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

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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.