Yubo Li

A review on phytochemistry, pharmacology and toxicology studies of Aconitum.

A number of species belonging to herbal genus Aconitum are well‐known and popular for their medicinal benefits in Indian, Vietnamese, Korean, Japanese, Tibetan and Chinese systems of medicine. It is a valuable drug as well as an unpredictable toxic material. It is therefore imperative to understand and control the toxic potential of herbs from this genus. In this review, the ethnomedicinal, phytochemistry, pharmacology, structure activity relationship and toxicology studies of Aconitum were presented to add to knowledge for their safe application.

Screening, Verification, and Optimization of Biomarkers for Early Prediction of Cardiotoxicity Based on Metabolomics

Drug-induced cardiotoxicity seriously affects human health and drug development. However, many conventional detection indicators of cardiotoxicity exhibit significant changes only after the occurrence of severe heart injuries. Therefore, we investigated more sensitive and reliable indicators for the evaluation and prediction of cardiotoxicity. We created rat cardiotoxicity models in which the toxicity was caused by doxorubicin (20 mg/kg), isoproterenol (5 mg/kg), and 5-fluorouracil (125 mg/kg). We collected data from rat plasma samples based on metabolomics using ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry. Following multivariate statistical and integration analyses, we selected 39 biomarker ions of cardiotoxicity that predict cardiotoxicity earlier than biochemical analysis and histopathological assessment. Because drugs with different toxicities may cause similar metabolic changes compared with other noncardiotoxic models (hepatotoxic and nephrotoxic models), we obtained 10 highly specific biomarkers of cardiotoxicity. We subsequently used a support vector machine (SVM) to develop a predictive model to verify and optimize the exclusive biomarkers. l-Carnitine, 19-hydroxydeoxycorticosterone, LPC (14:0), and LPC (20:2) exhibited the strongest specificities. The prediction rate of the SVM model is as high as 90.0%. This research provides a better understanding of drug-induced cardiotoxicity in drug safety evaluations and secondary development and demonstrates novel ideas for verification and optimization of biomarkers via metabolomics.

Determination of imperatorin in rat plasma by reversed-phase high-performance liquid chromatography after oral administration of Radix Angelicae dahuricae extract

A simple high-performance liquid chromatographic (HPLC) method has been developed for the determination of imperatorin in rat plasma and applied to a pharmacokinetic study in rats after administration of Radix Angelicae dahuricae extract. After addition of fluocinonide as an internal standard (IS), plasma samples were extracted with diethyl ether. HPLC analysis of the extracts was performed on a Diamonsil C18 analytical column using methanol-water (70:30, v/v) as the mobile phase. The UV detector was set at 254 nm. The standard curve was linear over the range 0.04-4.0 microg/mL. The lower limit of quantification was 0.04 microg/mL. The HPLC method developed could be easily applied to the determination and pharmacokinetic study of imperatorin in rat plasma after giving the animals Radix Angelicae dahuricae extract.

Plasma metabolic profiling analysis of nephrotoxicity induced by acyclovir using metabonomics coupled with multivariate data analysis

Acyclovir (ACV) is an antiviral agent. However, its use is limited by adverse side effect, particularly by its nephrotoxicity. Metabonomics technology can provide essential information on the metabolic profiles of biofluids and organs upon drug administration. Therefore, in this study, mass spectrometry-based metabonomics coupled with multivariate data analysis was used to identify the plasma metabolites and metabolic pathways related to nephrotoxicity caused by intraperitoneal injection of low (50mg/kg) and high (100mg/kg) doses of acyclovir. Sixteen biomarkers were identified by metabonomics and nephrotoxicity results revealed the dose-dependent effect of acyclovir on kidney tissues. The present study showed that the top four metabolic pathways interrupted by acyclovir included the metabolisms of arachidonic acid, tryptophan, arginine and proline, and glycerophospholipid. This research proves the established metabonomic approach can provide information on changes in metabolites and metabolic pathways, which can be applied to in-depth research on the mechanism of acyclovir-induced kidney injury.

A systematic strategy for screening and application of specific biomarkers in hepatotoxicity using metabolomics combined with ROC curves and SVMs

Current studies that evaluate toxicity based on metabolomics have primarily focused on the screening of biomarkers while largely neglecting further verification and biomarker applications. For this reason, we used drug-induced hepatotoxicity as an example to establish a systematic strategy for screening specific biomarkers and applied these biomarkers to evaluate whether the drugs have potential hepatotoxicity toxicity. Carbon tetrachloride (5 ml/kg), acetaminophen (1500 mg/kg), and atorvastatin (5 mg/kg) are established as rat hepatotoxicity models. Fifteen common biomarkers were screened by multivariate statistical analysis and integration analysis-based metabolomics data. The receiver operating characteristic curve was used to evaluate the sensitivity and specificity of the biomarkers. We obtained 10 specific biomarker candidates with an area under the curve greater than 0.7. Then, a support vector machine model was established by extracting specific biomarker candidate data from the hepatotoxic drugs and nonhepatotoxic drugs; the accuracy of the model was 94.90% (92.86% sensitivity and 92.59% specificity) and the results demonstrated that those ten biomarkers are specific. 6 drugs were used to predict the hepatotoxicity by the support vector machines model; the prediction results were consistent with the biochemical and histopathological results, demonstrating that the model was reliable. Thus, this support vector machine model can be applied to discriminate the between the hepatic or nonhepatic toxicity of drugs. This approach not only presents a new strategy for screening-specific biomarkers with greater diagnostic significance but also provides a new evaluation pattern for hepatotoxicity, and it will be a highly useful tool in toxicity estimation and disease diagnoses.

Rapid classification and identification of complex chemical compositions in traditional Chinese medicine based on UPLC-Q-TOF/MS coupled with data processing techniques using the KuDieZi injection as an example

The KuDieZi (KDZ) injection is prepared by extracting and processing Ixeris sonchifolia [Ixeris sonchifolia Hance] belonging to Lactuca genus of the Asteraceae family. The KDZ injection is a single-herb preparation in which the components are analysed by liquid chromatography-mass spectrometry. However, the chemical compositions of this preparation are complex and diverse. Hence, data processing is complicated and time consuming; furthermore, data processing cannot provide a systematic, accurate and repeatable method to rapidly classify and identify chemical constituents of the injection. In our study, the main components, particularly flavonoids, organic acids, amino acids and nucleosides in the KDZ injection, were rapidly classified and identified by data processing technology based on UPLC-Q-TOF/MS. After we reviewed lots of studies and collected information on the fragments, then compared them with the mass spectrometric analyses of the standards, the rules of diagnostic fragments (DFs) and neutral losses (NLs) of the four substances were found and summarised. A rapid classification and identification method of the chemical composition in the KDZ injection was then constructed using the DF filter (DFF) and the NL filter (NLF). This method was applied to analyse the KDZ injection. A total of 31 chemical components, which included 8 flavonoids, 13 organic acids, 6 amino acids and 4 nucleosides, were obtained. DFF and NLF were used to rapidly classify and identify chemical substances in the KDZ fingerprint. With this method, we effectively solved the technical difficulties in fingerprint resolution caused by complex components and low levels in traditional Chinese medicine (TCM). In addition, this study provided a novel approach for further studies on TCM.

Metabonomics study on nephrotoxicity induced by intraperitoneal and intravenous cisplatin administration using rapid resolution liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (RRLC-Q-TOF-MS)

Cisplatin is a well-known chemotherapeutic agent in cancer therapy. It is commonly administered intraperitoneally and intravenously in the clinic. The use of cisplatin is limited by its side effects, particularly its nephrotoxicity. In this study, mass spectrometry-based metabonomics coupled with multivariate statistical analysis was used to find biomarkers of kidney injury and further applied to investigate on the disturbed metabolic pathways, which were induced by single intraperitoneal or intravenous injection of cisplatin to rats with the dosage of 6 mg kg−1. It was found that sixteen biomarkers were changed because of drug administration. Among these sixteen biomarkers, eight biomarkers, including LPC(20:3), creatinine, LPC(14:0), LPC(18:3), LPC(22:5), arachidonic acid, proline and tryptophan, were found to be related to biochemical indicators of nephrotoxicity using Pearson correlation analysis. The identified biomarkers were mainly involved in valine, leucine and isoleucine biosynthesis, metabolism of sphingolipid, arginine and proline, glycerophospholipid, tryptophan, and arachidonic acid. In addition, the disturbed pathways were found to be time- and intraperitoneal or intravenous administration-dependent. The present result shows that mass spectrometry-based metabonomics approaches could be applied to study changes in metabolites and metabolic pathways associated with intraperitoneal or intravenous injection of cisplatin.

A novel approach to the simultaneous extraction and non-targeted analysis of the small molecules metabolome and lipidome using 96-well solid phase extraction plates with column-switching technology

This study combines solid phase extraction (SPE) using 96-well plates with column-switching technology to construct a rapid and high-throughput method for the simultaneous extraction and non-targeted analysis of small molecules metabolome and lipidome based on ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry. This study first investigated the columns and analytical conditions for small molecules metabolome and lipidome, separated by an HSS T3 and BEH C18 columns, respectively. Next, the loading capacity and actuation duration of SPE were further optimized. Subsequently, SPE and column switching were used together to rapidly and comprehensively analyze the biological samples. The experimental results showed that the new analytical procedure had good precision and maintained sample stability (RSD<15%). The method was then satisfactorily applied to more widely analyze the small molecules metabolome and lipidome to test the throughput. The resulting method represents a new analytical approach for biological samples, and a highly useful tool for researches in metabolomics and lipidomics.

A rapid and integrated pyramid screening method to classify and identify complex endogenous substances with UPLC/Q-TOF MS-based metabolomics

Metabolomics plays a role in disease diagnosis, safety and efficacy of drug evaluation, and microbial research. Liquid chromatography-mass spectrometry (MS) is the main analysis tool in metabolomics studies. Given the existence of many different categories of endogenous metabolites is isomers of one another, some problems on classification and identification have emerged. These problems result in high false-positive results, as well as a complex and time-consuming substance identification process. Accordingly, this study reviewed literature and retrieved databases to identify endogenous substances in the same category accompanied by a certain mass range (MR) and mass defect range (MDR), as well as an identical or similar fragmentation pattern in the mass spectrum [i.e., characteristic and neutral loss (NL) fragments]. We conducted different MS/MS collision energies to analyze different categories of endogenous substances to discover and summarize their fragmentation patterns. We then used the MR and MDR of the parent ion, diagnostic fragments (together with their MDR), and NL as screening tools to establish a pyramid screening method (PSM) for the rapid classification and identification of metabolites. Finally, we compared the PSM with the conventional identification method through known compounds in the literature. PSM was found to solve the key problem in metabolomics to some extent, namely, the classification and identification of substances. This method also facilitated the further development of metabolomics and provides a new perspective on the screening and identification of target components in other complex samples.

The classification and identification of complex chemical compositions in yanhusuo herb using UPLC-Q-TOF/MS

The yanhusuo herb used in our study is derived from the yanhusuos dried tubers, which belong to the Poppy Corydalis genus, and it is one of the traditional Chinese medicines (TCM) for relieving pain and promoting the circulation of blood and qi. The main components of yanhusuo include tetrahydroprotoberberine alkaloids, protoberberine alkaloids, protopine alkaloids and aporphine alkaloids. In this study, we aimed to realise the classification and identification of the alkaloid components in the yanhusuo herb by characteristic fragments and neutral losses using UPLC-Q-TOF/MS technology. After extensive review of the literature and some reference experiments, we found the fragmentation pattern of several alkaloids and the information of their corresponding fragment ions. Then, we determined the type of compound according to the type of fragment ions and the fragmentation pattern; thus we identified the compounds. Finally, we obtained 19 kinds of alkaloid compositions, including 12 kinds of tetrahydroprotoberberine alkaloids, 4 kinds of protoberberine alkaloids, 2 kinds of protopine alkaloids and 1 kind of aporphine alkaloid. In addition, analysis of the composition of yanhusuo was performed, which effectively solved the technical difficulties in the fingerprint analysis of TCM. This accurate and reliable method can provide a foundation for controlling the quality of different batches of the original ingredients.