How to determine entrapment efficiency in lipid and polymeric nanoparticles: a methodological guide

• Definition of critical aspects in the determination of drug load/entrapment efficiency • Review of the available separation and drug quantification methodologies • Case studies on NLC and chitosan PNP illustrate development process • Guidance in drug load/entrapment efficiency method development in form of a decision tree In nanomedicine, accurately determining the entrapment efficiency (EE) and drug load (DL) of nanoparticles (NPs) is crucial for assessing their efficacy. EE represents the amount of drug encapsulated within the particles relative to the total drug used during manufacturing. Various methods, both direct and indirect, are employed for EE determination, each with its advantages and challenges. Separation techniques such as (ultra-)centrifugation, ultrafiltration, dialysis, and size exclusion chromatography are commonly used for isolating NPs from the suspension medium. Additionally, direct determination methods like high-performance liquid chromatography and UV/Vis spectroscopy may offer precise quantification of drug content within the particles. Lipid and polymeric NPs pose distinct challenges in EE determination, requiring careful consideration of factors such as solubility, stability, and particle density. Drawing attention to critical challenges and method validation, this work addresses the specific characteristics and methodological considerations for lipid and polymeric NPs. Through a comparative analysis of commonly used techniques, it underscores the importance of method validation and the need for standardised protocols. A decision tree in evidence-based selection of an EE methodology is proposed. In essence, this article serves as a comprehensive guide for scientists in the field of nanoparticle-based drug delivery, elucidating key considerations in formulation development, methodological approaches, and critical challenges for ensuring reproducibility and reliability in NP-based therapeutics.