Zhiguo Hou

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.

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.

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.

A Novel Method for Evaluating the Cardiotoxicity of Traditional Chinese Medicine Compatibility by Using Support Vector Machine Model Combined with Metabonomics

Traditional biochemical and histopathological tests have been used to evaluate the safety of traditional Chinese medicine (TCM) compatibility for a long time. But these methods lack high sensitivity and specificity. In the previous study, we have found ten biomarkers related to cardiotoxicity and established a support vector machine (SVM) prediction model. Results showed a good sensitivity and specificity. Therefore, in this study, we used SVM model combined with metabonomics UPLC/Q-TOF-MS technology to build a rapid and sensitivity and specificity method to predict the cardiotoxicity of TCM compatibility. This study firstly applied SVM model to the prediction of cardiotoxicity in TCM compatibility containing Aconiti Lateralis Radix Praeparata and further identified whether the cardiotoxicity increased after Aconiti Lateralis Radix Praeparata combined with other TCM. This study provides a new idea for studying the evaluation of the cardiotoxicity caused by compatibility of TCM.

Metabonomic study of the effects of different acupuncture directions on therapeutic efficacy

Posterior circulation ischemia (PCI) is a common clinical ischemic cerebrovascular disease that can endanger the lives of patients in severe cases. Our previous research found that needling the Fengchi (GB20) acupoint presents a significant effect on PCI and that different acupuncture directions can exert different effects. To investigate the biological mechanism of acupuncture directions, rapid resolution liquid chromatography coupled with quadrupole time-of-flight mass spectrometry-based metabonomic techniques are used to analyze the metabolic profiles of urine samples. The urine samples were obtained from 30 healthy control subjects, 60 PCI patients before and after treatment of different acupuncture directions. Six metabolites, including LPE (22:6), estrone, uric acid, vanillylmandelic acid, N-acetyl-l-tyrosine, and 4-hydroxyphenylacetylglutamine were identified as potential biomarkers of acupuncture treatment of PCI. Acupuncture treatment of PCI patients significantly changed the levels of these potential biomarkers. Moreover, different acupuncture directions showed different effects on the contents of these biomarkers. These results strongly support the belief that acupuncture direction performs an important function in acupuncture intervention. The findings provide new insights into the mechanism of acupuncture treatment and reveal that acupuncture manipulation results in various curative.

Screening and verification of linearly dependent biomarkers with acute toxicity induced by Aconiti Radix based on liquid chromatography-mass spectrometry-based metabolite profiling

Aconiti Radix, with its unique anti-inflammatory and analgesic effects, is a well-known form of traditional medication; however, improper use of the Aconiti Radix drug often leads to severe acute toxicity. Raw Aconiti Radix ethanol extraction is the most toxic ingredient, followed by raw Aconiti Radix water extraction; processed products have less toxic ingredients. Current clinical examinations primarily use biochemical tests and histopathological examination, but such approaches lack specificity, are time-consuming, and have low sensitivity, which can easily lead to false positive results. Therefore, a fast and accurate way to evaluate acute toxicity is needed. We have established a method that combines metabonomics with trend analysis of a gavage concentration series to find and validate acute toxicity biomarkers of Aconiti Radix. The purpose of this study is to identify Aconiti Radix acute toxicity biomarkers based on UPLC-Q-TOF-MS metabonomics technology. We use relative amounts of biomarkers with dosage and degree of toxicity to determine a dose-dependent trend; these substances may be exclusive Aconiti Radix acute toxicity biomarkers. These exclusive biomarkers were validated both in water extraction of Aconiti Radix and drug incompatibility with Aconiti Radix Cocta–Pinelliae rhizoma couple medicines; ultimately, the acute toxicity biomarkers (shikimic acid, L-acetylcarnitine, LysoPC (22:5), L-valine) were determined. This new method provides a better way to discover and validate specific metabonomics endogenous small molecule compounds.

A practical and novel “standard addition” strategy to screen pharmacodynamic components in traditional Chinese medicine using Heishunpian as an example

The study of pharmacodynamic components in traditional Chinese medicine (TCM) is very important for future drug development and quality control of TCM. The present study established a new method for screening pharmacodynamic components (PCs) in TCM based on the strategy of standard addition (SA). The novel strategy was then applied to screen anti-inflammatory PCs in Heishunpian (HSP), the processed product of Aconitum carmichaeli Debx. We initially screened target components (TCs, possible PCs) by analyzing the correlation between UPLC-Q-TOF-MS fingerprints of chemical components and the anti-inflammatory efficacies of different HSP extracts. On the basis of spectrum-effect relationship (SER) analysis and TC determination, we added TC quantitatively into HSP extracts, evaluated the pharmacodynamic contribution ratios of TCs related to the original TCM using a mouse ear edema model, and compared them with the contribution ratio of the TC standard. The anti-inflammatory PCs of HSP were then defined. Results showed that hypaconitine, deoxyaconitine and chasmanine were anti-inflammatory PCs in HSP with positive relations with HSP efficacy. Thus, SA was used to systematically evaluate the effect of chemical ingredients in TCM. The proposed method presents simple operation, strong feasibility and reliability, and provides a new approach for screening PCs in TCM in a manner that highlights the complexity and multi-component effects of TCM.