The zebrafish model has attracted much attention due to its strong reproductive ability, short research cycle, and ease of maintenance. It has always been an important vertebrate model system, often used to carry out human disease research. Its motor behavior features have the advantages of being simpler, more intuitive, and quantifiable. In recent years, it has received widespread attention in the study of traditional Chinese medicine(TCM)for the treatment of sleep disorders, neurodegenerative diseases, fatigue, epilepsy, and other diseases. This paper reviews the characteristics of zebrafish motor behavior and its applications in the pharmacodynamic verification and mechanism research of TCM extracts, active ingredients, and TCM compounds, as well as in active ingredient screening and safety evaluation. The paper also analyzes its advantages and disadvantages, with the aim of improving the breadth and depth of zebrafish and its motor behavior applications in the field of TCM research.
Zebrafish/physiology* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/therapeutic use* ; Disease Models, Animal ; Drug Evaluation, Preclinical/methods* ; Animals ; Sleep Wake Disorders/physiopathology* ; Epilepsy/physiopathology* ; Neurodegenerative Diseases/physiopathology* ; Fatigue/physiopathology* ; Behavior, Animal/physiology* ; Motor Activity/physiology*

Traditional Chinese medicine(TCM), with diverse structural types of active components and remarkable clinical efficacy, holds a significant position in the pharmacological research. As the key substances, active components of TCM are of great importance in revealing the material basis of TCM efficacy and mechanism of action. However, the conventional approaches of discovering active components in TCM are characterized by tedious procedures, lengthy cycles, and unclear mechanisms, which struggle to meet the current demands for drug development. In recent years, major breakthroughs have been made in target discovery technologies, and new drug targets are constantly being discovered, which has facilitated the development of target-driven approaches. The target-guided active component discovery strategy provides a new paradigm for discovering active components in TCM. This article systematically summarizes two mainstream target-based technologies-virtual screening and ligand fishing-for TCM active component discovery. By analyzing relevant application cases, this article evaluates the strengths and limitations of each technology. The review aims to provide frameworks for expediting bioactive component discovery in complex systems like TCM, so as to accelerate the development of innovative drugs based on the active components of TCM and promote the modernization and internationalization of TCM.
Drugs, Chinese Herbal/pharmacology* ; Humans ; Medicine, Chinese Traditional ; Drug Discovery/methods* ; Animals

The study of active substances in traditional Chinese medicine(TCM) is the foundation of TCM pharmacology, TCM quality control, and new drug development, and it is also one of the most popular directions in TCM modernization research in recent years. Due to their diverse chemical compositions and complex component-effect relationships, Chinese medicines often require the comprehensive use of multidisciplinary technologies such as chemistry, biology, and information science to reveal their active substances and targets. In this paper, we review the innovative breakthroughs made in the past 30 years in the discovery strategies and technological means for the research of active substances in traditional Chinese medicine from the perspective of technological changes, and focus on the new direction of the research of active substances in traditional Chinese medicine under the paradigm shift of scientific research brought about by artificial intelligence, with the aim of promoting research in related fields to move in the direction of more accurate, efficient, and intelligent, and providing innovative ideas for the research of active substances in traditional Chinese medicine under the new situation.
Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal/pharmacology* ; Humans ; Artificial Intelligence ; Drug Discovery/methods* ; Animals

As a relatively novel technique for drug delivery, the needle-free injection technique is characterized by transporting the drug liquid to the designated subcutaneous position through a high-speed micro-jet. Although this technique has been applied in many fields, the research on its drug dispersion mechanism and injection performance is insufficient. The presented study aims to identify critical parameters during the injection process and describe their influence on the injection effect. The injection completion rate and performance of a needle-free injector under various operating conditions were compared based on mouse experiments. The results show that the nozzle diameter imposes a more significant influence on jet characteristics than other injection parameters. Moreover, the injection completion rate increases with the nozzle diameter. The nozzle diameters of 0.14 mm and 0.25 mm correspond to injection completion rates of 89.7% and 95.8%, respectively. Furthermore, by analyzing the rate of blood glucose change in the tested mice, it is found that insulin administration through the needle-free injection can achieve a drug effect duration longer than 120 min, which is better than that obtained using conventional needle-syringe technique. In summary, the obtained conclusions can provide an important reference for the optimal design and extending application of the air-powered needle-free injector.
Animals ; Mice ; Insulin/administration & dosage* ; Needles ; Injections, Subcutaneous/methods* ; Injections, Jet/instrumentation* ; Drug Delivery Systems/instrumentation* ; Blood Glucose/analysis* ; Equipment Design

Drug administration via hollow microneedles (HMN) have the advantages of painlessness, avoidance of first-pass effect, capability of sustained infusion, and no need for professional personnel operation. In addition, HMN can also be applied in the fields of body fluid extraction and biosensors, showing broad application prospects. However, traditional manufacturing technologies cannot meet the demand for low-cost mass production of HMN, limiting its widespread application. This paper reviews the main manufacturing technologies used for HMN in recent years, which include photolithography and etching, laser etching, sputtering and electroplating, micro-molding, three-dimensional (3D) printing and drawing lithography. It further analyzes the characteristics and limitations of existing manufacturing technologies and points out that the combination of various manufacturing technologies can improve production efficiency to a certain extent. In addition, this paper looks forward to the future trends of HMN manufacturing technology and proposes possible directions for its development. In conclusion, it is expected that this review can provide new ideas and references for follow-up research.
Printing, Three-Dimensional ; Needles ; Humans ; Drug Delivery Systems/methods* ; Equipment Design ; Microinjections/methods*

Studies on specific transient receptor potential melastatin 2 (TRPM2) channel inhibitors can deepen our understanding of the pathological mechanism of related diseases, and allow discovery of novel, effective targets and drugs for therapy. The development of TRPM2 channel inhibitors can be broadly classified into four categories with distinct characteristics: reutilization and structural modification of homologous ion channel modulators to produce a diverse array of TRPM2 channel inhibitors with strong inhibitory effects; TRPM2 channel inhibitors based on channel gating mechanism with high specificity; inhibitors identified through high-throughput screening with novel chemical structures; inhibitors developed from natural antioxidants with higher safety. In recent years, the application of computer-aided drug design has significantly accelerated the development of TRPM2 channel inhibitors. Several promising compounds such as ZA18, A1 and D9 have been discovered, and it is expected that more potent and selective TRPM2 channel inhibitor scaffolds will be discovered in the future. This article reviews the advances on the studies of TRPM2 channel inhibitors, aiming to provide insights for further research and clinical application of TRPM2 channel inhibitors.
TRPM Cation Channels/antagonists & inhibitors* ; Humans ; Drug Design

The unique characteristics of the deep space environment, microgravity, cosmic radiation, and extreme temperature fluctuations, are emerging as major driving forces for pharmaceutical innovation. These factors provide new avenues for optimizing drug formulations, improving crystal structure quality, and accelerating the discovery of therapeutic targets. Advances in deep space research not only help overcome critical bottlenecks in terrestrial drug development but also promote progress in structure-based drug design and deepen understanding of cellular stress-response mechanisms. Current progress in space-based pharmaceutical research primarily includes the study of disease mechanisms under microgravity, protein crystallization in microgravity, and drug development utilizing deep space radiation and resources. However, the operational complexity, high costs, and limited data reproducibility of space experiments remain key challenges hindering widespread application. Looking ahead, with the integration of automation, artificial intelligence analysis, and on-orbit manufacturing, deep space drug development is expected to achieve greater scalability and precision, opening a new frontier in biopharmaceutical science.
Drug Design ; Drug Development/methods* ; Humans ; Weightlessness ; Space Flight ; Artificial Intelligence ; Extraterrestrial Environment

The glucagon receptor (GCGR) is a critical target for the treatment of metabolic disorders such as Type 2 Diabetes Mellitus (T2DM) and obesity. Activation of GCGR enhances systemic insulin sensitivity through paracrine stimulation of insulin secretion, presenting a promising avenue for treatment. However, the discovery of effective GCGR agonists remains a challenging and resource-intensive process, often requiring time-consuming wet-lab experiments to synthesize and screen potential compounds. Recent advances in artificial intelligence technologies have demonstrated great potential in accelerating drug discovery by streamlining screening and efficiently predicting bioactivity. In the present work, we propose DeepGCGR, a two-layer deep learning model that leverages graph convolutional networks (GCN) integrated with a multiple attention mechanism to expedite the identification of GCGR agonists. In the first layer, the model predicts the bioactivity of various compounds against GCGR, efficiently filtering large chemical libraries to identify promising candidates. In the second layer, DeepGCGR classifies high bioactive compounds based on their functional effects on GCGR signaling, identifying those with potential agonistic or antagonistic effects. Moreover, DeepGCGR was specifically applied to identify novel GCGR-regulating compounds for the treatment of T2DM from natural products derived from traditional Chinese medicine (TCM). The proposed method will not only offer an effective strategy for discovering GCGR-targeting compounds with functional activation properties but also provide new insights into the development of T2DM therapeutics.
Deep Learning ; Drug Discovery/methods* ; Humans ; Diabetes Mellitus, Type 2/metabolism* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/pharmacology*

Artificial intelligence (AI) has emerged as a transformative technology in accelerating drug discovery and development within natural medicines research. Natural medicines, characterized by their complex chemical compositions and multifaceted pharmacological mechanisms, demonstrate widespread application in treating diverse diseases. However, research and development face significant challenges, including component complexity, extraction difficulties, and efficacy validation. AI technology, particularly through deep learning (DL) and machine learning (ML) approaches, enables efficient analysis of extensive datasets, facilitating drug screening, component analysis, and pharmacological mechanism elucidation. The implementation of AI technology demonstrates considerable potential in virtual screening, compound optimization, and synthetic pathway design, thereby enhancing natural medicines' bioavailability and safety profiles. Nevertheless, current applications encounter limitations regarding data quality, model interpretability, and ethical considerations. As AI technologies continue to evolve, natural medicines research and development will achieve greater efficiency and precision, advancing both personalized medicine and contemporary drug development approaches.
Biological Products/pharmacology* ; Artificial Intelligence ; Humans ; Drug Discovery/methods* ; Machine Learning ; Deep Learning

Network pharmacology has gained widespread application in drug discovery, particularly in traditional Chinese medicine (TCM) research, which is characterized by its "multi-component, multi-target, and multi-pathway" nature. Through the integration of network biology, TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elucidation of action mechanisms, establishing a novel research paradigm for TCM modernization. The rapid advancement of machine learning, particularly revolutionary deep learning methods, has substantially enhanced artificial intelligence (AI) technology, offering significant potential to advance TCM network pharmacology research. This paper describes the methodology of TCM network pharmacology, encompassing ingredient identification, network construction, network analysis, and experimental validation. Furthermore, it summarizes key strategies for constructing various networks and analyzing constructed networks using AI methods. Finally, it addresses challenges and future directions regarding cell-cell communication (CCC)-based network construction, analysis, and validation, providing valuable insights for TCM network pharmacology.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Network Pharmacology/methods* ; Humans ; Drugs, Chinese Herbal/chemistry* ; Drug Discovery