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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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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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Ligand-Mediated Nucleation and Growth of Palladium Metal Nanoparticles
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理性设计更安全的无机纳米粒子通过机械建模-信息机器学习的机械建模.

Joseph Cave1,2, Anne Christiono3, Carmine Schiavone1,4

  • 1Mathematics in Medicine Program, Department of Medicine, Houston Methodist Research Institute, Houston, Texas 77030, United States.

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|June 3, 2025
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概括

一个机器学习 (ML) 框架预测了无机纳米粒子 (NP) 的毒性. 整合生理学基础的药理动力学 (PBPK) 建模可以提高对器官特异性纳米毒性的预测,帮助更安全的NP设计.

关键词:
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科学领域:

  • 纳米技术纳米技术
  • 毒理学 毒理学 毒理学
  • 计算生物学 计算生物学

背景情况:

  • 评估无机纳米粒子 (NP) 的安全性对于临床应用至关重要.
  • 现有的NP毒性评估方法耗时且资源密集.
  • 需要预测模型来简化新型纳米材料的安全性评估.

研究的目的:

  • 开发一种机器学习 (ML) 框架,用于预测无机NP的体外和体内毒性.
  • 确定影响NP毒性的关键物理化学性质和实验条件.
  • 整合基于生理学的药理动力学 (PBPK) 建模用于器官特定的体内毒性预测.

主要方法:

  • 经过训练和验证的二元分类ML模型使用精选的体外细胞毒性数据集.
  • 进行了可解释性分析,以确定NP毒性决定因素和结构毒性关系.
  • 将PBPK模型集成到ML管道中,以估计器官特异性的NP暴露,用于体内预测.

主要成果:

  • ML框架准确地预测了不同无机NP的体外NP毒性.
  • 可解释性分析揭示了控制NP诱导毒性的关键因素.
  • 基于PBPK的ML模型显示了对特定器官纳米毒性的可靠预测.

结论:

  • 开发的基于PBPK的ML框架为NP安全评估提供了一种简化方法.
  • 这种方法有助于合理设计更安全的无机NP.
  • 该框架有可能加速纳米材料的临床转化.