Quantitative Structure-retention Relationship Research Articles

Modelling of chromatographic and electrophoretic behaviour of imidazoline and alpha adrenergic receptors ligands under different acid-base conditions

Background and Purpose: The ligands of the imidazoline and α-adrenergic receptors are mainly imidazoline and guanidine derivatives, known as centrally-acting antihypertensives and compounds with potential use in various neurological disorders. The extent of their ionisation has a major influence on their behaviour in the different analytical systems. The main objective of this work was to compare the mechanism of chromatographic retention and electrophoretic mobility under acidic, neutral and basic conditions. Experimental Approach: Multiple Linear Regression and Partial Least Squares Regression were applied for the QSRR (quantitative structure-retention relationship) and QSMR (quantitative structure-mobility relationship) modelling and to select the most important molecular parameters describing the chromatographic and electrophoretic behaviour of the investigated compounds. Key Results: The most important molecular descriptors, such as the chemical composition of the compounds, lipophilicity, polarizability and molecular branching, in the selected QSRR models showed that an important insight into the retention behaviour can be derived from the 0D-, 1D- and 2D-descriptors. The electrophoretic mobility could be explained by 2D- and 3D-descriptors, which provide information on the molecular mass, size and complexity, as well as on the influence of charge transfer and electronic properties on the migration behaviour. Conclusion: All created QSRR/QSMR models met the stringent validation criteria and showed high potential in describing the chromatographic and electrophoretic behaviour of investigated compounds.

Estimation of passive gastrointestinal absorption of new dual DNA gyrase and topoisomerase IV inhibitors using PAMPA and biopartitioning micellar chromatography and quantitative structure-retention relationship analysis

DNA gyrase and topoisomerase IV play significant role in maintaining the correct structure of DNA during replication and they have been identified as validated targets in antibacterial drug discovery. Inadequate pharmacokinetic properties are responsible for many failures during drug discovery and their estimation in the early phase of this process maximizes the chance of getting useful drug candidates. Passive gastrointestinal absorption of a selected group of thirteen dual DNA gyrase and topoisomerase IV inhibitors was estimated using two in vitro tests – parallel artificial membrane permeability assay (PAMPA) and biopartitioning micellar chromatography (BMC). Due to good correlation between obtained results, passive gastrointestinal absorption of remaining ten compounds was estimated using only BMC. With this experimental setup, it was possible to identify compounds with high values of retention factors (k) and highest expected passive gastrointestinal absorption, and compounds with low values of k for which low passive gastrointestinal absorption is predicted. Quantitative structure-retention relationship (QSRR) modelling was performed by creating multiple linear regression (MLR), partial least squares (PLS) and support vector machines (SVM) models. Descriptors with the highest influence on retention factor were identified and their interpretation can be used for the design of new compounds with improved passive gastrointestinal absorption.

Evaluation of Physicochemical Properties of Ipsapirone Derivatives Based on Chromatographic and Chemometric Approaches.

Drug discovery is a challenging process, with many compounds failing to progress due to unmet pharmacokinetic criteria. Lipophilicity is an important physicochemical parameter that affects various pharmacokinetic processes, including absorption, metabolism, and excretion. This study evaluated the lipophilic properties of a library of ipsapirone derivatives that were previously synthesized to affect dopamine and serotonin receptors. Lipophilicity indices were determined using computational and chromatographic approaches. In addition, the affinity to human serum albumin (HSA) and phospholipids was assessed using biomimetic chromatography protocols. Quantitative Structure-Retention Relationship (QSRR) methodologies were used to determine the impact of theoretical descriptors on experimentally determined properties. A multiple linear regression (MLR) model was calculated to identify the most important features, and genetic algorithms (GAs) were used to assist in the selection of features. The resultant models showed commendable predictive accuracy, minimal error, and good concordance correlation coefficient values of 0.876, 0.149, and 0.930 for the validation group, respectively.

Target and suspect screening of psychoactive substances in seizures and oral fluid exploiting retention time prediction and LC-MS/MS analysis

BackgroundNovel psychoactive substances (NPS) are a group of substances, mainly of synthetic origin, characterized by toxicological properties extremely dangerous. The main difficulty in recognizing NPS in seizures and biological samples lies in their dynamic nature, related to the continuous synthesis and introduction on the market of new drugs, often with very similar structures to existing ones. The aim of this study was the creation of a robust and versatile method for the analysis of traditional drugs and NPS in different matrices. ResultsBoth target analysis and suspect screening methodologies were developed. The strategy used for suspect screening allowed to collect data through a scheduled multi reaction monitoring (sMRM) survey which triggered the collection of enhanced product ion (EPI) spectra when a compound met information dependent acquisition (IDA) criteria. The retention time of the different drugs, which was crucial to define the sMRM survey scan parameters, was predicted with a Quantitative Structure Retention (Chromatographic) Relationship (QSRR) model by Multiple Linear Regression. The model was validated through the evaluation of training set predictions, an external validation set and a leave-one out strategy; the results showed that the method fit for its purpose. The target method was validated in oral fluid as a testing matrix, with excellent results in term of recovery, accuracy, precision and matrix effect. Finally, the performances of the suspect method were evaluated by analysing a mixture containing 8 reference standards not included in the initial dataset, as well as seizures and real oral fluid samples. Four NPS were putatively identified in the analysed samples. SignificanceThe advantage of the proposed approach is the possibility of quantifying 65 classical drugs of abuse and NPS and, at the same time, detect and putatively identify 146 additional drugs in one single LC-MS/MS run. This is an innovative strategy for multi analyte detection and enables detection of low concentrations of drugs in complex biological matrices such as oral fluid. Considering the highly dynamic drug market, a strength of this strategy is that the analytical method can be kept up to date through the addition of new compounds based on the last drug monitoring bodies alerts without the need of authentic standards.

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 0.749min 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.

Modeling of small molecule's affinity to phospholipids using IAM-HPLC and QSRR approach enhanced by similarity-based machine algorithms

Immobilized artificial membrane chromatography (IAM) has been proposed as a more biosimilar alternative to classical lipophilicity measurement. Determination of small molecule's affinity to phospholipids can be supported for predicting their behavior in the human body. Therefore, a better understanding of the molecular interaction mechanism between small xenobiotics and phospholipids can accelerate drug discovery. Here, the quantitative structure-retention relationships (QSRR) approach was integrated with mechanistic descriptors calculated using Chemicalize software to propose an easy-to-interpretation QSRR model. Considering the heterogeneous character of the data set, locally weighted least squares kernel regression belonging to similarity-based machine learning methods have been applied. The results showed that lipophilicity, charge, and maximum projection area determine molecule binding to phospholipids. Full validation of the obtained model based on OECD recommendations has been performed and the applicability domain was defined using the probability-oriented distance-based approach. The high values of predictive squared correlation coefficient (Q2), and small root mean square error of prediction (RMSEP), 0.812 and 6.739, respectively, confirmed that the obtained QSRR model is not well-fitted to the training data but also showed prediction power. Additionally, only 1.5% of molecules from the training set and 2.8% from the validation test are outside the applicability domain, confirming great predictive abilities.

Support Vector Models-Based Quantitative Structure–Retention Relationship (QSRR) in the Development and Validation of RP-HPLC Method for Multi-component Analysis of Anti-diabetic Drugs

Support Vector Models-Based Quantitative Structure–Retention Relationship (QSRR) in the Development and Validation of RP-HPLC Method for Multi-component Analysis of Anti-diabetic Drugs

QSPR models for predicting the Kovats retention indices of synthetic ester derivatives based on pyrethrin essential oil

ABSTRACT In this study, the chromatographic characteristics of 100 different pyrethroids including ester derivatives of cyclopropanecarboxylic acids were analyzed by measuring their logarithmic kovats retention index (log KRI) using a quantitative structure-retention relationship (QSRR). The log KRI of the studied pyrethroids were modeled by genetic algorithm-structure retention relationships (GA-QSRR) based on linear and nonlinear regression models. The descriptors such as HNar, H0v, and H5p, which express the GETAWAY (geometry, topology, and atom-weights assembly) compound descriptors, have a reasonable correlation with the log KRI. We assessed the predictive strength of the BP-ANN model and demonstrated the potential of the model using various statistical parameters. The statistical parameters such as Q2F1, Q2F2, Q2F3 , AAD, RMSE and CCC were used to evaluate the predictive ability of the BP-ANN model. In predicting the log KRI of pyrethroids, the results indicated that the BP-ANN model is more reliable and accurate than the BW-MLR model.

Simultaneously quantifying hundreds of acylcarnitines in multiple biological matrices within ten minutes using ultrahigh-performance liquid-chromatography and tandem mass spectrometry

Acylcarnitines are metabolic intermediates of fatty acids and branched-chain amino acids having vital biofunctions and pathophysiological significances. Here, we developed a high-throughput method for quantifying hundreds of acylcarnitines in one run using ultrahigh performance liquid chromatography and tandem mass spectrometry (UPLC-MS/MS). This enabled simultaneous quantification of 1136 acylcarnitines (C0–C26) within 10-min with good sensitivity (limit of detection < 0.7 fmol), linearity (correlation coefficient > 0.992), accuracy (relative error < 20%), precision (coefficient of variation (CV), CV < 15%), stability (CV < 15%), and inter-technician consistency (CV < 20%, n = 6). We also established a quantitative structure-retention relationship (goodness of fit > 0.998) for predicting retention time (tR) of acylcarnitines with no standards and built a database of their multiple reaction monitoring parameters (tR, ion-pairs, and collision energy). Furthermore, we quantified 514 acylcarnitines in human plasma and urine, mouse kidney, liver, heart, lung, and muscle. This provides a rapid method for quantifying acylcarnitines in multiple biological matrices.

Liquid chromatographic retention time prediction models to secure and improve the feature annotation process in high-resolution mass spectrometry

The development of quantitative structure-retention relationship (QSRR) models has, until recently, required an adequate selection of molecular descriptors necessarily obtained based on a known chemical structure. However, these complex descriptors are not always available nor calculable when the high-resolution mass spectrometry (HRMS) annotation process is underway. Depending on the level of annotation, many structures or even various molecular formulas could be candidates. To secure and improve the annotation process and to save time, a QSRR model (using only 0D molecular descriptors) to predict retention times in reverse-phase liquid chromatography (RPLC) based on the molecular formula was developed, and a general QSRR annotation-based methodology was also proposed.

ANFIS-Based QSRR Modelling for Kovats Retention Index Prediction in Gas Chromatography

This study aims to evaluate the implementation and effectiveness of the Adaptive Neuro-Fuzzy Inference System (ANFIS) based Quantitative Structure Retention Relationship (QSRR) to predict the Kovats retention index of compounds in gas chromatography. The model was trained using 340 essential oil compounds and their molecular descriptors. The evaluation of the ANFIS models revealed promising results, achieving an R2 of 0.974, an RMSE of 48.12, and an MAPE of 3.3% on the testing set. These findings highlight the ANFIS approach as remarkably accurate in its predictive capacity for determining the Kovats retention index in the context of gas chromatography. This study provides valuable perspectives on the efficiency of retention index prediction through ANFIS-based QSRR methods and the potential practicality in compound analysis and chromatographic optimization.

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).

Impact of structural similarity on the accuracy of retention time prediction

Quantitative Structure-Retention Relationships offer a valuable tool for de-risking chromatographic methods in relation to newly formed or hypothetical compounds, arising from synthetic processes or formulation activities. They can also be used to identify optimal separation conditions, or in support of structural elucidation. In this contribution, we provide a systematic study of the relationship between the accuracy of the retention model, the size of the training set and its structural similarity to the predicted compound. We compare structural similarity expressed either on a fingerprint basis (e.g., Tanimoto index), or by Euclidean distance calculated from of subset of molecular descriptors. The results presented indicate that accurate and predictive models can be built from a small dataset containing as few as 25 compounds, provided that the training set is structurally similar to the test compound. When the training set contains compounds selected by minimizing the Euclidean distance calculated from 3 descriptors most correlated with the retention time, root mean square error of 0.48 min and correlation coefficient of 0.9464 were observed for the test sets of 104 compounds. Moreover, these models meet the Tropsha predictivity criteria. These findings potentially bring the prediction of retention times within the practical reach of pharmaceutical analysts involved in chromatographic method development. We also present an optimisation approach to select algorithm settings in order to minimize the prediction error and ensure model predictivity.

RT-Ensemble Pred: A tool for retention time prediction of metabolites on different LC-MS systems

Liquid chromatography-mass spectrometry (LC-MS) could provide a large amount of information to assist in metabolites identification. Different liquid chromatographic methods (CMs) could produce different retention times to the same metabolite. To predict the retention time of local dataset by online datasets has become a trend, but the datasets downloaded from different databases were differences in quantity levels. And the imbalanced data could produce bad influence in model prediction. Thus, based on quantitative structure-retention relationships (QSRRs), an ensemble model, named RT-Ensemble Pred, has been successfully built to predict retention time of different LC-MS systems in this study. A total of 76, 807 metabolites (76, 909 retention times) have been collected across 9 CMs, and 19 natural products and 1 antifungal drug (20 retention times) have been collected to test the model applicability. An ensemble sampling was applied for the preprocessing procedure to solve the problem of imbalanced data. Based on the ensemble sampling, RT-Ensemble Pred could better utilize online datasets for the prediction of retention time. RT-Ensemble Pred was built based on the online datasets and tested by local dataset. The predictive accuracy of RT-Ensemble Pred was higher than the models without any sampling methods. The results showed that RT-Ensemble Pred could predict the metabolites which was not included in the database and the metabolites which were from new CMs. It could also be used for the prediction of other compounds beside metabolites. Furthermore, a tool of RT-Ensemble Pred was packed and can be freely downloaded at https://gitlab.com/mikic93/rt-ensemble-pred. It provides convenience for the users who need to predict the retention time of metabolites.

A practical strategy enabling more reliable identification of ginsenosides from Panax quinquefolius flower by dimension-enhanced liquid chromatography/mass spectrometry and quantitative structure-retention relationship-based retention behavior prediction

To accurately identify the metabolites is crucial in a number of research fields, and discovery of new compounds from the natural products can benefit the development of new drugs. However, the preferable phytochemistry or liquid chromatography/mass spectrometry approach is time-/labor-extensive or receives unconvincing identifications. Herein, we presented a strategy, by integrating offline two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (2D-LC/IM-QTOF-MS), exclusion list-containing high-definition data-dependent acquisition (HDDDA-EL), and quantitative structure-retention relationship (QSRR) prediction of the retention time (tR), to facilitate the in-depth and more reliable identification of herbal components and thus to discover new compounds more efficiently. Using the saponins in Panax quinquefolius flower (PQF) as a case, high orthogonality (0.79) in separating ginsenosides was enabled by configuring the XBridge Amide and CSH C18 columns. HDDDA-EL could improve the coverage in MS2 acquisition by 2.26 folds compared with HDDDA (2933 VS 1298). Utilizing 106 reference compounds, an accurate QSRR prediction model (R2 = 0.9985 for the training set and R2 = 0.88 for the validation set) was developed based on Gradient Boosting Machine (GBM), by which the predicted tR matching could significantly reduce the isomeric candidates identification for unknown ginsenosides. Isolation and establishment of the structures of two malonylginsenosides by NMR partially verified the practicability of the integral strategy. By these efforts, 421 ginsenosides were identified or tentatively characterized, and 284 thereof were not ever reported from the Panax species. The current strategy is thus powerful in the comprehensive metabolites characterization and rapid discovery of new compounds from the natural products.

Quantitative structure-retention relationship by databases of illegal additives

The importance of small molecule retention time in detecting illegal additives cannot be overstated. In order to provide a basis for identification in small-scale databases lacking corresponding chemical standards, a quantitative structure-retention relationship (QSRR) model was developed using two liquid chromatography databases under the same column but with different mobile phases. The QSRR model workflow includes feature extraction, feature selection, model construction and evaluation. Molecular descriptors were calculated by molecules optimized using Consistent force field (CFF) and Chemistry at HARvard Macromolecular Mechanicets (CHARMm) force field during the feature extraction stage. Two selection algorithms, minimum redundancy-maximum relevance (MRMR) and F-test, were compared during the feature selection stage. Five categories of machine learning algorithms, Regression Trees (Reg-T), Support Vector Machines (SVM), Gaussian Process Regression Models (GPR), Ensembles of Trees, and Kernel approximation models, were compared during the model construction phase resulting in 14 models being obtained. The best-performing model was found to be the Exponential Gaussian Process Regression Model (E-GPR). This was tested on Database 1 with coefficient of determination (R2)= 0.84 ± 0.02 and on Database 2 with R2 = 0.83 ± 0.03. The results indicate that this QSRR model can accurately predict retention times for small molecules within small-scale databases.

Facilitating structural elucidation of small environmental solutes in RPLC-HRMS by retention index prediction

Implementing effective environmental management strategies requires a comprehensive understanding of the chemical composition of environmental pollutants, particularly in complex mixtures. Utilizing innovative analytical techniques, such as high-resolution mass spectrometry and predictive retention index models, can provide valuable insights into the molecular structures of environmental contaminants. Liquid Chromatography-High-Resolution Mass Spectrometry is a powerful tool for the identification of isomeric structures in complex samples. However, there are some limitations that can prevent accurate isomeric structure identification, particularly in cases where the isomers have similar mass and fragmentation patterns. Liquid chromatographic retention, determined by the size, shape, and polarity of the analyte and its interactions with the stationary phase, contains valuable 3D structural information that is vastly underutilized. Therefore, a predictive retention index model is developed which is transferrable to LC-HRMS systems and can assist in the structural elucidation of unknowns. The approach is currently restricted to carbon, hydrogen, and oxygen-based molecules <500 g mol−1. The methodology facilitates the acceptance of accurate structural formulas and the exclusion of erroneous hypothetical structural representations by leveraging retention time estimations, thereby providing a permissible tolerance range for a given elemental composition and experimental retention time. This approach serves as a proof of concept for the development of a Quantitative Structure-Retention Relationship model using a generic gradient LC approach. The use of a widely used reversed-phase (U)HPLC column and a relatively large set of training (101) and test compounds (14) demonstrates the feasibility and potential applicability of this approach for predicting the retention behaviour of compounds in complex mixtures. By providing a standard operating procedure, this approach can be easily replicated and applied to various analytical challenges, further supporting its potential for broader implementation.

Identification of endogenous carbonyl steroids in human serum by chemical derivatization, hydrogen/deuterium exchange mass spectrometry and the quantitative structure-retention relationship

Steroids are tetracyclic aliphatic compounds, and most of them contain carbonyl groups. The disordered homeostasis of steroids is closely related to the occurrence and progression of various diseases. Due to high structural similarity, low concentrations in vivo, poor ionization efficiency, and interference from endogenous substances, it is very challenging to comprehensively and unambiguously identify endogenous steroids in biological matrix. Herein, an integrated strategy was developed for the characterization of endogenous steroids in serum based on chemical derivatization, ultra-performance liquid chromatography quadrupole Exactive mass spectrometry (UPLC-Q-Exactive-MS/MS), hydrogen/deuterium (H/D) exchange, and a quantitative structure-retention relationship (QSRR) model. To enhance the mass spectrometry (MS) response of carbonyl steroids, the ketonic carbonyl group was derivatized by Girard T (GT). Firstly, the fragmentation rules of derivatized carbonyl steroid standards by GT were summarized. Then, carbonyl steroids in serum were derivatized by GT and identified based on the fragmentation rules or by comparing retention time and MS/MS spectra with those of standards. H/D exchange MS was utilized to distinguish derivatized steroid isomers for the first time. Finally, a QSRR model was constructed to predict the retention time of the unknown steroid derivatives. With this strategy, 93 carbonyl steroids were identified from human serum, and 30 of them were determined to be dicarbonyl steroids by the charge number of characteristic ions and the number of exchangeable hrdrogen or comparing with standards. The QSRR model built by the machine learning algorithms has an excellent regression correlation, thus the accurate structures of 14 carbonyl steroids were determined, among which three steroids were reported for the first time in human serum. This study provides a new analytical method for the comprehensive and reliable identification of carbonyl steroids in biological matrix.

Characterization and identification of alkaloids in Phellodendri Chinensis Cortex and Phellodendri Amurensis Cortex based on UHPLC-IM-Q-TOF-MS

A strategy combining collision cross section(CCS) prediction and quantitative structure-retention relationship(QSRR) model for quinoline and isoquinoline alkaloids was established based on UHPLC-IM-Q-TOF-MS and applied to Phellodendri Chinensis Cortex and Phellodendri Amurensis Cortex. The strategy included the following three steps.(1) The molecular features were extracted by the &quot;find features&quot; algorithm.(2) The potential quinoline and isoquinoline alkaloids were screened by filtering the original characteristic ions extracted from Phellodendri Chinensis Cortex and Phellodendri Amurensis Cortex by the established CCS vs m/z prediction interval.(3) According to the retention time of candidate compounds predicted by QSRR model, the chemical constituents were identified in combination with the characteristic fragment ions and pyrolysis law of secondary mass spectrometry. With the strategy, a total of 80 compounds were predicted, and 15 were identified accurately. The strategy is effective for the identification of small analogs of traditional Chinese medicine.