Jiansong Fang

Objective: Alzheimer’s disease (AD) is an incurable neurodegenerative disease, and Bushen-Yizhi formula (BSYZ) has shown clinical potential for AD treatment, yet its bioactive components and pharmacological mechanisms remain unproven. To explore the therapeutic mechanism of BSYZ against AD-like cognitive impairment, a systems pharmacology framework was established via the integration of UPLC-Q-TOF-MS/MS, network analysis, machine learning and experimental validation. Materials and methods: Chemical composition of BSYZ was characterized by UPLC-Q-TOF-MS/MS. A scopolamine (SCOP)-induced mouse model of AD-like cognitive impairment was used to evaluate neuroprotective effects via behavioral, histopathological, and biochemical assays. AD-associated targets were identified by differential expression analysis and WGCNA. Core targets were screened using 127 machine learning models with SHAP interpretation and validated by RT-qPCR. Stage-stratified transcriptomic analysis assessed gene expression across AD stages. Molecular docking and 100-ns MD simulations explored binding of core components to targets. Results: One hundred five compounds were identified from BSYZ. In SCOP-treated mice, BSYZ ameliorated cognitive deficits, oxidative stress, cholinergic dysfunction, and neuroinflammation. Bioinformatics uncovered 81 potential AD targets, from which seven core genes (HIPK2, CXCR4, CCKBR, PTPN13, SFN, ALB, ALDH1A1) were refined. RT-qPCR confirmed differential mRNA expression, and stage-stratified analysis revealed dynamic changes during AD progression. Molecular docking and MD suggested that isoquercitrin and epicatechin gallate bind to CXCR4 and CCKBR, providing computational evidence. Conclusion: These findings support the multi-component, multi-target neuroprotective effects of BSYZ against AD-like cognitive impairment, providing a basis for further preclinical development and mechanistic investigation.

Natural medicines have been widely investigated for Alzheimer's disease (AD), but the development of this field and the relationship between bibliometric themes and experimental evidence remain insufficiently characterized. This study aimed to map the research landscape and thematic evolution of natural medicine research for AD through bibliometric analysis and to further examine representative mechanistic evidence through a focused narrative synthesis. MethodsPublications in the Web of Science Core Collection from January 1, 2001, to April 28, 2025 were analyzed using VOSviewer and CiteSpace. Bibliometric findings were used to guide a focused narrative synthesis of original experimental studies. A total of 3316 publications involving 16,858 authors, 3594 institutions, and 106 countries or regions were identified, with China contributing the most publications. Research has shifted from acetylcholinesterase inhibition and antioxidant effects toward broader AD‐related mechanisms, while computational approaches such as network pharmacology and molecular docking have become increasingly represented in this research field. Gut microbiota‐related research has increased but remains an emerging direction rather than a dominant hotspot. Experimental studies of representative compounds, medicinal herbs, and formulae mainly involved A β and tau pathology, cholinergic dysfunction, oxidative stress, neuroinflammation, mitochondrial dysfunction, and autophagy. Clinical evidence varied substantially, with EGb 761 and Huperzine A being more extensively studied than many agents supported mainly by preclinical evidence. Computational approaches were increasingly used for hypothesis generation, but their predictions require experimental validation. Natural medicine research for AD has become increasingly mechanism‐oriented and methodologically diverse. Future studies should strengthen experimental validation, improve standardization and pharmacokinetic characterization, and conduct adequately powered clinical trials to support translation of preclinical findings.

Traditional Chinese medicine (TCM) has gained increasing attention due to several severe cases of herb-induced liver injury over the last few decades. Due

The prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties plays a critical role in early-stage drug discovery. While artificial intelligence (AI) has demonstrated transformative potential in revolutionizing this field, existing computational tools remain constrained by limitations in throughput, interpretability, and immobilized modeling frameworks. To address these challenges, we developed ADMETPred, an innovative platform that integrates machine learning and graph neural networks to deliver rapid, accurate, and comprehensive ADMET profiling. Trained on rigorously curated datasets comprising 120,616 compounds, ADMETPred employs 189 models combining LightGBM, XGBoost, Random Forest, and graph attention network to predict 27 drug pharmacokinetic, metabolism, and toxicity endpoints. Compared with current tools, ADMETPred demonstrates superior predictive accuracy by leveraging multi-algorithm synergy, high-throughput batch processing capabilities with parallelized architecture, and customizable workflows for improved prediction flexibility. Notably, the platform integrates an interpretable, attention-driven substructure highlighting module to bridge predictions with actionable structural optimization insights. Case studies spanning post-market drug surveillance, natural product toxicity screening, and lead compound preclinical safety assessment demonstrated alignment with experimental and clinical evidence. In summary, ADMETPred provides a practical resource to enhance early-stage drug development by combining lowered usage barriers with reliable ADMET profiling, freely accessible at http://admetpred.pumc.ai-tcm.cn/ .

Herein, the intermolecular radical tandem reaction of tertiary aniline and olefin promoted by benzophenone, which acts as a photosensitizer to accelerate single‐electron transfer, hydrogen atom transfer, and α ‐aminoalkyl radical formation is reported. The reaction proceeds under mild conditions, exhibits a broad substrate tolerance and high diastereoselectivity, as well as confirms that hydrogen gas is produced in the reaction system, providing a convenient and efficient method for the synthesis of tetrahydroquinoline derivatives.

Recent research has implicated mitochondrial DNA copy number (mtDNA-CN) and Tau protein levels in the blood as potential biomarkers for early Alzheimer’s disease (AD) risk assessment, correlating with metabolite profiles. However, intermediary metabolites mediating these associations remain elusive. Employing a two-sample and a mediation Mendelian randomization (MR) analysis of the IEU OpenGWAS database, involving 383,476 participants from a genome-wide association study (GWAS) and an exome-wide association study (ExWAS), we identified intermediary metabolites linking mtDNA-CN and Tau.Meanwhile, the effects of mediating metabolites on HT22 cell viability and its mitochondrial morphology were also assessed in conjunction with in vitro experiments. Our study revealed an association of mtDNA-CN on Tau (OR = 3.102, 95% CI: 1.016–9.472, P = 0.047), as well as on other 31 metabolites such as 3-Hydroxybutyrate (3HB), Docosahexaenoic acid (DHA), Acetate, Albumin, Apolipoprotein A-I (APOA1), and so on. Notably, 3HB was further implicated in a relationship with Tau (OR = 6.030, 95% CI: 1.054–34.491, P = 0.043), acting as a mediator between mtDNA-CN and Tau. In vitro experiments demonstrated that 3HB positively sustained HT22 cell viability by MTT assay and mitigated mitochondrial swelling under low glucose conditions, as observed via HIS-SIM. In Western blot (WB) and quantitative real-time PCR (qPCR) assays, phosphorylation levels of Tau at serine 262 (p-Tau262) and serine 396 (p-Tau396) were tended to decline following 3HB intervention. Additionally, a positive correlation was identified between 3HB concentration and mtDNA-CN. These findings underscore the potential of 3HB as a biomarker and mediator in early AD risk assessment. Moreover, 3HB’s ability to enhance cell viability, maintain mitochondrial morphology, decrease phosphorylated Tau protein expression and increase mtDNA-CN under stressful conditions, suggesting its therapeutic potential in improving the imbalance of energy metabolism in the AD brain.

Traditional Chinese medicine (TCM) has been a cornerstone of health care for centuries, valued for its preventive and therapeutic properties. However, recent decades have revealed significant toxicological concerns associated with TCMs due to their complex chemical compositions. Traditional QSAR (quantitative structure-activity relationships) models, which predict toxicity based on chemical structures, face challenges with the intricate nature of TCM compounds. In this study, we effectively resolved this issue by correlating the toxicity of TCMs with advanced analytical descriptors from electron ionization mass spectra (EI-MS) data. The optimal classification model achieved a balanced accuracy of over 0.74. Through interpretable machine learning models, we identified specific toxic components, such as 13-hexyloxacyclotridec-10-en-2-one and loliolide. We applied molecular dynamics (MD) simulations to explore the interactions of identified toxic components with crucial protein targets, using hepatic cytochrome P450 3A4 as an example. This novel approach not only enhances our understanding of the toxicological profiles of TCMs but also maximizes their therapeutic benefits while minimizing adverse effects. More importantly, our findings support the application of analytical descriptor-based machine learning in predicting the toxicity of unknown mixtures in the real environment.

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder for which no effective therapy is currently available. Given that various attempts to target beta-amyloid (Aβ) have been unsuccessful in clinical trials, other potential pathogenic factors such as brain energy metabolism (EM) have attracted increasing attention. Traditional Chinese medicines, including danggui-shaoyao-san (DSS), play a notable role in AD. However, it remains unclear whether DSS exerts therapeutic effects on AD through EM regulation. In this study, we conducted behavioural tests, Nissl staining, haematoxylin and eosin staining, and thioflavin S staining, in APP/PS1 mice to assess the pharmacodynamic effect of DSS on AD. Subsequently, we integrated the drug target network of herbal ingredients in DSS and evaluated their absorption, distribution, metabolism, excretion, and toxicity properties to identify the core ingredients. We used proteomic and metabolomic approaches to explore the potential mechanisms of action of DSS against AD. Consequently, we verified the mechanism underlying EM using qPCR, western blotting, and ELISA. In vivo experimental results revealed that DSS ameliorated cognitive impairment in APP/PS1 mice, attenuated neuronal apoptosis, and reduced Aβ burden. Furthermore, the drug-target network comprised 6,514 drug-target interactions involving 1,118 herbal ingredients and 218 AD genes, of which 253 were identified as the core ingredients in DSS. The proteomic results implied that DSS could act on EM to alleviate AD, and targeted energy metabolomics suggested that DSS regulated 47 metabolites associated with EM. Mechanistically, we found that DSS could regulate the GSK3β/PGC1α signalling pathway to improve brain glucose uptake and mitigate mitochondrial dysfunction and oxidative stress, ultimately promoting EM to treat AD. Our study is the first to integrate multi-omics approaches to reveal that DSS could regulate the GSK3β/PGC1α signalling pathway to exert therapeutic effects in AD through the promotion of EM, thereby providing new insights into the mechanism of action of DSS against AD.