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相关概念视频

Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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相关实验视频

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A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
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预测人类清除使用在模型与减少偏差的预测.

Franco Lombardo1, Jörg Bentzien2, Giuliano Berellini2

  • 1CmaxDMPK, LLC, Framingham , Massachusetts 01701, United States.

Molecular pharmaceutics
|January 29, 2024
PubMed
概括

使用随机森林模型预测人类清理是可行的,达到3.3.3的几何平均折叠误差. 模型对脏清除的化合物显示出更高的准确性,为药物开发提供了现实的评估.

科学领域:

  • 药理动力学和药物新陈代谢
  • 计算化学和化学信息学
  • 毒理学和临床前研究

背景情况:

  • 准确预测人类清除对于有效的药物开发,降低成本和动物试验至关重要.
  • 现有的in silico方法由于培训数据的结构相似性,往往难以普遍适用.
  • 有大量的人类药理动力学 (PK) 数据的数据集对于稳健的模型开发至关重要.

研究的目的:

  • 开发和验证可靠的随机森林 (RF) 模型,以使用大型PK数据集预测人类清除.
  • 通过将结构相似的化合物排除在训练套件中来评估射频模型的一般适用性.
  • 使用符合性预测评估预测性能和置信区间.

主要方法:

  • 使用1340种化合物与人体静脉注射PK数据的RF模型的开发.
  • 从训练套件中系统地去除结构类似物和类似化合物,以测试通用性.
  • 在343个化合物的准前性测试集上进行验证.
  • 合规预测的应用用于模型适用性评估和置信区间的确定.

主要成果:

  • 射频模型,即使不包括结构相似的化合物,也预测了人类清除,几何平均折叠误差 (GMFE) 为3.3.
  • 当应用分类模型时,对通过清除的化合物 (GMFE为2.3) 的预测准确性更高.
关键词:
在ADME预测预测.人类的清除.在Silico预测预测中在生物体内清除.机器学习是机器学习.预测 信心 预测信心随机的森林随机的森林

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  • 研发的in silico射频模型表现出与从人类肝细胞或临床前体内数据进行缩放相当的性能.

    • 结论:

    • 随机森林模型为预测人类清除提供了一个现实的,广泛适用的方法.
    • 从培训数据中排除结构类比,可以产生更可靠的前预测.
    • 符合性预测增强了模型适用性评估,并为预测提供了关键的置信区间.