Non-clinical pharmacology of traditional Chinese medicine (TCM) formulae plays a crucial role in elucidating their therapeutic mechanisms. With the integration of cutting-edge disciplines such as systems biology, big data, and artificial intelligence, the research depth and breadth in this field continue to expand. However, technical standards for non-clinical pharmacology research on TCM formulae have not yet been established. To address this, technical guidelines for non-clinical pharmacology research of TCM formulae were formed, aiming to scientifically standardize research content, elevate research quality, and facilitate the mutual translation between clinical and basic research. Guided by integrative pharmacology of TCM and driven by clinical value, these guidelines employ multi-dimensional qualitative pharmacokinetics (PK) and pharmacodynamics (PD) studies alongside quantitative "PK-PD" studies of key components. This approach enables the transition from panoramic description to precise mechanistic research, establishing a complete evidence chain system for the effectiveness of TCM formulae and demonstrating their holistic action characteristics of "multi-component, multi-pathway, multi-target". Meanwhile, the guidelines emphasize that animal models for TCM research should ensure stability and reproducibility. Additionally, pharmacodynamic evaluation is characterized by its measurability and quantifiability. The mechanisms of action at the molecular level should be in-depth and interpretable. These aspects collectively provide guidance for basic non-clinical pharmacology research on TCM formulae and the development of new TCM drugs. The formulation of these guidelines leveraged the research strengths of the drafting units' teams, incorporated literature review findings, and achieved consensus through extensive discussions among multidisciplinary experts. Broad expert opinions from universities, research institutes, and enterprises engaged in new drug development were solicited, ensuring the guidelines' practicality, standardization, scientific rigor, and feasibility.

Safflower (Carthamus tinctorius. L) possesses anti-tumor, anti-thrombotic, anti-oxidative, immunoregulatory, and cardio-cerebral protective effects. It is used clinically for the treatment of cardio-cerebrovascular disease in China. This study aimed to investigate the effects and mechanisms of action of safflower extract on myocardial ischemia–reperfusion (MIR) injury in a left anterior descending (LAD)-ligated model based on integrative pharmacology study and ultra-performance liquid chromatography–quadrupole time-of-flight-tandem mass spectrometer (UPLC-QTOF-MS/MS). Safflower (62.5, 125, 250 mg/kg) was administered immediately before reperfusion. Triphenyl tetrazolium chloride (TTC)/Evans blue, echocardiography, terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) assay, lactate dehydrogenase (LDH) ability, and superoxide dismutase (SOD) levels were determined after 24 h of reperfusion. Chemical components were obtained using UPLC-QTOF-MS/MS. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. Quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting were used to analyze mRNA and protein levels, respectively. Safflower dose-dependently reduced myocardial infarct size, improved cardiac function, decreased LDH levels, and increased SOD levels in C57/BL6 mice. A total of 11 key components and 31 hub targets were filtered based on the network analysis. Comprehensive analysis indicated that safflower alleviated inflammatory effects by downregulating the expression of NFκB1, IL-6, IL-1β, IL-18, TNFα, and MCP-1 and upregulating NFκBia, and markedly increased the expression of phosphorylated PI3K, AKT, PKC, and ERK/2, HIF1α, VEGFA, and BCL2, and decreased the level of BAX and phosphorylated p65. Safflower shows a significant cardioprotective effect by activating multiple inflammation-related signaling pathways, including the NFκB, HIF-1α, MAPK, TNF, and PI3K/AKT signaling pathways. These findings provide valuable insights into the clinical applications of safflower.

Although there are differences in the appearance of Mountain-Cultivated Ginseng (MCG) and Garden-Cultivated Ginseng (GCG), it is very difficult to distinguish them when the samples are processed to slices or powder. Moreover, there is significant price difference between them, which leads to the widespread adulteration or falsification in the market. Thus, the authentication of MCG and GCG is crucial for the effectiveness, safety, and quality stability of ginseng. In the present study, a headspace solid-phase microextraction gas chromatography mass spectrometry (HS-SPME-GC-MS) coupled with chemometrics approach was developed to characterize the volatile component profiles in MCG and GCG with 5-,10-,15-growth years, and subsequently to discover differentiating chemical markers. As a result, we characterized, for the first time, 46 volatile components from all the samples by using the NIST database and the Wiley library. The base peak intensity chromatograms were subjected to multivariate statistical analysis to comprehensively compare the chemical differences among the above samples. MCG_{5-,10-,15-years} and GCG_{5-,10-,15-years} samples were mainly divided into two groups by unsupervised principal component analysis (PCA), and 5 potential cultivation-dependent markers were discovered based on orthogonal partial least squares-discriminant analysis (OPLS-DA). Moreover, MCG_{5-,10-,15-years} samples were divided into three blocks, and 12 potential growth-year-dependent markers enabled differentiation. Similarly, GCG_{5-,10-,15-years} samples were also separated into three groups, and six potential growth-year-dependent markers were determined. The proposed approach could be applied to directly distinguish MCG and GCG with different growth years and to identify the differentiation chemo-markers, which is an important criterion for evaluating the effectiveness, safety, and quality stability of ginseng.

Background: Honghema is a traditional Chinese medicinal material and is also widely used by ethnic minority communities in Guizhou Province, where it is commonly known as “Reib ndad gunb” and “Uab Detdend”. The botanical origins were the whole plants of Laportea bulbifera (Siebold & Zucc.) Wedd. (L. bulbifera) and Girardinia diversifolia (Link) Friis (G. diversifolia). Due to morphological similarities, Urtica mairei H. Lév. (U. mairei) was often mixed as easily confusable species in actual market circulation. However, systematic research regarding the differences in chemical composition between the two botanical origins, as well as between them and their easily confusable species remain limited. This study aimed to comprehensively characterise the chemical constituents of L. bulbifera, G. diversifolia, and U. mairei, and to identify significantly differential chemical constituents for their discrimination. Methods: Data acquisition was performed using ultra-performance liquid chromatography–mass spectrometry (UPLC-MS). Multivariate statistical analyses combined with reference standard comparison were performed to investigate the differences in chemical profiles among L. bulbifera, G. diversifolia and U. mairei, identify the key differential constituents leading to the differences, and clarify their relative abundance information. Results: A total of 133, 77, and 59 chemical constituents were identified from L. bulbifera, G. diversifolia, and U. mairei, respectively. These compounds comprised flavonoids, organic acids, carbohydrates, organonitrogen compounds, terpenoids, coumarins, lipids, steroids, and other types of compounds. Flavonoids constituted the highest proportion of the compounds in L. bulbifera, whereas organic acids were the most abundant in G. diversifolia and U. mairei. Through multivariate statistical analysis, 32 inter-group differential components between L. bulbifera and G. diversifolia, 27 between L. bulbifera and U. mairei, and 13 between G. diversifolia and U. mairei were screened and identified. The core components contributing to these differences included Baimaside, Roxburic acid, 1-Galloyl-glucose, Protocatechuic acid 3-glucoside, and Schaftoside. Venn diagram analysis revealed 20 characteristic differential constituents of L. bulbifera, mainly represented by flavonoids; 6 characteristic differential constituents of G. diversifolia, primarily comprising terpenoids and organic acids; and 4 characteristic differential constituents of U. mairei, mainly represented by carbohydrates. Conclusion: Significant differences were observed among the chemical profiles of the three plant species. The method established in this study can effectively identify the chemical compositions of Honghema and its easily confusable species, revealing the key components responsible for the variations in their chemical profiles. This study provides a basis for elucidating the material foundation of Honghema and offers an effective analytical approach and valuable reference data for distinguishing Honghema from its easily confusable species.