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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

Table 1:

Overview of variables and their symbols used in the machine learning models

Variable Unit Symbol Levels or Ranges
Nanoparticle’s properties
 Type of nanoparticles - Type Inorganic; Organic; Hybrid
 Core materials of nanoparticles - MAT Gold, Dendrimers, Liposomes, Polymeric, Hydrogels, Other Organic Material, Other Inorganic Material
 Shape of nanoparticles - Shape Spherical, Rod, Plate, Others
 Hydrodynamic diameter nm HD [5, 456]
 Zeta potential mV ZP [0, 274]
 Charge - Charge Positive; Negative; Neutral
Tumor therapy strategies
 Targeting strategy - TS Passive, Active
 Tumor model - TM Allograft Heterotopic, Allograft Orthotopic, Xenograft Heterotopic, Xenograft Orthotopic
 Cancer type - CT Brain, Breast, Cervix, Colon, Liver, Lung, Ovary, Pancreas, Prostate, Skin
 Tumor weight g TW [0.02, 5.09]
 Tumor size cm TSiz [0.02, 1.8]
Dosing regimen
 Dose mg/kg Dose [0.001, 1220]
 Body weight g BW [16, 35]
 Administrated route - AR IV
PBPK model parameters
  Release rate constant of tumor cells 1/h KTRES_rel [0.0001, 14]
  Maximum uptake rate constant of tumor cells 1/h KTRES_max [0.001, 25]
  Hill coefficient of tumor cells - KTRES_n [0.01, 10]
  Time reaching half maximum uptake rate of tumor cells h KTRES_50 [0.00001, 180]
Tumor delivery efficiency
 DE at 24 hours %ID DE24 [0.008, 50]
 DE at 168 hours %ID DE168 [0.003, 23.8]
 Maximum DE %ID DEmax [0.008, 56.9]

Abbreviations: DE, delivery efficiency; ID, injected dose; NPs, nanoparticles; PBPK, Physiologically based pharmacokinetic.