Introduction: A significant proportion of severe asthma patients require oral corticosteroids (OCS) to maintain symptom control. Prolonged OCS predisposes patients to hypothalamicpituitary-adrenal axis suppression which can be lifethreatening. Biologic therapies are effective steroid-sparing options suitable for select severe asthma patients. We evaluated steroid tapering outcomes and adrenal function during biologic therapy in a tertiary severe asthma clinic. Results: A total of 43 patients were included. Median age was 66 years (interquartile range [IQR] 60–74), and 65% were male. Mepolizumab was the most used (17 patients), followed by benralizumab (15), tezepelumab (6), dupilumab (4), and omalizumab (1). Median baseline prednisolone dose was 10 mg (IQR 5–10). Median lowest dose achieved was 3 mg (IQR 0–3), sustained for at least 4 weeks in 90.7% of patients. Median reduction in prednisolone dose was 5 mg (IQR 5–7.25). In all, 42% of patients successfully discontinued maintenance steroids. Adrenal function was assessed using morning serum cortisol (n = 37) and/or SST (n = 28). Evidence of adrenal insufficiency was identified in 19 patients (44%), including 15 patients with failed SST and four with low morning cortisol suggestive of adrenal suppression. Morning serum cortisol was indeterminate in four patients. A total of 17 patients were referred to endocrinology. During steroid tapering, 11 asthma-related hospital admissions and eight emergency department presentations occurred, involving 10 patients. Conclusion Biologics facilitate the reduction of the OCS burden in severe asthma. Tertiary adrenal suppression is common during steroid tapering and requires prompt biochemical assessment and endocrinology referral. Structured monitoring of adrenal function is essential during biologicfacilitated steroid tapering in routine clinical practice.
Biological Products ; Steroids ; Asthma

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*

Traditional Chinese medicine formula (TCMF) represents a fundamental component of Chinese medical practice, incorporating medical knowledge and practices from both Han Chinese and various ethnic minorities, while providing comprehensive insights into health and disease. The foundation of TCMF lies in its holistic approach, manifested through herbal compatibility theory, which has emerged from extensive clinical experience and evolved into a highly refined knowledge system. Within this framework, Chinese herbal medicines exhibit intricated characteristics, including multi-component interactions, diverse target sites, and varied biological pathways. These complexities pose significant challenges for understanding their molecular mechanisms. Contemporary advances in artificial intelligence (AI) are reshaping research in traditional Chinese medicine (TCM), offering immense potential to transform our understanding of the molecular mechanisms underlying TCMFs. This review explores the application of AI in uncovering these mechanisms, highlighting its role in compound absorption, distribution, metabolism, and excretion (ADME) prediction, molecular target identification, compound and target synergy recognition, pharmacological mechanisms exploration, and herbal formula optimization. Furthermore, the review discusses the challenges and opportunities in AI-assisted research on TCMF molecular mechanisms, promoting the modernization and globalization of TCM.
Artificial Intelligence ; Drugs, Chinese Herbal/pharmacokinetics* ; Humans ; Medicine, Chinese Traditional ; Animals

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

Natural product-based drug combinations (NPDCs) present distinctive advantages in treating complex diseases. While high-throughput screening (HTS) and conventional computational methods have partially accelerated synergistic drug combination discovery, their applications remain constrained by experimental data fragmentation, high costs, and extensive combinatorial space. Recent developments in artificial intelligence (AI), encompassing traditional machine learning and deep learning algorithms, have been extensively applied in NPDC identification. Through the integration of multi-source heterogeneous data and autonomous feature extraction, prediction accuracy has markedly improved, offering a robust technical approach for novel NPDC discovery. This review comprehensively examines recent advances in AI-driven NPDC prediction, presents relevant data resources and algorithmic frameworks, and evaluates current limitations and future prospects. AI methodologies are anticipated to substantially expedite NPDC discovery and inform experimental validation.
Artificial Intelligence ; Biological Products/chemistry* ; Humans ; Drug Combinations ; Drug Discovery/methods* ; Machine Learning ; Algorithms

Due to its synergistic effects and reduced side effects, combination therapy has become an important strategy for treating complex diseases. In traditional Chinese medicine (TCM), the "monarch, minister, assistant, envoy" compatibilities theory provides a systematic framework for drug compatibility and has guided the formation of a large number of classic formulas. However, due to the complex compositions and diverse mechanisms of action of TCM, it is difficult to comprehensively reveal its potential synergistic patterns using traditional methods. Synergistic prediction based on molecular compatibility theory provides new ideas for identifying combinations of active compounds in TCM. Compared to resource-intensive traditional experimental methods, artificial intelligence possesses the ability to mine synergistic patterns from multi-omics and structural data, providing an efficient means for modeling and optimizing TCM combinations. This paper systematically reviews the application progress of AI in the synergistic prediction of TCM active compounds and explores the challenges and prospects of its application in modeling combination relationships, thereby contributing to the modernization of TCM theory and methodological innovation.
Artificial Intelligence ; Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal/pharmacology* ; Humans ; Drug Synergism

Traditional Chinese medicine (TCM) demonstrates distinctive advantages in disease prevention and treatment. However, analyzing its biological mechanisms through the modern medical research paradigm of "single drug, single target" presents significant challenges due to its holistic approach. Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks, overcoming the limitations of reductionist research models and showing considerable value in TCM research. Recent integration of network target computational and experimental methods with artificial intelligence (AI) and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology. The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles. This review, centered on network targets, examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships, alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae, syndromes, and toxicity. Looking forward, network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics, potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
Artificial Intelligence ; Medicine, Chinese Traditional ; Humans ; Network Pharmacology/methods* ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Multiomics

OBJECTIVES: To explore the mechanism of Qingda Granules (QDG) for alleviating brain damage in spontaneously hypertensive rats (SHRs). METHODS: Twelve 5-week-old SHRs were randomized into SHR control group and SHR+QDG group treated with QDG by gavage at the daily dose of 0.9 g/kg for 12 weeks. The control rats, along with 6 age-matched WKY rats, were treated with saline only. Blood pressure changes of the rats were monitored, and pathologies and neuronal apoptosis in the cerebral cortex were examined with HE staining and TUNEL staining. Cerebral cortical expressions of miR-124 and STAT3 mRNA were detected using RT-qPCR, and the protein expressions of NeuN, STAT3, Bcl-2, Bax, and cleaved caspase-3 were detected with immunohistochemistry and Western blotting. In a HT22 cell model of oxygen and glucose deprivation/reoxygenation (OGD/R), the effects of QDG on cell viability and apoptosis, expressions of miR-124 and STAT3 mRNA, and protein expressions of STAT3, Bcl-2, Bax, and cleaved caspase-3 were evaluated using CCK8 assay, Hoechst 33342 staining, RT-qPCR, and Western blotting. RESULTS: Compared with WKY rats, SHRs had significantly elevated systolic blood pressure, diastolic blood pressure and mean arterial pressure with significantly increased neuronal apoptosis in the cerebral cortex, reduced expressions of NeuN, miR-124 and Bcl-2, and enhanced expressions of STAT3, Bax and cleaved caspase-3 (P<0.05). All these changes in the SHRs were significantly ameliorated by treatment with QDG (P<0.05). In the HT22 cell model, QDG treatment obviously reduced OGD/R-induced cell apoptosis, increased the expressions of miR-124 and Bcl-2, and suppressed the elevation of protein expressions of STAT3, Bax and cleaved caspase-3. CONCLUSIONS QDG inhibits cerebral cortical neuronal apoptosis and thereby attenuates brain damage in SHR rats by modulating the miR-124/STAT3 signaling axis.
Animals ; Rats, Inbred SHR ; MicroRNAs/metabolism* ; STAT3 Transcription Factor/metabolism* ; Signal Transduction/drug effects* ; Drugs, Chinese Herbal/pharmacology* ; Rats ; Apoptosis/drug effects* ; Rats, Inbred WKY ; Male ; Hypertension

OBJECTIVES: To investigate the protective effect of Yiqi Yangyin Huazhuo Tongluo Formula (YYHT) against high glucose-induced injury in mouse renal podocytes (MPC5 cells) and the possible mechanism. METHODS: Adult Wistar rats were treated with 19, 38, and 76 g/kg YYHT or saline via gavage for 7 days to prepare YYHT-medicated or blank sera for treatment of MPC5 cells cultured in high glucose (30 mmol/L) prior to transfection with a miR-21a-5p inhibitor or a miR-21a-5p mimic. The changes in miR-21a-5p expressions and the mRNA levels of FoxO1, PINK1, and Parkin in the treated cells were detected with qRT-PCR, and the protein levels of nephrin, podocin, FoxO1, PINK1, and Parkin were detected with Western blotting. Autophagic activity in the cells were evaluated with MDC staining. The effect of miR-21a-5p mimic on FoxO1 transcription and the binding of miR-21a-5p to FoxO1 were examined with luciferase reporter gene assay and radioimmunoprecipitation assay. RESULTS: MPC5 cells exposed to high glucose showed significantly increased miR-21a-5p expression, lowered expressions of FoxO1, PINK1, and Parkin1 mRNAs, and reduced levels of FoxO1, PINK1, parkin, nephrin, and podocin proteins and autophagic activity. Treatment of the exposed cells with YYHT-medicated sera and miR-21a-5p inhibitor both significantly enhanced the protein expressions of nephrin and podocin, inhibited the expression of miR-21a-5p, increased the mRNA and protein expressions of FoxO1, PINK1 and Parkin, and upregulated autophagic activity of the cells. Transfection with miR-21a-5p mimic effectively inhibited the transcription of FoxO1 and promoted the binding of miR-21a-5p to FoxO1 in MPC5 cells, and these effects were obviously attenuated by treatment with YYHT-medicated sera. CONCLUSIONS YYHT-medicated sera alleviate high glucose-induced injury in MPC5 cells by regulating miR-21a-5p/FoxO1/PINK1-mediated mitochondrial autophagy.
Animals ; MicroRNAs/genetics* ; Podocytes/pathology* ; Drugs, Chinese Herbal/pharmacology* ; Autophagy/drug effects* ; Rats, Wistar ; Protein Kinases/metabolism* ; Rats ; Forkhead Box Protein O1 ; Mice ; Mitochondria/drug effects* ; Ubiquitin-Protein Ligases/metabolism* ; Glucose ; Diabetic Nephropathies ; Male ; Membrane Proteins/metabolism* ; Intracellular Signaling Peptides and Proteins