Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Pharmacometrics to improve clinical benefit assessment in oncology
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Pharmacometrics group)ORCID iD: 0000-0002-4654-1131
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 [en]
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: urn:nbn:se:uu:diva-336420ISBN: 978-91-513-0191-4 (print)OAI: oai:DiVA.org:uu-336420DiVA, id: diva2:1165865
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
List of papers
1. A Pharmacometric Framework for Axitinib Exposure, Efficacy, and Safety in Metastatic Renal Cell Carcinoma Patients
Open this publication in new window or tab >>A Pharmacometric Framework for Axitinib Exposure, Efficacy, and Safety in Metastatic Renal Cell Carcinoma Patients
2017 (English)In: CPT: Pharmacometrics & Systems Pharmacology, ISSN 2163-8306, Vol. 6, no 6, p. 373-382Article in journal (Refereed) Published
Abstract [en]

The relationships between exposure, biomarkers (vascular endothelial growth factor (VEGF), soluble VEGF receptors (sVEGFR)-1, -2, -3, and soluble stem cell factor receptor (sKIT)), tumor sum of longest diameters (SLD), diastolic blood pressure (dBP), and overall survival (OS) were investigated in a modeling framework. The dataset included 64 metastatic renal cell carcinoma patients (mRCC) treated with oral axitinib. Biomarker timecourses were described by indirect response (IDR) models where axitinib inhibits sVEGFR-1, -2, and -3 production, and VEGF degradation. No effect was identified on sKIT. A tumor model using sVEGFR-3 dynamics as driver predicted SLD data well. An IDR model, with axitinib exposure stimulating the response, characterized dBP increase. In a time-to-event model the SLD timecourse predicted OS better than exposure, biomarker- or dBP-related metrics. This type of framework can be used to relate pharmacokinetics, efficacy, and safety to long-term clinical outcome in mRCC patients treated with VEGFR inhibitors.

National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-329102 (URN)10.1002/psp4.12193 (DOI)000404186100004 ()28378918 (PubMedID)
Funder
Swedish Cancer SocietyEU, FP7, Seventh Framework Programme, FP7/2007-2013
Available from: 2017-09-08 Created: 2017-09-08 Last updated: 2018-01-13Bibliographically approved
2. PK-PD Modeling of Individual Lesion FDG-PET Response to Predict Overall Survival in Patients With Sunitinib-treated Gastrointestinal Stromal Tumor
Open this publication in new window or tab >>PK-PD Modeling of Individual Lesion FDG-PET Response to Predict Overall Survival in Patients With Sunitinib-treated Gastrointestinal Stromal Tumor
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.

National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:uu:diva-303400 (URN)10.1002/psp4.12057 (DOI)000381564500002 ()27299707 (PubMedID)
Available from: 2016-09-19 Created: 2016-09-19 Last updated: 2018-01-10Bibliographically approved
3. Pharmacometric Modeling of Liver Metastases' Diameter, Volume, and Density and Their Relation to Clinical Outcome in Imatinib-Treated Patients With Gastrointestinal Stromal Tumors.
Open this publication in new window or tab >>Pharmacometric Modeling of Liver Metastases' Diameter, Volume, and Density and Their Relation to Clinical Outcome in Imatinib-Treated Patients With Gastrointestinal Stromal Tumors.
Show others...
2017 (English)In: CPT: pharmacometrics & systems pharmacology, ISSN 2163-8306, Vol. 6, no 7, p. 449-457Article in journal (Refereed) Published
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-329103 (URN)10.1002/psp4.12195 (DOI)28379635 (PubMedID)
Funder
Swedish Cancer Society
Available from: 2017-09-08 Created: 2017-09-08 Last updated: 2018-01-13
4. A Minimal Continuous-Time Markov Pharmacometric Model.
Open this publication in new window or tab >>A Minimal Continuous-Time Markov Pharmacometric Model.
2017 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 19, no 5, p. 1424-1435Article in journal (Refereed) Published
Abstract [en]

In this work, an alternative model to discrete-time Markov model (DTMM) or standard continuous-time Markov model (CTMM) for analyzing ordered categorical data with Markov properties is presented: the minimal CTMM (mCTMM). Through a CTMM reparameterization and under the assumption that the transition rate between two consecutive states is independent on the state, the Markov property is expressed through a single parameter, the mean equilibration time, and the steady-state probabilities are described by a proportional odds (PO) model. The mCTMM performance was evaluated and compared to the PO model (ignoring Markov features) and to published Markov models using three real data examples: the four-state fatigue and hand-foot syndrome data in cancer patients initially described by DTMM and the 11-state Likert pain score data in diabetic patients previously analyzed with a count model including Markovian transition probability inflation. The mCTMM better described the data than the PO model, and adequately predicted the average number of transitions per patient and the maximum achieved scores in all examples. As expected, mCTMM could not describe the data as well as more flexible DTMM but required fewer estimated parameters. The mCTMM better fitted Likert data than the count model. The mCTMM enables to explore the effect of potential predictive factors such as drug exposure and covariates, on ordered categorical data, while accounting for Markov features, in cases where DTMM and/or standard CTMM is not applicable or conveniently implemented, e.g., non-uniform time intervals between observations or large number of categories.

Keywords
NONMEM, non-linear mixed effects models, ordered categorical data, pharmacokinetic-pharmacodynamic, serial correlations
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-329101 (URN)10.1208/s12248-017-0109-1 (DOI)000408407300016 ()28634883 (PubMedID)
Funder
Swedish Cancer Society
Available from: 2017-09-08 Created: 2017-09-08 Last updated: 2018-01-13Bibliographically approved
5. A pharmacometric analysis of patient-reported outcomes in breast cancer patients through item response theory
Open this publication in new window or tab >>A pharmacometric analysis of patient-reported outcomes in breast cancer patients through item response theory
Show others...
2018 (English)In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 35, no 6, article id 122Article in journal (Refereed) Published
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.

National Category
Cancer and Oncology
Identifiers
urn:nbn:se:uu:diva-280823 (URN)10.1007/s11095-018-2403-8 (DOI)000367842000028 ()
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

Open Access in DiVA

fulltext(2236 kB)142 downloads
File information
File name FULLTEXT01.pdfFile size 2236 kBChecksum SHA-512
285270f94543440e55235976e0c67a268cc6321ef4a349c32a933f7ac640045591e780ff4c3253de1931349d2992e17bd198d406b16e808ba70056c4ce3d8baf
Type fulltextMimetype application/pdf
Buy this publication >>

Search in DiVA

By author/editor
Schindler, Emilie
By organisation
Department of Pharmaceutical Biosciences
Health Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 142 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 369 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf