CircRNA hsa_circ_0014130 function as a miR-132-3p sponge for playing oncogenic roles in bladder cancer via upregulating KCNJ12 expression.

The modern categories of endogenous non-coding RNAs, namely circular RNAs (circRNAs), involved within the carcinogenesis and progression of various human cancers. The fundamental aim of the current investigation was the evaluation of the hsa_circ_0014130 expressions, their biological functions, and potential regulatory network in bladder cancer. The level of expression for hsa_circ_0014130 was evaluated by qRT-PCR, and its relationships to clinicopathological features and survival outcomes of cases experiencing cancer of the bladder were scrutinized. The impact of hsa_circ_0014130 expressions on biological attitudes of bladder cancer cells in vitro was investigated. The interactions between hsa_circ_0014130 and microRNA (miRNA) sponge, miRNA, and its direct targets were determined by RNA pull-down as well as luciferase reporter gene assay. The correlations of their expression were determined by Pearson's correlation analysis. Rescue experiments were carried out to identify the biological roles of the regulation network. The expressions of hsa_circ_0014130 were markedly ameliorated in bladder cancer samples and linked with aggressive characteristics and unfavorable survival. Ectopic expression of hsa_circ_0014130 clearly enhanced the differentiation, proliferative, migratory, invasive potential of the cell in bladder cancer, and the development of tumor xenograft in vivo, while malignant biological behaviors were inhibited by hsa_circ_0014130 knockdown. The expression of hsa_circ_0014130 was tied to miR-132-3p in a negative manner with the cells and tissues of bladder cancer. hsa_circ_0014130 function as a competitive endogenous RNA for miR-132-3p to play oncogenic roles in bladder cancer cells. On the other hand, KCNJ12 was a straightforward target of miR-132-3p at the downstream, and the expressions of KCNJ12 were inversely related to that of miR-132-3p. Furthermore, a significantly positive correlation was found between hsa_circ_0014130 and KCNJ12 mRNA expression. More importantly, the oncogenic impact of hsa_circ_0014130 on bladder cancer cells was partly suppressed by ectopic expression of miR-132-3p or KCNJ12 knockdown. The underlined data revealed that hsa_circ_0014130 exerted its biological roles by regulating miR-132-3p/KCNJ12 expression. Further research revealed hsa_circ_0014130/miR-132-3p/KCNJ12 axis has participated in the Epithelial-mesenchymal transition (EMT) progress and GSK3ß/AKT signaling pathway. hsa_circ_0014130 works as a sponge of miR-132-3p to advance the oncogenesis and metastasis of bladder cancer by regulation of the KCNJ12 expression. These achievements might ameliorate the comprehension of tumor pathogenesis and provide novel therapeutic targets for cancer of the bladder.

Effect of alprazolam on rat serum metabolic profiles.

We developed a serum metabolomic method by gas chromatography-mass spectrometry (GC-MS) to evaluate the effect of alprazolam in rats. The GC-MS with HP-5MS (0.25 µm × 30 m × 0.25 mm) mass was conducted in electron impact ionization (EI) mode with electron energy of 70 eV, and full-scan mode with m/z 50-550. The rats were randomly divided to four groups, three alprazolam-treated groups and a control group. The alprazolam-treated rats were given 5, 10 or 20 mg/kg (low, medium, high) of alprazolam by intragastric administration each day for 14 days. The serum samples were corrected on the seventh and fourteenth days for metabolomic study. The blood was collected for biochemical tests. Then liver and brain were rapidly isolated and immersed for pathological study. Compared with the control group, on the seventh and fourteen days, the levels of d-glucose, 9,12-octadecadienoic acid, butanoic acid, l-proline, d-mannose and malic acid had changed, indicating that alprazolam induced energy metabolism, fatty acid metabolism and amino acid metabolism perturbations in rats. There was no significant difference for alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, urea and uric acid between controls and alprazolam groups. According to the pathological results, alprazolam is not hepatotoxic. Metabolomics could distinguish different alprazolam doses in rats.

Inhibitory Effect of Apigenin on Losartan Metabolism and CYP2C9 Activity in vitro.

BACKGROUND: CYP2C9 is one of the most important phase I drug-metabolizing enzymes in liver. The objective of this work was to investigate the effects of apigenin on the metabolism of losartan and human CYP2C9 and rat CYP2C11 activity in vitro. METHODS: Different concentrations of apigenin were added to a 100 mmol/l Tris-HCl reaction mixture containing 2 pmol/ml recombinant human CYP2C9.1, 0.25 mg/ml human liver microsomes or 0.5 mg/ml rat liver microsomes to determine the half maximal inhibition or a half-maximal inhibitory concentration (IC50) on the metabolism of losartan. In addition, diclofenac used as CYP2C9 substrate was performed to determine the effects of apigenin on CYP2C9. RESULTS: The results showed that apigenin has the inhibitory effect on the metabolism of losartan in vitro, the IC50 was 7.61, 4.10 and 11.07 µmol/l on recombinant CYP2C9 microsomes, human liver microsomes and rat liver microsomes, respectively. Meanwhile, apigenin's mode of action on human CYP2C9 activity was competitive for the substrate diclofenac. In contrast to its potent inhibition of CYP2C9 in humans (9.51 µmol/l), apigenin had lesser effects on CYP2C11 in rat (IC50 = 15.51 µmol/l). CONCLUSION: The observations imply that apigenin has the inhibitory effect on the metabolism of losartan and CYP2C9 activity in vitro. More attention should be paid as to when losartan should be administrated combined with apigenin.

Differential effects of ketoconazole, itraconazole and voriconazole on the pharmacokinetics of imatinib and its main metabolite GCP74588 in rat.

The aim of the study was to develop a high performance liquid chromatography method for simultaneous determination of imatinib and CGP74588 in rat serum and study the inhibition effects of ketoconazole, itraconazole and voriconazole on pharmacokinetics of imatinib and CGP74588 in rats. In our study, we found that ketoconazole caused a significant increase (63.4%) in the AUC of imainib and a 28.8% increase in Cmax, which was greater than that of itraconazole but lower than that of voriconazole. When co-administered with voriconazole, pharmacokinetic parameters of imatinib were not significantly altered except for a 36.8% increase in the Cmax of imtinib. The Cmax of CGP74588 was decreased by 55.8% and AUC(0-∞) 49.7%, while the Vz/F and CLz/F values were increased by 1.7-fold and 1.1-fold, respectively. Itraconazole did not significantly influence the pharmacokinetic parameters of imatinib and CGP74588. The difference may be related to the different variation of inhibition sites of the three azole antifungal agents on CYP3A4 and P-gp. In clinical, when imatinib was co-administrated with ketoconazole or voriconazole, dose adjustment of imatinib should be taken into account.

5-[(Z)-2,3-Dimeth-oxy-benzyl-idene]-1,2,4-triazolo[3,2-b][1,3]thia-zol-6(5H)-one.

The title compound, C(13)H(11)N(3)O(3)S, was synthesized from 1H-1,2,4-triazole-5-thiol in a one pot reaction. The fused thia-zolo[3,2-b][1,2,4]triazole system is essentially coplanar with the benzene ring: they enclose an inter-planar angle of 1.37 (13)°. The olefinic double bond is in a Z conformation. In the crystal, C-H⋯N hydrogen bonds link the mol-ecules into double layers parallel to the ab plane.

The effect of anlotinib on the pharmacokinetic profile of oxycodone and the underlying mechanism.

PURPOSE: Cancer patients experience pain during medical treatment. Therefore, anticancer drugs and painkillers are often prescribed together. This study aims to determine the interaction between anlotinib and oxycodone and reveal the underlying mechanism. METHODS: UPLC-MS/MS, an efficient and sensitive method, was used for the simultaneous determination of oxycodone and oxycodone metabolites. Sprague-Dawley rats were given oxycodone with or without anlotinib. Then, UPLC-MS/MS was used to determine the blood concentration of oxycodone. To study the interaction mechanism, rat and human liver microsomes (HLMs) were used for determining enzyme kinetics. RESULTS: Long-term administration of oxycodone combined with anlotinib resulted in significantly increased pharmacokinetic parameters AUC(0-t), AUC(0-∞), and Cmax for oxycodone, indicating that anlotinib inhibited oxycodone. In vitro kinetic measurements indicated that anlotinib inhibited the metabolism of oxycodone through a mixed mechanism. Further studies indicated that in HLMs, anlotinib strongly inhibited the metabolism of oxycodone. CONCLUSION: This study showed that anlotinib inhibited the metabolism of oxycodone both in vitro and in vivo. It is recommended that the dose of oxycodone should be reconsidered when oxycodone is combined with anlotinib in clinical practice.

Application of back-propagation artificial neural network and curve estimation in pharmacokinetics of losartan in rabbit.

In order to develop pharmacokinetic model, a well-known multilayer feed-forward algorithm back-propagation artificial neural networks (BP-ANN) was applied to the pharmacokinetics of losartan in rabbit. The plasma concentrations of losartan in twelve rabbits, which were divided into two groups and given losartan 2 mg/kg by intravenous (Iv) and intragastrical (Ig) administration, were determined by LC-MS. The BP-ANN model included one input layer, hidden layers, and one output layer was constructed and compared with curve estimation based on the time-concentration data of losartan. The results showed the BP-ANN model had high goodness of fit index and good coherence (R > 0.99) between forecasted concentration and measured concentration both in Iv and Ig administration. The residuals of each concentrations generated by BP-ANN model were all smaller than Curve estimation. The pharmacokinetic result showed there was no significant difference between measured and simulated pharmacokinetic parameters including AUC(0-t), AUC(0-∞), MRT(0-t), MRT(0-∞), T1/2 V and Cmax (P > 0.05). In conclusion, the BP-ANN model has remarkably accurate predictions ability, which better than Curve estimation, and can be used as a utility tool in pharmacokinetic experiment.