Abstract B9: Tumor growth rate analysis enables cost efficient design of preclinical pharmacology studies.

Abstract

Abstract Xenograft models are widely used in the oncology drug discovery and development process. However, the lack of available information regarding the xenograft models, such as growth curves, standard of care treatment results, IHC analysis of biomarkers, as well as target gene expression, mutation, and amplification, hampers the selection of suitable models for accurate evaluation of therapeutic molecules. We have created XenoBase™ that combines public cell line profiling data with our own pharmacology data on >180 xenograft models. A searching engine is also built in the database to search for models based on gene mutation, expression, amplification, as well as SOC information, types (orthotopic, subcutaneous, systemic) of the xenograft models. The XenoBase™ will enable informed decision in selecting the most relevant models for the development of targeted therapeutics. Armed with hundreds of xenograft studies available in the XenoBase™, we analyzed the tumor growth rate of each study to compare the results with traditional T/C analysis. The T/C analysis is the current standard, and a T/C value less than 0.42 is widely accepted as an indication of efficacy in evaluating a test article. However, this T/C analysis is limited in using only the data points on one day, overlooking the fact that tumor growth curves are generated over a long period of time (months) and with many days of data collection. To take advantage of the tumor growth curves, we analyzed tumor growth rate utilizing every data point, and derived growth rate based on slopes. Our analysis with the XenoBase™ data indicated that the growth rate based approach is more powerful in evaluating test articles for efficacy compared to T/C analysis or area under the curve (AUC) analysis. If adopted by the industry, this approach may save hundreds of millions of dollars spent in excess number of animals and prolonged observations that may not be necessary. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):B9. Citation Format: Sheng Guo, Jun Li, Juan Zhang, Wubin Qian, Qian Shi. Tumor growth rate analysis enables cost efficient design of preclinical pharmacology studies. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr B9.