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

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.

项目概要和摘要 许多研究表明，基于纳米颗粒（NP）的药物制剂在诊断和治疗方面是有效的。 在实验室动物中治疗癌症，但将动物结果转化为临床成功的程度较低。 由于该领域存在两个基本挑战，即纳米粒子向肿瘤的递送效率低以及缺乏 建立一个强大的计算模型来解释不同物种之间的 NP 药代动力学 (PK) 差异，从而 允许人们预测肿瘤的传递并将结果从动物推断到人类。 建议开发一种稳健的、经过验证的、可预测的基于生理学的通用药代动力学 (PBPK) 我们的假设是，组织分布与肿瘤有关。 通过使用数百个数据集进行训练，可以使用通用 PBPK 模型来预测不同 NP 的传递 采用先进的数学方法，例如基于贝叶斯的马尔可夫链蒙特卡罗 (MCMC) 使用物种和性别特定的生理和/或生理模拟和/或人工神经网络（ANN）方法 NP 特定的物理化学参数旨在实现这一目标 1： 为雄性和雌性荷瘤小鼠中的 NP 开发基于贝叶斯的稳健通用 PBPK 模型。 目标 2：为男性和女性 NP 开发基于贝叶斯的稳健且可预测的通用 PBPK 模型 目标 3：验证和优化 Bayesian-PBPK-。 具有新实验数据的 ANN 模型并将其转换为基于 Web 的界面 在目标 1 中，贝叶斯 MCMC。 方法将用于确保模型参数经过严格优化且无偏差。在目标 2 中，我们将进行测试。 假设结合人工智能方法（例如 ANN）将显着改善 Bayesian-PBPK 模型的预测精度、效率和适用范围在目标 3 中，我们将进行。 最近，我们在荷瘤小鼠中进行了 PK 和组织分布实验来验证我们的模型。 荷瘤小鼠中 NP 的简单 PBPK 模型和包含 376 个数据集的纳米肿瘤数据库。 这些研究使得这个提议非常可行，因为：（1）它是一个新的应用。 癌症纳米医学中的贝叶斯-MCMC 和 ANN 方法（2）提供了比较潜在性别的工具； NP肿瘤递送的差异；（3）该模型将具有“预测性”，这使得它与之前的不同 主要是“相关”分析的研究；(4) 该模型将转换为基于网络的界面 促进其应用到更广泛的受众，该项目意义重大，因为它解决了一个关键问题。 癌症纳米药物的递送效率低，这一直是过去 20 年来取得进展的关键障碍 这个项目具有广泛的影响，因为它将极大地提高我们对关键的基本理解。 NP 肿瘤递送的因素和任何潜在的性别依赖性，并将提供一个切实的工具来改善 设计具有更高肿瘤递送效率的纳米粒子以加速癌症纳米药物的临床转化 从动物到人类，并减少/消除纳米医学研究中的动物实验。