TCMSID: a simplified integrated database for drug discovery from traditional chinese medicine

Text Mining and Drug Discovery Analysis: A Comprehensive Approach to Investigate Diabetes-Induced Osteoporosis

Purpose: Osteoporosis (OP) and diabetes are prevalent diseases in orthopedic and endocrinology departments, with OP potentially arising as a complication of diabetes. However, the mechanism underlying diabetes-induced osteoporosis (DOP) remains enigmatic, and drug discovery in this domain is restricted, hindering research into the DOP's etiology and treatment. With the ultimate goal of preventing OP in diabetic patients, the objective of this study is to mine the genes and pathways linked to DOP using bioinformatics and databases. Method: The present study employed text mining as the initial approach to retrieve genes commonly associated with diabetes and OP. Subsequently, functional annotation was conducted to investigate the roles and functionalities. In order to explore the interactions between proteins relevant to DOP, we constructed protein-protein interaction (PPI) networks. Furthermore, to obtain key genes and candidate drugs for DOP treatment, we conducted drug-gene interaction (DGI) analysis, complemented by a thorough examination of transcriptional factors (TFs)-miRNA co-regulation. Results: The results through text mining revealed 110 genes that are commonly associated with both diabetes and OP. Subsequent enrichment analysis narrowed down the list to 95 symbols that were involved in PPI analysis. After DGI analysis, we identified 7 genes targeted by 11 drugs, which represent candidates for treating DOP. Conclusion: This study unveils ANDECALIXIMAB, SILTUXIMAB, OLOKIZUMAB, SECUKINUMAB, and IXEKIZUMAB as promising potential drugs for DOP treatment, demonstrating the significance of utilizing text mining and pathway analysis to investigate disease mechanisms and explore existing therapeutic options.

Mechanism investigation of Duhuo Jisheng pill against rheumatoid arthritis based on a strategy for the integration of network pharmacology, molecular docking and in vivo experimental verification

CONTEXT

Duhuo Jisheng pill (DHJS) is a classic traditional Chinese medicine (TCM) formula for rheumatoid arthritis (RA). The effective components and therapeutic mechanisms of DHJS for treating RA are still unclear.

OBJECTIVE

To explore the potential mechanism of DHJS against RA by means of network pharmacology and experimental verification.

MATERIALS AND METHODS

A network pharmacology and molecular docking analysis based on phytochemistry was used to elucidate the mechanism of DHJS against RA. The targets of DHJS anti-RA active ingredient were obtained by searching TCMSP, ETCM and TCMSID. The RA model induced by collagen was established in Wistar rats. The rats in the DHJS group were administered doses of 0.5, 1.0 and 2.0 g/kg for a period of 10 d. The expression of targets was measured with Western blot.

RESULTS

Network pharmacology analysis showed that the anti-RA effect of DHJS was mediated by targets involved in immunity, inflammation and apoptosis, as well as PI3K-Akt and NF-κB signalling pathways. Of 2.0 g/kg DHJS significantly alleviated the ankle inflammation (IL-6: 62.73 ± 8.39 pg/mL, IL-1β: 50.49 ± 11.47 pg/mL, TNF-α: 16.88 ± 3.05 pg/mL, IL-17A: 12.55 ± 1.87 pg/mL, IL-10: 16.24 ± 3.00 pg/mL), comparing with the model group (IL-6: 92.02 ± 13.25 pg/mL, IL-1β: 71.85 ± 4.12 pg/mL, TNF-α: 25.64 ± 3.69 pg/mL, IL-17A: 22.14 ± 4.56 pg/mL, IL-10: 9.51 ± 3.03 pg/mL) (p < 0.05). Moreover, the protein expression of p-PI3K, p-AKT and p-p65 significantly decreased after DHJS administration.

CONCLUSIONS

DHJS could alleviate the collagen-induced arthritis (CIA) by the PI3K/AKT/NF-κB signalling pathway.

Multi-level advances in databases related to systems pharmacology in traditional Chinese medicine: a 60-year review

The therapeutic effects of traditional Chinese medicine (TCM) involve intricate interactions among multiple components and targets. Currently, computational approaches play a pivotal role in simulating various pharmacological processes of TCM. The application of network analysis in TCM research has provided an effective means to explain the pharmacological mechanisms underlying the actions of herbs or formulas through the lens of biological network analysis. Along with the advances of network analysis, computational science has coalesced around the core chain of TCM research: formula-herb-component-target-phenotype-ZHENG, facilitating the accumulation and organization of the extensive TCM-related data and the establishment of relevant databases. Nonetheless, recent years have witnessed a tendency toward homogeneity in the development and application of these databases. Advancements in computational technologies, including deep learning and foundation model, have propelled the exploration and modeling of intricate systems into a new phase, potentially heralding a new era. This review aims to delves into the progress made in databases related to six key entities: formula, herb, component, target, phenotype, and ZHENG. Systematically discussions on the commonalities and disparities among various database types were presented. In addition, the review raised the issue of research bottleneck in TCM computational pharmacology and envisions the forthcoming directions of computational research within the realm of TCM.

Discovering the Active Ingredients of Medicine and Food Homologous Substances for Inhibiting the Cyclooxygenase-2 Metabolic Pathway by Machine Learning Algorithms

Cyclooxygenase-2 (COX-2) and microsomal prostaglandin E2 synthase (mPGES-1) are two key targets in anti-inflammatory therapy. Medicine and food homology (MFH) substances have both edible and medicinal properties, providing a valuable resource for the development of novel, safe, and efficient COX-2 and mPGES-1 inhibitors. In this study, we collected active ingredients from 503 MFH substances and constructed the first comprehensive MFH database containing 27,319 molecules. Subsequently, we performed Murcko scaffold analysis and K-means clustering to deeply analyze the composition of the constructed database and evaluate its structural diversity. Furthermore, we employed four supervised machine learning algorithms, including support vector machine (SVM), random forest (RF), deep neural networks (DNNs), and eXtreme Gradient Boosting (XGBoost), as well as ensemble learning, to establish 640 classification models and 160 regression models for COX-2 and mPGES-1 inhibitors. Among them, ModelA_ensemble_RF_1 emerged as the optimal classification model for COX-2 inhibitors, achieving predicted Matthews correlation coefficient (MCC) values of 0.802 and 0.603 on the test set and external validation set, respectively. ModelC_RDKIT_SVM_2 was identified as the best regression model based on COX-2 inhibitors, with root mean squared error (RMSE) values of 0.419 and 0.513 on the test set and external validation set, respectively. ModelD_ECFP_SVM_4 stood out as the top classification model for mPGES-1 inhibitors, attaining MCC values of 0.832 and 0.584 on the test set and external validation set, respectively. The optimal regression model for mPGES-1 inhibitors, ModelF_3D_SVM_1, exhibited predictive RMSE values of 0.253 and 0.35 on the test set and external validation set, respectively. Finally, we proposed a ligand-based cascade virtual screening strategy, which integrated the well-performing supervised machine learning models with unsupervised learning: the self-organized map (SOM) and molecular scaffold analysis. Using this virtual screening workflow, we discovered 10 potential COX-2 inhibitors and 15 potential mPGES-1 inhibitors from the MFH database. We further verified candidates by molecular docking, investigated the interaction of the candidate molecules upon binding to COX-2 or mPGES-1. The constructed comprehensive MFH database has laid a solid foundation for the further research and utilization of the MFH substances. The series of well-performing machine learning models can be employed to predict the COX-2 and mPGES-1 inhibitory capabilities of unknown compounds, thereby aiding in the discovery of anti-inflammatory medications. The COX-2 and mPGES-1 potential inhibitor molecules identified through the cascade virtual screening approach provide insights and references for the design of highly effective and safe novel anti-inflammatory drugs.