We present further study of a subset of carbapenems, arising from a previously reported machine learning approach, with regard to their mouse pharmacokinetic profiling and subsequent study in a mouse model of sub-acute Mycobacterium tuberculosis infection. Pharmacokinetic metrics for such small molecules were compared to those for meropenem and biapenem, resulting in the selection of two carbapenems to be assessed for their ability to reduce M. tuberculosis bacterial loads in the lungs of infected mice. The original syntheses of these two carbapenems were optimized to provide multigram quantities of each compound. One of the two experimental carbapenems, JSF-2204, exhibited efficacy equivalent to that of meropenem, while both were inferior to rifampin. The lessons learned in this study point toward the need to further enhance the pharmacokinetic profiles of experimental carbapenems to positively impact in vivo efficacy performance.
我们对一组碳青霉烯亚类进行了进一步研究,这些碳青霉烯亚类是通过先前报道的机器学习方法得出的,涉及它们在小鼠药代动力学分析及随后在亚慢性结核分枝杆菌感染小鼠模型中的研究。这些小分子的药代动力学指标与美罗培南和比阿培南进行了比较,结果选择了两种碳青霉烯亚类,以评估它们减少感染小鼠肺部结核分枝杆菌负荷的能力。这两种碳青霉烯亚类的原始合成经过优化,以提供每种化合物的多克数量。两种实验性碳青霉烯亚类之一,JSF-2204,表现出与美罗培南相当的功效,而两种亚类均不及利福平。这项研究所学到的经验表明,需要进一步增强实验性碳青霉烯亚类的药代动力学指标,以对in vivo的功效表现产生积极影响。