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Zhoumeng Lin,1– 4,* Wei-Chun Chou,1– 4,* Yi-Hsien Cheng,3,4 Chunla He,5 Nancy A Monteiro-Riviere,6,7 Jim E Riviere7,8 1Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA; 2Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL, USA; 3Institute of Computational Comparative Medicine, Kansas State University, Manhattan, KS, USA; 4Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, USA; 5Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA; 6Nanotechnology Innovation Center of Kansas State, Kansas State University, Manhattan, KS, USA; 7Center for Chemical Toxicology Research and Pharmacokinetics, North Carolina State University, Raleigh, NC, USA; 8 1Data Consortium, Kansas State University, Olathe, KS, USA*These authors contributed equally to this workCorrespondence: Zhoumeng Lin, Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, 1225 Center Dr., Gainesville, FL, 32610, USA, Tel +1 352-273-6160, Email linzhoumeng@ufl.eduBackground: Low delivery efficiency of nanoparticles (NPs) to the tumor is a critical barrier in the field of cancer nanomedicine. Strategies on how to improve NP tumor delivery efficiency remain to be determined.Methods: This study analyzed the roles of NP physicochemical properties, tumor models, and cancer types in NP tumor delivery efficiency using multiple machine learning and artificial intelligence methods, using data from a recently published Nano-Tumor Database that contains 376 datasets generated from a physiologically based pharmacokinetic (PBPK) model.Results: The deep neural network model adequately predicted the delivery efficiency of different NPs to different tumors and it outperformed all other machine learning methods; including random forest, support vector machine, linear regression, and bagged model methods. The adjusted determination coefficients (R2) in the full training dataset were 0.92, 0.77, 0.77 and 0.76 for the maximum delivery efficiency (DEmax), delivery efficiency at 24 h (DE24), at 168 h (DE168), and at the last sampling time (DETlast). The corresponding R2 values in the test dataset were 0.70, 0.46, 0.33 and 0.63, respectively. Also, this study showed that cancer type was an important determinant for the deep neural network model in predicting the tumor delivery efficiency across all endpoints (19– 29%). Among all physicochemical properties, the Zeta potential and core material played a greater role than other properties, such as the type, shape, and targeting strategy.Conclusion: This study provides a quantitative model to improve the design of cancer nanomedicine with greater tumor delivery efficiency. These results help to improve our understanding of the causes of low NP tumor delivery efficiency. This study demonstrates the feasibility of integrating artificial intelligence with PBPK modeling approaches to study cancer nanomedicine.Graphical Abstract: Keywords: artificial intelligence, machine learning, physiologically based pharmacokinetic modeling, nanomedicine, drug delivery, nanotechnology |
