根据数据预测临床前和临床模型中的结核病药物疗效。
Predicting tuberculosis drug efficacy in preclinical and clinical models from data.
作者信息
Goh Janice J N, Patel Anu, Ngara Bernard, van Wijk Rob C, Strydom Natasha, Wang Qianwen, Van Nhi, Washington Tracy M, Nuermberger Eric L, Aldridge Bree B, Roubert Christine, Sarathy Jansy, Dartois Véronique, Savic Rada M
机构信息
Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
Department of Molecular Biology and Microbiology, Tufts University School of Medicine, and Stuart B. Levy Center for Integrated Management of Antimicrobial Resistance Boston, Boston, MA, USA.
出版信息
iScience. 2025 Jan 30;28(3):111932. doi: 10.1016/j.isci.2025.111932. eCollection 2025 Mar 21.
Multiple potency assays are used to evaluate compounds against , but a consensus on clinically relevant assays is lacking. We aimed to identify an assay signature that predicts preclinical efficacy and early clinical outcome. Thirty-one unique assays were compiled for 10 TB drugs. EC values were compared to pharmacokinetic-pharmacodynamic (PK-PD)-model-derived EC values from mice evaluated via multinomial regression. External validation of best-performing assay combinations was performed using five new TB drugs. Best-performing assay signatures for acute and subacute infections were described by assays that reproduce conditions found in macrophages and foamy macrophages and chronic infection by the caseum assay. Subsequent simulated mouse bacterial burden over time using predicted EC was within 2-fold of observations. This study helps us identify clinically relevant assays and prioritize successful drug candidates, saving resources and accelerating clinical success.
多种效价测定法用于评估化合物对……的效果，但目前缺乏关于临床相关测定法的共识。我们旨在确定一种能够预测临床前疗效和早期临床结果的测定法特征。针对10种抗结核药物编制了31种独特的测定法。将半数效应浓度（EC）值与通过多项回归评估的小鼠药代动力学-药效学（PK-PD）模型得出的EC值进行比较。使用5种新型抗结核药物对表现最佳的测定法组合进行外部验证。急性和亚急性感染的最佳表现测定法特征由能够重现巨噬细胞和泡沫巨噬细胞中条件的测定法描述，而慢性感染则由干酪样物质测定法描述。随后使用预测的半数效应浓度（EC）模拟小鼠随时间的细菌负荷，其结果在观察值的2倍以内。这项研究有助于我们确定临床相关测定法，并对成功的候选药物进行优先级排序，从而节省资源并加速临床成功。
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