PLGA公司
化学
控制释放
乙醇酸
肽
动力学
生物相容性材料
溶剂
体外
色谱法
剂型
乳酸
聚合物
组合化学
化学工程
材料科学
生物医学工程
纳米技术
生物化学
有机化学
医学
物理
工程类
量子力学
生物
细菌
遗传学
作者
Meenakshi Goel,Dennis Leung,Amin Famili,Debby P. Chang,Purnendu K. Nayak,Mohammad H. Al‐Sayah
标识
DOI:10.1016/j.ejpb.2021.05.008
摘要
Poly (lactic-co-glycolic acid) (PLGA), a biocompatible and biodegradable polymer, is one of the most commonly used vehicles for controlled-release (CR) implantable dosage forms. Drug molecules formulated in such CR vehicles are released slowly over an extended period of time - often months to years - posing challenges for batch release and quality control testing. Thus, reliable and reproducible accelerated testing methods are required to bridge this gap during early formulation development. This work describes the development of an accelerated in vitro release testing method to predict the real-time in vitro release of a synthetic peptide from a 6-month CR PLGA implant formulation. While accelerated methods have been previously reported for PLGA-based formulations, this work describes a unique case of an aggregation-prone peptide, which required careful attention to the impact of different conditions on both release kinetics and peptide stability. This method describes a suitable combination of release conditions that could help in understanding the release profiles of such peptides prone to aggregation. Parameters including pH, buffer species, temperature, and addition of organic co-solvents and surfactants were evaluated separately and in combination for their ability to achieve complete peptide release within 2 weeks while accurately recapitulating release rate, profile and peptide stability. The accelerated release method that gave the best agreement with real-time release was a mixed media of co-solvent (5% tetrahydrofuran), surfactant (5% TritonX-100) and elevated temperature (50 °C) in a neutral buffer (PBS pH 7.4). This optimized accelerated release method achieved complete release of the peptide load within 14-21 days compared to 3- to 6-months of real-time release and could discriminate critical differences in release behavior between different CR formulations to guide formulation and process development.
科研通智能强力驱动
Strongly Powered by AbleSci AI