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Supplementary file 5_GEODE: an in silico tool that translates in vitro to in vivo predictions of tuberculosis antibiotic combination efficacy.zip

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https://figshare.com/articles/dataset/Supplementary_file_5_GEODE_an_in_silico_tool_that_translates_in_vitro_to_in_vivo_predictions_of_tuberculosis_antibiotic_combination_efficacy_zip/30382537
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IntroductionTuberculosis (TB) remains the primary cause of death due to infectious disease in the world. TB, while treatable, requires an extended course of multiple antibiotics, taking 6–9 months, and many antibiotic regimens have deleterious side effects. Treatment is complicated by co-infection, emerging drug resistance, and compliance issues; accordingly, the identification of new and optimal regimens has been a recent focus. Rodent models of TB (e.g., mouse, rabbit) do not mimic some severe pathologies well, while nonhuman primate models are costly. Several computational and in vitro tools have been developed to explore drug regimen design and efficacy for TB, each providing insight into human disease dynamics. MethodsHere we briefly review existing tools and introduce a novel, integrated approach combining in vitro predictions of drug pharmacokinetics, pharmacodynamics and drug-drug interactions with a granuloma-scale computational model (GranSim). Our method captures in vivo dynamics to test how well systematic in vitro data predict granuloma-scale outcomes such as CFU burden and sterilization time. To evaluate in vitro measurements under various growth conditions and to compare to clinical and experimental datasets, we simulated five well-known regimens in our pipeline: HRZM, BPaMZ, RMZE, BPaL and HRZE. ResultsWe find that in vitro measurements of antibiotic regimen pharmacodynamics under specific growth conditions can be used to simulate virtual granulomas consistent with low-burden human and primate granulomas. DiscussionThis work provides a novel tool that can be used to quickly and efficiently evaluate drug regimens for TB.

引言 结核病(Tuberculosis,简称TB)仍是全球范围内感染性疾病致死的首要病因。结核病虽可治疗,但需采用多种抗生素的长期疗程,时长6至9个月,且多数抗生素治疗方案会产生严重不良反应。合并感染、新发耐药性以及治疗依从性问题使得治疗过程更为复杂,因此,开发新型最优治疗方案成为近期的研究热点。结核病啮齿动物模型(如小鼠、家兔)无法很好地复现部分严重病理特征,而非人灵长类动物模型则成本高昂。目前已开发出多种计算工具与体外(in vitro)实验工具,用于探究结核病的治疗方案设计与疗效,各工具均可为人类疾病的动态变化提供研究视角。 方法 本文首先简要综述现有工具,并介绍一种新型整合方法:将药物药代动力学(pharmacokinetics)、药效动力学(pharmacodynamics)与药物相互作用的体外预测结果,与肉芽肿尺度计算模型(GranSim)相结合。本方法可捕捉体内(in vivo)动态变化,用于评估系统性体外数据对肉芽肿尺度结局(如菌落形成单位(Colony-Forming Unit,CFU)负荷与灭菌时间)的预测效果。为评估不同生长条件下的体外测量结果,并与临床及实验数据集进行对比,我们在研究流程中模拟了五种知名治疗方案:HRZM、BPaMZ、RMZE、BPaL与HRZE。 结果 本研究发现,在特定生长条件下对抗生素治疗方案的药效动力学进行体外测量,可用于模拟与低负荷人类及灵长类肉芽肿相符的虚拟肉芽肿。 讨论 本研究开发了一种新型工具,可快速高效地评估结核病的治疗方案。
创建时间:
2025-10-17
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