Traditional Chinese medicine (TCM) is an integral part of mainstream medicine in China. Due to its worldwide use, potential impact on healthcare and opportunities for new drug development, TCM is also of great international interest. Recently, a new era for modernisation of TCM was launched with the successful completion of the Good Practice in Traditional Chinese Medicine Research in the Post-genomic Era (GP-TCM) project, the European Union’s Seventh Framework Programme (FP7) coordination action on TCM research. This 3.5-year project that involved inputs from over 200 scientists resulted in the production of 20 editorials and in-depth reviews on different aspects of TCM that were published in a special issue of Journal of Ethnopharmacology (2012; volume 140, issue 3). In this narrative review, we aim to summarise the findings of the FP7 GP-TCM project and highlight the relevance of TCM to modern medicine within a historical and international context. Advances in TCM research since the 1950s can be characterised into three phases: Phase I (1950s-1970s) was fundamental for developing TCM higher education, research and hospital networks in China; Phase II (1980s-2000s) was critical for developing legal, economic and scientific foundations and international networks for TCM; and Phase III (2011 onwards) is concentrating on consolidating the scientific basis and clinical practice of TCM through interdisciplinary, interregional and intersectoral collaborations. Taking into account the quality and safety requirements newly imposed by a globalised market, we especially highlight the scientific evidence behind TCM, update the most important milestones and pitfalls, and propose integrity, integration and innovation as key principles for further modernisation of TCM. These principles will serve as foundations for further research and development of TCM, and for its future integration into tomorrow’s medicine.

Autophagy is an important homeostatic cellular recycling mechanism responsible for degrading unnecessary or dysfunctional cellular organelles and proteins in all living cells. In addition to its vital homeostatic role, this degradation pathway also involves in various human disorders, including metabolic conditions, neurodegenerative diseases, cancers and infectious diseases. Therefore, the comprehensive understanding of autophagy process, autophagy-related modulators and corresponding pathway and disease information will be of great help for identifying the new autophagy modulators, potential drug candidates, new diagnostic and therapeutic targets. In recent years, some autophagy databases providing structural and functional information were developed, but the specific databases covering autophagy modulator (proteins, chemicals and microRNAs)-related target, pathway and disease information do not exist. Hence, we developed an online resource, Human Autophagy Modulator Database (HAMdb, http://hamdb.scbdd.com ), to provide researchers related pathway and disease information as many as possible. HAMdb contains 796 proteins, 841 chemicals and 132 microRNAs. Their specific effects on autophagy, physicochemical information, biological information and disease information were manually collected and compiled. Additionally, lots of external links were available for more information covering extensive biomedical knowledge. HAMdb provides a user-friendly interface to query, search, browse autophagy modulators and their comprehensive related information. HAMdb will help researchers understand the whole autophagy process and provide detailed information about related diseases. Furthermore, it can give hints for the identification of new diagnostic and therapeutic targets and the discovery of new autophagy modulators. In a word, we hope that HAMdb has the potential to promote the autophagy research in pharmacological and pathophysiological area.

Background: Complex chronic diseases such as rheumatoid arthritis have become a major challenge in medicine and for the pharmaceutical industry. New impulses for drug development are needed. Objective: A systems biology approach is explored to find subtypes of rheumatoid arthritis patients enabling a development towards more personalized medicine. Methods: Blood samples of 33 rheumatoid arthritis (RA) patients and 16 healthy volunteers were collected. The RA patients were diagnosed according to Chinese medicine (CM) theory and divided into 2 groups, the RA Heat and RA Cold group. CD4⁺ T-cells were used for a total gene expression analysis. Metabolite profiles were measured in plasma using gas chromatography/mass spectrometry. Multivariate statistics was employed to find potential biomarkers for the RA Heat and RA Cold phenotype. A comprehensive biologic interpretation of the results is discussed. Results: The genomics and metabolomics analysis showed statistically relevant different gene expression and metabolite profiles between healthy controls and RA patients as well as between the RA Heat and RA Cold group. Differences were found in the regulation of apoptosis. In the RA Heat group caspase 8 activated apoptosis seems to be stimulated while in the RA Cold group apoptosis seems to be suppressed through the Nrf2 pathway. Conclusions: RA patients could be divided in 2 groups according to CM theory. Molecular differences between the RA Cold and RA Heat groups were found which suggest differences in apoptotic activity. Subgrouping of patients according to CM diagnosis has the potential to provide opportunities for better treatment outcomes by targeting Western or CM treatment to specific groups of patients.

Current strategies for drug discovery have reached a bottleneck where the paradigm is generally “one gene, one drug, one disease.” However, using holistic and systemic views, network pharmacology may be the next paradigm in drug discovery. Based on network pharmacology, a combinational drug with two or more compounds could offer beneficial synergistic effects for complex diseases. Interestingly, traditional chinese medicine (TCM) has been practicing holistic views for over 3,000 years, and its distinguished feature is using herbal formulas to treat diseases based on the unique pattern classification. Though TCM herbal formulas are acknowledged as a great source for drug discovery, no drug discovery strategies compatible with the multidimensional complexities of TCM herbal formulas have been developed. In this paper, we highlighted some novel paradigms in TCM-based network pharmacology and new drug discovery. A multiple compound drug can be discovered by merging herbal formula-based pharmacological networks with TCM pattern-based disease molecular networks. Herbal formulas would be a source for multiple compound drug candidates, and the TCM pattern in the disease would be an indication for a new drug.