uu.seUppsala University Publications
Change search
Refine search result
1234567 1 - 50 of 326
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 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 use