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首页> 外文期刊>Journal of chemical information and modeling >Chemoinformatic Analysis of NCI Preclinical Tumor Data: Evaluating Compound Efficacy from Mouse Xenograft Data, NCI-60 Screening Data, and Compound Descriptors
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Chemoinformatic Analysis of NCI Preclinical Tumor Data: Evaluating Compound Efficacy from Mouse Xenograft Data, NCI-60 Screening Data, and Compound Descriptors

机译:NCI临床前肿瘤数据的化学信息学分析:从小鼠异种移植数据,NCI-60筛选数据和化合物描述符评估化合物功效

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摘要

We provide a chemoinformatic examination of the NCI public human tumor xenograft data to explore relationships between small molecules, treatment modality, efficacy, and toxicity. Efficacy endpoints of tumor weight reduction (TW) and survival time increase (ST) compared to tumor bearing control mice were augmented by a toxicity measure, defined as the survival advantage of treated versus control animals (TX). These endpoints were used to define two independent therapeutic indices (TIs) as the ratio of efficacy (TW or ST) to toxicity (TX). Linear models predictive of xenograft endpoints were successfully constructed (0.67
机译:我们提供了NCI公共人类肿瘤异种移植数据的化学信息学检查,以探索小分子,治疗方式,功效和毒性之间的关系。与荷瘤对照小鼠相比,减轻肿瘤重量(TW)和延长生存时间(ST)的功效终点通过毒性指标得以提高,该毒性指标定义为治疗组与对照组动物(TX)的生存优势。这些终点被用来定义两个独立的治疗指标(TIs),即功效(TW或ST)与毒性(TX)之比。使用由NCI 60细胞试验中的治疗方式,化学信息描述符和体外细胞生长抑制变量组成的模型,成功构建了预测异种移植终点的线性模型(0.67

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