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Quan W, Wang Y, Chen YH, Shao Q, Gong YZ, Hu JW, Liu WH, Wu ZJ, Wang J, Ma SB, Li XQ. Screening of rosmarinic acid from Salvia miltiorrhizae acting on the novel target TRPC1 based on the 'homology modelling-virtual screening-molecular docking-affinity assay-activity evaluation' method. PHARMACEUTICAL BIOLOGY 2023; 61:155-164. [PMID: 36604840 PMCID: PMC9828776 DOI: 10.1080/13880209.2022.2160769] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 11/14/2022] [Accepted: 12/15/2022] [Indexed: 06/17/2023]
Abstract
CONTEXT Salvia miltiorrhizae Bunge (Lamiaceae) is a traditional Chinese medicine (TCM) for the treatment of 'thoracic obstruction'. Transient receptor potential canonical channel 1 (TRPC1) is a important target for myocardial injury treatment. OBJECTIVE This work screens the active component acting on TRPC1 from Salvia miltiorrhizae. MATERIALS AND METHODS TCM Systems Pharmacology Database and Analysis Platform (TCMSP) was used to retrieve Salvia miltiorrhiza compounds for preliminary screening by referring to Lipinski's rule of five. Then, the compound group was comprehensively scored by AutoDock Vina based on TRPC1 protein. Surface plasmon resonance (SPR) was used to determine the affinity of the optimal compound to TRPC1 protein. Western blot assay was carried out to observe the effect of the optimal compound on TRPC1 protein expression in HL-1 cells, and Fura-2/AM detection was carried out to observe the effect of the optimal compound on calcium influx in HEK293 cells. RESULTS Twenty compounds with relatively good characteristic parameters were determined from 202 compounds of Salvia miltiorrhiza. Rosmarinic acid (RosA) was obtained based on the molecular docking scoring function. RosA had a high binding affinity to TRPC1 protein (KD value = 1.27 µM). RosA (50 μM) could reduce the protein levels (417.1%) of TRPC1 after oxygen-glucose deprivation/reperfusion (OGD/R) in HL-1 cells and it could inhibit TRPC1-mediated Ca2+ influx injury (0.07 ΔRatio340/380) in HEK293 cells. DISCUSSION AND CONCLUSIONS We obtained the potential active component RosA acting on TRPC1 from Salvia miltiorrhizae, and we speculate that RosA may be a promising clinical candidate for myocardial injury therapy.
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Affiliation(s)
- Wei Quan
- Department of Pharmacy, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
- Department of Pharmacology, School of Pharmacy, Air Force Medical University, Xi’an, China
| | - Yuan Wang
- Department of Neurosurgery, Wuhan No.1 Hospital, Wuhan, China
| | - Yu-han Chen
- Department of Pharmacy, Xijing Hospital, Air Force Medical University, Xi’an, China
| | - Qing Shao
- Xi’an Mental Health Center, School of Medicine, Xi’an Jiaotong University, Xi’an, China
| | - Yang-ze Gong
- Xi’an Mental Health Center, School of Medicine, Xi’an Jiaotong University, Xi’an, China
| | - Jie-wen Hu
- Xi’an Mental Health Center, School of Medicine, Xi’an Jiaotong University, Xi’an, China
| | - Wei-hai Liu
- Department of Pharmacy, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Zi-jun Wu
- Department of Pharmacy, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Jie Wang
- Department of Pharmacy, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Shan-bo Ma
- Department of Pharmacy, Xijing Hospital, Air Force Medical University, Xi’an, China
| | - Xiao-qiang Li
- Department of Pharmacology, School of Pharmacy, Air Force Medical University, Xi’an, China
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Chen Z, Jiang Y, Zhang X, Zheng R, Qiu R, Sun Y, Zhao C, Shang H. ResNet18DNN: prediction approach of drug-induced liver injury by deep neural network with ResNet18. Brief Bioinform 2021; 23:6457162. [PMID: 34882224 DOI: 10.1093/bib/bbab503] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/27/2021] [Accepted: 11/02/2021] [Indexed: 01/22/2023] Open
Abstract
Drug-induced liver injury (DILI) has always been the focus of clinicians and drug researchers. How to improve the performance of the DILI prediction model to accurately predict liver injury was an urgent problem for researchers in the field of medical research. In order to solve this scientific problem, this research collected a comprehensive and accurate dataset of DILI with high recognition and high quality based on clinically confirmed DILI compound datasets, including 1446 chemical compounds. Then, the residual neural network with 18-layer by using more 5-layer blocks (ResNet18) with deep neural network (ResNet18DNN) model was proposed to predict DILI, which was an improved model for DILI prediction through vectorization of compound structure image. In predicting DILI, the ResNet18DNN learned greatly and outperformed the existing state-of-the-art DILI predictors. The results of DILI prediction model based on ResNet18DNN showed that the AUC (area under the curve), accuracy, recall, precision, F1-score and specificity of the training set were 0.973, 0.992, 0.995, 0.994, 0.995 and 0.975; those of test set were, respectively, 0.958, 0.976, 0.935, 0.947, 0.926 and 0.913, which were better than the performance of previously published described DILI prediction models. This method adopted ResNet18 embedding method to vectorize molecular structure images and the evaluation indicators of Resnet18DNN were obtained after 10 000 iterations. This prediction approach will greatly improve the performance of the predictive model of DILI and provide an accurate and precise early warning method for DILI in drug development and clinical medication.
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Affiliation(s)
- Zhao Chen
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Yin Jiang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Xiaoyu Zhang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Rui Zheng
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Ruijin Qiu
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Yang Sun
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Chen Zhao
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Hongcai Shang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China.,College of Integrated Traditional Chinese and Western Medicine, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China
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