The Use of Model-Based Tumor-Size Metrics to Predict Survival

Ribba, Benjamin; Holford, Nicholas H. G.; Mentré, France

doi:10.1038/clpt.2014.111

Cited by 19 publications

(26 citation statements)

References 9 publications

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“…developing new lesions. ECOG performance status also had a strong influence on survival (Figure S3), as reported by others for other indications . Additional significant covariates for OS were the SLD at enrolment and the predicted TSR ( t ) (Figures S3), in line with what has been reported previously .…”

Section: Discussionsupporting

confidence: 86%

“…The model captured the pronounced reduction in life expectancy once new lesions appeared ( Figure S3) with median OS of 21 vs. 12 months, for patients not developing any new lesion vs. developing new lesions. ECOG performance status also had a strong influence on survival ( Figure S3), as reported by others for other indications [8,11,16,37,38]. Additional significant covariates for OS were the SLD at enrolment and the predicted TSR(t) ( Figures S3), in line with what has been reported previously [8,[11][12][13].…”

Section: Discussionsupporting

confidence: 86%

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Models for change in tumour size, appearance of new lesions and survival probability in patients with advanced epithelial ovarian cancer

Zecchin

Gueorguieva

Enas

et al. 2016

Brit J Clinical Pharma

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AIMSThe aims of this study were (i) to develop a modelling framework linking change in tumour size during treatment to survival probability in metastatic ovarian cancer; and (ii) to model the appearance of new lesions and investigate their relationship with survival and disease characteristics. METHODSData from a randomized Phase III clinical trial comparing carboplatin monotherapy to gemcitabine plus carboplatin combotherapy in 336 patients with metastatic ovarian cancer were used. A population model describing change in tumour size based on drug treatment information was established and its relationship with time to appearance of new lesions and survival were investigated with time to event models. RESULTSThe tumour size profiles were well characterized as evaluated by visual predictive checks. Metastasis in the liver at enrolment and change in tumour size up to week 12 were predictors of time to appearance of new lesions. Survival was predicted based on the patient tumour size and ECOG performance status at enrolment and on appearance of new lesions during treatment and change in tumour size up to week 12. Tumour size and survival data from a separate study were adequately predicted. CONCLUSIONSThe proposed models simulate tumour dynamics following treatment and provide a link to the probability of developing new lesions as well as to survival. The models have potential to be used for optimizing the design of late phase clinical trials in metastatic ovarian cancer based on early phase clinical study results and simulation. British Journal of Clinical Pharmacology WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• Change in tumour size (CTS) following chemotherapy has been demonstrated to be predictive of overall survival (OS) in several tumour types and its relationship with drug exposure can be quantified using a drug-disease modelling approach. A corresponding analysis is not available for metastatic ovarian cancer.• Such a modelling framework can be used to predict the outcome of a Phase III trial, based on CTS and OS observed in Phase II.• The value of including the status of non-target lesions and presence or absence of new lesions during treatment as predictors of survival has recently been investigated for metastatic renal cell carcinoma. WHAT THIS STUDY ADDS• The proposed modelling framework established a relationship between CTS, time to appearance of new lesions and OS in patients with metastatic ovarian cancer.• The time to development of new lesions was predicted with a time-to-event model including covariates indicating the location of the metastases at enrolment and the tumour size change during treatment.• OS probability was predicted using information on tumour size and ECOG status at enrolment and CTS and appearance of new lesions during treatment.

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Section: Discussionsupporting

confidence: 86%

Section: Discussionsupporting

confidence: 86%

Models for change in tumour size, appearance of new lesions and survival probability in patients with advanced epithelial ovarian cancer

Zecchin

Gueorguieva

Enas

et al. 2016

Brit J Clinical Pharma

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“…For these reasons, TTG was not tested on OS here. Instead, the tumor timecourse was identified to be the best predictor for OS, as previously suggested …”

Section: Discussionmentioning

confidence: 78%

A Pharmacometric Framework for Axitinib Exposure, Efficacy, and Safety in Metastatic Renal Cell Carcinoma Patients

Schindler

Amantea

Karlsson

et al. 2017

CPT Pharmacometrics Syst. Pharmacol.

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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. (ClinicalTrial.gov identifier NCT00569946.)

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“…In addition, such static early predictors are unlikely to predict PFS and OS outcomes in treatments that involve different timeframes for the onset of effect, such as immune modulators. Therefore, the validity of these predictors needs to be evaluated further in such cases.The use of a predictor's full-time course to dynamically predict the disease time course and therefore outcome, shown recently for gastrointestinal stromal tumors [34], gastroenteropancreatic neuroendocrine tumors [75], and metastatic prostate cancer [76], has been recommended to overcome some of these limitations and also to enable simulations of different scenarios [74]. However, as discussed in previous sections, the optimal incorporation of the course of time variant predictors into this quantitative framework requires a joint modeling exercise.…”

Section: Clinical Endpoint Modelsmentioning

confidence: 99%

“…Most reported studies used variables based on predefined time points as predictors, such as the change in tumor size after 6-8 weeks of treatment or the time to tumor growth. However, such predictors have some limitations in their ability to accurately predict outcome, as previously discussed by others [36,74]. In addition, such static early predictors are unlikely to predict PFS and OS outcomes in treatments that involve different timeframes for the onset of effect, such as immune modulators.…”

Section: Clinical Endpoint Modelsmentioning

confidence: 99%

Bringing Model-Based Prediction to Oncology Clinical Practice: A Review of Pharmacometrics Principles and Applications

et al. 2015

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Despite much investment and progress, oncology is still an area with significant unmet medical needs, with new therapies and more effective use of current therapies needed. The emergent field of pharmacometrics combines principles from pharmacology (pharmacokinetics [PK] and pharmacodynamics [PD]), statistics, and computational modeling to support drug development and optimize the use of already marketed drugs. Although it has gained a role within drug development, its use in clinical practice remains scarce. The aim of the present study was to review the principal pharmacometric concepts and provide some examples of its use in oncology. Integrated population PK/PD/disease progression models as part of the pharmacometrics platform provide a powerful tool to predict outcomes so that the right dose can be given to the right patient to maximize drug efficacy and reduce drug toxicity. Population models often can be developed with routinely collected medical record data; therefore, we encourage the application of such models in the clinical setting by generating close collaborations between physicians and pharmacometricians. The Oncologist 2016;21:220-232Implications for Practice: The present review details how the emerging field of pharmacometrics can integrate medical record data with predictive pharmacological and statistical models of drug response to optimize and individualize therapies. In order to make this routine practice in the clinic, greater awareness of the potential benefits of the field is required among clinicians, together with closer collaboration between pharmacometricians and clinicians to ensure the requisite data are collected in a suitable format for pharmacometrics analysis.

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The Use of Model-Based Tumor-Size Metrics to Predict Survival

Cited by 19 publications

References 9 publications

Models for change in tumour size, appearance of new lesions and survival probability in patients with advanced epithelial ovarian cancer

Models for change in tumour size, appearance of new lesions and survival probability in patients with advanced epithelial ovarian cancer

A Pharmacometric Framework for Axitinib Exposure, Efficacy, and Safety in Metastatic Renal Cell Carcinoma Patients

Bringing Model-Based Prediction to Oncology Clinical Practice: A Review of Pharmacometrics Principles and Applications

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