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

Zhoumeng Lin1,2,3,4, Wei-Chun Chou1,2,3,4, Yi-Hsien Cheng3,4

  • 1Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.

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|April 1, 2022
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Summary

Improving nanoparticle (NP) delivery to tumors is key for cancer nanomedicine. This study used AI to predict NP delivery efficiency, finding cancer type and NP properties like Zeta potential are crucial for better drug design.

Keywords:
artificial intelligencedrug deliverymachine learningnanomedicinenanotechnologyphysiologically based pharmacokinetic modeling

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Materials Science

Background:

  • Low nanoparticle (NP) delivery efficiency to tumors is a major obstacle in cancer nanomedicine.
  • Developing strategies to enhance NP tumor delivery is critical for effective cancer treatment.

Purpose of the Study:

  • To analyze the influence of NP physicochemical properties, tumor models, and cancer types on NP tumor delivery efficiency.
  • To develop a predictive model for NP tumor delivery using machine learning and artificial intelligence.

Main Methods:

  • Utilized a Nano-Tumor Database with 376 datasets from a physiologically based pharmacokinetic (PBPK) model.
  • Applied multiple machine learning and artificial intelligence methods, including deep neural networks, random forest, and support vector machines.

Main Results:

  • A deep neural network model accurately predicted NP delivery efficiency, outperforming other methods (R² up to 0.92 in training, 0.70 in testing).
  • Cancer type significantly impacted delivery efficiency predictions (19-29%).
  • Zeta potential and core material were key physicochemical properties influencing delivery efficiency.

Conclusions:

  • Developed a quantitative model to enhance the design of cancer nanomedicine for improved tumor delivery.
  • Enhanced understanding of factors contributing to low NP tumor delivery efficiency.
  • Demonstrated the integration of AI with PBPK modeling for cancer nanomedicine research.