Endpoint in ovarian cancer xenograft model predicted by nighttime motion metrics

Abstract

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.