Exploring the impact of study design on unperturbed tumour growth inhibition modelling
Zinnia P Parra-Guillen (1), Victor Mangas-SanJuan (1), Iñaki F Troconiz (1), Gary Mo (2), Celine Pitou (2), Philip W Iversen (3), Johan E Wallin (2)
(1) Pharmacometrics & Systems Pharmacology Group, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain, (2) Global PK/PD & Pharmacometrics, Eli Lilly and Company, (3) Lilly Research laboratories, Eli Lilly and Company
Objectives: Xenograft and syngenic models are being increasingly used in drug development to evaluate the antitumour effects of oncological compounds. To characterise the pharmacokinetic/pharmacodynamic drug effect, the Simeoni tumour growth inhibition (TGI) model [1] is commonly used. An accurate description of the tumour dynamics in the absence of drug is a key step before characterising TGI drug effects. However, experimental designs are frequently limited both in duration and number of animals, potentially influencing parameter estimation. The objective of this study was to evaluate the impact of sampling schema and number of animals on the precision of parameter estimates, as well as exploring alternative sampling schemas.
Methods: Longitudinal tumour volume from 28 different cell lines corresponding to 10 different tumour types- was modelled using the Simeoni TGI model in NONMEM 7.3. Parameter precision of the different studies was evaluated considering (i) the standard schema of measurements every two days (Q2D), (ii) twice per week (BIW) or once per week (QW) in PFIM [2] and keeping the original number of mice per study (from 7 to 287). Additionally, parameter precision was evaluated when optimising the sampling times assuming 8 and 10 samples per study.
Results: Good precision was observed for the typical parameters (mean relative standard error, RSE, below 10%) in all evaluated scenarios. Largest imprecision was detected on the linear growth rate for those tumours with a low exponential growth rate. Moreover, precision on the interindividual variability (IIV) parameters was highly dependent on the number of experimental mice, with a RSE between 50-60% for standard experiments of 8-10 mice. The BIW sampling schema was proven adequate, showing little impact on parameter precision with a mean absolute loss <5% and a relative loss <25% for all parameters except for residual error estimate. When performing optimal design, clusters of sampling points around the initial and latest possible collection designtimes was observed, highlighting the importance of early and late measurements on parameter precision.
Conclusions: Measuring tumour volume twice per week allows for an adequate estimation of population parameters. However, precision of IIV parameters highly depended on the number of mice in the study rather than number of measurements. The results can be generalized to any tumour type cell line regardless of the dynamics of growth and variability.
References:
[1] Simeoni et al. Cancer Research (2004)
[2] Bazzoli et al. Comp Methods Programs Biomed (2010)