Identification of impurities in macrolides by liquid chromatography-mass spectrometric detection and prediction of retention times of impurities by constructing quantitative structure-retention relationship (QSRR)

Quantitative Structure–Retention Relationship Analysis of Polycyclic Aromatic Compounds in Ultra-High Performance Chromatography

A comparative quantitative structure–retention relationship (QSRR) study was carried out to predict the retention time of polycyclic aromatic hydrocarbons (PAHs) using molecular descriptors. The molecular descriptors were generated by the software Dragon and employed to build QSRR models. The effect of chromatographic parameters, such as flow rate, temperature, and gradient time, was also considered. An artificial neural network (ANN) and Partial Least Squares Regression (PLS-R) were used to investigate the correlation between the retention time, taken as the response, and the predictors. Six descriptors were selected by the genetic algorithm for the development of the ANN model: the molecular weight (MW); ring descriptor types nCIR and nR10; radial distribution functions RDF090u and RDF030m; and the 3D-MoRSE descriptor Mor07u. The most significant descriptors in the PLS-R model were MW, RDF110u, Mor20u, Mor26u, and Mor30u; edge adjacency indice SM09_AEA (dm); 3D matrix-based descriptor SpPosA_RG; and the GETAWAY descriptor H7u. The built models were used to predict the retention of three analytes not included in the calibration set. Taking into account the statistical parameter RMSE for the prediction set (0.433 and 0.077 for the PLS-R and ANN models, respectively), the study confirmed that QSRR models, associated with chromatographic parameters, are better described by nonlinear methods.

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.

The Quality Control of Midecamycin and the Predictive Demarcation between Its Impurities and Components

Midecamycin is a 16-membered macrolide antibiotic. It can inhibit the synthesis of bacterial proteins by blocking up the activity of peptidyl transferase in the 50S ribosome. We used high-resolution mass spectrometry to analyze midecamycin, and quantitatively analyzed of each component of midecamycin produced by 18 different companies. The developed methods were validated by assessing linearity, limit of quantitation (LOQ), accuracy, precision, and robustness. Good separations were achieved for all components. Ten components of midecamycin were identified, and the contents of these components were determined in midecamycin produced by different companies. The demarcation between impurities and components of midecamycin was not clear. A ligand-docking model was used for predicting the impurities and components of midecamycin. Components and impurities were docked with the target. The results reported in this article may be important for quality control and the predictive demarcation between impurities and components of midecamycin.

Strategies for structure elucidation of small molecules based on LC–MS/MS data from complex biological samples

LC–MS/MS is a major analytical platform for metabolomics, which has become a recent hotspot in the research fields of life and environmental sciences. By contrast, structure elucidation of small molecules based on LC–MS/MS data remains a major challenge in the chemical and biological interpretation of untargeted metabolomics datasets. In recent years, several strategies for structure elucidation using LC–MS/MS data from complex biological samples have been proposed, these strategies can be simply categorized into two types, one based on structure annotation of mass spectra and for the other on retention time prediction. These strategies have helped many scientists conduct research in metabolite-related fields and are indispensable for the development of future tools. Here, we summarized the characteristics of the current tools and strategies for structure elucidation of small molecules based on LC–MS/MS data, and further discussed the directions and perspectives to improve the power of the tools or strategies for structure elucidation.

Improved structural annotation of triterpene metabolites of traditional Chinese medicine in vivo based on quantitative structure-retention relationships combined with characteristic ions: Alismatis Rhizoma as an example

As a fast, sensitive and selective method, liquid chromatography-tandem high-resolution mass spectrometry (LC-HRMS) has been used for studying the in vivo metabolism of traditional Chinese medicine (TCM). However, the rapid discovery and characterization of metabolites, especially isomers, remain challenging due to their complexity and low concentration in vivo. This study proposed a strategy to improve the structural annotation of prototypes and metabolites through characteristic ions and a quantitative structure-retention relationship (QSRR) model, and Alismatis Rhizoma (AR) triterpenes were used as an example. This strategy consists of four steps. First, based on an in-house database reported previously, prototypes and metabolites in biosamples were preliminarily identified. Second, the candidate structures of prototype compounds and metabolites were determined by characteristic ions, databases or potential metabolic pathways. Then, a QSRR model was established to predict the retention times of the proposed structure. Finally, the structures of unknown prototypes and metabolites were determined by matching experimental retention times with the predicted values. The QSRR model built by the genetic algorithm-multiple linear regression (GA-MLR) has excellent regression correlation (R² = 0.9966). Based on this strategy, a total of 118 compounds were identified, including 47 prototypes and 71 metabolites, among which 61 unknown compounds were reasonably characterized. The typical compound identified by this strategy was successfully validated using a triterpene standard. This strategy can improve the annotation confidence of in vivo metabolites of TCM and facilitate further pharmacological research.

Suspect screening of environmental contaminants by UHPLC-HRMS and transposable Quantitative Structure-Retention Relationship modelling

A Quantitative Structure-Retention Relationship (QSRR) model is proposed and aims at increasing the confidence level associated to the identification of organic contaminants by Ultra-High Performance Liquid Chromatography hyphenated to High Resolution Mass Spectrometry (UHPLC-HRMS) in environmental samples under a suspect screening approach. The model was built from a selection of 8 easily accessible physicochemical descriptors, and was validated from a set of 274 organic compounds commonly found in environmental samples. The proposed predictive figure approach is based on the mobile phase composition at solute elution (expressed as % acetonitrile), that has the major advantage of making the model reusable by other laboratories, since the elution composition is independent of both the column geometry and the UHPLC-system. The model quality was assessed and was altered neither by the columns from different lots, nor by the complex matrices of environmental water samples. Then, the solute retention of any organic compound present in water samples is expected to be predicted within ± 14.3% acetonitrile by our model. Solute retention can therefore be used as a supplementary tool for the identification of environmental contaminants by UHPLC-HRMS, in addition to mass spectrometry data already used in the suspect screening approach.

Prediction of Chromatography Conditions for Purification in Organic Synthesis Using Deep Learning

In this research, a process for developing normal-phase liquid chromatography solvent systems has been proposed. In contrast to the development of conditions via thin-layer chromatography (TLC), this process is based on the architecture of two hierarchically connected neural network-based components. Using a large database of reaction procedures allows those two components to perform an essential role in the machine-learning-based prediction of chromatographic purification conditions, i.e., solvents and the ratio between solvents. In our paper, we build two datasets and test various molecular vectorization approaches, such as extended-connectivity fingerprints, learned embedding, and auto-encoders along with different types of deep neural networks to demonstrate a novel method for modeling chromatographic solvent systems employing two neural networks in sequence. Afterward, we present our findings and provide insights on the most effective methods for solving prediction tasks. Our approach results in a system of two neural networks with long short-term memory (LSTM)-based auto-encoders, where the first predicts solvent labels (by reaching the classification accuracy of 0.950 ± 0.001) and in the case of two solvents, the second one predicts the ratio between two solvents (R2 metric equal to 0.982 ± 0.001). Our approach can be used as a guidance instrument in laboratories to accelerate scouting for suitable chromatography conditions.

A review of pretreatment and analysis of macrolides in food (Update Since 2010)

Macrolides are versatile broad-spectrum antibiotics whose activity stems from the presence of a macrolide ring. They are widely used in veterinary medicine to prevent and treat disease. However, because of their improper use and the absence of effective regulation, these compounds pose a threat to human health and the environment. Consequently, simple, quick, economical, and effective techniques are required to analyze macrolides in animal-derived foods, biological samples, and environmental samples. This paper presents a comprehensive overview of the pretreatment and analytical methods used for macrolides in various sample matrices, focusing on the developments since 2010. Pretreatment methods mainly include liquid-liquid extraction, solid-phase extraction, matrix solid-phase dispersion, and microextraction methods. Detection and quantification methods mainly include liquid chromatography (coupled to mass spectrometry or other detectors), electrochemical methods, capillary electrophoresis, and immunoassays. Furthermore, a comparison between the pros and cons of these methods and prospects for future developments are also discussed.

Universal response method for the quantitative analysis of multi-components in josamycin and midecamycin using liquid chromatography coupled with charged aerosol detector

Josamycin and midecamycin are consisted of three groups of components with different ultraviolet maximum absorption wavelengths (λ_max), which are 231 nm, 280 nm and 205 nm. The quantitative analysis of all these components is challengeable due to the absence of the respective reference substances. To address this problem, universal and reliable methods were developed using high performance liquid chromatography coupled with charged aerosol detector (HPLC-CAD) for the quantitative analysis of components in josamycin and midecamycin. The chromatographic conditions and CAD parameters setting were optimized. Subsequently, the components were identified using HPLC coupled with ion trap/time-of-flight mass spectrometry (IT/TOF MS). The developed methods were validated by assessing linearity, limit of quantitation (LOQ), accuracy, precision and robustness. Good separations were achieved for all components and the adjustment of the filter valve and power function value efficiently improved sensitivity. The developed methods were more comprehensive than current HPLC-UV method. The experimental results demonstrated good linearity with coefficients of determination (R²) greater than 0.999 in the range of 0.002-0.30 mg mL^-1. The limits of detection (LOD) were ranging from 1.8 to 2.0 μg·mL^-1. The intra-day and inter-day RSD values were less than 2.0 % (n = 6) and 5.6 % (n = 9) respectively. The recoveries were 95.0 %-124.0 % at the spiked concentration levels of 0.05 %, 0.50 %, 0.10 % and 2.5 % with relative standard deviations (RSDs, n = 3) lower than 2.0 %. Finally, the developed methods were successfully applied to the quantitative analysis of minor components and used main components (leucomycin A₃ and midecamycin A₁) as alternative reference substance of minor components. The overall results demonstrated that the HPLC-CAD was a good alternative for the quantitative analysis of multi-components in 16-membered macrolides.

Development of a method of analysis for profiling of the impurities in phenoxymethylpenicillin potassium based on the analytical quality by design concept combined with the degradation mechanism of penicillins

Accurate analysis of all of the impurities present in a substance is critical for controlling the impurity profiles of drugs. Penicillins can easily yield a formidable array of degradation-related impurities (DRIs) with significantly different polarities and charge properties, which renders identifying each one a complicated matter. In this work, phenoxymethylpenicillin potassium (Pen V) was selected to find a way to quickly establish a robust analysis method for the impurity profiling of penicillin. Based on the analytical quality by design (AQbD) concept and the degradation mechanism of the drug, structures of all of the DRIs were first proposed. Then Pen V and its detected DRIs were separated and identified by liquid chromatography-tandem mass spectrometry method (LC-MS). Characteristic fragment ions and mass fragmentation process of Pen V and its detected DRIs were summarized. In addition, a quantitative structure-retention relationship (QSRR) model was constructed to predict the retention times of undetected impurities and to evaluate whether the chromatographic system can separate them. Finally, a stability-indicating high-performance liquid chromatography (HPLC) method was developed that can separate all of the DRIs of Pen V.

Qualitative and quantitative assessment of related substances in the Compound Ketoconazole and Clobetasol Propionate Cream by HPLC-TOF-MS and HPLC

Related substances in pharmaceutical formulations are associated with their safety, efficacy and stability. However, there is no overall study already published on the assessment of related substances in the Compound Ketoconazole and Clobetasol Propionate Cream. In this work, a reliable HPLC-TOF-MS qualitative method was developed for the analysis of related substances in this preparation with a quick and easy extraction procedure. Besides the active pharmaceutical ingredients, two compounds named ketoconazole impurity B′ optical isomer and ketoconazole impurity E were identified. Furthermore, a new HPLC method for qualitative and quantitative assessment on related substances and degradation products, which were found in the stability test, was established and validated. The single standard to determine multi-components method was applied in the quantitative analysis, which was an effective way for reducing cost and improving accuracy. This study can provide a creative idea for routine analysis of quality control of the Compound Ketoconazole and Clobetasol Propionate Cream.

Characterization of a new component and impurities in josamycin by trap-free two-dimensional liquid chromatography coupled to ion trap time-of-flight mass spectrometry

RATIONALE

The toxicities of the impurities of a drug will affect the clinical effects and cause potential health risk; therefore, it is essential to study profiles of the impurities. In this study, a new structural type of component and two acid degradation impurities in josamycin were discovered and characterized for the further improvement of official monographs in pharmacopoeias.

METHODS

The component and acid degradation impurities in josamycin were separated and preliminary characterized by trap-free two-dimensional liquid chromatography coupled to high-resolution ion trap time-of-flight mass spectrometry (2D LC/IT-TOF MS) in both positive and negative electrospray ionization mode. The eluent of each peak from the first dimensional chromatographic system was trapped by a switching valve and subsequently transferred to the second dimensional chromatographic system, which was connected to the mass spectrometer. Full scan MS was firstly conducted to obtain the exact m/z values of the molecules. Then LC/MS/MS and LC/MS/MS/MS experiments were performed on the compounds of interest.

RESULTS

A new structural type of component, which was named as josamycin A, and two acid degradation impuritiess, which were identified as impurity I and impurity II, were discovered in josamycin. Their structures and fragmentation pattern were deduced according to MSⁿ data. Furthermore, josamycin A was synthesized and impurity I was separated by preparative HPLC. The structures of josamycin A and the impurities were confirmed by ¹ H NMR and ¹³ C NMR data.

CONCLUSIONS

Josamycin A was produced when the hydroxyl group on the macrolide of josamycin was oxidized into a carbonyl group. Impurity I and impurity II were produced by the loss of one molecule of acetyl mycaminose from josamycin and josamycin A, respectively. Compared with josamycin, the experimental results showed that josamycin A had a higher antibacterial activity with similar cytotoxicity, while impurity I had no antibacterial activity but a higher cytotoxicity. As a result, the control of impurity I is significant.

Multi-analytical strategy for unassigned peaks using physical/mathematical separation, fragmental rules and retention index prediction: An example of sesquiterpene metabolites characterization in Cyperus rotundus

Comprehensive two-dimensional gas chromatography- mass spectrometry (GC × GC-qMS) can provide powerful physical separation, signal enhancement, and spectral identification for analytes in complex samples. Unassigned peaks are commonly presented in the untargeted profile after a single run with EI-MS spectral matching and retention index (RI) confirmation. The procedure proposed in this work can be applied as a general method for suggesting or narrowing down the candidates of unassigned GC × GC-qMS peaks. To begin, peak purity detection and chemometric resolution are employed to acquire pure mass spectra. In addition, the fragmental rules and in-silico spectra from structures are available for annotating certain unassigned peaks with reference spectra that are not observed in commercial databases. Furthermore, the procedure proposed in this work allows for in silico RI calculation by means of random forest (RF) analysis based on the retention data under the same chromatographic conditions. The calculated RIs can aid in analysis when the RI information of peaks of interest is not available in retention data libraries. Using the proposed strategy, certain unassigned peaks can be attributed to sesquiterpene metabolites in an in-house database for Cyperus rotundus.