Title : Liposomal formulation and quantitative HPLC analysis of random peptide mixtures for antibacterial drug development
Abstract:
The rising antibiotic resistance among bacterial pathogens presents a growing global health concern, necessitating the exploration of novel therapeutic approaches. One promising alternative to traditional antibiotics is the use of peptides. Random peptide mixtures (RPMs), characterized by their structural diversity resulting from randomized amino acid assembly, offer a potential advantage by limiting the development of bacterial resistance. These mixtures can be efficiently encapsulated in nanostructured carriers such as liposomes—biocompatible, biodegradable vesicles widely utilized in biomedical applications. Liposomal encapsulation enhances peptide stability, protects against enzymatic degradation, and can improve therapeutic efficacy while minimizing side effects. A critical aspect of using RPMs in such systems is their accurate quantification, which was the primary focus of this study. Liposomes composed of soy phosphatidylcholine (SPC) were prepared using the thin-film hydration technique and used to encapsulate RPM. Due to the random structure of RPMs, standard high-performance liquid chromatography (HPLC) methods are not applicable. To address this, liposomal formulations were purified and subjected to hydrochloric acid hydrolysis, releasing encapsulated peptides and breaking them down into individual amino acids. Two analytical strategies were evaluated: fluorescence-based detection of phenylalanine, and UV detection following derivatization of phenylalanine and lysine with 2,4,6-trinitrobenzenesulfonic acid (TNBS). Both approaches enabled reliable quantification of peptide content post-hydrolysis. In summary, we developed and validated two HPLC-based methods for quantifying RPM content in liposomal formulations, offering useful tools for further development of peptide-based antibacterial therapies. This work has been supported by Polish National Centre of Research & Development as a part of EuroNanoMed III project (grant number ENM3/V/33/Antineuropatho/2023).
