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Pharmacometric Modeling of Liver Metastases' Diameter, Volume, and Density and Their Relation to Clinical Outcome in Imatinib-Treated Patients With Gastrointestinal Stromal Tumors
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Pharmacometrics group)ORCID iD: 0000-0002-4654-1131
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Pharmacometrics group)
Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
Department of Radiology, Papworth Hospital NHS Foundation Trust, Cambridge University Health Partners, Cambridge, United Kingdom.
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2017 (English)In: CPT: pharmacometrics & systems pharmacology, ISSN 2163-8306, Vol. 6, no 7, p. 449-457Article in journal (Refereed) Published
Abstract [en]

Three‐dimensional and density‐based tumor metrics have been suggested to better discriminate tumor response to treatment than unidimensional metrics, particularly for tumors exhibiting nonuniform size changes. In the developed pharmacometric modeling framework based on data from 77 imatinib‐treated gastrointestinal patients, the time‐courses of liver metastases' maximum transaxial diameters, software‐calculated actual volumes (Vactual) and calculated ellipsoidal volumes were characterized by logistic growth models, in which imatinib induced a linear dose‐dependent size reduction. An indirect response model best described the reduction in density. Substantial interindividual variability in the drug effect of all response assessments and additional interlesion variability in the drug effect on density were identified. The predictive ability of longitudinal tumor unidimensional and three‐dimensional size and density on overall survival (OS) and progression‐free survival (PFS) were compared using parametric time‐to‐event models. Death hazard increased with increasing Vactual. This framework may guide early clinical interventions based on three‐dimensional tumor responses to enhance benefits for patients with gastrointestinal stromal tumors (GIST).

Place, publisher, year, edition, pages
2017. Vol. 6, no 7, p. 449-457
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-329103DOI: 10.1002/psp4.12195ISI: 000406417400006PubMedID: 28379635OAI: oai:DiVA.org:uu-329103DiVA, id: diva2:1139733
Funder
Swedish Cancer SocietyAvailable from: 2017-09-08 Created: 2017-09-08 Last updated: 2018-09-20Bibliographically 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, EmilieKrishnan, Sreenath MadathilFriberg, Lena E

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