Summary of drug release modeling approaches and key findings.
| Model/approach | Research highlights | Performance metrics | Implications | References |
|---|---|---|---|---|
| Korsmeyer-Peppas, Weibull, and others | The 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, Polynomials | The Hyperbolic Tangent Function model best fit for poly(lactic-co-glycolic acid) (PLGA) nanoparticle release kinetics. | Best fit for complex datasets | Provides 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 implants | Enables precise design of implantable drug delivery systems. | [92] |
| Zero-order, first-order, Korsmeyer-Peppas | The Korsmeyer-Peppas model is widely used for mass transfer mechanisms. | Foundational for empirical modeling | Essential for initial screening and formulation development. | [67] |
AB: Conceptualization, Data curation, Investigation, Visualization, Writing—original draft. YA: Resources, Methodology, Software, Validation, Writing—review & editing. TKB: Conceptualization, Software, Supervision, Writing—original draft, Writing—review & editing. MTI: Conceptualization, Supervision, Project administration, Writing—review & editing. All authors read and approved the submitted version.
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