Details

Autor(en) / Beteiligte

• Nwagwu, Chibueze D.
• Defensor, Erwin
• Carbonell, Anne-Marie
• Carbonell, Shawn

Titel

Abstract A21: Digital vivarium cloud platform facilitates nonclinical endpoint assessment in an ovarian carcinoma xenograft model with ascites

Ist Teil von

• Clinical cancer research, 2020-07, Vol.26 (13_Supplement), p.A21-A21

Erscheinungsjahr

2020

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Abstract Background: Successful translation of nonclinical data is reliant upon the validity of measures for assessing disease morbidity and endpoint in mouse oncology models. Conventional means of endpoint evaluation (i.e., tumor weight, volume of ascites, or clinical signs) can be subjective and unreliable. Here we demonstrate continuous automated measurement of motion reliably predicts endpoint; is correlative with efficacy in an ovarian cancer xenograft model with ascites; and can be used to improve adherence to the three Rs, the guiding principles utilized to ensure animal welfare when conducting nonclinical research. Methods: Six groups of athymic mice were intraperitoneally inoculated with increasing numbers of ES-2 ovarian cancer cells or control (PBS) and housed in a homecage on a continuous monitoring platform. Leveraging the platform’s videographic and electronic records, we retrospectively analyzed night-time motion data. Based on these analyses, we established a Motion Threshold for noticeable decline in the motion metric. Relative to the Motion Threshold, the following parameters were defined: motion loss post induction (MLPI) and motion loss from endpoint (MLFE). These were in turn analyzed for their ability to predict endpoint in comparison to conventional parameters. Upon validation, the Motion Threshold was applied to a subsequent study to analyze its ability to predict efficacy. Results: In the first study, MLPI showed significant correlation with endpoint when graphed on a linear regression plot (R2 = 0.87; p < 0.0001), while conventional metrics showed low correlations with endpoint: number of tumor nodules (R2 = 0.1334; p = 0.1133), volume of ascites (R2 = 0.2524; p = 0.0240), tumor weight (R2 = 0.1162; p = 0.1413). Additionally, we found the MLFE predicted endpoint earlier than the first onset of clinical signs (p < 0.0001). A subsequent study was performed utilizing the Motion Threshold to analyze the efficacy of cisplatin in combination with OS2966 versus vehicle or both therapies alone. OS2966 + cisplatin significantly extended normal activity of mice (according to the Motion Threshold) compared to vehicle (p = 0.003). The Motion Threshold was also shown to correlate with standard survival curve (R2 = 0.86; p < 0.0001) and was superior to conventional parameters for determining intergroup differences. Conclusion: The results demonstrate the high sensitivity, reproducibility, and objectivity of continuous collection of the motion metric. These results are relevant as they enhance study interpretation and reduce time to humane endpoint. The use of digital metrics also increases statistical power, which can be used to potentially reduce the number of animals required for cancer research, hence facilitating faithfulness to the three Rs. Citation Format: Chibueze D. Nwagwu, Erwin Defensor, Anne-Marie Carbonell, Shawn Carbonell. Digital vivarium cloud platform facilitates nonclinical endpoint assessment in an ovarian carcinoma xenograft model with ascites [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr A21.