用数字测量和生物标志物提高心理药理学临床试验成功率:未来的方法
Jacob E Reiter1, Stefanie Nickels2, Benjamin W Nelson2,3
1Verily Life Sciences LLC, South San Francisco, CA, USA. jreitere@gmail.com.
NPP - digital psychiatry and neuroscience
|December 8, 2025
概括
数字测量和数字生物标记 (DBM) 可以通过解决诊断问题,主观终点和高安慰剂率来改善精神病药物试验. 这些客观措施提供了更敏感的结果和个性化的治疗方法.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 数字健康数字健康
背景情况:
- 精神病学临床试验的成功率很低,阻碍了精神药物药物开发.
- 主要挑战包括诊断异质性,主观终点和高安慰剂反应率.
研究的目的:
- 探索数字测量和数字生物标志物 (DBM) 如何克服精神病学临床试验中的重大障碍.
- 识别DBM以改善患者分层,客观结果测量和安慰剂反应预测.
主要方法:
- 该研究回顾了DBM在精神病学研究中解决异质性,主观性和安慰剂效应的潜力.
- 专注于DBMs用于识别治疗反应亚型,并提供客观的生理和行为数据.
主要成果:
- DBMs可以为精神病亚型提供有针对性的药物开发.
- 与健康的有意义方面 (MAH) 相关的目标DBM可以作为敏感的,监管所接受的终点.
- 对安慰剂反应的预测性DBM可以增强丰富策略并减少安慰剂效应.
结论:
- DBMs提供了一种有希望的方法来提高精神病药物试验的成功率.
- 工业,学术界和监管机构之间的合作对于成功实施DBM至关重要.
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