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A pharmacometric analysis of patient-reported outcomes in breast cancer patients through item response theory
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 Pharmaceutical Biosciences.
Genentech Inc, South San Francisco, CA USA..
Genentech Inc, South San Francisco, CA USA..
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2018 (English)In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 35, no 6, article id 122Article in journal (Refereed) Published
Resource type
Text
Abstract [en]

Purpose

An item response theory (IRT) pharmacometric framework is presented to characterize Functional Assessment of Cancer Therapy-Breast (FACT-B) data in locally-advanced or metastatic breast cancer patients treated with ado-trastuzumab emtansine (T-DM1) or capecitabine-plus-lapatinib.

Methods

In the IRT model, four latent well-being variables, based on FACT-B general subscales, were used to describe the physical, social/family, emotional and functional well-being. Each breast cancer subscale item was reassigned to one of the other subscales. Longitudinal changes in FACT-B responses and covariate effects were investigated.

Results

The IRT model could describe both item-level and subscale-level FACT-B data. Non-Asian patients showed better baseline social/family and functional well-being than Asian patients. Moreover, patients with Eastern Cooperative Oncology Group performance status of 0 had better baseline physical and functional well-being. Well-being was described as initially increasing or decreasing before reaching a steady-state, which varied substantially between patients and subscales. T-DM1 exposure was not related to any of the latent variables. Physical well-being worsening was identified in capecitabine-plus-lapatinib-treated patients, whereas T-DM1-treated patients typically stayed stable.

Conclusion

The developed framework provides a thorough description of FACT-B longitudinal data. It acknowledges the multi-dimensional nature of the questionnaire and allows covariate and exposure effects to be evaluated on responses.

Place, publisher, year, edition, pages
2018. Vol. 35, no 6, article id 122
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:uu:diva-280823DOI: 10.1007/s11095-018-2403-8ISI: 000367842000028OAI: oai:DiVA.org:uu-280823DiVA, id: diva2:915125
Conference
The American Conference on Pharmacometrics 2015 (ACoP6), October 3 to 8, 2015, Virginia, USA
Available from: 2016-03-29 Created: 2016-03-15 Last updated: 2018-08-02Bibliographically approved
In thesis
1. Pharmacometrics to improve clinical benefit assessment in oncology
Open this publication in new window or tab >>Pharmacometrics to improve clinical benefit assessment in oncology
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The high attrition rate in oncology drug development calls for new approaches that would increase the understanding of drugs’ efficacy and safety profiles. This thesis focuses on the development of pharmacometric models to characterize and quantify the relationships between drug exposure, circulating and imaging biomarkers, adverse effects, overall survival (OS), and patient-reported outcomes (PROs).

In axitinib-treated metastatic renal cell carcinoma patients, exposure-driven changes in soluble VEGF receptor 3 were linked to tumor size dynamics, which could in turn predict OS better than biomarker- or hypertension-related predictors. In sunitinib-treated gastro-intestinal stromal tumor (GIST) patients, the tumor metabolic response was sensitive to sunitinib dosing schedule and a substantial inter-lesion variability was quantified. A more pronounced decrease in tumor metabolism for the lesion that best responds to treatment after one week was predictive of longer OS. In imatinib-treated GIST patients, tumor volume better detected size changes of liver metastases and were slightly more predictive of OS than conventional tumor diameters, while tumor density had no predictive value.

A new modeling approach, the minimal continuous-time Markov model (mCTMM), was developed to facilitate the analysis of ordered categorical scores with Markovian features, e.g. fatigue or hand-foot syndrome grades. The mCTMM is applicable when existing approaches are not appropriate (non-uniform assessment intervals) or not easily implemented (variables with large number of categories).

An item response theory pharmacometric framework was established to describe longitudinal item-level data of a PRO questionnaire, the Functional Assessment of Cancer Therapy-Breast (FACT-B). Four correlated latent well-being variables characterized the multi-dimensional nature of FACT-B. When applied to data from breast cancer patients, the progression of physical well-being was typically better in patients treated with ado-trastuzumab emtansine (T-DM1) than with capecitabine-plus-lapatinib-treated patients. No relationship was identified between T-DM1 exposure and any of the latent variables.

In summary, the developed models advance the use of pharmacometrics in assessing the clinical benefit of anti-cancer therapies. They provide a quantitative understanding of the desired and adverse responses to drugs, and their relationships to exposure and long-term clinical outcome. Such frameworks may help to early assess response to therapy and optimize dosing strategies for investigational or existing therapies.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2018. p. 73
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 243
Keywords
nonlinear mixed effect models, NONMEM, pharmacokinetics, pharmacodynamics, VEGF, SLD, targeted therapies, IRT, FDG-PET, SUVmax
National Category
Health Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-336420 (URN)978-91-513-0191-4 (ISBN)
Public defence
2018-02-16, B/B42, Biomedicinskt centrum, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2018-01-24 Created: 2017-12-13 Last updated: 2018-03-07

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Schindler, EmilieFriberg, Lena E.Karlsson, Mats O.

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