Ulf G
    Wåhlander, Karin
    Eriksson, Henry
    Schulman, Sam
    Karlsson, Mats O
    Uppsala University, Medicinska vetenskapsområdet, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Pharmacokinetics of ximelagatran and relationship to clinical response in acute deep vein thrombosis.2005In: Clin Pharmacol Ther, ISSN 0009-9236, Vol. 77, no 4, p. 279-90Article in journal (Refereed)
  • 46.
    Cullberg, Marie
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Eriksson, Ulf G.
    Wåhlander, Karin
    Eriksson, Henry
    Schulman, Sam
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Pharmacokinetics of ximelagatran and relationship to clinical response in acute vein thrombosis2005In: Clinical Pharmacology and Therapeutics, ISSN 0009-9236, E-ISSN 1532-6535, Vol. 77, no 4, p. 279-290Article in journal (Refereed)
    Abstract [en]

    OBJECTIVE:

    Our objective was to characterize the pharmacokinetics of melagatran, the active form of the oral direct thrombin inhibitor ximelagatran, and the relationship between melagatran exposure and clinical outcome in patients with acute deep vein thrombosis.

    METHODS:

    A population pharmacokinetic analysis was performed on samples from patients with deep vein thrombosis participating in a randomized dose-finding study (THRombin Inhibitor in Venous thrombo-Embolism [THRIVE I]). Patients received fixed doses of oral ximelagatran (24, 36, 48, or 60 mg twice daily) for 12 to 16 days. Thrombus size was evaluated by venography before and after treatment. Exposure-response curves were characterized for the probability of regression, no change, and progression of the thrombus extension and of having a bleeding-related event, by use of logistic regression models.

    RESULTS:

    The pharmacokinetics of melagatran (1836 samples in 264 patients) was predictable, without significant time or dose dependencies. Clearance after oral administration (population mean, 27.3 L/h) was correlated with creatinine clearance (P < 10(-6)), and volume of distribution (population mean, 176 L) was correlated with body weight (P = 2 x 10(-5)). Gender, age, or smoking did not significantly influence melagatran pharmacokinetics after the influence of renal function and body weight was accounted for. Unexplained interpatient variability values in total plasma clearance and bioavailability were 19% and 21%, respectively. The median area under the plasma melagatran concentration versus time curve across all patients and dose levels was 3.22 h x micromol/L (5th-95th percentiles, 1.35-7.69). There was no significant relationship between area under the plasma concentration versus time curve and change in thrombus extension (P = .59) or bleeding-related events (P = .77), and the estimated exposure-response curves were relatively flat.

    CONCLUSIONS:

    The pharmacokinetics of melagatran in patients with acute deep vein thrombosis was predictable after oral ximelagatran administration. Shallow exposure-response curves for efficacy and bleeding indicate that there is no need for individualized dosing or therapeutic drug monitoring in the patient population studied.

  • 47.
    Cullberg, Marie
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Wählby, Ulrika
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Eriksson, Ulf G.
    Utility of ecarin clotting time, an ex vivo coagulation test, for pharmacokinetic analysis of the direct thrombin inhibitor melagatranArticle in journal (Refereed)
  • 48.
    Cullberg, Marie
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Wåhlander, Karin
    Lundström, Torbjörn
    Wall, Ulrika
    Eriksson, Ulf G.
    Karlsson, Mats O.
    Ximelagatran in extended secondary prevention of venous thromboembolism: Pharmacokinetics, pharmacodynamics and relationships to clinical eventsManuscript (Other academic)
  • 49. Dahl, Svein G.
    et al.
    Aarons, Leon
    Gundert-Remy, Ursula
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Schneider, Yves-Jacques
    Steimer, Jean-Louis
    Troconiz, Inaki F.
    Incorporating Physiological and Biochemical Mechanisms into Pharmacokinetic-Pharmacodynamic Models: A Conceptual Framework2010In: Basic & Clinical Pharmacology & Toxicology, ISSN 1742-7835, E-ISSN 1742-7843, Vol. 106, no 1, p. 2-12Article, review/survey (Refereed)
    Abstract [en]

    The aim of this conceptual framework paper is to contribute to the further development of the modelling of effects of drugs or toxic agents by an approach which is based on the underlying physiology and pathology of the biological processes. In general, modelling of data has the purpose (1) to describe experimental data, (2a) to reduce the amount of data resulting from an experiment, e.g. a clinical trial and (2b) to obtain the most relevant parameters, (3) to test hypotheses and (4) to make predictions within the boundaries of experimental conditions, e.g. range of doses tested (interpolation) and out of the boundaries of the experimental conditions, e.g. to extrapolate from animal data to the situation in man. Describing the drug/xenobiotic-target interaction and the chain of biological events following the interaction is the first step to build a biologically based model. This is an approach to represent the underlying biological mechanisms in qualitative and also quantitative terms thus being inherently connected in many aspects to systems biology. As the systems biology models may contain variables in the order of hundreds connected with differential equations, it is obvious that it is in most cases not possible to assign values to the variables resulting from experimental data. Reduction techniques may be used to create a manageable model which, however, captures the biologically meaningful events in qualitative and quantitative terms. Until now, some success has been obtained by applying empirical pharmacokinetic/pharmacodynamic models which describe direct and indirect relationships between the xenobiotic molecule and the effect, including tolerance. Some of the models may have physiological components built in the structure of the model and use parameter estimates from published data. In recent years, some progress toward semi-mechanistic models has been made, examples being chemotherapy-induced myelosuppression and glucose-endogenous insulin-antidiabetic drug interactions. We see a way forward by employing approaches to bridge the gap between systems biology and physiologically based kinetic and dynamic models. To be useful for decision making, the 'bridging' model should have a well founded mechanistic basis, but being reduced to the extent that its parameters can be deduced from experimental data, however capturing the biological/clinical essential details so that meaningful predictions and extrapolations can be made.

  • 50.
    Dansirikul, Chantaratsamon
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Silber, Hanna E
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Approaches to handling pharmacodynamic baseline responses2008In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 35, no 3, p. 269-283Article in journal (Refereed)
    Abstract [en]

    A few approaches for handling baseline responses are available for use in pharmacokinetic (PK)-pharmacodynamic (PD) analysis. They include: (method 1-B1) estimation of the typical value and interindividual variability (IIV) of baseline in the population, (B2) inclusion of the observed baseline response as a covariate acknowledging the residual variability, (B3) a more general version of B2 as it also takes the IIV of the baseline in the population into account, and (B4) normalization of all observations by the baseline value. The aim of this study was to investigate the relative performance of B1-B4. PD responses over a single dosing interval were simulated from an indirect response model in which a drug acts through stimulation or inhibition of the response according to an Emax model. The performance of B1-B4 was investigated under 22 designs, each containing 100 datasets. NONMEM VI beta was used to estimate model parameters with the FO and the FOCE method. The mean error (ME, %) and root mean squared error (RMSE, %) of the population parameter estimates were computed and used as an indicator of bias and imprecision. Absolute ME (|ME|) and RMSE from all methods were ranked within the same design, the lower the rank value the better method performance. Average rank of each method from all designs was reported. The results showed that with B1 and FOCE, the average of |ME| and RMSE across all typical individual parameters and all conditions was 5.9 and 31.8%. The average rank of |ME| for B1, B2, B3, and B4 was 3.7, 3.8, 3.3, and 5.2 for the FOCE method, and 4.6, 4.3, 4.7, and 6.4 for the FO method. The smallest imprecision was noted with the use of B1 (rank of 3.1 for FO, and 2.9 for FOCE) and increased, in order, with B3 (3.9-FO and 3.6-FOCE), B2 (4.8-FO; 4.7-FOCE), and B4 (6.4-FO; 6.5-FOCE). We conclude that when considering both bias and imprecision B1 was slightly better than B3 which in turn was better than B2. Differences between these methods were small. B4 was clearly inferior. The FOCE method led to a smaller bias, but no marked reduction in imprecision of parameter estimates compared to the FO method.

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