Quantitative structure–retention relationships of flavonoids unraveled by immobilized artificial membrane chromatography

Cross-column density functional theory-based quantitative structure-retention relationship model development powered by machine learning

Quantitative structure-retention relationship (QSRR) modeling has emerged as an efficient alternative to predict analyte retention times using molecular descriptors. However, most reported QSRR models are column-specific, requiring separate models for each high-performance liquid chromatography (HPLC) system. This study evaluates the potential of machine learning (ML) algorithms and quantum mechanical (QM) descriptors to develop QSRR models that can predict retention times across three different reversed-phase HPLC columns under varying conditions. Four machine learning methods-partial least squares (PLS) regression, ridge regression (RR), random forest (RF), and gradient boosting (GB)-were compared on a dataset of 360 retention times for 15 aromatic analytes. Molecular descriptors were calculated using density functional theory (DFT). Column characteristics like particle size and pore size and experimental conditions like temperature and gradient time were additionally used as descriptors. Results showed that the GB-QSRR model demonstrated the best predictive performance, with Q2 of 0.989 and root mean square error of prediction (RMSEP) of 0.749 min on the test set. Feature analysis revealed that solvation energy (SE), HOMO-LUMO energy gap (∆E HOMO-LUMO), total dipole moment (Mtot), and global hardness (η) are among the most influential predictors for retention time prediction, indicating the significance of electrostatic interactions and hydrophobicity. Our findings underscore the efficiency of ensemble methods, GB and RF models employing non-linear learners, in capturing local variations in retention times across diverse experimental setups. This study emphasizes the potential of cross-column QSRR modeling and highlights the utility of ML models in optimizing chromatographic analysis.

Mechanistic Chromatographic Column Characterization for the Analysis of Flavonoids Using Quantitative Structure-Retention Relationships Based on Density Functional Theory

This work aimed to unravel the retention mechanisms of 30 structurally different flavonoids separated on three chromatographic columns: conventional Kinetex C18 (K-C18), Kinetex F5 (K-F5), and IAM.PC.DD2. Interactions between analytes and chromatographic phases governing the retention were analyzed and mechanistically interpreted via quantum chemical descriptors as compared to the typical 'black box' approach. Statistically significant consensus genetic algorithm-partial least squares (GA-PLS) quantitative structure retention relationship (QSRR) models were built and comprehensively validated. Results showed that for the K-C18 column, hydrophobicity and solvent effects were dominating, whereas electrostatic interactions were less pronounced. Similarly, for the K-F5 column, hydrophobicity, dispersion effects, and electrostatic interactions were found to be governing the retention of flavonoids. Conversely, besides hydrophobic forces and dispersion effects, electrostatic interactions were found to be dominating the IAM.PC.DD2 retention mechanism. As such, the developed approach has a great potential for gaining insights into biological activity upon analysis of interactions between analytes and stationary phases imitating molecular targets, giving rise to an exceptional alternative to existing methods lacking exhaustive interpretations.

Enhanced Protection of Biological Membranes during Lipid Peroxidation: Study of the Interactions between Flavonoid Loaded Mesoporous Silica Nanoparticles and Model Cell Membranes

Flavonoids, polyphenols with anti-oxidative activity have high potential as novel therapeutics for neurodegenerative disease, but their applicability is rendered by their poor water solubility and chemical instability under physiological conditions. In this study, this is overcome by delivering flavonoids to model cell membranes (unsaturated DOPC) using prepared and characterized biodegradable mesoporous silica nanoparticles, MSNs. Quercetin, myricetin and myricitrin have been investigated in order to determine the relationship between flavonoid structure and protective activity towards oxidative stress, i.e., lipid peroxidation induced by the addition of hydrogen peroxide and/or Cu²⁺ ions. Among investigated flavonoids, quercetin showed the most enhanced and prolonged protective anti-oxidative activity. The nanomechanical (Young modulus) measurement of the MSNs treated DOPC membranes during lipid peroxidation confirmed attenuated membrane damage. By applying a combination of experimental techniques (atomic force microscopy—AFM, force spectroscopy, electrophoretic light scattering—ES and dynamic light scattering—DLS), this work generated detailed knowledge about the effects of flavonoid loaded MSNs on the elasticity of model membranes, especially under oxidative stress conditions. Results from this study will pave the way towards the development of innovative and improved markers for oxidative stress-associated neurological disorders. In addition, the obtained could be extended to designing effective delivery systems of other high potential bioactive molecules with an aim to improve human health in general.

Biological Activity and Physicochemical Properties of Dipeptidyl Nitrile Derivatives Against Pancreatic Ductal Adenocarcinoma Cells

Background:

Pancreatic cancer is one of the most aggressive types with high mortality in patients. Therefore, studies to discover new drugs based on cellular targets have been developed to treat this disease. Due to the importance of Cysteine Protease (CP) to several cellular processes in cancer cells, CP inhibitors have been studied as novel alternative approaches for pancreatic cancer therapy.

Objective:

The cytostatic potential of new CP inhibitors derived from dipeptidyl nitriles is analyzed in vitro using pancreatic cancer (MIA PaCa-2) cells.

Methods:

The cytotoxic and cytostatic activities were studied using MTT colorimetric assay in 2D and 3D cultures. Colony formation, migration in Boyden chamber and cell cycle analysis were applied to further study the cytostatic activity. The inhibition of cysteine proteases was evaluated with Z-FR-MCA selective substrate, and ROS evaluation was performed with DCFH-DA fluorophore. Permeability was investigated using HPLC-MS to obtain log kw. Combination therapy was also evaluated using the best compound with gemcitabine.

Results:

The inhibition of intracellular CP activity by the compounds was confirmed, and the cytostatic effect was established with cell cycle retention in the G1 phase. CP inhibitors were able to reduce cell proliferation by 50% in the clonogenic assay, and the same result was achieved for the migration assay, without any cytotoxic effect. The Neq0554 inhibitor was also efficient to increase the gemcitabine potency in the combination therapy. Physicochemical properties using an artificial membrane model quantified 1.14 ≥ log Kw ≥ 0.75 for all inhibitors (also confirmed using HPLC-MS analysis) along with the identification of intra and extracellular metabolites. Finally, these dipeptidyl nitrile derivatives did not trigger the formation of reactive oxygen species, which is linked to genotoxicity.

Conclusion:

Altogether, these results provide a clear and favorable picture to develop CP inhibitors in pre-clinical assays.