Prediction of HPLC retention times of tebipenem pivoxyl and its degradation products in solid state by applying adaptive artificial neural network with recursive features elimination

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.

Virtual Screening of C. Sativa Constituents for the Identification of Selective Ligands for Cannabinoid Receptor 2

The selective targeting of the cannabinoid receptor 2 (CB2) is crucial for the development of peripheral system-acting cannabinoid analgesics. This work aimed at computer-assisted identification of prospective CB2-selective compounds among the constituents of Cannabis Sativa. The molecular structures and corresponding binding affinities to CB1 and CB2 receptors were collected from ChEMBL. The molecular structures of Cannabis Sativa constituents were collected from a phytochemical database. The collected records were curated and applied for the development of quantitative structure-activity relationship (QSAR) models with a machine learning approach. The validated models predicted the affinities of Cannabis Sativa constituents. Four structures of CB2 were acquired from the Protein Data Bank (PDB) and the discriminatory ability of CB2-selective ligands and two sets of decoys were tested. We succeeded in developing the QSAR model by achieving Q² 5-CV > 0.62. The QSAR models helped to identify three prospective CB2-selective molecules that are dissimilar to already tested compounds. In a complementary structure-based virtual screening study that used available PDB structures of CB2, the agonist-bound, Cryogenic Electron Microscopy structure of CB2 showed the best statistical performance in discriminating between CB2-active and non-active ligands. The same structure also performed best in discriminating between CB2-selective ligands from non-selective ligands.

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.

Characterization of pharmaceutic structured triacylglycerols by high-performance liquid chromatography/tandem high-resolution mass spectrometry and its application to structured fat emulsion injection

RATIONALE

Structured triacylglycerols (STAGs) are a complex mixture of triacylglycerols which are esterified by long-chain fatty acids and medium-chain fatty acids with the same glycerol molecular backbone. As a kind of lipid pharmaceutic excipients, STAGs are used in the pharmaceutical industry as major components of structured fat emulsion injections and play an important role in pharmaceutic energy material because they improve nutritional status with faster elimination in a safe way. The composition and proportion of triacylglycerols in STAGs are closely related to the stability of pharmaceutical preparations and curative effects in the clinic. Therefore, it is necessary to characterize pharmaceutic STAGs using a rapid and accurate method.

METHODS

An analytical method for rapid and accurate determination of triacylglycerols in pharmaceutic STAGs was developed using high-performance liquid chromatography/tandem high-resolution mass spectrometry (HPLC/HRMS). Triacylglycerol components could be well separated on a Waters Xterra MS C8 (2.1 × 100 mm, 3.5 μm) column. Four-dimensional HPLC/HRMS data (high-resolution m/z, MS² data, retention time and isotopic intensity distribution) were used to identify triacylglycerols using Lipid Data Analyzer (LDA) software and the LIPID MAPS database. Then, these identified triacylglycerol components were relatively quantified by their corresponding normalized peak areas using representative standard curves of structurally similar standard substances.

RESULTS

Forty-seven kinds of triacylglycerol components in pharmaceutic STAGs and structured fat emulsion injection were identified and relatively quantified by this method. It has been shown that their retention times are in good correlation with the number of carbon atoms and carbon-carbon double bonds. The main components in pharmaceutic STAGs and structured fat emulsion injection were triacylglycerols containing both medium-chain fatty acids and long-chain fatty acids, while the other components, including triacylglycerols containing three medium-chain fatty acids and triacylglycerols containing three long-chain fatty acids, were relatively low.

CONCLUSIONS

This study has provided a rapid and accurate approach for the identification and quality control of pharmaceutic STAGs and structured fat emulsion injection and this approach can be extended to other lipid pharmaceutic excipients and used as an effective and reasonable control to guarantee the quality of pharmaceutical preparations.

Cyclodextrins as multifunctional excipients: Influence of inclusion into β-cyclodextrin on physicochemical and biological properties of tebipenem pivoxil

A novel approach for drug design based on the oral carbapenem analog tebipenem pivoxil (TP) has been proposed. The formation of the tebipenem pivoxil-β-cyclodextrin (TP-β-CD) complex resulted in changes concerning physicochemical properties of TP, which is significant for planning the development of an innovative pharmaceutical formulation as well as in the modifications of biological activity profile of the studied delivery system. The inclusion of TP into β-cyclodextrin (β-CD) was confirmed by spectral (infrared and Raman spectroscopies) and thermal method (differential scanning calorimetry). Precise indications of TP domains responsible for interaction with β-CD were possible through a theoretical approach. The most important physicochemical modifications obtained as an effect of TP inclusion were changes in solubility and its rate depending on acceptor fluids, and an increase in chemical stability in the solid state. Biologically essential effects of TP and β-CD interactions were decreased TP permeability through Caco-2 cell monolayers with the use of efflux effect inhibition and increased antibacterial activity. The proposed approach is an opportunity for development of the treatment in resistant bacterial infections, in which along with physicochemical modifications induced by a drug carrier impact, a carrier synergy with a pharmacological potential of an active pharmaceutical substance could be used.

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)

Macrolides are multicomponent drugs whose impurity control is always a challenge demanding analysis method with good sensitivity and selectivity. Three separate, sensitive, accurate liquid chromatography tandem mass spectrometry methods (LC-MS) were developed for the measurement of three 16-membered ring macrolides (josamycin, josamycin propionate and midecamycin acetate) and related substances in commercial samples. The characteristics of impurities in macrolides were summarized as useful guidance for the impurity analysis of this class of drugs. For each drug, a large number of unknown components have been detected with the high-sensitive MS detector and possible structures of the majority of them were postulated based on the summarized fragmentation rules of 16-membered ring macrolides. A QSRR model was constructed by multilinear regression to predict the retention times of identified impurities which were not detected by the LC-MS methods, without obtaining their reference standards. Satisfactory performance was obtained during leave-one-out cross-validation with a predictive ability (Q²) of 0.95. The generalisation ability of the model was further confirmed by an average error of 2.3% in external prediction. The best QSRR model, based on eight molecular descriptors, exhibited a promising predictive performance and robustness.

Quantitative structure-retention relationship model for the determination of naratriptan hydrochloride and its impurities based on artificial neural networks coupled with genetic algorithm

Mathematical modeling of Quantitative Structure - Property Relationships met great interest in fields of in silico drug design and more recently, pharmaceutical analysis. In our approach we proposed automated method of creation Quantitative Structure-Retention Relationship (QSRR) for analysis of triptans, selective serotonin 5-HT₁ receptor agonists used for the treatment of acute headache. The method was created using hybrid machine learning approach, namely Genetic algorithm (GA) coupled with artificial neutral networks (ANN). Performance of proposed hybrid GA-ANN model was evaluated with predicting relative retention times of naratriptan hydrochloride impurities. Several ANN types were coupled with GA and tested: single-layer ANN (SL-ANN), double-layer ANN (D-ANN) and higher order architectures: pi-sigma ANN (PS-ANN) and sigma-pi-sigma ANN (SPS-ANN). Partial Least Squares (PLS) method was used as a reference. The separation of naratriptan hydrochloride and its related products (impurities and degradation products) was obtained by developing a gradient high-performance liquid chromatography method with diode-array detector (HPLC-DAD). Degradation products during acid-basic hydrolysis were identified with an electrospray ionization tandem mass spectrometry (Q-TOF-MS/MS) detector. Independent data for outer validation of QSRR model was obtained from the determination of related products of sumatriptan succinate via an HPLC-DAD method. Accuracy of QSRR was measured by inner-validation on naratriptan data and outer validation on sumatriptan succinate samples. The best performing model were PS-ANN and SPS-ANN with mean errors of 8% (Q₂=0.87) and 15% (Q₂=0.77) on an inner-validation data set, respectively. Validation on similar samples from an outer validation data set of sumatriptan succinate impurities gave mean errors of 18% (R²_pred=0.64) and 17% (R²_pred=0.63) for the PS-ANN and SPS-ANN models, respectively.