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: 10640223
• 负责人: Zhoumeng Lin
• 金额: $ 34.87万
• 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/10640223
• 关键词: Acceleration; Accounting; Address; Animal Experimentation; Animals; Antineoplastic Agents; Artificial Intelligence; Artificial nanoparticles; Bayesian Analysis; Biodistribution; Breast Osteosarcoma; Cancer Patient; Chemicals; Clinical; Computer Models; Data; Data Set; Databases; Dependence; Development; Diagnosis; Diameter; 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; Parameter Estimation; 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 intelligence method; artificial neural network; 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.

项目总结和摘要 许多研究已经表明，基于纳米颗粒（NP）的药物制剂在诊断和治疗中是有效的。 在实验室动物中治疗癌症，但动物结果转化为临床成功率很低。这部分是 由于该领域中的两个基本挑战，即纳米颗粒向肿瘤的递送效率低和缺乏纳米颗粒的生物相容性。 一个稳健的计算模型，以解释不同物种之间的NP药代动力学（PK）差异， 使人们能够预测肿瘤的转移，并将结果从动物推断到人类。的目的 建议开发一种稳健的、经验证的和预测性的通用生理药代动力学（PBPK） 雄性和雌性荷瘤小鼠中的NP模型。我们的假设是组织分布和肿瘤 通过用数百个数据集进行训练，可以用通用PBPK模型预测不同NP的递送 使用先进的数学方法，如基于贝叶斯的马尔可夫链蒙特卡罗（MCMC） 模拟和/或人工神经网络（ANN）方法，使用物种和性别特异性生理和 NP特定理化参数。为实现这一目标，制定了三个具体目标。目标1： 在雄性和雌性荷瘤小鼠中开发用于NP的基于贝叶斯的稳健通用PBPK模型。 目的2：建立一个基于贝叶斯的、鲁棒的、可预测的男性和女性NP的通用PBPK模型 移植到荷瘤小鼠身上。目的3：验证和优化贝叶斯-PBPK- 人工神经网络模型与新的实验数据，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严格优化和无偏。在目标2中，我们将测试 假设人工智能方法，如人工神经网络的结合将显着改善 预测精度、效率和适用范围。在目标3中，我们将 在荷瘤小鼠中进行PK和组织分布实验以验证我们的模型。最近，我们发布了一份 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。 这些研究使这一建议具有很高的可行性。该项目的新颖之处在于：（1）它是一种新的应用， Bayesian-MCMC和ANN方法在癌症纳米医学中的应用;（2）它提供了一种比较潜在性别的工具 NP肿瘤递送的差异;（3）该模型将是“预测性的”，这使得它不同于以前的模型。 研究，主要是“相关”的分析;（4）该模型将被转换为一个基于网络的界面， 使其应用于更广泛的受众。这个项目意义重大，因为它解决了一个关键问题， 癌症纳米药物的低递送效率，这是过去20年来进展的关键障碍 年这个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肿瘤递送的因素和任何潜在的性别依赖性，并将提供一个有形的工具，以改善 设计具有更高肿瘤递送效率的纳米颗粒，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。

项目成果

Pharmacokinetics and tumor delivery of nanoparticles.

• DOI: 10.1016/j.jddst.2023.104404
• 发表时间: 2023-04
• 期刊: Journal of drug delivery science and technology
• 影响因子: 5
• 作者: L. Yuan;Qiran Chen;J. Riviere;Zhoumeng Lin

Impact of protein coronas on nanoparticle interactions with tissues and targeted delivery.

• DOI: 10.1016/j.copbio.2023.103046
• 发表时间: 2023-12
• 期刊: Current opinion in biotechnology
• 影响因子: 7.7
• 作者: Wei-Chun Chou;Zhoumeng Lin

Predicting Nanoparticle Delivery to Tumors Using Machine Learning and Artificial Intelligence Approaches.

• DOI: 10.2147/ijn.s344208
• 发表时间: 2022
• 期刊: International journal of nanomedicine
• 影响因子: 8
• 作者: Lin Z;Chou WC;Cheng YH;He C;Monteiro-Riviere NA;Riviere JE
• 通讯作者: Riviere JE

Development of a multi-route physiologically based pharmacokinetic (PBPK) model for nanomaterials: a comparison between a traditional versus a new route-specific approach using gold nanoparticles in rats.

• DOI: 10.1186/s12989-022-00489-4
• 发表时间: 2022-07-08
• 期刊: PARTICLE AND FIBRE TOXICOLOGY
• 影响因子: 10
• 作者: Chou, Wei-Chun;Cheng, Yi-Hsien;Riviere, Jim E.;Monteiro-Riviere, Nancy A.;Kreyling, Wolfgang G.;Lin, Zhoumeng
• 通讯作者: Lin, Zhoumeng

Integration of In Vitro and In Vivo Models to Predict Cellular and Tissue Dosimetry of Nanomaterials Using Physiologically Based Pharmacokinetic Modeling.

• DOI: 10.1021/acsnano.2c07312
• 发表时间: 2022-12-27
• 期刊: ACS NANO
• 影响因子: 17.1
• 作者: Lin, Zhoumeng;Aryal, Santosh;Cheng, Yi-Hsien;Gesquiere, Andre J.
• 通讯作者: Gesquiere, Andre J.