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.

项目总结与摘要

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.