Endotoxin detection in nanoliposomes using diluted Limulus Amebocyte Lysate and isothermal titration calorimetry
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Arquímedes Velásquez
Abstract
The increasing use of nanomaterial-based products, such as nanoliposome vaccines, has amplified the need for reliable endotoxin testing, particularly for intravenous applications. Conventional Limulus Amebocyte Lysate (LAL) assays can be compromised by nanomaterials or formulation components that mask endotoxins, highlighting the need for alternative detection strategies. Here, we evaluated isothermal titration calorimetry (ITC) for detecting endotoxins encapsulated in two types of nanoliposomes (NLPs) and assessed the influence of individual NLP components on LAL enzymatic activity and viscosity. NLPs were synthesized in the presence of endotoxins, dialyzed to remove non-bound endotoxins, and characterized using high-performance liquid chromatography (HPLC) and attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) to confirm endotoxin incorporation. Dynamic light scattering (DLS), transmission electron microscopy (TEM), and atomic force microscopy (AFM) were used to assess size, morphology, and aggregation behavior Endotoxins produce with C 16:0 and C18:1 lipids were found to integrate into NLPs, impeding enzymatic reactions in LAL assays, with loading varying according to NLP type. For ITC detection, NLPs were disrupted using 0.2 % Triton X in a 3:2 nanoliposome-to-detergent ratio followed by brief ultrasonication. Despite the additional time and energy required for ITC experiments, the ITC provides a sensitive alternative to LAL, decreasing reliance on horseshoe crab reagents, visual inspection, and chromophores, while remaining insensitive to turbidity and broadly applicable to both organic and inorganic nanomaterials.
Acknowledgments
Microbiología Farmaceutica de Costa Rica.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: AV: Investigations; RPR: investigations, FB: formal analysis and writing; AA. Investigations and writing; YRCU: methodology, JRVB: writing and review and editing; supervisions, formal analysis writing and review and editing.
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Use of Large Language Models, AI and Machine Learning Tools: Chat GPT to correct english.
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Conflict of interest: Not applicable.
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Research funding: None declared.
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Data availability: Under request.
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