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Wellhagen, G. J., Kjellsson, M. C. & Karlsson, M. O. (2019). A Bounded Integer Model for Rating and Composite Scale Data. AAPS Journal, 21(4), Article ID 74.
Open this publication in new window or tab >>A Bounded Integer Model for Rating and Composite Scale Data
2019 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 21, no 4, article id 74Article in journal (Refereed) Published
Abstract [en]

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

Keywords
Bounded integer model, Categorical data, Composite scale, Nonlinear mixed-effects modelling, Probit regression, Rating scale
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-388766 (URN)10.1208/s12248-019-0343-9 (DOI)000470776700002 ()31172350 (PubMedID)
Funder
Swedish Research Council, 2018-03317
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-08-12Bibliographically approved
Thorsted, A., Bouchene, S., Tano, E., Castegren, M., Lipcsey, M., Sjölin, J., . . . Nielsen, E. I. (2019). A non-linear mixed effect model for innate immune response: In vivo kinetics of endotoxin and its induction of the cytokines tumor necrosis factor alpha and interleukin-6. PLoS ONE, 14(2), Article ID e0211981.
Open this publication in new window or tab >>A non-linear mixed effect model for innate immune response: In vivo kinetics of endotoxin and its induction of the cytokines tumor necrosis factor alpha and interleukin-6
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2019 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 14, no 2, article id e0211981Article in journal (Refereed) Published
Abstract [en]

Endotoxin, a component of the outer membrane of Gram-negative bacteria, has been extensively studied as a stimulator of the innate immune response. However, the temporal aspects and exposure-response relationship of endotoxin and resulting cytokine induction and tolerance development is less well defined. The aim of this work was to establish an in silico model that simultaneously captures and connects the in vivo time-courses of endotoxin, tumor necrosis factor alpha (TNF-alpha), interleukin-6 (IL-6), and associated tolerance development. Data from six studies of porcine endotoxemia in anesthetized piglets (n = 116) were combined and used in the analysis, with purified endotoxin (Escherichia coli O111: B4) being infused intravenously for 1-30 h in rates of 0.063-16.0 mu g/kg/h across studies. All data were modelled simultaneously by means of importance sampling in the non-linear mixed effects modelling software NONMEM. The infused endotoxin followed one-compartment disposition and non-linear elimination, and stimulated the production of TNF-alpha to describe the rapid increase in plasma concentration. Tolerance development, observed as declining TNF-alpha concentration with continued infusion of endotoxin, was also driven by endotoxin as a concentration-dependent increase in the potency parameter related to TNF-alpha production (EC50). Production of IL-6 was stimulated by both endotoxin and TNF-a, and four consecutive transit compartments described delayed increase in plasma IL-6. A model which simultaneously account for the time-courses of endotoxin and two immune response markers, the cytokines TNF-alpha and IL-6, as well as the development of endotoxin tolerance, was successfully established. This model-based approach is unique in its description of the time-courses and their interrelation and may be applied within research on immune response to bacterial endotoxin, or in pre-clinical pharmaceutical research when dealing with study design or translational aspects.

National Category
Physiology
Identifiers
urn:nbn:se:uu:diva-379038 (URN)10.1371/journal.pone.0211981 (DOI)000459330800014 ()30789941 (PubMedID)
Funder
Swedish Foundation for Strategic Research
Available from: 2019-03-11 Created: 2019-03-11 Last updated: 2019-03-11Bibliographically approved
Germovsek, E., Ambery, C., Yang, S., Beerahee, M., Karlsson, M. O. & Plan, E. L. (2019). A Novel Method for Analysing Frequent Observations from Questionnaires in Order to Model Patient-Reported Outcomes: Application to EXACT (R) Daily Diary Data from COPD Patients. AAPS Journal, 21(4), Article ID UNSP 60.
Open this publication in new window or tab >>A Novel Method for Analysing Frequent Observations from Questionnaires in Order to Model Patient-Reported Outcomes: Application to EXACT (R) Daily Diary Data from COPD Patients
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2019 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 21, no 4, article id UNSP 60Article in journal (Refereed) Published
Abstract [en]

Chronic obstructive pulmonary disease (COPD) is a progressive lung disease with approximately 174 million cases worldwide. Electronic questionnaires are increasingly used for collecting patient-reported-outcome (PRO) data about disease symptoms. Our aim was to leverage PRO data, collected to record COPD disease symptoms, in a general modelling framework to enable interpretation of PRO observations in relation to disease progression and potential to predict exacerbations. The data were collected daily over a year, in a prospective, observational study. The e-questionnaire, the EXAcerbations of COPD Tool (EXACT (R)) included 14 items (i.e. questions) with 4 or 5 ordered categorical response options. An item response theory (IRT) model was used to relate the responses from each item to the underlying latent variable (which we refer to as disease severity), and on each item level, Markov models (MM) with 4 or 5 categories were applied to describe the dependence between consecutive observations. Minimal continuous time MMs were used and parameterised using ordinary differential equations. One hundred twenty-seven COPD patients were included (median age 67years, 54% male, 39% current smokers), providing approximately 40,000 observations per EXACT (R) item. The final model suggested that, with time, patients more often reported the same scores as the previous day, i.e. the scores were more stable. The modelled COPD disease severity change over time varied markedly between subjects, but was small in the typical individual. This is the first IRT model with Markovian properties; our analysis proved them necessary for predicting symptom-defined exacerbations.

Keywords
EXACT questionnaire, IRT, Markov model, NONMEM, PRO
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:uu:diva-383495 (URN)10.1208/s12248-019-0319-9 (DOI)000466176800001 ()31028495 (PubMedID)
Available from: 2019-05-17 Created: 2019-05-17 Last updated: 2019-05-17Bibliographically approved
Krause, A., Kloft, C., Huisinga, W., Karlsson, M., Pinheiro, J., Bies, R., . . . Musser, B. J. (2019). Comment on Jaki et al., A proposal for a new PhD level curriculum on quantitative methods for drug development. Pharmaceutical Statistics 17 (5):593-606, Sep/Oct 2018, DOI:10.1002/pst.1873 [Letter to the editor]. Pharmaceutical statistics, 18(3), 278-281
Open this publication in new window or tab >>Comment on Jaki et al., A proposal for a new PhD level curriculum on quantitative methods for drug development. Pharmaceutical Statistics 17 (5):593-606, Sep/Oct 2018, DOI:10.1002/pst.1873
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2019 (English)In: Pharmaceutical statistics, ISSN 1539-1604, E-ISSN 1539-1612, Vol. 18, no 3, p. 278-281Article in journal, Letter (Other academic) Published
Place, publisher, year, edition, pages
John Wiley & Sons, 2019
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-389924 (URN)10.1002/pst.1940 (DOI)000470930500001 ()30932340 (PubMedID)
Available from: 2019-08-01 Created: 2019-08-01 Last updated: 2019-08-01Bibliographically approved
Arshad, U., Chasseloup, E., Nordgren, R. & Karlsson, M. O. (2019). Development of visual predictive checks accounting for multimodal parameter distributions in mixture models. Journal of Pharmacokinetics and Pharmacodynamics, 46(3), 241-250
Open this publication in new window or tab >>Development of visual predictive checks accounting for multimodal parameter distributions in mixture models
2019 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 46, no 3, p. 241-250Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
SPRINGER/PLENUM PUBLISHERS, 2019
Keywords
Visual predictive checks, Mixture models, Multimodal parameter distributions, Pharmacokinetics, Pharmacodynamics
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-385960 (URN)10.1007/s10928-019-09632-9 (DOI)000468597100003 ()30968312 (PubMedID)
Available from: 2019-06-19 Created: 2019-06-19 Last updated: 2019-06-19Bibliographically approved
Abrantes, J. A., Jönsson, S., Karlsson, M. & Nielsen, E. I. (2019). Handling interoccasion variability in model-based dose individualization using therapeutic drug monitoring data. British Journal of Clinical Pharmacology, 85(6), 1326-1336
Open this publication in new window or tab >>Handling interoccasion variability in model-based dose individualization using therapeutic drug monitoring data
2019 (English)In: British Journal of Clinical Pharmacology, ISSN 0306-5251, E-ISSN 1365-2125, Vol. 85, no 6, p. 1326-1336Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
John Wiley & Sons, 2019
Keywords
NONMEM, pharmacokinetics, population analysis, therapeutic drug monitoring
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:uu:diva-381215 (URN)10.1111/bcp.13901 (DOI)000468974200029 ()30767254 (PubMedID)
Available from: 2019-04-05 Created: 2019-04-05 Last updated: 2019-06-24Bibliographically approved
Gottipati, G., Berges, A. C., Yang, S., Chen, C., Karlsson, M. & Plan, E. L. (2019). Item Response Model Adaptation for Analyzing Data from Different Versions of Parkinson's Disease Rating Scales. Pharmaceutical research, 36(9), Article ID 135.
Open this publication in new window or tab >>Item Response Model Adaptation for Analyzing Data from Different Versions of Parkinson's Disease Rating Scales
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2019 (English)In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 36, no 9, article id 135Article in journal (Refereed) Published
Abstract [en]

Purpose: The aim of this work was to allow combination of information from recent and historical trials in Parkinson's Disease (PD) by developing bridging methodology between two versions of the clinical endpoint.

Methods: A previously developed Item Response Model (IRM), that described longitudinal changes in Movement Disorder Society (MDS) sponsored revision of Unified Parkinson's Disease Rating Scale (UPDRS) [MDS-UPDRS] data from the De Novo PD cohort in Parkinson's Progression Markers Initiative, was first adapted to describe baseline UPDRS data from two clinical trials, one in subjects with early PD and another in subjects with advanced PD. Assuming similar IRM structure, items of the UPDRS version were mapped to those in the MDS-UPDRS version. Subsequently, the longitudinal changes in the placebo arm of the advanced PD study were characterized.

Results: The parameters reflecting differences in the shared items between endpoints were successfully estimated, and the model diagnostics indicated that mapping was better for early PD subjects (closer to De Novo cohort) than for advanced PD subjects. Disease progression for placebo in advanced PD patients was relatively shallow.

Conclusion: An IRM able to handle two variants of clinical PD endpoints was developed; it can improve the utilization of data from diverse sources and diverse disease populations.

Place, publisher, year, edition, pages
SPRINGER/PLENUM PUBLISHERS, 2019
Keywords
Data integration, disease progression, item response theory, movement disorder society (sponsored revision) - Unified parkinson's disease rating scale, parkinson's disease
National Category
Neurology
Identifiers
urn:nbn:se:uu:diva-390377 (URN)10.1007/s11095-019-2668-6 (DOI)000475976900001 ()31317279 (PubMedID)
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-08-12Bibliographically approved
Ibrahim, M. M. A., Ueckert, S., Freiberga, S., Kjellsson, M. C. & Karlsson, M. O. (2019). Model-Based Conditional Weighted Residuals Analysis for Structural Model Assessment. AAPS Journal, 21(3), Article ID UNSP 34.
Open this publication in new window or tab >>Model-Based Conditional Weighted Residuals Analysis for Structural Model Assessment
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2019 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 21, no 3, article id UNSP 34Article in journal (Refereed) Published
Abstract [en]

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

Keywords
conditional weighted residuals, diagnostics, model evaluation, nonlinear mixed effects models, prediction bias, structural model
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-367054 (URN)10.1208/s12248-019-0305-2 (DOI)000460184300001 ()30815754 (PubMedID)
Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2019-03-25Bibliographically approved
Brekkan, A., Lopez-Lazaro, L., Plan, E. L., Nyberg, J., Kankanwadi, S. & Karlsson, M. (2019). Pharmacokinetic and Pharmacodynamic Sensitivity of Pegfilgrastim. AAPS Journal
Open this publication in new window or tab >>Pharmacokinetic and Pharmacodynamic Sensitivity of Pegfilgrastim
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2019 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416Article in journal (Refereed) Submitted
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-381432 (URN)
Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2019-04-11
Duthaler, U., Suenderhauf, C., Karlsson, M. O., Hussner, J., zu Schwabedissen, H. M., Krahenbuhl, S. & Hammann, F. (2019). Population pharmacokinetics of oral ivermectin in venous plasma and dried blood spots in healthy volunteers. British Journal of Clinical Pharmacology, 85(3), 626-633
Open this publication in new window or tab >>Population pharmacokinetics of oral ivermectin in venous plasma and dried blood spots in healthy volunteers
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2019 (English)In: British Journal of Clinical Pharmacology, ISSN 0306-5251, E-ISSN 1365-2125, Vol. 85, no 3, p. 626-633Article in journal (Refereed) Published
Abstract [en]

Aims

The anthelminthic ivermectin is receiving new attention as it is being repurposed for new indications such as mass drug administrations for the treatment of scabies or in malaria vector control. As its pharmacokinetics are still poorly understood, we aimed to characterize the population pharmacokinetics of ivermectin in plasma and dried blood spots (DBS), a sampling method better suited to field trials, with special focus on the influence of body composition and enterohepatic circulation.

Methods

We performed a clinical trial in 12 healthy volunteers who each received a single oral dose of 12 mg ivermectin, and collected peripheral venous and capillary DBS samples. We determined ivermectin concentrations in plasma and DBS by liquid chromatography tandem mass spectrometry using a fully automated and scalable extraction system for DBS sample processing. Pharmacokinetic data were analysed using non-linear mixed effects modelling.

Results

A two-compartment model with a transit absorption model, first-order elimination, and weight as an influential covariate on central volume of distribution and clearance best described the data. The model estimates (inter-individual variability) for a 70 kg subject were: apparent population clearance 7.7 (25%) l h(-1), and central and peripheral volumes of distribution 89 (10%) l and 234 (20%) l, respectively. Concentrations obtained from DBS samples were strongly linearly correlated (R-2 = 0.97) with plasma concentrations, and on average 30% lower.

Conclusion

The model accurately depicts population pharmacokinetics of plasma and DBS concentrations over time for oral ivermectin. The proposed analytical workflow is scalable and applicable to the requirements of mass drug administrations.

Keywords
Drug analysis, parasitology < Infectious diseases, pharmacokinetics, pharmacometrics, tropical diseases < Infectious diseases
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-379025 (URN)10.1111/bcp.13840 (DOI)000458936500016 ()30566757 (PubMedID)
Available from: 2019-03-12 Created: 2019-03-12 Last updated: 2019-03-12Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-1258-8297

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