A comparative study of the predictive performance of different descriptor calculation tools: Molecular-based elution order modeling and interpretation of retention mechanism for isomeric compounds from METLIN database

In the pharmaceutical industry, the need for analytical standards is a bottleneck for comprehensive evaluation and quality control of intermediate and end products. These are complex mixtures containing structurally related molecules. In this regard, chromatographic peak annotation, especially for critical pairs of isomers and closest structural analogs, can be supported by using a Quantitative Structure Retention Relationship (QSRR) approach. In our study, we investigated the fundamental basis of the reversed-phase (RP) retention mechanism for 1141 isomeric compounds from the METLIN SMRT dataset. Nine different descriptor calculation tools combined with different feature selection methods (genetic algorithm (GA), stepwise, Boruta) and machine learning (ML) approaches (support vector machine (SVM), multiple linear regression (MLR), random forest (RF), XGBoost) were applied to provide a reliable molecular structure-based interpretation of RP retention behaviour of the isomeric compounds. Strict internal and external validation metrics were used to select models with the best predictive capabilities (rtest > 0.73, order of elution > 60 %). For the developed models, mean absolute errors were in the range of 60 to 110 s. Stepwise and GA showed the most suitable performance as descriptor selection methods, while SVM and XGBoost modeling gave satisfactory predictive characteristics in most cases. Validation performed on the published experimental data for structurally related pharmaceutical compounds confirmed the best accuracy of MLR modeling in combination with GA feature selection of general physico-chemical properties. The resulting models will be useful for the prediction of separation and identification of structurally related compounds in pharmaceutical analysis, providing a simultaneous understanding of the interaction mechanisms leading to their retention under RP conditions.

Analysis of Primary Liquid Chromatography Mass Spectrometry Data by Neural Networks for Plant Samples Classification

Plant samples are potential sources of physiologically active secondary metabolites and their classification is an extremely important task in traditional medicine and other fields of research. In the production of herbal drugs, different plant parts of the same or related species can serve as adulterants for primary plant material. The use of highly informative and relatively easily accessible tools, such as liquid chromatography and low-resolution mass spectrometry, helps to solve these tasks by means of fingerprint analysis. In this study, to reveal specific plant part features for 20 species from one family (Apiaceae), and to preserve the maximum information content, two approaches are suggested. In both cases, minimal raw data pretreatment, including rescaling of time and m/z axes and cutting off some uninformative regions, was applied. For the support vector machine (SVM) method, tensor unfolding was required, while neural networks (NNs) were able to work directly with squared heatmaps as input data. Moreover, five data augmentation variants are proposed, to overcome the typical problem of a lack of data. As a result, a comparable F1-score close to 0.75 was achieved by SVM and two employed NN architectures. Eight marker compounds belonging to chlorophylls, lipids, and coumarin apio-glucosides were tentatively identified as characteristic of their corresponding sample groups: roots, stems, leaves, and fruits. The proposed approaches are simple, information-saving and can be applied to a broad type of tasks in metabolomics.

An application of the standardised reference extract quantification strategy in the quality control of ginseng infusions by liquid chromatography with mass spectrometric detection

INTRODUCTION

Limited availability of individual standards is a bottleneck for quality control of functional foods and natural medicines. The use of standard mixtures or secondary standards is a possible alternative in this case. Earlier, an approach known as standardised reference extract (RE) strategy was introduced for HPLC-UV analysis of different plant materials; however, its application in HPLC-MS analysis has not been investigated.

OBJECTIVE

To establish an HPLC-MS-based RE method for determination of ginsenoside content in ginseng infusions using commercially available extract reference material of Panax quinquefolius L.

RESULTS

The developed HPLC-MS method was validated as precise (1.1%-9.4% intra-day variation; 1.6%-12.8% inter-day variation) and highly sensitive [limit of detection (LOD): 1-40 ng/mL; limit of quantification (LOQ): 4-120 ng/mL]. The stability of samples was satisfactory (5.7%-16.3%). The RE quantification method was compared with the external standard method, and the obtained difference was not significant, mostly in the range of 5%-10%. Matrix effects for the diluted samples of RE and ginseng infusions, determined via the standard addition method, were in the range of 85%-115% and 80%-126%, respectively, and were also positively correlated with the ginsenoside concentration. Eleven batches of ginseng infusions from different manufacturers were analysed using the established method.

CONCLUSION

The method for HPLC-MS-based ginsenoside quantification using RE as a secondary standard was established for the first time. The results of this study demonstrate that the application of the standardised RE strategy in HPLC-MS can minimise the matrix effect-related error in addition to the cost-effective quality control of herbal products, foods, and traditional medicines.

A new PARAFAC-based algorithm for HPLC-MS data treatment: herbal extracts identification

INTRODUCTION

Role of highly informative high-performance liquid chromatography mass spectrometry (HPLC-MS) methods in quality control is increasing. Complex herbal products and formulations can simultaneously contain extracts from different plants. Therefore, due to the leads to lack of commercial standards it is important to develop novel approaches for comprehensive treatment of big datasets.

OBJECTIVE

The aim of this study is to create a straightforward and information-saving algorithm for the identification of plants extracts in commercial products.

MATERIAL AND METHODS

In total, 34 samples, including Glycyrrhiza glabra and Panax ginseng dried roots; and Abrus precatorius dried leaves, their double and triple mixtures and flavoured oolong tea samples were analysed by HPLC-MS and combined in a three-dimensional dataset (retention time-mass-to-charge ratio (m/z)-samples). This dataset was subjected to smoothing and denoising techniques and further decomposed using parallel factor analysis (PARAFAC).

RESULTS

Samples were divided into eight clusters; loading matrices were interpreted and the presence of the most characteristic triterpene glycoside groups was demonstrated and supported by the characteristic chromatogram approach. The occurrence of Abrus precatorius and G. glabra additives in flavoured tea was confirmed.

CONCLUSION

Developed HPLC-MS-PARAFAC method is potentially reliable and an efficient tool for handling untreated experimental data and its future development may lead to more comprehensive evaluation of chemical composition and quality control of food additives and other complex mixtures.

Nerve agent markers screening after accumulation in garden cress (Lepidium sativum) used as a model plant object

In this research an accumulation of nerve agent markers in garden cress (Lepidium sativum) as a model plant object was studied using LC-QTOF hybrid system. For the determination of methylphosphonic acid and alkyl methylphosphonates, which are specific markers of sarin, soman, VR and VX, simple and sensitive approach was developed. Direct analysis of aqueous extracts on the reversed phase column with polar endcapping allowed to achieve satisfactory retention factor for methylphosphonic acid, which has high polarity and is usually very weakly retained on the ordinary reversed phase columns. Application of the QTOF mass spectrometer with high mass resolution led to the increase in the accuracy of the conducted measurements. The HPLC-HRMS technique developed exclusively for this study has been validated for linearity, limit of detection, limit of quantification, precision, accuracy and matrix effect prior to the analysis of plant extract samples. Hydroponic growth model was employed to examine accumulation of nerve agent markers in garden cress. It was found that after elimination of nerve agent markers from the plant growth medium, garden cress was able to store these substances for at least 5 weeks providing high retrospectivity of the analysis. Moreover, during the cress growth, no metabolization of alkyl methylphosphonates was observed. This allows not only to reveal the fact of nerve agents release into environment, but also to define its type after a long period of time.

"Dilute-and-shoot" rapid-separation liquid chromatography tandem mass spectrometry method for fast detection of thiodiglycolic acid in urine

A new sensitive rapid-separation liquid chromatography tandem mass spectrometry approach for the determination of thiodiglycolic acid (TDGA) in urine has been developed. The use of the "dilute-and-shoot" method helps to shorten the sample preparation stage and provides a sensitive and direct approach for TDGA determination in urine. Chromatographic separation of the analyte and other urine compounds was achieved using a reverse-phase liquid chromatography column with mobile phases consisting of 0.1% formic acid in water and acetonitrile in a gradient elution mode. For the identification and quantification of TDGA electrospray ionization-tandem mass spectrometry monitoring, two precursor-to-product ion transitions were used. The method demonstrates good linearity and has a detection limit of 50 ng mL⁻¹ in urine.

A validated LC-MS/MS method for rapid determination of methotrexate in human saliva and its application to an excretion evaluation study

A sensitive and simple method for the methotrexate quantification was developed using aminopterin as internal standard. Methotrexate is an anticancer agent that is widely used in a variety of human cancers including primary central nervous system lymphoma. The compound was quantified by liquid-chromatography coupled to electrospray ionization (positive ion-mode) low-energy collision dissociation-tandem mass spectrometry. Quantitative detection was by multiple reaction monitoring of the transitions of the [M+H]+ ion of MTX to its common product ion at m/z 308.4 and of aminopterin at m/z 441.2→m/z 294.0. The method demonstrated linearity over at least three orders of magnitude and had a detection limit of 1ng/ml for methotrexate. A run time of less than 8.0min for each sample made it possible to analyze a large number of human saliva samples per day. Application of this procedure was demonstrated to a saliva excretion study of methotrexate on the samples obtained after an intravenously administration of 1mg/kg/dose of methotrexate to six patients with acute lymphoblastic leukemia.