Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA.
Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
Sci Rep. 2021 Mar 11;11(1):5643. doi: 10.1038/s41598-021-84827-0.
Tuberculosis (TB) is the deadliest infectious disease worldwide. The design of new treatments for TB is hindered by the large number of candidate drugs, drug combinations, dosing choices, and complex pharmaco-kinetics/dynamics (PK/PD). Here we study the interplay of these factors in designing combination therapies by linking a machine-learning model, INDIGO-MTB, which predicts in vitro drug interactions using drug transcriptomics, with a multi-scale model of drug PK/PD and pathogen-immune interactions called GranSim. We calculate an in vivo drug interaction score (iDIS) from dynamics of drug diffusion, spatial distribution, and activity within lesions against various pathogen sub-populations. The iDIS of drug regimens evaluated against non-replicating bacteria significantly correlates with efficacy metrics from clinical trials. Our approach identifies mechanisms that can amplify synergistic or mitigate antagonistic drug interactions in vivo by modulating the relative distribution of drugs. Our mechanistic framework enables efficient evaluation of in vivo drug interactions and optimization of combination therapies.
结核病（TB）是全球最致命的传染病。新的结核病治疗方法的设计受到大量候选药物、药物组合、剂量选择和复杂的药代动力学/药效学（PK/PD）的阻碍。在这里，我们通过将一个使用药物转录组学预测体外药物相互作用的机器学习模型 INDIGO-MTB 与一个名为 GranSim 的药物 PK/PD 和病原体-免疫相互作用的多尺度模型联系起来，研究这些因素在设计组合疗法中的相互作用。我们从药物扩散、病变内的空间分布和针对各种病原体亚群的活性的动力学计算体内药物相互作用评分（iDIS）。针对非复制细菌评估的药物方案的 iDIS 与临床试验中的疗效指标显著相关。我们的方法通过调节药物的相对分布，确定了可以在体内放大协同或减轻拮抗药物相互作用的机制。我们的机制框架使我们能够有效地评估体内药物相互作用并优化组合疗法。
