Modeling and Simulation Workbench for NONMEM: Tutorial on Pirana, PsN, and Xpose

ABSTRACT.In this study, we report the development of the first item response theory (IRT) model within a pharmacometrics framework to characterize the disease progression in multiple sclerosis (MS), as measured by Expanded Disability Status Score (EDSS). Data were collected quarterly from a 96-week phase III clinical study by a blinder rater, involving 104,206 item-level observations from 1319 patients with relapsing-remitting MS (RRMS), treated with placebo or cladribine. Observed scores for each EDSS item were modeled 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 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. The IRT model was able to describe baseline and longitudinal EDSS data on item and total level. The 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 eight items contained 80% of information for the given range of disabilities. This study has illustrated that IRT modeling 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.

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“…The simulation-based diagnostics were realized using computer-intensive statistical methods available in the Perl-coded program PsN (16).…”

Section: Modeling Methodologymentioning

confidence: 99%

Application of Item Response Theory to Modeling of Expanded Disability Status Scale in Multiple Sclerosis

Novakovic

Krekels

Munafo

et al. 2016

AAPS J

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“…Further simulation-based diagnostics were performed by visual predictive checks (VPCs) using PsN tools (17). Monte Carlo simulations of 200 datasets were generated using the final models, and 95% prediction intervals were obtained around the median, the 2.5th and the 97.5th percentiles at individual item level (the proportion of the subjects within each score category) as well as at total score level (sum of the scores from all the MDS-UPDRS assessments) level and compared with the same metrics calculated from the original data.…”

Section: Model Building and Evaluationmentioning

confidence: 99%

Modeling a Composite Score in Parkinson’s Disease Using Item Response Theory

Gottipati

Karlsson

Plan

2017

AAPS J

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Abstract.In the current work, we present the methodology for development of an Item Response Theory model within a non-linear mixed effects framework to characterize the longitudinal changes of the Movement Disorder Society (sponsored revision) of Unified Parkinson's Disease Rating Scale (MDS-UPDRS) endpoint in Parkinson's disease (PD). The data were obtained from Parkinson's Progression Markers Initiative database and included 163,070 observations up to 48 months from 430 subjects belonging to De Novo PD cohort. The probability of obtaining a score, reported for each of the items in the questionnaire, was modeled as a function of the subject's disability. Initially, a single latent variable model was explored to characterize the disease progression over time. However, based on the understanding of the questionnaire set-up and the results of a residuals-based diagnostic tool, a three latent variable model with a mixture implementation was able to adequately describe longitudinal changes not only at the total score level but also at each individual item level. The linear progression rates obtained for the patient-reported items and the non-sided items were similar, each of which roughly take about 50 months for a typical subject to progress linearly from the baseline by one standard deviation. However for the sided items, it was found that the better side deteriorates quicker than the disabled side. This study presents a framework for analyzing MDS-UPDRS data, which can be adapted to more traditional UPDRS data collected in PD clinical trials and result in more efficient designs and analyses of such studies.

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“…This package currently uses PopED (28,29), NONMEM (26), PsN (30), and R (31) to handle the various tasks inherent to simulating and evaluating the MBAOD process. Parallel computational processes available in the MBAOD R-package and compilation in Clanguage of the scripts were also used to shorten runtimes.…”

Section: Tools Usedmentioning

confidence: 99%

Model-Based Adaptive Optimal Design (MBAOD) Improves Combination Dose Finding Designs: an Example in Oncology

Pierrillas

Fouliard

Chenel

et al. 2018

AAPS J

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Abstract.Design of phase 1 combination therapy trials is complex compared to single therapy trials. In this work, model-based adaptive optimal design (MBAOD) was exemplified and evaluated for a combination of paclitaxel and a hypothetical new compound in a phase 1 study to determine the best dosing regimen for a phase 2 trial. Neutropenia was assumed as the main toxicity and the dose optimization process targeted a 33% probability of grade 4 neutropenia and maximal efficacy (based on preclinical studies) by changing the dose amount of both drugs and the dosing schedule for the new drug. Different starting conditions (e.g., initial dose), search paths (e.g., maximal change in dose intensity per step), and stopping criteria (e.g., B3 + 3 rule^) were explored. The MBAOD approach was successfully implemented allowing the possibility of flexible designs with the modification of doses and dosing schedule throughout the trial. The 3 + 3 rule was shown to be highly conservative (selection of a dosing regimen with at least 90% of the possible maximal efficacy in less than 21% of the cases) but also safer (selection of a toxic design in less than 2% of the cases). Without the 3 + 3 rule, better performance was observed (>67% of selected designs were associated with at least 90% of possible maximal efficacy) while the proportion of DLTs per trial was similar. Overall, MBAOD is a promising tool in the context of dose finding studies of combination treatments and was showed to be flexible enough to be associated with requirements imposed by clinical protocols.

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Modeling and Simulation Workbench for NONMEM: Tutorial on Pirana, PsN, and Xpose

Cited by 541 publications

References 15 publications

Application of Item Response Theory to Modeling of Expanded Disability Status Scale in Multiple Sclerosis

Application of Item Response Theory to Modeling of Expanded Disability Status Scale in Multiple Sclerosis

Modeling a Composite Score in Parkinson’s Disease Using Item Response Theory

Model-Based Adaptive Optimal Design (MBAOD) Improves Combination Dose Finding Designs: an Example in Oncology

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