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Updated: Mar 29, 2026
Multi-Scale Modification of Metallic Implants With Pore Gradients, Polyelectrolytes and Their Indirect Monitoring In vivo
Published on: July 1, 2013
PLGA-Based In Situ-Forming Implants, a Quality by Design Perspective.
Nayelli Campos-Morales1, Luz Graciela Cervantes-Pérez2, Alicia Sánchez-Mendoza2
1Ciencias Farmacéuticas, Universidad Autónoma Metropolitana, Unidad Xochimilco, Calzada del Hueso 1100, Colonia Villa Quietud, Alcaldía Coyoacán, Mexico City C.P. 04960, Mexico.
Poly(lactic-co-glycolic acid) (PLGA) in situ-forming implants (ISFIs) show promise for drug delivery. Quality by Design (QbD) principles can improve formulation predictability and clinical translation by prioritizing critical material attributes.
Area of Science:
- Biomaterials Science
- Pharmaceutical Technology
- Drug Delivery Systems
Background:
- In situ-forming implants (ISFIs) based on poly(lactic-co-glycolic acid) (PLGA) are advanced parenteral drug delivery systems.
- PLGA-based ISFIs offer minimally invasive administration but face challenges in formulation complexity, variability, and release prediction.
- Existing reviews lack a comprehensive Quality by Design (QbD) approach focused on risk prioritization for ISFI development.
Purpose of the Study:
- To examine the application of QbD principles to solvent-exchange PLGA-based ISFIs.
- To identify critical material attributes (CMAs) influencing implant formation, burst release, and long-term drug release.
- To provide a structured perspective for rational formulation design and improved clinical translation.
Main Methods:
- Systematic review of QbD principles applied to PLGA-based ISFIs.
- Identification and risk-based prioritization of CMAs affecting ISFI performance.
- Discussion of design of experiments (DoE) in ISFI formulation.
Main Results:
- Formulation-driven CMAs (polymer properties, drug characteristics, solvent choice) significantly impact ISFI performance.
- Burst release is identified as a critical CMA affecting safety, efficacy, and translational robustness.
- Formulation parameters demonstrate greater influence than process parameters on ISFI outcomes.
Conclusions:
- QbD application is crucial for enhancing the predictability and reproducibility of PLGA-based ISFIs.
- Prioritizing formulation-driven CMAs supports rational design and successful clinical translation.
- This review offers a framework for optimizing ISFI development through a QbD approach.
