(M1130-06-36) Tracking the Microstructural Changes and Degradation Behaviors of Bioresorbable Subdermal Pellets Under In Vitro and In Vivo Conditions

Purpose: The development of controlled-/extended-release bioresorbable implants has presented itself as a promising system for the continuous, long-acting delivery of drugs for various indications, including HIV prophylaxis. These controlled drug delivery systems can be used for pre-exposure prophylaxis (PrEP) to reduce the risk of HIV infection. By manipulating the surface coating properties and core composition, precise control can be achieved over implant biodegradation and drug diffusion into the surrounding tissue from the implant core. Hence, characterization of microstructure and surface properties of such controlled-release implants is critical in formulation development. This proof-of-concept study utilizes innovative artificial intelligence (AI)-based image analysis of an antiretroviral drug loaded pellet-based implant to assess the evolution of the pellet coating and internal microstructures during in vivo and in vitro exposure studies to provide a mechanistic understanding of the critical quality attributes (CQA’s) associated with drug release kinetics and biodegradation of pellets.

Methods: Bioresorbable pellet-based implants with a biodegradable polymer coating were non-invasively investigated using X-ray microscopy (XRM) and AI-based image analysis. One scan captured three pellets after in vitro exposure studies in physiological PBS buffer at three different time points, and the second XRM scan captured three pellets retrieved from rats for in vivo exposure studies at similar time points. Image analysis was performed using digiM I2S^TM software to characterize different material phases. Microstructure characteristics quantified from the segmented data sets include size and spatial distributions, pellet porosity, coating thickness uniformity, volume fractions of pellet fractures, and density uniformity.

Results: The volume of the pellet core remaining after 1 week and 1 month from both in vivo and in vitro studies was plotted against time in Figure 1. Highly comparable decreases in sample volume were identified between the two studies, where in vitro and in vivo samples showed a 35% and 32% decrease in sample volume from 1-week to 1-month samples, respectively. The increase in sample volume seen after 1 week of in vivo release is suggestive of polymer swelling in the in vivo environment. Pore size distributions in Figure 2a-b revealed a decrease in pore sizes with increased time of in vitro exposure. Pellets retrieved after 1 week and 1 month from in vivo exposure show comparable pore sizes below D50 levels, as seen by the distribution in Figure 2c.

Conclusion: AI-based analysis has been shown as a powerful approach to understand the microstructure and drug release behavior of the pellet-based implants to expedite the development, screening, and optimization of such products. Microstructural evaluation from imaging data can be used to provide a mechanistic understanding of the degradation of the implant polymer matrix and how it influences the drug release. The AI-based imaging technique used here can be broadly applied in the early stages of controlled-release product development to predict product performance between various formulations in a quick time, unlike traditional, time-consuming in vitro testing methods to model the drug release through the implant matrix and into the systemic circulation.


Implant volume measured from µCT image analysis remaining after 1 week and 1 month from in vitro (purple line) and in vivo (green line) exposure studies.


a) Cumulative % pore size distribution from in vitro pellets, b) Volume % pore size distribution from in vitro pellets, c) Cumulative % high intensity particle size distribution from in vivo pellets, d) Volume % high intensity particle size distribution from in vivo pellets.