Optimal Design for Informative Protocols in Xenograft Tumor Growth Inhibition Experiments in Mice.
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.
INSERM, IAME, UMR 1137, F-75018, Paris, France. giulia.lestini@inserm.fr.|Université Paris Diderot, IAME, UMR 1137, Sorbonne Paris Cité, F-75018, Paris, France. giulia.lestini@inserm.fr.|Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di Pavia, Pavia, Italy. giulia.lestini@inserm.fr.
Giulia
G
Lestini
INSERM, IAME, UMR 1137, F-75018, Paris, France.|Université Paris Diderot, IAME, UMR 1137, Sorbonne Paris Cité, F-75018, Paris, France.
France
F
Mentré
Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di Pavia, Pavia, Italy.
Paolo
P
Magni
D000818:Animals; D000970:Antineoplastic Agents / Q000008:administration & dosage*; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000008:administration & dosage*; D045744:Cell Line, Tumor; D005472:Fluorouracil / Q000008:administration & dosage*; D045325:HCT116 Cells; D006801:Humans; D051379:Mice; D008819:Mice, Nude; D017239:Paclitaxel / Q000008:administration & dosage*; D047368:Tumor Burden / Q000187:drug effects* / Q000502:physiology; D023041:Xenograft Model Antitumor Assays / Q000379:methods*
D000970:Antineoplastic Agents; D017239:Paclitaxel; D005472:Fluorouracil
Fisher information matrix; oncology; optimal design; pharmacodynamic; tumor growth inhibition models