使用二次智能TM预测非目标受体相互作用的临床结果
Will S Redfern1, Chris E Pollard1, Mark Holbrook1
1Certara Predictive Technologies, Certara UK Limited, Level 2-Acero, 1 Concourse Way, Sheffield S1 2BJ, United Kingdom.
Journal of pharmacological and toxicological methods
|November 22, 2024
概括
现在可以从非目标活动中预测药物的不良影响. 一个新的定量框架将药物度与受体结合进行比较,从意见转向基于证据的风险评估,以实现更安全的药物开发.
科学领域:
- 药理学和药物发现
- 毒理学和风险评估
- 计算化学计算化学
背景情况:
- 意外药物相互作用 (非目标活性) 的不良影响在药物开发中构成了重大挑战.
- 目前对非目标效应的风险评估往往是主观的,缺乏标准化的定量方法.
- 了解药物度和受体占用之间的关系对于预测治疗疗效和不良事件至关重要.
研究的目的:
- 开发一个定量框架来预测药物目标外活动引起的不良影响.
- 建立一个一致的,基于证据的方法来评估与二次药理学相关的风险.
- 为了能够在不同的受体,化合物和评估器中进行可靠的风险评估.
主要方法:
- 开发了一种定量方法,将测试化合物的预期血度与其非目标活性进行比较.
- 这种比较与针对特定受体的参考药物进行了基准比较,以确定治疗疗效.
- 对30多种药物在100个受体 (172个调制) 的数据进行了精选和评估,使用未结合的血度与Ki比作为受体占用量的替代品.
主要成果:
- 建立了一个定量框架,以对非目标相互作用进行一致的风险评估.
- 该方法将二次药理学评估从基于意见的方法转变为基于证据的方法.
- 证明了α1A-腺素受体对抗性的原理,将其与姿势低血压联系起来.
结论:
- 开发的定量框架提供了一种可靠的方法来预测非目标药物活动的不良影响.
- 这种基于证据的方法提高了药物安全性评估的一致性和可靠性.
- 该方法代表了二级药理学领域的重大进步.
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