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Systematic Modeling and Design Evaluation of Unperturbed Tumor Dynamics in Xenografts
Zinnia P Parra-Guillen1, Victor Mangas-Sanjuan1, Maria Garcia-Cremades1
1Pharmacometrics and Systems Pharmacology Research Unit, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain (Z.P.P.-G.,V.M.-S.,M.G.-C., I.F.T.); Navarra Institute for Health Research, Pamplona, Spain (Z.P.P.-G.,V.M.-S.,M.G.-C., I.F.T.); Global PK/PD & Pharmacometrics (G.M., C.P., J.E.W.) and Lilly Research Laboratories (P.W.I.), Eli Lilly and Company, Indianapolis, USA Solna, Sweden.
Abstract:
Xenograft mice are largely used to evaluate the efficacy of oncological drugs during preclinical phases of drug discovery and development. Mathematical models provide a useful tool to quantitatively characterize tumor growth dynamics and also optimize upcoming experiments. To the best of our knowledge, this is the first report where unperturbed growth of a large set of tumor cell lines (n = 28) has been systematically analyzed using a previously proposed model of nonlinear mixed effects (NLME). Exponential growth was identified as the governing mechanism in the majority of the cell lines, with constant rate values ranging from 0.0204 to 0.203 day-1 No common patterns could be observed across tumor types, highlighting the importance of combining information from different cell lines when evaluating drug activity. Overall, typical model parameters were precisely estimated using designs in which tumor size measurements were taken every 2 days. Moreover, reducing the number of measurements to twice per week, or even once per week for cell lines with low growth rates, showed little impact on parameter precision. However, a sample size of at least 50 mice is needed to accurately characterize parameter variability (i.e., relative S.E. values below 50%). This work illustrates the feasibility of systematically applying NLME models to characterize tumor growth in drug discovery and development, and constitutes a valuable source of data to optimize experimental designs by providing an a priori sampling window and minimizing the number of samples required.
Insights
This study analyzed tumor growth in 28 cell lines using nonlinear mixed-effects (NLME) models. Exponential growth was common, and NLME models can optimize preclinical drug discovery experiments.
Area of Science:
- Oncology
- Pharmacology
- Mathematical Biology
Background:
- Xenograft mice are crucial for preclinical drug testing in oncology.
- Mathematical modeling aids in understanding tumor growth dynamics and experiment optimization.
- Systematic analysis of unperturbed tumor growth across multiple cell lines is needed.
Purpose of the Study:
- To systematically analyze the unperturbed growth of 28 tumor cell lines using a nonlinear mixed-effects (NLME) model.
- To characterize tumor growth dynamics and optimize experimental designs for preclinical drug discovery.
- To assess the impact of sampling frequency and sample size on parameter estimation precision.
Main Methods:
- Application of a nonlinear mixed-effects (NLME) model to analyze tumor growth data from 28 xenograft mouse models.
- Evaluation of exponential growth as the primary mechanism for tumor progression.
- Simulation studies to determine optimal sampling schedules and required sample sizes for accurate parameter estimation.
Main Results:
- Exponential growth was the predominant growth mechanism across most cell lines, with rates varying from 0.0204 to 0.203 day⁻¹.
- No consistent growth patterns were observed across different tumor types, emphasizing the need for diverse cell line data.
- Precise parameter estimation was achieved with measurements every 2 days; reducing frequency had minimal impact on precision, but ~50 mice are needed for variability characterization.
Conclusions:
- Nonlinear mixed-effects (NLME) models are feasible for systematic tumor growth characterization in drug discovery.
- The findings provide a data-driven approach to optimize experimental designs, including sampling windows and minimizing sample sizes.
- This study offers valuable insights for improving the efficiency and accuracy of preclinical oncological drug development.
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