基于quinazoline 的 Aurora 激酶抑制剂的计算机辅助优化,以提高代谢稳定性
Song Chen1, Hongjin Zhai1, Shuhan You1
1State Key Laboratory of Anti-Infective Drug Discovery and Development, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510006, PR China.
European journal of medicinal chemistry
|January 9, 2026
概括
机器学习确定了 quinazoline Aurora 激酶抑制剂 9h 上的代谢部位. 优化的衍生品,如4q,显示出改善的代谢稳定性和强大的体内抗瘤功效.
科学领域:
- 药用化学 医学化学
- 药理学 药理学是指药理学的学科.
- 计算化学的计算化学
背景情况:
- 基于quinazoline的奥罗拉激酶抑制剂9h在体外和体内表现出显著的有效性.
- 由于代谢稳定性不佳和半衰期短,9h的发展受到限制.
研究的目的:
- 为了提高Aurora激酶抑制剂9h.的代谢稳定性和药理动力学概况.
- 为了确定新的quinazoline衍生品,改善药物类似性质,用于潜在的癌症治疗.
主要方法:
- 机器学习驱动的in silico模型被用来预测9h的代谢热点.
- 为了优化,应用了电子同位素替代和代谢阻断策略.
- 进行了体外肝脏微小体稳定性试验,大鼠的药理动力学研究,以及体内瘤生长抑制试验.
主要成果:
- 化合物4q是一种新型的quinazoline衍生物,在老鼠和人类显微体中分别增加了14倍和4.5倍的半衰期.
- 4q在老鼠中表现出有利的药理动力学,包括5.5小时的口服半衰期和58%的口服生物可用性.
- 4q在体内实现了56.7%的瘤生长抑制,保持了强大的紫外线激酶抑制活性.
结论:
- 优化的quinazoline衍生品4q克服了9h的局限性,显示了增强的代谢稳定性和有利的药理动力学特性.
- 4q在体内保持强大的抗瘤疗效,代表了进一步开发药物的有希望的候选人,以对抗奥罗拉激酶驱动的癌症.
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