Determination of Drug Release Kinetics from Nanoparticles: Overcoming Pitfalls of the Dynamic Dialysis Method

脂质体 化学 透析管 脂质双层 药物输送 生物物理学 药品 纳米载体 动力学 透析 纳米颗粒 膜透性 毒品携带者 生物膜 双层 磁导率 纳米技术 药理学 材料科学 生物化学 有机化学 医学 外科 物理 量子力学 生物
作者
Sweta Modi,Bradley D. Anderson
出处
期刊:Molecular Pharmaceutics [American Chemical Society]
卷期号:10 (8): 3076-3089 被引量:294
标识
DOI:10.1021/mp400154a
摘要

Dynamic dialysis is one of the most common methods for the determination of release kinetics from nanoparticle drug delivery systems. Drug appearance in the "sink" receiver compartment is a consequence of release from the nanoparticles into the dialysis chamber followed by diffusion across the dialysis membrane. This dual barrier nature inherent in the method complicates data interpretation and may lead to incorrect conclusions regarding nanoparticle release half-lives. Although the need to consider the barrier properties of the dialysis membrane has long been recognized, there is insufficient quantitative appreciation for the role of the driving force for drug transport across that membrane. Reversible nanocarrier binding of the released drug reduces the driving force for drug transport across the dialysis membrane leading to a slower overall apparent release rate. This may lead to the conclusion that a given nanoparticle system will provide a sustained release in vivo when it will not. This study demonstrates these phenomena using model lipophilic drug-loaded liposomes varying in lipid composition to provide variations in bilayer permeability and membrane binding affinities. Model simulations of liposomal transport as measured by dynamic dialysis were conducted to illustrate the interplay between the liposome concentration, membrane/water partition coefficient, and the apparent release rate. Reliable determination of intrinsic liposomal bilayer permeability coefficients for lipophilic drugs by dynamic dialysis requires validation of drug release kinetics at varying nanoparticle concentration and the determination of membrane binding coefficients along with appropriate mechanism-based mathematical modeling to ensure the reliability and proper interpretation of the data.
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