通过基于结构和基于基的建模,识别具有有利功效和药理动学的抗结核药物
bioRxiv : the preprint server for biology
|February 20, 2025
概括
研究人员开发了一种新的计算管道,以确定治疗Mycobacterium结核病感染的潜在候选药物. 这种方法成功提名了93种不同的抑制剂,其中12种显示出有希望的抗菌疗效和药物动力学特征.
科学领域:
- 药用化学 医学化学
- 计算机化药物发现技术
- 传染性疾病 传染性疾病
背景情况:
- 对结核病 (TB) 的药物发现在同时优化多个分子性质方面面临挑战.
- 识别新疗法需要高效的命中发现管道,考虑有效性和药物动力学概况.
研究的目的:
- 开发和应用基于混合结构和配体的计算管道,用于识别β-ketoacyl合成酶KasA的新型抑制剂.
- 解决在成功发现阶段针对Mycobacterium结核病感染开发新疗法至关重要的多个标准.
主要方法:
- 开发了一种混合管道,结合了基于结构和配体的方法.
- 贝叶斯优化引导的对接和机器学习组合模型被用于化合物提名.
- 模型预测了体外抗菌疗效 (最小抑制度,MIC) 和小鼠的药理动力学 (PK) 血暴露.
主要成果:
- 该管道应用于Enamine HTS库 (210万个分子),选择了93种不同的化合物.
- 实验验证表明成功率很高:PK为41%,MIC为19%.
- 十二种化合物符合MIC和PK的命中类标准,作为药物发现的有希望的起点.
结论:
- 开发的计算管道对于提名具有理想抗菌和药物动力学特性的候选药物是有效的.
- 这种方法加速了新型抗结核治疗药物的成功发现阶段.
- 这些已识别的药物化合物为进一步开发针对Mycobacterium tuberculosis的药物开发计划提供了坚实的基础.
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