| Traditional Chinese Medicine(TCM)is an important source of modern drug research and development with a long history and the exact effect,which is used for the treatment of different diseases through multi-component,multi-target and multipathways.However,the composition and the overall pharmacological effects of TCM is complex.According to the complexity of TCM,to find out the key efficacy of its material basis and the important targets for treating the diseases is a primary purpose of TCM study.Due to the difficulty in extraction and separation of TCM ingredients and the traditional pharmacodynamics experiment is time-consuming and expensive,so it is urgent to use an efficient molecular simulation method to carry out the research.In this study,Crataegus pinnatifida Bge.and Salvia miltiorrhiza Bge.were selected as the research objects.Using the molecular modeling techniques,based on the related targets of hyperlipidemia,we proposed a novel method,namely"TCM-target effects relationship spectrum".The standardized and quantitative construction methods of TCM-target effects relationship spectrum is discussed,which was applied to analyze the relationship between effective ingredients and targets and explain the mechanism of multi-target action of TCM.This study mainly carried out the following four aspects:based on the single target activity identification of TCM;the construction of TCM-target effects relationship spectrum of hyperlipidemia;based on the cell experiments to identify lipid-lowering activity of the TCM ingredients;the construction of absorbing prediction models based on the drug transporters.1.Based on the single target activity identification of TCM.Firstly,Purine nucleoside phosphorylase(PNP)was used as research vectors to establish the molecular simulation method of finding potential active ingredients from traditional Chinese medicine.Meanwhile,the cell experiments were used to identify activity of the potential ingredients.For the PNP,seven active compounds were selected with active value and structural diversity as the training set and used to generate Hiphop pharmacophore model by using DS 4.0.The test set was applied for evaluating the generated ten pharmacophore models and employed to screen potential PNP inhibitors from Traditional Chinese Medicine Database(TCMD).Then,the hits were further filtered by Lipinski’s rule,and 1810 TCM compounds were obtained.The molecular docking models of PNP was used to further screen the results of pharmacophore and to analyze the interaction of between the compounds and PNP protein.Based on the screening rules of the docking score of positive compound of PNP and the key amino acids,PNP-Comp.1 from Green tea and Ginkgo biloba.was selected as the most potential PNP inhibitor.Then,MD simulation was implemented to analyze the binding stability of PNP-PNP-Comp.1 under dynamic conditions.Finally,two cell lines including Molt-4 T cells and K562 B cells were used to test the activity of PNP-Comp.1.From the result,the optimal concentration of PNP-Comp.1 was 10 μM,which displayed statistically significant inhibitory effect in Molt-4 cells,but has no effect in K562 cells,indicating that PNP-Comp.1 was a potential PNP inhibitor.This study was used to provides an example for the analysis of the interaction between the lipid-lowering targets and the TCM component involved in this article,which provided the basis and guidance for the next construction of TCM-target effects relationship spectrum.2.The construction of TCM-target effects relationship spectrum of hyperlipidemia.The TCM-target effects relationship spectrum was used to indicate the relationship between TCM ingredients and targets,which was utilized to clarify the material basis and action mechanism of TCM.To construct a standardized and quantitative TCM-target effects relationship spectrum,based on the related eight targets of hyperlipidemia,the Crataegus pinnatifida Bge.and Salvia miltiorrhiza Bge.were selected as the research objects by using the pharmacophore model and docking method.Due to the different molecular modeling methods have different data bases according to the different lipid-lowering targets,we introduced a probabilistic assignment curve(PAC)method and constructed a PAC curve model for different lipid-lowering targets.PAC curve models are empirically derived functions that translate a measure(e.g.,as fit value and docking score)into a probability of a compound being active.Then,we introduced a probabilistic fusion method to combine the docking-based and pharmacophore-based target prediction result,which is based on Belief Theory.Thus,the cumulative probability(C-value)was produced that is a fuse performance of the two measures,which was used to build the TCM-target effects relationship spectrum.In conclusion,we constructed a standardized and quantitative TCM-target effects relationship spectrum,which provided the new ideas for illustrating the multi-target mechanism for the lipid-lowering effects of TCM.3.Based on the cell experiments to identify lipid-lowering activity of the TCM ingredients.In this study,HepG2 cell lines was used to evaluate the lipid-lowering activity of ingredients from TCM.Firstly,the hyperlipidemia model of HepG2 cell line was constructed by using sodium oleate with concentration of 60 μg·mL-1 as the inducer for 24h.And,MTT was used to detecte the cytotoxicity of compounds from TCM.The positive compound was used to evaluate the accuracy of lipid-lowering efficacy.Subsequently,triglyceride(TG)content was used as the evaluation index to determine the lipid-lowering effect of the compound compared with the model group.The potential active compounds based on the TCM-target effects relationship spectrum of the Crataegus pinnatifida Bge.and Salvia miltiorrhiza Bge.were evaluated the lipid-lowering efficacy.From the result,Comp.1-Crataegus pinnatifida Bge.could significantly decrease the TG content of 47.85%in the optimal concentration of 40μmol·L-1,and the difference was statistically significant.Comp.1-Salvia miltiorrhiza Bge.could the significantly decrease the TG content of 20.88%in the optimal concentration of 10 μmol·L-1.In this study,HepG2 cell line was used to evaluate the lipid-lowering activity of components from Crataegus pinnatifida Bge.and Salvia miltiorrhiza Bge.and two compounds were verified the lipid-lowering activity,which further validated the reliability of the TCM-target effects relationship spectrum and provided provided the basis and direction for the component compatibility and new drug design of lipid-lowering.4.The construction of absorbing prediction models based on the drug transporters.The core of a successful drug is the inner activity,and the features of ADMET is the carrier.Therefore,the ADMET prediction is critical in the drug design process.Among the ADMET properties,due to the low intestinal absorption rate,about 50%of drugs end up in the clinical trials.Therefore,the prediction of the absorption properties of drugs is particularly important in the development of drugs discovery.In this study,a substrate prediction models based on the absorption transporters such as PepT1,MCT1,and OATP1B1 were constructed using the support vector machine method.In the process of model building,we explored the data partitioning,descriptor selection,data preprocessing,and model optimization to obtain the optimal qualitative model with high accuracy,sensitivity,and specificity.The optimal model was used to predict the substrate of the absorption transporters from Crataegus pinnatifida Bge.and Salvia miltiorrhiza Bge,which provide the guidance for identifying the absorption properties of TCM,and to evaluate the accuracy of the TCM-target effects relationship spectrum.In this study,we proposed a novel method and created a standardized and quantitative TCM-target effects relationship spectrum,which was applied in the field of the hyperlipemia.Then,combined with cell experiment and ADMET prediction,we discovered the potential active compound and key targets of Crataegus pinnatifida Bge.and Salvia miltiorrhiza Bge and clarified the material basis and action mechanism of the TCM. |
PDF Full Text Request