An application of QSRR approach and multiple linear regression method for lipophilicity assessment of flavonoids

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.

Construction and application of a QSRR approach for identifying flavonoids

A quantitative structure retention relationship (QSRR) method was developed to identify flavonoid isomers auxiliary using an ultra high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method based on the linear relationships between the Ln(k') values of flavonoids and their hydrogen bonding energy (XAH) and dissolution energy (ES). Chromatographic separation was achieved with a Hypersil GOLD C18 (100 mm × 2.1 mm, 1.9 µm) column and Agilent SB-C18 (2.1 ×50 mm, 1.8 µm) column on a Dionex Ultimate 3000 RSLC chromatograph. Compounds were eluted isocratically using a mobile phase containing 0.1% formic acid/water solution and methanol at a ratio of 55:45 (v/v). Mass spectrometry was performed in the negative and positive ionization modes on a Thermo Fisher Q Exactive Orbitrap mass spectrometer equipped with an electrospray ionization interface. The established QSRR model was Ln(k') = 5.6163 + 0.0469ES - 0.0984XAH, with a determination coefficient (R2) of 0.9981, adjusted determination coefficient (adjR2) of 0.9976, and corrected root mean square error of 0.0682. The determination coefficient of the leave-one-out (LOO) cross-validation (Q2LOO) was 0.9976, and the cross-verification root mean square error was 0.0754. Simulated samples containing 7 flavonoids were used to validate the feasibility of the method. The classical method (UHPLC-MS/MS combined the CD software and the mzCloud, mzVault and Chemspider databases) was used to identify the seven flavonoids in the simulated samples. This classic identification strategy cannot provide accurate identification results, which provided multiple identification results for each compound in the simulated samples. On the basis of the results, the 7 flavonoids were accurately identified by the established QSRR model, and the reference standards were used to validate it. The relative error of retention time(RE(tR)) between the model calculation and experimental results was less than 10%. This method effectively complements and improves the classical methods, that UHPLC-MS/MS combined the CD software and the mass spectra databases were used to identify flavonoids identification.

A multi-target QSRR approach to model retention times of small molecules in RPLC

Quantitative structure-retention relationship models (QSRR) have been utilized as an alternative to costly and time-consuming separation analyses and associated experiments for predicting retention time. However, achieving 100 % accuracy in retention prediction is unrealistic despite the existence of various tools and approaches. The limitations of vast data availability and time complexity hinder the use of most algorithms for retention prediction. Therefore, in this study, we examined and compared two approaches for modelling retention time using a dataset of small molecules with retention times obtained at multiple conditions, referred to as multi-targets (five pH levels: 2.7, 3.5, 5, 6.5, and 8 at gradient times of 20 min of mobile phase). The first approach involved developing separate models for predicting retention time at each condition (single-target approach), while the second approach aimed to learn a single model for predicting retention across all conditions simultaneously (multi-target approach). Our findings highlight the advantages of the multi-target approach over the single-target modelling approach. The multi-target models are more efficient in terms of size and learning speed compared to the single-target models. These retention prediction models offer two-fold benefits. Firstly, they enhance knowledge and understanding of retention times, identifying molecular descriptors that contribute to changes in retention behaviour under different pH conditions. Secondly, these approaches can be extended to address other multi-target property prediction problems, such as multi-quantitative structure Property(X) relationship studies (mt-QS(X)R).

Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods

Complex systems exist widely, including medicines from natural products, functional foods, and biological samples. The biological activity of complex systems is often the result of the synergistic effect of multiple components. In the quality evaluation of complex samples, multicomponent quantitative analysis (MCQA) is usually needed. To overcome the difficulty in obtaining standard products, scholars have proposed achieving MCQA through the "single standard to determine multiple components (SSDMC)" approach. This method has been used in the determination of multicomponent content in natural source drugs and the analysis of impurities in chemical drugs and has been included in the Chinese Pharmacopoeia. Depending on a convenient (ultra) high-performance liquid chromatography method, how can the repeatability and robustness of the MCQA method be improved? How can the chromatography conditions be optimized to improve the number of quantitative components? How can computer software technology be introduced to improve the efficiency of multicomponent analysis (MCA)? These are the key problems that remain to be solved in practical MCQA. First, this review article summarizes the calculation methods of relative correction factors in the SSDMC approach in the past five years, as well as the method robustness and accuracy evaluation. Second, it also summarizes methods to improve peak capacity and quantitative accuracy in MCA, including column selection and two-dimensional chromatographic analysis technology. Finally, computer software technologies for predicting chromatographic conditions and analytical parameters are introduced, which provides an idea for intelligent method development in MCA. This paper aims to provide methodological ideas for the improvement of complex system analysis, especially MCQA.

LC-ESI-QTOF/MS characterization of antimicrobial compounds with their action mode extracted from vine tea (Ampelopsis grossedentata) leaves

Vine tea (Ampelopsis grossedentata) is a tea plant cultivated south of the Chinese Yangtze River. It has anti-inflammatory properties and is used to normalize blood circulation and detoxification. The leaves of vine tea are the most abundant source of flavonoids, such as dihydromyricetin and myricetin. However, as the main bioactive flavonoid in vine tea, dihydromyricetin was the main focus of previous research. This study aimed to explore the antibacterial activities of vine tea against selected foodborne pathogens. The antimicrobial activity of vine tea extract was evaluated by the agar well diffusion method. Cell membrane integrity and bactericidal kinetics, along with physical damage to the cell membrane, were also observed. The extract was analyzed using a high-performance liquid chromatography-diode array detector (HPLC-DAD), and the results were confirmed using a modified version of a previously published method that combined liquid chromatography and electrospray-ionized quadrupole time-of-flight mass spectrometry (LC-ESI-QTOF/MS). Cell membrane integrity and bactericidal kinetics were determined by releasing intracellular material in suspension and monitoring it at 260 nm using an ultraviolet (UV) spectrophotometer. A scanning electron microscope (SEM) was used to detect morphological alterations and physical damage to the cell membrane. Six compounds were isolated successfully: (1) myricetin (C15H10O8), (2) myricetin 3-O-rhamnoside (C21H20O12), (3) 5,7,8,3,4-pentahydroxyisoflavone (C15H10O7), (4) dihydroquercetin (C15H12O7), (5) 6,8-dihydroxykaempferol (C15H10O8), and (6) ellagic acid glucoside (C20H16O13). Among these bioactive compounds, C15H10O7 was found to have vigorous antimicrobial activity against Bacillus cereus (AS11846) and Staphylococcus aureus (CMCCB26003). A dose-dependent bactericidal kinetics with a higher degree of absorbance at optical density 260 (OD260) was observed when the bacterial suspension was incubated with C15H10O7 for 8 h. Furthermore, a scanning electron microscope study revealed physical damage to the cell membrane. In addition, the action mode of C15H10O7 was on the cell wall of the target microorganism. Together, these results suggest that C15H10O7 has vigorous antimicrobial activity and can be used as a potent antimicrobial agent in the food processing industry.

Thermodynamics-Based Model Construction for the Accurate Prediction of Molecular Properties From Partition Coefficients

Developing models for predicting molecular properties of organic compounds is imperative for drug development and environmental safety; however, development of such models that have high predictive power and are independent of the compounds used is challenging. To overcome the challenges, we used a thermodynamics-based theoretical derivation to construct models for accurately predicting molecular properties. The free energy change that determines a property equals the sum of the free energy changes (ΔG_Fs) caused by the factors affecting the property. By developing or selecting molecular descriptors that are directly proportional to ΔG_Fs, we built a general linear free energy relationship (LFER) for predicting the property with the molecular descriptors as predictive variables. The LFER can be used to construct models for predicting various specific properties from partition coefficients. Validations show that the models constructed according to the LFER have high predictive power and their performance is independent of the compounds used, including the models for the properties having little correlation with partition coefficients. The findings in this study are highly useful for applications in drug development and environmental safety.

Deep Q-learning for the selection of optimal isocratic scouting runs in liquid chromatography

An important challenge in chromatography is the development of adequate separation methods. Accurate retention models can significantly simplify and expedite the development of adequate separation methods for complex mixtures. The purpose of this study was to introduce reinforcement learning to chromatographic method development, by training a double deep Q-learning algorithm to select optimal isocratic scouting runs to generate accurate retention models. These scouting runs were fit to the Neue-Kuss retention model, which was then used to predict retention factors both under isocratic and gradient conditions. The quality of these predictions was compared to experimental data points, by computing a mean relative percentage error (MRPE) between the predicted and actual retention factors. By providing the reinforcement learning algorithm with a reward whenever the scouting runs led to accurate retention models and a penalty when the analysis time of a selected scouting run was too high (> 1h); it was hypothesized that the reinforcement learning algorithm should by time learn to select good scouting runs for compounds displaying a variety of characteristics. The reinforcement learning algorithm developed in this work was first trained on simulated data, and then evaluated on experimental data for 57 small molecules - each run at 10 different fractions of organic modifier (0.05 to 0.90) and four different linear gradients. The results showed that the MRPE of these retention models (3.77% for isocratic runs and 1.93% for gradient runs), mostly obtained via 3 isocratic scouting runs for each compound, were comparable in performance to retention models obtained by fitting the Neue-Kuss model to all (10) available isocratic datapoints (3.26% for isocratic runs and 4.97% for gradient runs) and retention models obtained via a "chromatographer's selection" of three scouting runs (3.86% for isocratic runs and 6.66% for gradient runs). It was therefore concluded that the reinforcement learning algorithm learned to select optimal scouting runs for retention modeling, by selecting 3 (out of 10) isocratic scouting runs per compound, that were informative enough to successfully capture the retention behavior of each compound.

Offline two-dimensional liquid chromatography coupled with ion mobility-quadrupole time-of-flight mass spectrometry enabling four-dimensional separation and characterization of the multicomponents from white ginseng and red ginseng

Inherent complexity of plant metabolites necessitates the use of multi-dimensional information to accomplish comprehensive profiling and confirmative identification. A dimension-enhanced strategy, by offline two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (2D-LC/IM-QTOF-MS) enabling four-dimensional separations (2D-LC, IM, and MS), is proposed. In combination with in-house database-driven automated peak annotation, this strategy was utilized to characterize ginsenosides simultaneously from white ginseng (WG) and red ginseng (RG). An offline 2D-LC system configuring an Xbridge Amide column and an HSS T3 column showed orthogonality 0.76 in the resolution of ginsenosides. Ginsenoside analysis was performed by data-independent high-definition MSE (HDMSE) in the negative ESI mode on a Vion™ IMS-QTOF hybrid high-resolution mass spectrometer, which could better resolve ginsenosides than MSE and directly give the CCS information. An in-house ginsenoside database recording 504 known ginsenosides and 58 reference compounds, was established to assist the identification of ginsenosides. Streamlined workflows, by applying UNIFI™ to automatedly annotate the HDMSE data, were proposed. We could separate and characterize 323 ginsenosides (including 286 from WG and 306 from RG), and 125 thereof may have not been isolated from the Panax genus. The established 2D-LC/IM-QTOF-HDMSE approach could also act as a magnifier to probe differentiated components between WG and RG. Compared with conventional approaches, this dimension-enhanced strategy could better resolve coeluting herbal components and more efficiently, more reliably identify the multicomponents, which, we believe, offers more possibilities for the systematic exposure and confirmative identification of plant metabolites.

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.