From:  Mechanisms and kinetics of drug release from lignin-based hydrogels: a review

 Summary of drug release modeling approaches and key findings.

Model/approachResearch highlightsPerformance metricsImplicationsReferences
Korsmeyer-Peppas, Weibull, and othersThe Korsmeyer-Peppas model is the most effective for lipid-based nanoparticles (NLCs and liposomes).Adjusted R2: 0.95 (NLCs), 0.93 (liposomes)Highlights the reliability of conventional models for lipid-based systems.[89]
Decision tree regression (DTR), Quadratic Polynomial Regression (QPR), and Passive-Aggressive Regression (PAR)DTR outperformed QPR and PAR in predictive accuracy for drug release.R2: 0.99887 (DTR), 0.95382 (QPR), 0.94652 (PAR)Demonstrates the potential of machine learning for precise drug release modeling.[90]
Hyperbolic Tangent Function, Korsmeyer-Peppas, Weibull, PolynomialsThe Hyperbolic Tangent Function model best fit for poly(lactic-co-glycolic acid) (PLGA) nanoparticle release kinetics.Best fit for complex datasetsProvides a robust framework for understanding non-linear release in polymeric systems.[91]
Mechanistic modeling (phase inversion, hydrolysis)Non-uniform drug distribution and localized degradation significantly influence release profiles.High predictive accuracy for implantsEnables precise design of implantable drug delivery systems.[92]
Zero-order, first-order, Korsmeyer-PeppasThe Korsmeyer-Peppas model is widely used for mass transfer mechanisms.Foundational for empirical modelingEssential for initial screening and formulation development.[67]