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. Author manuscript; available in PMC: 2024 Apr 11.
Published in final edited form as: J Control Release. 2023 Jul 31;361:53–63. doi: 10.1016/j.jconrel.2023.07.040

Fig. 1.

Fig. 1.

Overview of the computational workflow to integrate machine learning and deep learning models with physiologically based pharmacokinetic (PBPK) modeling to predict delivery efficiency of nanoparticles (NPs) to the tumor site in tumor-bearing mice. (A) Step 1: Nano-Tumor Database, (B) Step 2: Development of AI-QSAR model, (C) Step 3: AI-assisted PBPK model. Abbreviations: DNN, deep neural network; RF, random forest; Adj-R2, adjusted coefficient of determination; RMSE, Root mean square error; KTRES_max, maximum uptake rate constant of tumor cells; KTRES_50, time reaching half maximum uptake rate of tumor cells; KTRES_n, Hill coefficient for the uptake of tumor cells; KTRES_rel, release rate constant of tumor cells.