Population pharmacokinetic–pharmacodynamic modelling in oncology: a tool for predicting clinical response

Bender, Brendan C.; Schindler, Emilie; Friberg, Lena E.

doi:10.1111/bcp.12258

Cited by 88 publications

(104 citation statements)

References 65 publications

Supporting

Mentioning

101

Contrasting

Order By: Relevance

“…Utilizing the complete time‐course curve for tumor size was found superior to using single dimension metrics such as baseline tumor size, rate of tumor shrinkage, time to regrowth, or best percent change in tumor size. Historically, the majority of oncology PKPD modeling efforts of OS have been focusing on linking survival to a single dimension tumor size metric 30, 31. It can be argued that looking at single dimension metrics ignores information, and may introduce bias as well as limit extrapolation of data 32, 33.…”

Section: Discussionmentioning

confidence: 99%

Exposure-Response Analysis of Necitumumab Efficacy in Squamous Non-Small Cell Lung Cancer Patients

Cosson

Wallin³

2017

CPT Pharmacometrics Syst. Pharmacol.

View full text Add to dashboard Cite

We sought to describe the exposure–response relationship of necitumumab efficacy in squamous non‐small cell lung cancer patients and evaluate intrinsic and extrinsic patient descriptors that may guide dosing. SQUIRE was a phase III study comparing necitumumab in combination with gemcitabine and cisplatin vs. gemcitabine and cisplatin alone in 1,014 patients. An integrated model for tumor size dynamics and overall survival was developed, where reduction in tumor size results in a decrease in survival hazard. The change in tumor size was characterized using linear growth and first‐order shrinkage. Overall survival was described using a combination of a Weibull function and Gompertz function for the hazard, with dynamic tumor size being a predictor for the hazard. Although body weight resulted in higher clearance and lower exposure, simulations showed that an 800 mg flat dose provided optimal response regardless of body weight.

show abstract

Section: Discussionmentioning

confidence: 99%

Exposure-Response Analysis of Necitumumab Efficacy in Squamous Non-Small Cell Lung Cancer Patients

Cosson

Wallin³

2017

CPT Pharmacometrics Syst. Pharmacol.

View full text Add to dashboard Cite

show abstract

“…Tumor growth and inhibition (TGI) models have been used to leverage data on early tumor size dynamics with the aim of optimizing the design of late‐phase trials 4, 5. Several models have been published that describe the time course of tumor size in RCC.…”

mentioning

confidence: 99%

Preclinical Modeling of Tumor Growth and Angiogenesis Inhibition to Describe Pazopanib Clinical Effects in Renal Cell Carcinoma

Ouerdani

Struemper

Suttle

et al. 2015

CPT Pharmacometrics Syst. Pharmacol.

View full text Add to dashboard Cite

The objective was to leverage tumor size data from preclinical experiments to propose a model of tumor growth and angiogenesis inhibition for the analysis of pazopanib efficacy in renal cell carcinoma (RCC) patients. We analyzed tumor data in mice with RCC CAKI‐2 cell line treated with pazopanib. Clinical tumor size data obtained in a subset of patients with RCC were also analyzed. A model accounting for the processes of tumor growth, angiogenesis, and drug effect was developed. The final tumor model was composed of two variables: the tumor and its vasculature. Our results show that, both in mice and in humans, pazopanib exhibits a dual mechanism of action, and parameter estimation values highlight the inherent difference between mice and humans on the time scale of tumor size response. We developed a semimechanistic tumor growth inhibition model that takes into account tumor angiogenesis in order to describe the effects of pazopanib in mice. Analyzing rich preclinical data with a semimechanistic model may be a relevant approach to facilitate the description of sparse clinical longitudinal tumor size data and to provide insights for the understanding of the drug mechanisms of action in patients.

show abstract

“…This allows estimation of population parameters that describe the average response and a measure of variability in the data. The importance of using this approach during drug development has been widely discussed in the literature; it is particularly attractive for unbalanced and sparse data 42, 43, 44…”

Section: Methodsmentioning

confidence: 99%

Advanced Methods for Dose and Regimen Finding During Drug Development: Summary of the EMA/EFPIA Workshop on Dose Finding (London 4–5 December 2014)

Musuamba

Manolis²,

Holford

et al. 2017

CPT Pharmacom & Syst Pharma

View full text Add to dashboard Cite

Inadequate dose selection for confirmatory trials is currently still one of the most challenging issues in drug development, as illustrated by high rates of late‐stage attritions in clinical development and postmarketing commitments required by regulatory institutions. In an effort to shift the current paradigm in dose and regimen selection and highlight the availability and usefulness of well‐established and regulatory‐acceptable methods, the European Medicines Agency (EMA) in collaboration with the European Federation of Pharmaceutical Industries Association (EFPIA) hosted a multistakeholder workshop on dose finding (London 4–5 December 2014). Some methodologies that could constitute a toolkit for drug developers and regulators were presented. These methods are described in the present report: they include five advanced methods for data analysis (empirical regression models, pharmacometrics models, quantitative systems pharmacology models, MCP‐Mod, and model averaging) and three methods for study design optimization (Fisher information matrix (FIM)‐based methods, clinical trial simulations, and adaptive studies). Pairwise comparisons were also discussed during the workshop; however, mostly for historical reasons. This paper discusses the added value and limitations of these methods as well as challenges for their implementation. Some applications in different therapeutic areas are also summarized, in line with the discussions at the workshop. There was agreement at the workshop on the fact that selection of dose for phase III is an estimation problem and should not be addressed via hypothesis testing. Dose selection for phase III trials should be informed by well‐designed dose‐finding studies; however, the specific choice of method(s) will depend on several aspects and it is not possible to recommend a generalized decision tree. There are many valuable methods available, the methods are not mutually exclusive, and they should be used in conjunction to ensure a scientifically rigorous understanding of the dosing rationale.

show abstract

Population pharmacokinetic–pharmacodynamic modelling in oncology: a tool for predicting clinical response

Cited by 88 publications

References 65 publications

Exposure-Response Analysis of Necitumumab Efficacy in Squamous Non-Small Cell Lung Cancer Patients

Exposure-Response Analysis of Necitumumab Efficacy in Squamous Non-Small Cell Lung Cancer Patients

Preclinical Modeling of Tumor Growth and Angiogenesis Inhibition to Describe Pazopanib Clinical Effects in Renal Cell Carcinoma

Advanced Methods for Dose and Regimen Finding During Drug Development: Summary of the EMA/EFPIA Workshop on Dose Finding (London 4–5 December 2014)

Contact Info

Product

Resources

About