A significant number of drugs fail during the clinical testing stage. To understand the attrition of drugs through the regulatory process, here we review and advance machine-learning (ML) and natural language-processing algorithms to investigate the importance of factors in clinical trials that are linked with failure in Phases II and III. We find that clinical trial phase transitions can be predicted with an average accuracy of 80%. Identifying these trials provides information to sponsors facing difficult decisions about whether these higher risk trials should be modified or halted. We also find common protocol characteristics across therapeutic areas that are linked to phase success, including the number of endpoints and the complexity of the eligibility criteria.

中文翻译：

---

通过机器学习进行临床试验的相变的关键指标。

在临床测试阶段大量药物失败。为了了解药物在整个监管过程中的消耗，我们在这里回顾并推进了机器学习（ML）和自然语言处理算法，以研究与II期和III期失败相关的临床试验中因素的重要性。我们发现临床试验的相变可以以80％的平均准确度进行预测。识别这些试验可为面临艰难决定的申办者提供信息，以决定是否应修改或停止这些较高风险的试验。我们还发现与阶段成功相关的跨治疗领域的通用协议特征，包括终点数量和资格标准的复杂性。