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Application of Item Response Theory to Modeling of Expanded Disability Status Scale in Multiple Sclerosis
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Pharmacometrics group)ORCID iD: 0000-0003-0665-6484
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Division of Pharmacology, Leiden Academic Centre of Drug Research, Leiden University, Leiden, Netherlands.
Merck Institute of Pharmacometrics, Lausanne, Switzerland.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.ORCID iD: 0000-0002-3712-0255
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2017 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 19, no 1, p. 172-179Article in journal (Other academic) Published
Abstract [en]

In this study, we report the development of the first IRT model within a NLME (Non Linear Mixed Effect) framework to characterize the disease progression in MS (as measured by EDSS). Data were collected from a 96-week Phase III clinical study, involving 104206 item-level observations from 1319 patients with relapsing-remitting MS, treated with placebo or cladribine. Observed scores for each EDSS item were modelled describing the probability of a given score as a function of patients’ (unobserved) disability using a logistic model. Longitudinal data from placebo arms were used to describe the disease progression over time and the model was then extended to cladribine arms in order to characterize the drug effect. Sensitivity with respect to patient disability was calculated as Fisher information for each EDSS item, which were ranked according to the amount of information they contained. IRT model was able to describe baseline and longitudinal EDSS data on item and total level. Final model suggested that cladribine treatment significantly slows disease-progression rate, with a 20% decrease in disease-progression rate compared to placebo, irrespective of exposure, and effects an additional exposure-dependent reduction in disability progression. Four out of 8 items contained 80% of information for the given range of disabilities. This study has illustrated that IRT modelling is specifically suitable for accurate quantification of disease status and description and prediction of disease progression in Phase 3 studies on RRMS, by integrating EDSS item-level data in a meaningful manner.

Place, publisher, year, edition, pages
2017. Vol. 19, no 1, p. 172-179
Keywords [en]
multiple sclerosis, disease progression model, Expanded Disability Status Scale, Item Response Theory, cladribine tablets
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-315622DOI: 10.1208/s12248-016-9977-zISI: 000392210900017PubMedID: 27634384OAI: oai:DiVA.org:uu-315622DiVA, id: diva2:1074980
Funder
EU, European Research CouncilAvailable from: 2017-02-16 Created: 2017-02-16 Last updated: 2018-01-13Bibliographically approved
In thesis
1. Longitudinal Models for Quantifying Disease and Therapeutic Response in Multiple Sclerosis
Open this publication in new window or tab >>Longitudinal Models for Quantifying Disease and Therapeutic Response in Multiple Sclerosis
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Treatment of patients with multiple sclerosis (MS) and development of new therapies have been challenging due to the disease complexity and slow progression, and the limited sensitivity of available clinical outcomes. Modeling and simulation has become an increasingly important component in drug development and in post-marketing optimization of use of medication. This thesis focuses on development of pharmacometric models for characterization and quantification of the relationships between drug exposure, biomarkers and clinical endpoints in relapse-remitting MS (RRMS) following cladribine treatment.

A population pharmacokinetic model of cladribine and its main metabolite, 2-chloroadenine, was developed using plasma and urine data. The renal clearance of cladribine was close to half of total elimination, and was found to be a linear function of creatinine clearance (CRCL).

Exposure-response models could quantify a clear effect of cladribine tablets on absolute lymphocyte count (ALC), burden of disease (BoD), expanded disability status scale (EDSS) and relapse rate (RR) endpoints. Moreover, they gave insight into disease progression of RRMS.

This thesis further demonstrates how integrated modeling framework allows an understanding of the interplay between ALC and clinical efficacy endpoints. ALC was found to be a promising predictor of RR. Moreover, ALC and BoD were identified as predictors of EDSS time-course. This enables the understanding of the behavior of the key outcomes necessary for the successful development of long-awaited MS therapies, as well as how these outcomes correlate with each other.

The item response theory (IRT) methodology, an alternative approach for analysing composite scores, enabled to quantify the information content of the individual EDSS components, which could help improve this scale. In addition, IRT also proved capable of increasing the detection power of potential drug effects in clinical trials, which may enhance drug development efficiency.

The developed nonlinear mixed-effects models offer a platform for the quantitative understanding of the biomarker(s)/clinical endpoint relationship, disease progression and therapeutic response in RRMS by integrating a significant amount of knowledge and data.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2017. p. 71
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 225
Keywords
nonlinear mixed-effects models, pharmacometrics, NONMEM, multiple sclerosis, cladribine, EDSS, item response theory, relapse rate, absolute lymphocyte count, total volume T2 lesions, burden of disease, power, sample size
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-316562 (URN)978-91-554-9836-8 (ISBN)
Public defence
2017-04-21, B42, Biomedicinskt Centrum, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2017-03-30 Created: 2017-03-03 Last updated: 2018-01-13

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