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PK-PD Modeling of Individual Lesion FDG-PET Response to Predict Overall Survival in Patients With Sunitinib-treated Gastrointestinal Stromal Tumor
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Pfizer Inc, La Jolla, CA USA..
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.
2016 (English)In: CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, ISSN 2163-8306, Vol. 5, no 4, p. 173-181Article in journal (Refereed) Published
Abstract [en]

Pharmacometric models were developed to characterize the relationships between lesion-level tumor metabolic activity, as assessed by the maximum standardized uptake value (SUVmax) obtained on [F-18]-fluorodeoxyglucose (FDG) positron emission tomography (PET), tumor size, and overall survival (OS) in 66 patients with gastrointestinal stromal tumor (GIST) treated with intermittent sunitinib. An indirect response model in which sunitinib stimulates tumor loss best described the typically rapid decrease in SUVmax during on-treatment periods and the recovery during off-treatment periods. Substantial interindividual and interlesion variability were identified in SUVmax baseline and drug sensitivity. A parametric time-to-event model identified the relative change in SUVmax at one week for the lesion with the most pronounced response as a better predictor of OS than tumor size. Based on the proposed modeling framework, early changes in FDG-PET response may serve as predictor for long-term outcome in sunitinib-treated GIST.

Place, publisher, year, edition, pages
2016. Vol. 5, no 4, p. 173-181
National Category
Pharmacology and Toxicology
Identifiers
URN: urn:nbn:se:uu:diva-303400DOI: 10.1002/psp4.12057ISI: 000381564500002PubMedID: 27299707OAI: oai:DiVA.org:uu-303400DiVA: diva2:971765
Available from: 2016-09-19 Created: 2016-09-19 Last updated: 2018-01-10Bibliographically 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
Keyword
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: 2018-01-24

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