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Updated: Mar 19, 2026
Development and Maintenance of a Preclinical Patient Derived Tumor Xenograft Model for the Investigation of Novel Anti-Cancer Therapies
Published on: September 30, 2016
Optimal Design for Informative Protocols in Xenograft Tumor Growth Inhibition Experiments in Mice
Giulia Lestini1,2,3, France Mentré4,5, Paolo Magni6
1INSERM, IAME, UMR 1137, F-75018, Paris, France. giulia.lestini@inserm.fr.
Abstract:
Tumor growth inhibition (TGI) models are increasingly used during preclinical drug development in oncology for the in vivo evaluation of antitumor effect. Tumor sizes are measured in xenografted mice, often only during and shortly after treatment, thus preventing correct identification of some TGI model parameters. Our aims were (i) to evaluate the importance of including measurements during tumor regrowth and (ii) to investigate the proportions of mice included in each arm. For these purposes, optimal design theory based on the Fisher information matrix implemented in PFIM4.0 was applied. Published xenograft experiments, involving different drugs, schedules, and cell lines, were used to help optimize experimental settings and parameters using the Simeoni TGI model. For each experiment, a two-arm design, i.e., control versus treatment, was optimized with or without the constraint of not sampling during tumor regrowth, i.e., "short" and "long" studies, respectively. In long studies, measurements could be taken up to 6 g of tumor weight, whereas in short studies the experiment was stopped 3 days after the end of treatment. Predicted relative standard errors were smaller in long studies than in corresponding short studies. Some optimal measurement times were located in the regrowth phase, highlighting the importance of continuing the experiment after the end of treatment. In the four-arm designs, the results showed that the proportions of control and treated mice can differ. To conclude, making measurements during tumor regrowth should become a general rule for informative preclinical studies in oncology, especially when a delayed drug effect is suspected.
Insights
Including tumor regrowth measurements in preclinical oncology studies improves antitumor effect evaluation. Continuing tumor growth inhibition (TGI) model experiments beyond treatment ensures more accurate parameter identification and reliable drug development.
Area of Science:
- Oncology
- Preclinical Drug Development
- Pharmacometrics
Background:
- Tumor growth inhibition (TGI) models are crucial for evaluating antitumor effects in preclinical oncology.
- Current TGI models often limit tumor size measurements to the treatment period, potentially hindering accurate parameter estimation.
- This limitation can impact the precise evaluation of drug efficacy and TGI model parameters.
Purpose of the Study:
- To assess the significance of incorporating tumor regrowth measurements into TGI models.
- To investigate optimal allocation proportions for mice in different experimental arms.
- To enhance the accuracy and informativeness of preclinical oncology studies.
Main Methods:
- Optimal design theory, utilizing the Fisher information matrix (PFIM 4.0), was applied.
- Published xenograft experiments with diverse drugs, schedules, and cell lines were analyzed.
- Two-arm (control vs. treatment) and four-arm designs were optimized with and without sampling during tumor regrowth ('short' vs. 'long' studies).
Main Results:
- Longer studies, including measurements during tumor regrowth up to 6g tumor weight, yielded smaller predicted relative standard errors compared to short studies.
- Optimal measurement times were identified within the tumor regrowth phase, underscoring the value of post-treatment data collection.
- Four-arm designs indicated that the proportions of control and treated mice can be varied for optimization.
Conclusions:
- Measurements during tumor regrowth are essential for informative preclinical oncology studies.
- Continuing experiments beyond the treatment phase is critical for accurate TGI model parameter identification.
- This approach is particularly important when delayed drug effects are suspected, leading to more robust drug development.
