Prediction of allergic reaction risk of Shenmai injection based on real-world evidence coupled with UPLC-Q-TOF-MS.

The allergic reaction (AR) of Chinese herbal injection (CHI) has become one of the most noticeable focuses of public health in China. However, it still remains a considerable controversy as to whether low-molecular-weight components in CHI have potential sensitization. In this study, the relationship between AR and low-molecular-weight component profile of Shenmai injection was explored by an interdisciplinary technology integrating real-world evidence and ultra-performance liquid chromatography-quadrupole time-of-flight mass spectroscopy (UPLC-Q-TOF-MS). The AR information of hospitalized patients was obtained by comprehensively analyzing real-world evidence from January 2015 to June 2019 at two Chinese hospitals. The UPLC-Q-TOF-MS was exploited to systematically investigate the low-molecular-weight component profile with 50-1500 m/z mass range, and 3725 MS1 peaks were detected. The optimized partial least squares discriminant analysis model was established to map the influence of low-molecular-weight components on AR. The results of this study showed that high levels of organic acids administered intravenously might be a potential risk factor for inducing AR. By using this method, Shenmai injection with high AR risk could be recognized precisely with 100% accuracy before clinical use.

Identification of high-risk patients for ADR induced by traditional Chinese medicine injection: a nested case-control study.

The adverse drug reaction (ADR) of traditional Chinese medicine injection (TCMI) has become one of the major concerns of public health in China. There are significant advantages for developing methods to improve the use of TCMI in routine clinical practice. The method of predicting TCMI-induced ADR was illustrated using a nested case-control study in 123 cases and 123 controls. The partial least squares regression (PLSR) models, which mapped the influence of basic characteristics and routine examinations to ADR, were established to predict the risk of ADR. The software was devised to provide an easy-to-use tool for clinic application. The effectiveness of the method was evaluated through its application to new patients with 95.7% accuracy of cases and 91.3% accuracy of controls. By using the method, the patients at high-risk could be conveniently, efficiently and economically recognized without any extra financial burden for additional examination. This study provides a novel insight into individualized management of the patients who will use TCMI.

A strategy to find novel candidate anti-Alzheimer's disease drugs by constructing interaction networks between drug targets and natural compounds in medical plants.

BACKGROUND: Alzheimer' disease (AD) is an ultimately fatal degenerative brain disorder that has an increasingly large burden on health and social care systems. There are only five drugs for AD on the market, and no new effective medicines have been discovered for many years. Chinese medicinal plants have been used to treat diseases for thousands of years, and screening herbal remedies is a way to develop new drugs. METHODS: We used molecular docking to screen 30,438 compounds from Traditional Chinese Medicine (TCM) against a comprehensive list of AD target proteins. TCM compounds in the top 0.5% of binding affinity scores for each target protein were selected as our research objects. Structural similarities between existing drugs from DrugBank database and selected TCM compounds as well as the druggability of our candidate compounds were studied. Finally, we searched the CNKI database to obtain studies on anti-AD Chinese plants from 2007 to 2017, and only clinical studies were included. RESULTS: A total of 1,476 compounds (top 0.5%) were selected as drug candidates. Most of these compounds are abundantly found in plants used for treating AD in China, especially the plants from two genera Panax and Morus. We classified the compounds by single target and multiple targets and analyzed the interactions between target proteins and compounds. Analysis of structural similarity revealed that 17 candidate anti-AD compounds were structurally identical to 14 existing approved drugs. Most of them have been reported to have a positive effect in AD. After filtering for compound druggability, we identified 11 anti-AD compounds with favorable properties, seven of which are found in anti-AD Chinese plants. Of 11 anti-AD compounds, four compounds 5,862, 5,863, 5,868, 5,869 have anti-inflammatory activity. The compound 28,814 mainly has immunoregulatory activity. The other six compounds have not yet been reported for any biology activity at present. DISCUSSION: Natural compounds from TCM provide a broad prospect for the screening of anti-AD drugs. In this work, we established networks to systematically study the connections among natural compounds, approved drugs, TCM plants and AD target proteins with the goal of identifying promising drug candidates. We hope that our study will facilitate in-depth research for the treatment of AD in Chinese medicine.

The identification and molecular mechanism of anti-stroke traditional Chinese medicinal compounds.

Stroke is a worldwide epidemic disease with high morbidity and mortality. The continuously exploration of anti-stroke medicines and molecular mechanism has a long way to go. In this study, in order to screen candidate anti-stroke compounds, more than 60000 compounds from traditional Chinese medicine (TCM) database were computationally analyzed then docked to the 15 known anti-stroke targets. 192 anti-stroke plants for clinical therapy and 51 current anti-stroke drugs were used to validate docking results. Totally 2355 candidate anti-stroke compounds were obtained. Among these compounds, 19 compounds are structurally identical with 16 existing drugs in which part of them have been used for anti-stroke treatment. Furthermore, these candidate compounds were significantly enriched in anti-stroke plants. Based on the above results, the compound-target-plant network was constructed. The network reveals the potential molecular mechanism of anti-stroke for these compounds. Most of candidate compounds and anti-stroke plants are tended to interact with target NOS3, PSD-95 and PDE5A. Finally, using ADMET filter, we identified 35 anti-stroke compounds with favorable properties. The 35 candidate anti-stroke compounds offer an opportunity to develop new anti-stroke drugs and will improve the research on molecular mechanism of anti-stroke.

Pharmacophore-Map-Pick: A Method to Generate Pharmacophore Models for All Human GPCRs.

GPCR-based drug discovery is hindered by a lack of effective screening methods for most GPCRs that have neither ligands nor high-quality structures. With the aim to identify lead molecules for these GPCRs, we developed a new method called Pharmacophore-Map-Pick to generate pharmacophore models for all human GPCRs. The model of ADRB2 generated using this method not only predicts the binding mode of ADRB2-ligands correctly but also performs well in virtual screening. Findings also demonstrate that this method is powerful for generating high-quality pharmacophore models. The average enrichment for the pharmacophore models of the 15 targets in different GPCR families reached 15-fold at 0.5 % false-positive rate. Therefore, the pharmacophore models can be applied in virtual screening directly with no requirement for any ligand information or shape constraints. A total of 2386 pharmacophore models for 819 different GPCRs (99 % coverage (819/825)) were generated and are available at http://bsb.kiz.ac.cn/GPCRPMD.

In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database.

There is a constant demand to develop new, effective, and affordable anti-cancer drugs. The traditional Chinese medicine (TCM) is a valuable and alternative resource for identifying novel anti-cancer agents. In this study, we aim to identify the anti-cancer compounds and plants from the TCM database by using cheminformatics. We first predicted 5278 anti-cancer compounds from TCM database. The top 346 compounds were highly potent active in the 60 cell lines test. Similarity analysis revealed that 75% of the 5278 compounds are highly similar to the approved anti-cancer drugs. Based on the predicted anti-cancer compounds, we identified 57 anti-cancer plants by activity enrichment. The identified plants are widely distributed in 46 genera and 28 families, which broadens the scope of the anti-cancer drug screening. Finally, we constructed a network of predicted anti-cancer plants and approved drugs based on the above results. The network highlighted the supportive role of the predicted plant in the development of anti-cancer drug and suggested different molecular anti-cancer mechanisms of the plants. Our study suggests that the predicted compounds and plants from TCM database offer an attractive starting point and a broader scope to mine for potential anti-cancer agents.

Xiaokeping mixture inhibits diabetic nephropathy in streptozotocin-induced rats through blocking TGF-ß1/Smad7 signaling.

BACKGROUND: Diabetic nephropathy (DN) is a major cause of chronic kidney failure and characterized by excessive deposition of extracellular matrix. Evidence have shown that transforming growth factor-ß1 (TGF-ß1) is a key mediator in the development of DN. However, treatment of DN by blocking the TGF-ß1/Smad7 pathway remains limited. Xiaokeping mixture (XKP), a traditional Chinese herbal compound, has been used for treatment in patients with DN for many years. METHODS: In the present study, TGF-ß1/Smad7 pathway analysis was used to evaluate the therapeutic effect of XKP on DN rats induced by streptozotocin and to address the underlying molecular mechanism. Male rats were divided into four groups: normal control, untreated control group (fed with high fat), irbesartan-treated DN, and XKP-treated DN, respectively. Levels of serum creatinine, blood urea nitrogen, urine protein of 24 hours, and triacylglycerol were detected. Pathological changes of renal tissues were observed by hematoxylin-eosin staining. Immunohistochemical and Western blot analysis were used to detect the expressions of TGF-ß1 and Smad7. RESULTS: The results demonstrated that XKP can effectively reduce the levels of glucose, serum creatinine, blood urea nitrogen, urine protein of 24 hours, and triacylglycerol. Further studies indicated that inhibition of DN in XKP-treated DN rats was associated with inhibition of TGF-ß1/Smad7 signaling as demonstrated by downregulation of TGF-ß1 but upregulation of Smad7. CONCLUSION: The data obtained from the present study indicate that XKP may be a therapeutic agent for DN.