Development of a web-based predictive model of nanoparticle delivery to tumors by integrating physiologically-based pharmacokinetic modeling with artificial intelligence

通过将基于生理学的药代动力学模型与人工智能相结合，开发基于网络的纳米粒子递送至肿瘤的预测模型

• 批准号: 10180594
• 负责人: Zhoumeng Lin
• 金额: $ 34.31万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10180594
• 关键词: Accounting; Address; Animal Experimentation; Animals; Antineoplastic Agents; Artificial Intelligence; Artificial nanoparticles; Bayesian Analysis; Biodistribution; Breast Osteosarcoma; Caliber; Cancer Patient; Chemicals; Clinical; Computer Models; Data; Data Set; Databases; Dependence; Development; Diagnosis; Dose; Drug Delivery Systems; Drug Formulations; Drug Kinetics; Drug Targeting; Drug or chemical Tissue Distribution; Ensure; Excretory function; Experimental Designs; Female; Formulation; Human; Knowledge; Laboratory Study; Malignant Neoplasms; Markov Chains; Markov chain Monte Carlo methodology; Medical; Metabolism; Methods; Modeling; Mus; Neural Network Simulation; Online Systems; Organ; Outcome; Output; Physiological; Process; Property; Public Health; Publications; Publishing; Research; Research Personnel; Rodent; Sex Differences; Site-Directed Mutagenesis; Statistical Methods; Subgroup; Technology; Testing; Training; Translations; Uncertainty; absorption; artificial neural network; base; cancer therapy; clinical translation; design; experimental study; graphical user interface; improved; interest; machine learning method; male; malignant breast neoplasm; mathematical methods; nano; nanoGold; nanomedicine; nanoparticle; nanoparticle delivery; novel; novel therapeutics; pharmacokinetic model; physiologically based pharmacokinetics; predictive modeling; sex; species difference; success; tool; tumor; user-friendly; web based interface; zeta potential; Accounting; Address; Animal Experimentation; Animals; Antineoplastic Agents; Artificial Intelligence; Artificial nanoparticles; Bayesian Analysis; Biodistribution; Breast Osteosarcoma; Caliber; Cancer Patient; Chemicals; Clinical; Computer Models; Data; Data Set; Databases; Dependence; Development; Diagnosis; Dose; Drug Delivery Systems; Drug Formulations; Drug Kinetics; Drug Targeting; Drug or chemical Tissue Distribution; Ensure; Excretory function; Experimental Designs; Female; Formulation; Human; Knowledge; Laboratory Study; Malignant Neoplasms; Markov Chains; Markov chain Monte Carlo methodology; Medical; Metabolism; Methods; Modeling; Mus; Neural Network Simulation; Online Systems; Organ; Outcome; Output; Physiological; Process; Property; Public Health; Publications; Publishing; Research; Research Personnel; Rodent; Sex Differences; Site-Directed Mutagenesis; Statistical Methods; Subgroup; Technology; Testing; Training; Translations; Uncertainty; absorption; artificial neural network; base; cancer therapy; clinical translation; design; experimental study; graphical user interface; improved; interest; machine learning method; male; malignant breast neoplasm; mathematical methods; nano; nanoGold; nanomedicine; nanoparticle; nanoparticle delivery; novel; novel therapeutics; pharmacokinetic model; physiologically based pharmacokinetics; predictive modeling; sex; species difference; success; tool; tumor; user-friendly; web based interface; zeta potential

PROJECT SUMMARY AND ABSTRACT Many studies have shown that nanoparticle (NP)-based drug formulations are effective in the diagnosis and treatment of cancer in lab animals, but the translation of animal results to clinical success is low. This is partly due to two fundamental challenges in this field, which are low delivery efficiency of NPs to the tumor and lack of a robust computational model to account for NP pharmacokinetic (PK) differences across species and thus allow one to predict tumor delivery and extrapolate the results from animals to humans. The objective of this proposal is to develop a robust, validated, and predictive generic physiologically based pharmacokinetic (PBPK) model for NPs in male and female tumor-bearing mice. Our hypothesis is that tissue distribution and tumor delivery of different NPs can be predicted with a generic PBPK model by training with hundreds of datasets with advanced mathematical methods, such as Bayesian-based Markov chain Monte Carlo (MCMC) simulations and/or artificial neural network (ANN) methods using species- and sex-specific physiological and NP-specific physicochemical parameters. Three specific aims were designed to achieve this objective. Aim 1: To develop a Bayesian-based robust generic PBPK model for NPs in male and female tumor-bearing mice. Aim 2: To develop a Bayesian-based robust and predictive generic PBPK model for NPs in male and female tumor-bearing mice by incorporating artificial intelligence. Aim 3: To validate and optimize the Bayesian-PBPK- ANN model with new experimental data and convert it to a web-based interface. In Aim 1, a Bayesian-MCMC method will be used to ensure model parameters are rigorously optimized and unbiased. In Aim 2, we will test the hypothesis that incorporation of artificial intelligence methods, such as ANN will significantly improve the prediction accuracy, efficiency, and applicable domain of the Bayesian-PBPK model. In Aim 3, we will conduct PK and tissue distribution experiments in tumor-bearing mice to validate our model. Recently, we published a simple PBPK model for NPs in tumor-bearing mice and a Nano-Tumor Database that contains 376 datasets. These studies make this proposal highly feasible. This project is novel because: (1) it is a new application of Bayesian-MCMC and ANN methods in cancer nanomedicine; (2) it provides a tool to compare potential sex differences in NP tumor delivery; (3) the model will be “predictive”, which makes it different from previous studies that were mostly “correlative” analysis; and (4) the model will be converted to a web-based interface to facilitate its application to a wider audience. This project is significant since it addresses a crucial problem of low delivery efficiency of cancer nanomedicines, which has been a critical barrier to progress over the last 20 years. This project has broad impacts because it will greatly improve our fundamental understanding of the key factors of NP tumor delivery and any potential sex-dependence, and will provide a tangible tool to improve the design of NPs with higher tumor delivery efficiency to accelerate clinical translation of cancer nanomedicines from animals to humans, and also reduce/eliminate animal experimentation in nanomedicine studies.

项目摘要和摘要 许多研究表明，基于纳米颗粒（NP）的药物配方在诊断和 实验室动物的癌症治疗，但动物结果向临床成功的转化很低。这部分是 由于该领域面临的两个基本挑战，它们是NP向肿瘤的递送效率较低而缺乏 强大的计算模型，以说明NP药代动力学（PK）之间的差异，因此 允许人们预测肿瘤递送，并推断动物的结果到人类。这个目的 提案是开发出强大，验证和预测性的基于生理的药代动力学（PBPK） 男性和雌性肿瘤小鼠NP的模型。我们的假设是组织分布和肿瘤 可以通过使用数百个数据集培训的通用PBPK模型来预测不同的NP 使用先进的数学方法，例如贝叶斯马尔可夫链蒙特卡洛（MCMC） 使用规格和性别特定的生理学和/或人工神经网络（ANN）方法 - NP特定的物理参数。设计了三个特定的目标来实现这一目标。目标1： 为雄性和雌性肿瘤小鼠开发基于贝叶斯的强大PBPK模型。 AIM 2：为男性和女性开发基于贝叶斯的NPS基于贝叶斯的鲁棒和预测性通用PBPK模型 通过纳入人工智能来承担肿瘤小鼠。目标3：验证和优化贝叶斯PBPK- 具有新的实验数据的ANN模型，并将其转换为基于Web的接口。在AIM 1中，贝叶斯MCMC 方法将用于确保模型参数被严格优化和公正。在AIM 2中，我们将测试 纳入艺术智能方法（例如ANN）的假设将显着改善 贝叶斯PBPK模型的预测准确性，效率和适用域。在AIM 3中，我们将进行 PK和组织分布实验在肿瘤小鼠中验证我们的模型。最近，我们出版了 简单的PBPK模型，用于含有肿瘤的小鼠和包含376个数据集的纳米肿瘤数据库。 这些研究使得这项提议非常可行。该项目是新颖的，因为：（1）它是 癌症纳米医学中的贝叶斯-MCMC和ANN方法； （2）它提供了比较潜在性别的工具 NP肿瘤递送的差异； （3）该模型将是“预测”，这使其与以前的不同 主要是“相关”分析的研究； （4）模型将转换为基于Web的接口 促进其在广泛的受众中的应用。该项目很重要，因为它解决了一个关键问题 癌症纳米药物的递送效率低，这是最近20次进步的关键障碍 年。该项目具有广泛的影响，因为它将大大提高我们对关键的基本理解 NP肿瘤递送和任何潜在的性别依赖性的因素，并将提供切实的工具来改善 具有较高肿瘤递送效率的NP的设计，以加速癌症纳米医学的临床翻译 从动物到人类，还减少/消除纳米医学研究中的动物实验。