Polymeric Nanoparticles for Controlled Release: Design-of-Experiments and Predictive Kinetic Modeling

PLGA nanoparticles; Design of Experiments; controlled release; ibuprofen; encapsulation efficiency; Higuchi; Korsmeyer–Peppas; kinetic modeling

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Dr. Ahmed Hameed AlSaeedi, Lecturer,
Department of Pharmaceutics, College of
Pharmacy, University of Hilla, Hilla, Iraq.
ahmed.alsaeedi@outlook.de,
https://orcid.org/0009-0001-9539-393X
Ahmad A. E. Alezzy,
Department of Pharmaceutics, College of
Pharmacy, Tikrit University, Tikrit, Iraq.
ahmad.eltayeeb@gmail.com ,
https://orcid.org/0009-0004-3930-288X

European Journal of Prosthodontics and Restorative Dentistry (2026) 34(4s),506-511

Polymeric Nanoparticles for
Controlled Release: Design-ofExperiments and Predictive
Kinetic Modeling

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Background: Poly(lactic-co-glycolic acid) (PLGA) nanoparticles are a leading platform in controlled drug delivery due to biocompatibility, biodegradability, and tunable properties (3,4). Yet, optimizing formulation variables to achieve precise release profiles is challenging because parameters interact and shift multiple CQAs simultaneously (7–10). Objective: To develop, optimize, and characterize ibuprofen-loaded PLGA nanoparticles using Design of Experiments (DoE), and to describe in-vitro release behavior and comparatively evaluate kinetic models. Methods: Nanoparticles were prepared by oil-in-water (O/W) emulsification– solvent evaporation (19). A 3³ full factorial DoE varied polymer concentration (1–3% w/v), drug loading (5–15% w/w), and sonication time (1–5 min). Responses were particle size, PDI, and encapsulation efficiency (EE%). The optimized formulation underwent in-vitro release in PBS (pH 7.4) and was fitted to zero-order, first-order, Higuchi, and Korsmeyer–Peppas models (7,28). Results: The optimized setting within the design space (1% polymer, 5% drug loading, 5 min sonication) produced 116 nm nanoparticles (PDI 0.17, EE 92%). Korsmeyer–Peppas fitting restricted to the first 60% of release (0–48 h) yielded n = 0.33 (R² = 0.92), consistent with diffusion-dominant transport over the fitted region. Conclusion: DoE coupled with transparent kinetic modeling provides a reproducible framework for tuning PLGA nanoparticle release, reducing trialand-error and supporting formulation design decisions in early development.

Received-23-05-2026 Revised-25-06-2026 Accepted-29-06-2026

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