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.

尽管有几种疗法在非临床研究中显示出希望，但卵巢癌的存活率仍然很低。迫切需要新技术来优化非临床研究到临床成功的转化。尽管大多数非临床设置都利用生理参数的主观测量方法，这可能会影响结果的准确性，但本研究使用客观的，无创的，基于云的数字饲养室监测平台实时评估了小鼠的身体活动。进行了一项初步的范围寻找研究，在该研究中，使用数字指标（例如运动和呼吸频率）在小鼠中接种了不同数量的卵巢癌细胞，以表征疾病的进展。测距研究的数据用于建立可能预测终端终点的运动阈值（MT）。使用MT，评估了顺铂和抗CD29抗体OS2966的疗效。结果表明，MT预测终点终点显着早于传统参数，并且与治疗功效相关。因此，连续运动监测可以灵敏地预测非临床卵巢癌模型的终末终点，并可用于药物功效测试。