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Evolving neural network optimization of cholesteryl ester separation by reversed-phase HPLC.

ABSTRACT: Cholesteryl esters have antimicrobial activity and likely contribute to the innate immunity system. Improved separation techniques are needed to characterize these compounds. In this study, optimization of the reversed-phase high-performance liquid chromatography separation of six analyte standards (four cholesteryl esters plus cholesterol and tri-palmitin) was accomplished by modeling with an artificial neural network-genetic algorithm (ANN-GA) approach. A fractional factorial design was employed to examine the significance of four experimental factors: organic component in the mobile phase (ethanol and methanol), column temperature, and flow rate. Three separation parameters were then merged into geometric means using Derringer's desirability function and used as input sources for model training and testing. The use of genetic operators proved valuable for the determination of an effective neural network structure. Implementation of the optimized method resulted in complete separation of all six analytes, including the resolution of two previously co-eluting peaks. Model validation was performed with experimental responses in good agreement with model-predicted responses. Improved separation was also realized in a complex biological fluid, human milk. Thus, the first known use of ANN-GA modeling for improving the chromatographic separation of cholesteryl esters in biological fluids is presented and will likely prove valuable for future investigators involved in studying complex biological samples.

SUBMITTER: Jansen MA

PROVIDER: S-EPMC2895920 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Lipid nanoparticles (LNPs) have shown great success as drug delivery systems, especially for mRNA vaccines, as those developed during the Covid-19 pandemics. Lipid analysis is critical to monitor the formulation process and control the quality of LNPs. The present study is focused on the development and validation of a high-performance liquid chromatography - diode array detector -evaporative light scattering detector (HPLC-DAD/ELSD) based method for the simultaneous quantification of 7 lipids, illustrating the main components of LNPs: ionizable lipids, the neutral co-lipid cholesterol, phospholipids, hydrophilic polymer-lipids for colloidal stability (e.g., a PEGylated lipid). In particular, this study focuses on two innovative synthetic lipids: a switchable cationic lipid (CSL3) which has demonstrated in vitro and in vivo siRNA transfection abilities, and the palmitic acid-grafted-poly(ethyloxazoline)5000 (PolyEtOx), used as an alternative polymer to address allergic reactions attributed to PEGylated lipids. The HPLC separation was achieved on a Poroshell C18 column at 50 °C using a step gradient of a mobile phase composed of water/methanol mixtures with 0.1% (v/v) trifluoroacetic acid (TFA). This method was validated following ICH Q2(R1) & (R2) guidelines in terms of linearity (R² ≥ 0.997), precision (relative standard deviation on peak areas < 5% for intermediate repeatability), accuracy (recoveries between 92.9% and 108.5%), and sensitivity. Indeed, low detection and quantitation limits were determined (between 0.02 and 0.04 µg and between 0.04 and 0.10 µg, respectively). Due to its high selectivity, this method allowed the analysis of lipid degradation products produced through degradation studies in basic, acidic, and oxidative conditions. Moreover, the method was successfully applied to the analysis of several liposome formulations at two key steps of the development process. Consequently, the reported HPLC method offers fast, versatile, selective and quantitative analysis of lipids, essential for development optimization, chemical characterization, and stability testing of LNP formulations.

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Evolving neural network optimization of cholesteryl ester separation by reversed-phase HPLC.

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