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大规模预测和测试药物活性对副作用的目标
Eugen Lounkine1, Michael J Keiser, Steven Whitebread
1Novartis Institutes for Biomedical Research, Cambridge, Massachusetts 02139, USA.
Nature
|June 23, 2012
概括
预测药物的副作用是一个挑战. 一种新的计算方法确定了意想不到的药物标,证实了其一半的预测,并揭示了新的药物标和不良反应联系,以实现更安全的药物发现.
科学领域:
- 药理学 药理学 是一个学科.
- 计算化学计算化学
- 毒理学 毒理学 毒理学
背景情况:
- 使用传统的实证方法,很难识别与药物不良反应 (ADRs) 相关的意外药物标 (非标).
- 数百种蛋白质与ADR有关,药物可以与多个目标相互作用,其中一些与传统指标无关.
研究的目的:
- 通过计算预测 656 种在市场上销售的药物在 73 个意外"副作用"目标上的活性.
- 开发一种新的关联指标,以优先考虑最能解释观察到的副作用的非目标.
- 创建一个全面的药物标药物不良反应网络.
主要方法:
- 利用计算策略预测药物活性在一组定义的副作用目标上.
- 通过专有数据库和新的实验分析验证了预测.
- 开发并应用一个关联度量来将新发现的非目标与特定的ADR联系起来.
主要成果:
- 大约50%的计算预测被实验证实,亲和度从1nM到30μM不等.
- 确定了新的药物向关联,包括二甲对循环氧化酶-1 (COX-1) 的抑制作为腹痛的原因.
- 通过血小板聚合试验验验证了通过血小板聚合试验确定的非目标相互作用的临床相关性.
结论:
- 计算方法有效地预测了意外的药物标及其与药物不良反应的关联.
- 这种策略可以识别潜在的ADRs的新机制,并优先考虑非目标目标,以降低药物发现风险.
- 开发的药物向药物不良反应网络为了解和减轻毒理学负担提供了宝贵的工具.
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