Despite several therapeutics showing promise in nonclinical studies, survival from ovarian cancer remains poor. New technologies are urgently needed to optimize the translation of nonclinical studies into clinical successes. While most nonclinical settings utilize subjective measures of physiological parameters, which can hamper the accuracy of the results, this study assessed the physical activity of mice in real time using an objective, non-invasive, cloud-based, digital vivarium monitoring platform. An initial range-finding study in which varying numbers of ovarian cancer cells were inoculated in mice was conducted to characterize disease progression using digital metrics such as motion and breathing rate. Data from the range-finding study were used to establish a motion threshold (MT) that might predict terminal endpoint. Using the MT, the efficacies of cisplatin and OS2966, an anti-CD29 antibody, were assessed. Results showed that MT predicted terminal endpoint significantly earlier than traditional parameters and correlated with therapeutic efficacy. Thus, continuous motion monitoring sensitively predicts terminal endpoint in nonclinical ovarian cancer models and could be applicable for drug efficacy testing.
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Data sets generated during and/or analyzed during this study may be sought from the corresponding author under the author’s discretion. The custom code used in this study is available upon request.
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We thank Vium, Inc. for funding this study via their Next Generation Disease Model Grant.
The authors declare the following competing interests: C.D.N., W.S.C. and M.Y.J. are past employees of OncoSynergy, Inc., the company that developed and owns OS2966. A.-M.E.C. is a current Executive and Shareholder of OncoSynergy, Inc. D.A.R. is a current employee of OncoSynergy, Inc. W.S.C. is a former Director and current Shareholder of OncoSynergy, Inc. E.D. is an employee of Vium, Inc., the corporation that funded this research and designed the Vium digital vivarium monitoring platform used in this study.
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Nwagwu, C.D., Defensor, E., Jiang, M.Y. et al. Endpoint in ovarian cancer xenograft model predicted by nighttime motion metrics. Lab Anim 49, 227–232 (2020). https://doi.org/10.1038/s41684-020-0594-1