Chromatographic Data Research Articles

An inulin-type fructan (AMP1-1) from Atractylodes macrocephala with anti-weightlessness bone loss activity

Atractylodes macrocephalon polysaccharides of alleviating weightlessness-induced bone loss (WIBL) are rarely reported. In this study, a neutral polysaccharide fragment named AMP1-1 was isolated and purified. Monosaccharide composition and gel permeation chromatography analysis indicated that AMP1-1 was composed of glucose and fructose with molecular weight of 1.433 kDa. Based on data of gas chromatography–mass spectrometer (GCMS), a linear backbone consisted of α-d-Glcp-(1→ and →1)-β-d-Fruf-2→ was discovered. Combining results from nuclear magnetic resonance (NMR), the inulin-type fructan AMP1-1 was identified as α-d-Glcp-1→(2-β-d-Fruf-1)7. Anti-WIBL activity of AMP1-1 was evaluated though analyzation of mechanical properties, BALP and TRAP 5b activities on femur. In vitro mRNA expression indicated that anti-WIBL activity of AMP1-1 was achieved by promote bone formation and inhibit bone resorption in primary osteoblasts and RAW264.7 cell lines under simulated weightlessness. In conclusion, the inulin-type fructan AMP1-1 with α-d-Glcp-1→(2-β-d-Fruf-1)7 had anti-WIBL activity via remodeling bone homeostasis.

Prediction Models for Brain Distribution of Drugs Based on Biomimetic Chromatographic Data.

The development of high-throughput approaches for the valid estimation of brain disposition is of great importance in the early drug screening of drug candidates. However, the complexity of brain tissue, which is protected by a unique vasculature formation called the blood–brain barrier (BBB), complicates the development of robust in silico models. In addition, most computational approaches focus only on brain permeability data without considering the crucial factors of plasma and tissue binding. In the present study, we combined experimental data obtained by HPLC using three biomimetic columns, i.e., immobilized artificial membranes, human serum albumin, and α1-acid glycoprotein, with molecular descriptors to model brain disposition of drugs. Kp,uu,brain, as the ratio between the unbound drug concentration in the brain interstitial fluid to the corresponding plasma concentration, brain permeability, the unbound fraction in the brain, and the brain unbound volume of distribution, was collected from literature. Given the complexity of the investigated biological processes, the extracted models displayed high statistical quality (R2 > 0.6), while in the case of the brain fraction unbound, the models showed excellent performance (R2 > 0.9). All models were thoroughly validated, and their applicability domain was estimated. Our approach highlighted the importance of phospholipid, as well as tissue and protein, binding in balance with BBB permeability in brain disposition and suggests biomimetic chromatography as a rapid and simple technique to construct models with experimental evidence for the early evaluation of CNS drug candidates.

Expansion of the application range of pyrolysis-gas chromatography to copolymer sequence determination: Acrylate copolymers

The sequence distribution, which describes the order of monomers in a copolymer, is notoriously hard to study. The current standard in polymer sequence determination, NMR, is limited to simple copolymers due to lack in resolution and sensitivity. Therefore, an alternative method to determine the sequence of complex copolymers in the form of a developed pyrolysis-GC-MS method was investigated. Pyrolysis-GC-MS was applied to various acrylate copolymers; in the obtained pyrograms clear trimer distributions were observed and identified. Based on these results the sequence of these systems was calculated. The determined sequence values were found to be in line with expectations, demonstrating that the method was suitable for the sequence determination of binary acrylate copolymers. The developed method was also applied on various tertiary acrylate copolymers. Tertiary copolymers form many more distinct trimers rendering assignment more challenging. Nevertheless, based on a combination of chromatographic and mass spectrometric data, all formed trimers were assigned. To the best of our knowledge, this is the first time that a tertiary acrylate is sequenced by pyrolysis-GC.

Hydrothermal generation of hydrocarbons in basement rocks, Southern Tuscany

Carbonaceous material in the form of graphitic carbon, amorphous carbon and liquid hydrocarbons occurs in the metamorphic rocks of Monti Romani in Southern Tuscany. Raman spectroscopic analyses show a contrast in structural ordering between carbon in the host rocks and carbon films and nodules at the contact with high temperature mineralized veins. Microscopy and gas chromatography additionally indicate liquid hydrocarbons, with a thermal maturity at the peak of the oil window. The association of hydrocarbons with high temperature fluids together with gas chromatographic and spectroscopic data indicate a probable hydrothermal origin of the oils, from the late Miocenic sediments that fill the Tafone Graben. A model is proposed in which hydrocarbons were generated along the fault that borders the Tafone Graben and then migrated toward the basements rocks at the footwall. The presence of 25-norhopane indicates biodegradation in the depth interval between about 100 and 1500 m. The hydrothermal generation of hydrocarbons could occur in other geothermal areas in Southern Tuscany where the presence of hydrocarbons has been reported but never fully explained.

Analysis and Research on the Reasons for the Increase of Com ponents in 500kV Transformer Oil

This paper analyzes the cause of the defect of component growth in a 500kV transformer oil. First, the historical operation of the main transformer is analyzed, and then the oil chromatography data is studied. The A-phase online chromatography shows that the acetylene content is in good agreement with the offline chromatography detection, showing an increasing trend. The disintegration inspection of the main phase A phase found that there were two obvious discharge breakdown points in the inner paper tube of the low-voltage coil. According to the analysis results, the A-phase transformer should be managed and controlled, especially the oil chromatography tracking period (once every two weeks) should be shortened, and the load should be controlled if necessary.

Data independent acquisition mass spectrometry in relation to potential diagnostic and prognostic biomarkers for high-grade serous ovarian cancer.

e17564 Background: Ovarian cancer (OC) is one of the most common causes of cancer-related death among women worldwide. High-grade serous ovarian cancer (HGSOC) represents the major OC histological type, and challenges associated with its early detection and prediction of clinical outcomes significantly contribute to the high prevalence and poor prognosis of OC. Lack of reliable biomarkers further exacerbates the low survival rate of patients with HGSOC. Methods: In this study, we performed liquid chromatography data independent acquisition tandem-mass spectrometry (LC-DIA-MS/MS) experiments on depleted serum samples [26 HGSOC cases, 24 healthy controls (HCs)] to identify potential diagnostic and prognostic biomarkers for HGSOC. Results: A total of 1,148 proteins were identified in all samples combined, representing one of the largest quantifications in the serum proteome of OC to date. Among them, 122 proteins showed differential expressions between the serum proteome of HGSOC patients and HCs, including 109 up- and 14 down-regulated proteins in HGSOC. These potential diagnostic biomarkers were involved in the pathways related to immune response, TGF- β and IGF signaling, and lipid metabolism. Moreover, we performed Kaplan-Meier survival analysis on the HGSOC patients with and without recurrence, and identified several potential prognostic biomarkers highly associated with progression free survival (PFS). Using independent cohort samples, we validated the expression levels of HGSOC serum biomarker candidates using targeted proteomics approaches (MRM and PRM). In vitro functional assays revealed that some of these biomarkers may play an essential role in cancer cell migration and proliferation in HGSOC. Conclusions: In summary, our LC-DIA-MS/MS analysis of the HGSOC serum proteome successfully identified novel diagnostic and prognostic biomarkers for HGSOC, some of which were experimentally shown to have functional relevance to the pathophysiology of OC.

Comprehensive profiling of phytochemicals in the fruits of Gardenia jasminoides Ellis and its variety using liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry.

The fruits of Gardenia jasminoides Ellis are an important herb medicine in Traditional Chinese Medicine (TCM) and have been used for thousands of years for clearing away heat and toxic materials. It mainly contains iridoids, pigments, organic acids, and flavonoids. Although belonging to one species, it has two kinds of cultivars and one variety widely distributed and sold. This study aims to develop anintegratedandefficientanalytical strategy for comprehensive profiling of phytochemicals and clarify the differences in all three populations. Based on reversed-phase ultra-high performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC/ESI-QTOFMS), an optimized analytical approach for comprehensive profiling of phytochemicals in the fruits of G. jasminoides was established in negative ionization mode. The holistic metabolites profiling was carried out on UHPLC/ESI-QTOFMS and data analysis program Progenesis QI, and a total of 80 metabolites were obtained and interpreted by chromatographic and tandem mass spectral data. The interpretation of metabolites comprises iridoids, pigments, organic acids, and flavonoids. Principal component analysis and partial least square-discriminant analysis were performed, and 19 main different components could be obtained to distinguish the three populations. Combined with non-targeted and targeted data analysis, the integrated strategy developed in this study was feasibly applied to discern differences in the profiles of the phytochemicals accumulating in the fruits of three populations of G. jasminoides.

Statistical Methods in the Study of Protein Binding and Its Relationship to Drug Bioavailability in Breast Milk.

Protein binding (PB) is indicated as the factor most severely limiting distribution in the organism, reducing the bioavailability of the drug, but also minimizing the penetration of xenobiotics into the fetus or the body of a breastfed child. Therefore, PB is an important aspect to be analyzed and monitored in the design of new drug substances. In this paper, several statistical analyses have been introduced to find the relationship between protein binding and the amount of drug in breast milk and to select molecular descriptors responsible for both pharmacokinetic phenomena. Along with descriptors related to the physicochemical properties of drugs, chromatographic descriptors from TLC and HPLC experiments were also used. Both methods used modification of the stationary phase, using bovine serum albumin (BSA) in TLC and human serum albumin (HSA) in HPLC. The use of the chromatographic data in the protein binding study was found to be positive —the most effective application of normal-phase TLC and HPLCHSA data was found. Statistical analyses also confirmed the prognostic value of affinity chromatography data and protein binding itself as the most important parameters in predicting drug excretion into breast milk.

Plasma Levels of Vitamin A in Early Pregnancy and Correlationship with Hypertensive Disorder.

Objective. Analyzing the vitamin A content in early pregnancy and finding out the relationship between the serum levels of vitamin A of pregnant women and hypertensive disorder. Method. A total of 4,188 pregnant women who had took part in vitamin A testing in Miyun District Hospital from November 2016 to March 2020 were collected. The serum levels of vitamin A were determined by high performance liquid chromatography, and clinical and testing data were collected for statistical analysis. The original data outcome was finally analyzed with the SPSS. Results. 266 Hypertensive disorder cases and 2836 normal pregnancy cases were analyzed with 27 cases of twin pregnancy, 315 cases without follow-up and 744 of diabetic pregnancies excluded. The 266 women were divided into four groups: 110 women were diagnosed gestational hypertension, 65 women were diagnosed preeclampsia, 78 women were diagnosed pregnancy with chronic hypertension, and 13 women were diagnosed chronic hypertension with preeclampsia. The results shows that vitamin A level of the hypertensive group was 0.46(±0.08) mg/L, 0.47 (±0.012) mg/L, 0.47 (±0.09) mg/L, and 0.52 (±0.012) mg/L, respectively, while the level of normal group was 0.44 (±0.09) mg/L. We found that there were differences between the normal pregnant group and the preeclampsia group with statistical significance (P < 0.05). The difference between the pregnancy with chronic hypertension group and the normal group was statistically significant (P < 0.05). The difference between the chronic hypertension with preeclampsia group and the normal group was also statistically significant (P < 0.05). Conclusion. Serum levels of vitamin A in early pregnant women have a certain correlation with the hypertensive disorder.

Semi-supervised Clustering Algorithm for Retention Time Alignment of Gas Chromatographic Data

Gas chromatography (GC) is an effective tool for the analysis of complex mixtures with a huge number of components. To keep tracking the chemical changes during the processes like plastic waste pyrolysis usually different sample states are profiled, but retention time drifts between the chromatograms make the comparability difficult. The aim of this study is to develop a fast and simple method to eliminate the time drifts between the chromatograms using easily accessible priori information. The proposed method is tested on GC chromatograms obtained by analysis of pyrolysis product (Mg/Y catalyst) of shredded real waste HDPE/PP/LDPE mixture. A modified k-means algorithm was developed to account the retention time drifts between samples (different sample states). The outcome of the retention time alignment is an averaged retention time for each peak from all the chromatograms which makes the comparison and further analysis (such as "fingerprinting") easier or possible.

Research on on-line monitoring data cleaning of transformer oil chromatogram based on machine learning and neural networks

Transformer oil chromatogram on-line monitoring device is a widely used sensing device to monitor the operation state of transformer. Due to the lack of hardware verification means, the on-line chromatographic data generally has the problem of drift. In actual operation, it is mostly used as the basis for trend judgment, but can not reflect the effective value of chromatographic measurement. In this paper, the LSTM neural networks is used to correct the on-line monitoring data of transformer oil chromatogram. The off-line test data combined with neural networks data cleaning technology are used to repeatedly feed back and calibrate the online data, so that the corrected chromatographic on-line number is steadily close to the off-line detection data, which greatly improves the availability and accuracy of on-line chromatographic data. The research results of this paper have important reference significance for the data cleaning of on-line monitoring and measurement of electrical equipment.

Near-Infrared Spectroscopy to Predict Provitamin A Carotenoids Content in Maize

Vitamin A deficiency (VAD) is a public health issue worldwide. Provitamin A (PVA) biofortified maize serves as an alternative to help combat VAD. Breeding efforts to develop maize varieties with high PVA carotenoid content combine molecular and phenotypic selection strategies. The phenotypic assessment of carotenoids is currently done using liquid chromatography, a precise but time- and resource-consuming methodology. Using near-infrared spectroscopy (NIRS) could increase the breeding efficiency. This study used ultra-performance liquid chromatography (UPLC) data from 1857 tropical maize genotypes as a training set and NIRS data to do an independent test of a set of 650 genotypes to predict PVA carotenoids using Bayesian and modified partial least square (MPLS) regression models. Both regression methods produced similar prediction accuracies for the total carotenoids (r2 = 0.75), lutein (r2 = 0.55), zeaxanthin (r2= 0.61), β-carotene (r2 = 0.22) and β-cryptoxanthin (BCX) (r2 = 0.57). These results demonstrate that Bayesian and MPLS regression of BCX on NIRS data can be used to predict BCX content, the current focus on PVA enhancement, and thus offers opportunities for high-throughput phenotyping at a low cost, especially in the early stages of PVA maize breeding pipeline when many genotypes must be screened.

Spatial-temporal data analysis of digital twin

Background: Digital Twin (DT) has proven to be one of the most promising technologies for routine monitoring and management of complex systems with uncertainties. Methods: Our work, which is mainly concerned with heterogeneous spatial-temporal data, focuses on exploring data utilization methodology in DT. The goal of this research is to summarize the best practices that make the spatial-temporal data analytically tractable in a systematic and quantifiable manner. Some methods are found to handle those data via jointly spatial-temporal analysis in a high-dimensional space effectively. We provide a concise yet comprehensive tutorial on spatial-temporal analysis considering data, theories, algorithms, indicators, and applications. The advantages of our spatial-temporal analysis are discussed, including model-free mode, solid theoretical foundation, and robustness against ubiquitous uncertainty and partial data error. Finally, we take the condition-based maintenance of a real digital substation in China as an example to verify our proposed spatial-temporal analysis mode. Results: Our proposed spatial-temporal data analysis mode successfully turned raw chromatographic data, which are valueless in low-dimensional space, into an informative high-dimensional indicator. The designed high-dimensional indicator could capture the ’insulation’ correlation among the sampling data over a long time span. Hence it is robust against external noise, and may support decision-making. This analysis is also adaptive to other daily spatial-temporal data in the same form. Conclusions: This exploration and summary of spatial-temporal data analysis may benefit the fields of both engineering and data science.

Identification of degradation products in flumazenil using LC-Q-TOF/MS and NMR: Degradation pathway elucidation

Flumazenil is an imidazobenzodiazepine derivative that antagonizes the actions of benzodiazepines. The degradation pathway elucidation plays an important role in the drug quality control. In this work, a reliable LC-Q-TOF/MS method was developed to separate and identify the degradation products of flumazenil generated in the stress testing conducted according to the ICH Q1A(R2) guideline. Fifteen degradation products were detected in total including three reported impurities and twelve unknown impurities. Based on the chromatographic and mass spectrometric data acquired, the structures of all the degradation products were identified. Besides, two major degradants were synthetized and further confirmed by NMR. The degradation pathways of flumazenil were elucidated, and the degradation characteristics of benzodiazepines were also discussed. The obtained results are of both great importance for the quality control of flumazenil and good reference for the degradation study of other benzodiazepines.

A Power Transformer Fault Prediction Method through Temporal Convolutional Network on Dissolved Gas Chromatography Data

The power transformer is an example of the key equipment of power grid, and its potential faults limit the system availability and the enterprise security. However, fault prediction for power transformers has its limitations in low data quality, binary classification effect, and small sample learning. We propose a method for fault prediction for power transformers based on dissolved gas chromatography data: after data preprocessing of defective raw data, fault classification is performed based on the predictive regression results. Here, Mish-SN Temporal Convolutional Network (MSTCN) is introduced to improve the accuracy during the regression step. Several experiments are conducted using data set from China State Grid. The discussion of the results of experiments is provided.

Implementation of chemometrics, design of experiments, and neural network analysis for prior process knowledge assessment, failure modes and effect analysis, scale-down model development, and process characterization for a chromatographic purification of Teriparatide.

Process understanding and characterization forms the foundation, ensuring consistent and robust biologics manufacturing process. Using appropriate modeling tools and machine learning approaches, the process data can be monitored in real time to avoid manufacturing risks. In this article, we have outlined an approach toward implementation of chemometrics and machine learning tools (neural network analysis) to model and predict the behavior of a mixed-mode chromatography step for a biosimilar (Teriparatide) as a case study. The process development data and process knowledge was assimilated into a prior process knowledge assessment using chemometrics tools to derive important parameters critical to performance indicators (i.e., potential quality and process attributes) and to establish the severity ranking for the FMEA analysis. The characterization data of the chromatographic operation are presented alongwith the determination of the critical, key and non- key process parameters, set points, operating, process acceptance and characterized ranges. The scale-down model establishment was assessed using traditional approaches and novel approaches like batch evolution model and neural network analysis. The batch evolution model was further used to demonstrate batch monitoring through direct chromatographic data, thus demonstrating its application for continuos process verification. Assimilation of process knowledge through a structured data acquisition approach, built-in from process development to continuous process verification was demonstrated to result in a data analytics driven model that can be coupled with machine learning tools for real time process monitoring. We recommend application of these approaches with the FDA guidance on stage wise process development and validation to reduce manufacturing risks.

Rapid and interference-free quantification of nine coumarins in Cnidii fructus using HPLC-DAD assisted with second-order calibration model

High performance liquid chromatography-diode array detection (HPLC-DAD) assisted with second-order calibration model based on alternating trilinear decomposition-assisted multivariate curve resolution (ATLD-MCR) model was proposed to quantify nine coumarins in traditional Chinese medicine (TCM) samples of Cnidii Fructus. This advanced analytical strategy shows good linearity (r > 0.9991), rapidity (t < 10.50 min) and accuracy, in low chromatographic resolution data even in the presence of potential interfering species. Satisfactory qualitative and quantitative results were achieved in the presence of complex matrix interferences from the TCM samples and the second-order advantage was fully exploited. The average spiked recoveries for most target analytes are in the range of 84.1%–108.0% with the standard deviations less than 6.9%. Furthermore, the quantitative results were further compared with a classical univariate calibration applied to a series of peak area data acquired from HPLC-UV detection. Statistical parameters and analytical figures of merit were calculated according to modern analytical protocols to validate the accuracy of the proposed analytical method. The results show that the proposed analytical method had the advantages of high-efficiency, low-cost and met the requirements of green analytical chemistry. Therefore, it promises to be a potential and universal way for qualitative and quantitative analysis of coumarins in pharmaceutical and food fields.

PHI-PLOT OPTIMIZATION STUDIES OF SOLVENT GRADIENT SIMULATED MOVING BED (SG-SMB) SEPARATION OF THE AMINO ACIDS TRYPTOPHAN AND TYROSINE

The phi-plot optimization approach has been effectively used as a search routine to determine the best operational settings for solvent-gradient simulated moving bed process (SG-SMB) separation of various molecular combinations, such as amino acids. Modulating the solvent strength (SG-SMB) improves the performance of the simulated moving bed process-SMB, resulting in a significant increase in purities and productivity, as well as a reduction in solvent consumption. The amino acids tryptophan and tyrosine were separated using the phi-plot optimization method, which has never been investigated before in the context of SG-SMB process separation. The mass transfer kinetic parameters were determined through the chromatographic dynamic data and incorporated into the SG-SMB simulator for the validation of the optimization conditions by the Phi-plot method. A series of optimization studies were carried out varying the solvent concentration and the process flow rates in the context of triangle theory. The dynamic stepwise SG-SMB simulator was used to evaluate the ideal operational conditions for separation using the phi-plot optimization approach. For the conditions studied, the new phi-plot optimization approach was able to determine the optimal operational settings for producing high-purity separations in the extract and raffinate, which was validated by the simulator. The optimization results of the studied conditions were plotted in the triangle theory separation regions which confirms the optimization technique as a viable search routine for such complex task of determination of the best operational conditions of SG-SMB.

Biomimetic chromatography-A novel application of the chromatographic principles.

Biomimetic chromatography is the name of the High Performance Liquid Chromatography (HPLC) methods that apply stationary phases containing proteins and phospholipids that can mimic the biological environment where drug molecules distribute. The applied mobile phases are aqueous organic with a pH of 7.4 to imitate physiological conditions that would be encountered in the human body. The calibrated retention of molecules on biomimetic stationary phases reveals a compound's affinity to proteins and phospholipids, which can be used to model the biological and environmental fate of molecules. This technology, when standardised, enables the prediction of in vivo partition and distribution behaviour of compounds and aids the selection of the best compounds for further studies to become a drug molecule. Applying biomimetic chromatographic measurements helps reduce the number of animal experiments during the drug discovery process. New biomimetic stationary phases, such as sphingomyelin and phosphatidylethanolamine, widen the application to the modelling of blood-brain barrier distribution and lung tissue binding. Recently, the measured properties have also been used to predict toxicity, such as phospholipidosis and cardiotoxicity. The aquatic toxicity of drugs and pesticides can be predicted using biomimetic chromatographic data. Biomimetic chromatographic separation methods may also be extended in the future to predict protein and receptor binding kinetics. The development of new biomimetic stationary phases and new prediction models will further accelerate the widespread application of this analytical method.

New HPLC, MS, and CDS Products Introduced in 2021–2022: A Brief Review

This installment describes newly introduced high-performance liquid chromatography (HPLC), mass spectrometry (MS), chromatography data systems (CDS), and related products in 2021–2022 and prior years. It summarizes their unique features and significant user benefits.