Release Mechanisms and Practical Percolation Threshold for Long-acting Biodegradable Implants: An Image to Simulation...

Area of Science:

• Materials Science
• Biomedical Engineering
• Pharmaceutical Sciences

Background:

• Characterizing long-acting drug formulations (6-12 months) is challenging due to time and cost constraints of real-time release studies.
• Efficient methods are needed to predict drug release profiles for optimizing formulation and process parameters.
• Biodegradable long-acting delivery systems require robust characterization of critical quality attributes.

Purpose of the Study:

• To develop and validate an image-based release modeling technique for predicting drug release from long-acting formulations.
• To investigate the impact of formulation and process parameters on initial burst release using rapid T0 image analysis.
• To identify critical quality attributes influencing implant performance in biodegradable long-acting delivery systems.

Main Methods:

• Utilized X-Ray Microscopy (XRM) and Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) for imaging T0 samples.
• Developed an image-based release modeling method for predicting release profiles.
• Employed an iterative correction method incorporating poly(lactic-co-glycolic acid) (PLGA) degradation.
• Designed a water stress test to study pore formation dynamics.

Main Results:

• XRM-based predictions showed good accuracy for initial burst release compared to in vitro tests.
• FIB-SEM imaging combined with iterative corrections achieved strong agreement between predicted and in vitro release data.
• Image-based simulations identified a critical percolation threshold influencing implant performance.
• The study demonstrated the feasibility of predicting long-term release from initial sample characterization.

Conclusions:

• Image-based release modeling offers an efficient alternative to traditional methods for characterizing long-acting drug formulations.
• The identified percolation threshold is a crucial critical quality attribute for biodegradable long-acting delivery systems.
• Accurate prediction of drug release profiles from T0 samples can significantly accelerate formulation development and optimization.
• Correlative imaging techniques enhance the predictive power of release modeling for complex drug delivery systems.