Nonclinical endpoint assessment utilizing a digital vivarium cloud platform in an ovarian cancer xenograft model.

person Chibueze Dominic Nwagwu OncoSynergy, Inc., Greenwich, CT info_outline Chibueze Dominic Nwagwu, Erwin Defensor, Laura Schaevitz, Shawn Carbonell

Authors person Chibueze Dominic Nwagwu OncoSynergy, Inc., Greenwich, CT info_outline Chibueze Dominic Nwagwu, Erwin Defensor, Laura Schaevitz, Shawn Carbonell Organizations OncoSynergy, Inc., Greenwich, CT, Vium, Inc., San Mateo, CA, Vium, Inc., San Mateo, CT Abstract Disclosures Research Funding Other Background: Translation of preclinical data is highly dependent on the validity of the metrics for assessing disease progression and endpoint in mouse oncology models. A digital cloud platform would provide an objective and non-invasive technology allowing real-time assessment of several parameters including motion, breathing, and activity. Here, we hypothesize digital metrics can serve to predict endpoint in an ovarian cancer xenograft model with ascites, thereby improving analysis and interpretation of therapeutic efficacy data and improved adherence to the Three Rs. Methods: Six groups of athymic mice were intraperitoneally inoculated with increasing numbers of ES-2 ovarian cancer cells or control and housed in the Vium Digital Vivarium. Leveraging Vium’s readily accessible videographic and electronic records we retrospectively analyzed night time motion data using the ROC curve on GraphPad Prism and R programming . The following parameters were defined: motion loss post induction (MLPI) and motion loss from endpoint (MLFE). These were in turn compared to the conventional parameters (ie, number of tumor nodules, tumor weight, volume of ascites, and clinical signs) in terms of predicting disease and endpoint. Results: MLPI showed significantly high correlation with endpoint when graphed on a linear regression plot (R 2 = 0.8671; p < 0.0001), while the conventional metrics showed low correlations with endpoint: number of tumor nodules (R 2 = 0.1334; p = 0.1133); volume of ascites (R 2 = 0.2524; p = 0.0240); tumor weight (R 2 = 0.1162; p = 0.1413). We also showed that MLFE was significantly higher than the days from onset of first clinical sign to endpoint. Analysis of additional digital metrics are underway. Conclusions: The results support the improved ability of a digital platform to predict endpoint over conventional metrics, and earlier than manually-recorded clinical signs. These results are important as they not only provide an objective parameter to enhance study interpretation, but they also reduce the time to humane endpoint. Additionally, the continuously recorded digital metrics increase statistical power, which can be leveraged to potentially reduce the number of animals required per study.