Isolation and identification of human metabolites from a novel anti-tumor candidate drug 5-chlorogenic acid injection by HPLC-HRMS/MSn and HPLC-SPE-NMR

A new approach to prediction riboflavin absorbance using imprinted polymer and ensemble machine learning algorithms

The molecularly imprinted polymer (MIP) is useful for measuring the amount of riboflavin (vitamin B2), in various samples using UV/Vis instruments. The practical optimization of the MIP synthesis conditions has a number of drawbacks, like the need to spend money, the need to spend time, the use of the compounds that cause contamination, needing laboratory equipment and tools. Using machine learning (ML) to predict the amount of riboflavin absorbance is a creative solution to overcome the problems and shortcomings of optimizing polymer synthesis conditions. In fact, by using the model without needing real work in the laboratory, the optimum laboratory conditions are determined, and as a result the maximized absorption of the riboflavin is obtained. In this paper, MIP was synthesized for selective extraction of the riboflavin, and UV/Vis spectrophotometry was used to quantitatively measure riboflavin absorbance. Various factors affect the performance of the polymer. The effect of six important factors, including the molar ratio of the template, the molar ratio of monomer, the molar ratio of cross-linker, loading time, stirring rate, and pH, were investigated. Then, using ensemble ML algorithms, like gradient boosting (GB), extra trees (ET), random forest (RF), and Ada boost (Ada) algorithms, an accurate model was created to predict the riboflavin absorption. Also, the mutual information feature selection method was used to determine the important features. The results of using feature selection method was shown that variables such as the molar ratio of the template, the molar ratio of the monomer, and the molar ratio of the cross-linker had a high effect on riboflavin absorbance. The GB and Ada boost algorithms performed better than ET and RF algorithms. After tuning the n-estimator hyper parameter (n-estimator = 300), the GB algorithm was shown an excellent performance in predicting the absorbance of riboflavin and the maximum R²-scoring of the model was obtained at 0.965995, the minimum of the mean absolute error (MAE), and mean square error (MSE) of the model respectively were obtained -0.003711 and -0.000078. Therefore, by using the proposed model, it is possible to predict riboflavin absorbance theoretically, and with high accuracy by changing the inputs of model, and using the model instead of working in the lab saves time, money, chemical compounds, and lab ware.

Comprehensive profiling and characterization of the absorbed components and metabolites in mice serum and tissues following oral administration of Qing-Fei-Pai-Du decoction by UHPLC-Q-Exactive-Orbitrap HRMS

Qing-Fei-Pai-Du decoction (QFPDD) is a Chinese medicine compound formula recommended for combating corona virus disease 2019 (COVID-19) by National Health Commission of the People's Republic of China. The latest clinical study showed that early treatment with QFPDD was associated with favorable outcomes for patient recovery, viral shedding, hospital stay, and course of the disease. However, the effective constituents of QFPDD remain unclear. In this study, an UHPLC-Q-Orbitrap HRMS based method was developed to identify the chemical constituents in QFPDD and the absorbed prototypes as well as the metabolites in mice serum and tissues following oral administration of QFPDD. A total of 405 chemicals, including 40 kinds of alkaloids, 162 kinds of flavonoids, 44 kinds of organic acids, 71 kinds of triterpene saponins and 88 kinds of other compounds in the water extract of QFPDD were tentatively identified via comparison with the retention times and MS/MS spectra of the standards or refereed by literature. With the help of the standards and in vitro metabolites, 195 chemical components (including 104 prototypes and 91 metabolites) were identified in mice serum after oral administration of QFPDD. In addition, 165, 177, 112, 120, 44, 53 constituents were identified in the lung, liver, heart, kidney, brain, and spleen of QFPDD-treated mice, respectively. These findings provided key information and guidance for further investigation on the pharmacologically active substances and clinical applications of QFPDD.

Current Advances in Naturally Occurring Caffeoylquinic Acids: Structure, Bioactivity, and Synthesis

Caffeoylquinic acids (CQAs) are a broad class of secondary metabolites that have been found in edible and medicinal plants from various families. It has been 100 years since the discovery of chlorogenic acid in 1920. In recent years, a number of naturally derived CQAs have been isolated and structurally elucidated. Accumulated evidence demonstrate that CQAs have a wide range of biological activities, such as antioxidation, antibacterial, antiparasitic, neuroprotective, anti-inflammatory, anticancer, antiviral, and antidiabetic effects. Up to date, some meaningful progresses on the biosynthesis and total synthesis of CQAs have also been made. Therefore, it is necessary to comprehensively summarize the structure, biological activity, biosynthesis, and chemical synthesis of CQAs. This review provides extensive coverage of naturally occurring CQAs discovered from 1990 until 2020. Modern isolation techniques, chemical data (including structure, biosynthesis, and total synthesis), and bioactivity are summarized. This would be helpful for further research of CQAs as potential pharmaceutical agents.

Chlorogenic acid effectively treats cancers through induction of cancer cell differentiation

Rationale: Inducing cancer differentiation is a promising approach to treat cancer. Here, we identified chlorogenic acid (CA), a potential differentiation inducer, for cancer therapy, and elucidated the molecular mechanisms underlying its differentiation-inducing effects on cancer cells.

Methods: Cancer cell differentiation was investigated by measuring malignant behavior, including growth rate, invasion/migration, morphological change, maturation, and ATP production. Gene expression was analyzed by microarray analysis, qRT-PCR, and protein measurement, and molecular biology techniques were employed for mechanistic studies. LC/MS analysis was the method of choice for chemical detection. Finally, the anticancer effect of CA was evaluated both in vitro and in vivo.

Results: Cancer cells treated with CA showed reduced proliferation rate, migration/invasion ability, and mitochondrial ATP production. Treating cancer cells with CA resulted in elevated SUMO1 expression through acting on its 3'UTR and stabilizing the mRNA. The increased SUMO1 caused c-Myc sumoylation, miR-17 family downregulation, and p21 upregulation leading to G₀/G₁ arrest and maturation phenotype. CA altered the expression of differentiation-related genes in cancer cells but not in normal cells. It inhibited hepatoma and lung cancer growth in tumor-bearing mice and prevented new tumor development in naïve mice. In glioma cells, CA increased expression of specific differentiation biomarkers Tuj1 and GFAP inducing differentiation and reducing sphere formation. The therapeutic efficacy of CA in glioma cells was comparable to that of temozolomide. CA was detectable both in the blood and brain when administered intraperitoneally in animals. Most importantly, CA was safe even at very high doses.

Conclusion: CA might be a safe and effective differentiation-inducer for cancer therapy. “Educating” cancer cells to differentiate, rather than killing them, could be a novel therapeutic strategy for cancer.

Chlorogenic Acid Improves the Regorafenib Effects in Human Hepatocellular Carcinoma Cells

Chlorogenic acid (CGA) is a polyphenol present in many human dietary foods. Several studies indicated a beneficial role of CGA in the prevention of cancer and an enhancement of chemotherapy when combined with CGA in the treatment of human hepatocarcinoma (HCC). Drug toxicity, resistance and subsequent disease progression represent a problem in HCC management, although treatment with the multikinase inhibitor Regorafenib improved overall survival. This study focused on the evaluation of the effects of combined treatment using both low Regorafenib concentrations and CGA as natural compound in HCC cells. The analysis of cell proliferation by Ki67 staining and cell cycle progression showed that CGA enhanced Regorafenib-mediated cell growth inhibition. Moreover, CGA potentiated the apoptotic effect of Regorafenib by the activation of the pro-apoptotic Annexin V, Bax and Caspase 3/7 and the inhibition of anti-apoptotic Bcl2 and Bcl-xL. Combined treatments were also effective in inhibiting cell motility. The mechanisms underlying the positive effects of combining CGA and Regorafenib were also addressed and an increased inhibition of MAPK (mitogen-activated protein kinase)and PI3K/Akt/mTORC (phosphatidylinositol-3-kinase (PI3K)/Akt and the mammalian target of rapamycin (mTOR) signaling was observed. Overall, these data demonstrated that co-treatment with Regorafenib and CGA enhanced Regorafenib action, reducing its cytotoxicity in HCC cells. In conclusion, this drug combination could be considered as a safe and more effective approach in HCC therapy.