To analyze the priority challenges in vaccine regulation in China and provide reference for the improvement of the national regulatory system.

A list of possible challenges in vaccine regulation in China was generated through literature review and in-depth interviews. Using the Hanlon method, expert consultation was conducted to assess the importance, severity and feasibility of intervention for these potential challenges to screen out priority challenges.

The expert response rate was 83.5%, the expert authority coefficient was 0.772, and the coordination coefficients for expert scoring on the importance, severity, and feasibility of intervention of potential challenges were 0.576, 0.517, and 0.510, respectively (P<0.05). According to the results of the Hanlon's analysis, the top five priority challenges in vaccine regulation in China were: inadequate development of regulatory human resources; an underdeveloped information disclosure and public communication mechanism; an incomplete quality management system within regulatory authorities; weak quality management capabilities in some enterprises; and insufficient coordination in accelerated regulatory processes.

This study suggests that strengthening the development of professional human resources, improving mechanisms for information disclosure and public communication, enhancing quality management system construction, guiding enterprises to implement self-regulation, and coordinating accelerated regulatory mechanisms are five key dimensions for systematically advancing vaccine regulatory capacity and comprehensively improving the level of vaccine regulatory governance in China.

To analyze the issues in the reporting of serious adverse events (SAEs) in pediatric drug clinical trials, propose standardized reporting strategies, ensure the safety and legitimate rights and interests of pediatric research participants, and improve the pharmacovigilance system for pediatric clinical trials.

An analysis was conducted on 136 SAEs reported to the medical ethics committee of our hospital from our hospital between January 1, 2015 and April 30, 2025. The analysis covered the gender and age of affected children, medical specialities involved, types of drugs, patterns of SAE occurrence, causal relationship with the study drug, reporting issues, and whether they were judged as suspected unexpected serious adverse reactions (SUSARs).

Among the 136 SAEs, the pediatric hematology specialty had the highest number of cases (35 cases); the number of male cases (77 cases) was higher than that of female cases (59 cases), and most occurred during childhood (86 cases); among the drug types, therapeutic biological products had the highest number of cases (76 cases); the most significant impact on research participants was the requirement for hospitalization (124 cases); the number of cases where SAEs were "definitely unrelated" to the trial drug was the highest (47 cases), and the most significant impact on clinical trials was "no change in dose, continue medication" (76 cases); 13 cases were judged as SUSARs.

It is necessary to establish a standardized reporting and handling system for SAEs in pediatric drug clinical trials, to develop SAE reporting forms suitable for children, and to attach great importance to the assessment of children's specific conditions. Sponsors, researchers, and ethics committees must fully understand the differences in SAE spectra between children and adults, strengthen safety monitoring, and maximize the protection of the rights and safety of pediatric subjects.

To analyze the current status, hotspots, and trends of artificial intelligence (AI) technologies applied in the field of drug clinical trials, providing a reference for the application of AI technologies in this domain.

A visual analysis of literature on AI applications in drug clinical trials was conducted using CiteSpace across Chinese databases (CNKI and Wanfang) and the English database (Web of Science).

A total of 135 Chinese and 122 English publications were included. The volume of Chinese publications on AI technologies in drug clinical trials exhibited fluctuating growth, while international English publications demonstrated steady and rapid growth. The institution with the highest number of domestic publications was the China National Medical Products Administration, while internationally, it was Harvard University. Collaborative networks among researchers were relatively decentralized both domestically and internationally. Domestic collaborations primarily occurred among pharmaceutical companies, exhibiting a fragmented overall structure. International collaborations were concentrated among universities, with regional clusters of intensive collaboration. Keyword co-occurrence and clustering analyses revealed both commonalities and differences in AI applications between domestic and international reaserch. Currently, machine learning technologies represent a hotspot in both domestic and international research. Applications in participant recruitment are observed globally, and machine learning is leveraged for data analysis to optimize clinical trials, assess outcomes, and diagnose diseases. Domestically, AI applications focused on clinical trial data management, information system design, and implementation. Internationally, key areas included patient recruitment, disease diagnosis within trials, and biomarker prediction. Domestic research emphasized compliance in AI applications, whereas international research prioritized the refinement of AI algorithmics.

The application of AI in drug clinical trials is evolving from foundational data processing toward complex scenario implementation. Future research may advance in areas such as standardization of data formats, data compliance, and the application of AI in complex clinical trial scenarios.

To establish an HPLC-PDA method for the simultaneous determination of 5-hydroxymethylfurfural, chlorogenic acid, caffeic acid, isochlorogenic acid A, cichoric acid, baicalin, oroxylin A-7-O-β-D-glucuronide, wogonoside, baicalein and glycyrrhizic acid in the Puqin Jiedu Lishi Granules.

The analysis was performed on a Waters Symmetry C₁₈ column (250 mm×4.6 mm, 5 μm) with gradient elution using acetonitrile and 0.2% phosphoric acid solution as the mobile phase. The flow rate was 1.0 mL·min^-1, the column temperature was 20 ℃, and the detection wavelengths were set at 284 nm, 327 nm, 280 nm and 254 nm.

The linear ranges of 5-hydroxymethylfurfural, chlorogenic acid, caffeic acid, isochlorogenic acid A, cichoric acid, baicalin, oroxylin A-7-O-β-D-glucuronide, wogonoside, baicalein and glycyrrhizic acid were 1.860~59.64, 5.425~173.6, 1.980~63.36, 1.890~60.24, 5.990~191.6, 33.30~1066, 6.440~206.0, 28.00~896.0, 2.044~65.40, 6.660~213.1 μg·mL^-1, respectively, with the correlation coefficients r≥0.999 5. The precision, stability, and reproducibility tests yielded satisfactory results. The average recoveries of the 10 components ranged from 98.15%~102.21%, with RSDs between 0.96%~1.91%.

This method is reliable, stable and reproducible, providing a reference for the quality control of Puqin Jiedu Lishi Granules.

To investigate the potential active ingredients and mechanism of action of Xiaobo Yuganzi Ddecoction in the treatment of type 2 diabetes mellitus (T2DM).

UPLC-Q-Exactive Orbitrap MS was employed to characterize the chemical constituents of Xiaobo Yuganzi Decoction. Potential targets of these chemical constituents and known T2DM-related targets were retrieved from public databases, and their overlapping targets were identified. A protein-protein interaction network was constructed to screen for core targets, followed by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. Cytoscape 3.9.1 software was utilized to construct a ‘drug-constituent-target-pathway’ network, and molecular docking was performed between chemical constituents and key targets to screen for potential active ingredients. Western blotting was employed to investigate the effects of Xiaobo Yuganzi Decoction on the expression levels of proteins related to the PI3K/AKT pathway in HepG2 cells.

A total of 39 chemical constituents were identified in Xiaobo Yuganzi Decoction, and 443 intersecting targets were obtained. These targets were primarily involved in biological processes such as protein phosphorylation. Molecular docking results indicated that jatrorrhizine, berberine, ellagic acid, bufotenidine, bufotenine, and palmatine exhibited good binding affinities to key protein receptors. Compared with the model group, Xiaobo Yuganzi Decoction significantly upregulated the expression of p-PI3K/PI3K and p-AKT/AKT proteins in HepG2 cells (P<0.05), consistent with the predictions from network pharmacology.

Potential active ingredients in Xiaobo Yuganzi Decoction, such as jatrorrhizine and ellagic acid, may target core proteins including SRC, HSP90AA1, and STAT3, thereby modulating the PI3K/AKT signaling pathway to exert therapeutic effects on T2DM.

To investigate the key solvents influencing the crystallization process of tofacitinib enantiomer and to explore the dominant crystal form.

Recrystallization and suspension competition methods were used to study the crystallization of tofacitinib enantiomer in different solvents. Powder X-ray diffraction and infrared spectroscopy were used to characterize the crystalline forms of tofacitinib enantiomer. The crystal structure and crystal habit of tofacitinib enantiomer were simulated.

The tofacitinib enantiomer synthesized by ethanol was stable. The crystal characterization parameters can be used as an index to measure the raw material quality of tofacitinib enantiomer. The cell parameters of tofacitinib enantiomer are a=8.559 1, b=27.011 4, c=7.157 5, α=γ=90°, β=68.210 1, belonging to the single monoclinic system with the space group P2_1/c.

Using ethanol as the crystallization solvent can effectively synthesize the dominant crystal form, so as to establish a crystallization process system more suitable for tofacitinib enantiomer, providing a theoretical basis and demonstration effect for the preparation of tofacitinib enantiomer.

To evaluate the in vivo dynamic change patterns and bioavailability of 5 main components in Yinqiao Qingre tablets in rat plasma.

A UPLC-TQ-MS method was established and validated for the simultaneous quantification of the 5 components in plasma. Rats were randomly divided into three groups: Group 1 was given Yinqiao Qingre tablets by intragastric administration (ig) at a dose of 0.153 7 g·kg^-1 crude drug; Group 2 was given a mixed solution of the 5 components at the same concentration as that in the first group via ig; gGroup 3 was intravenously injected (via tail vein) with the mixed solution of the 5 components at a dose of 1 mg·kg^-1. Plasma concentrations of the 5 components at different time points were determined. Pharmacokinetic parameters were calculated using a non-compartmental model, and absolute and relative bioavailability values were further computed.

The specificity of the 5 components analysis methods in plasma was good, the linear relationship was good (r>0.99), the extraction recovery and matrix effect RSD were less than 15%, and the precision, accuracy and stability within and between batches met the requirements. The average peak time (T_max) of the five components was 0.083~1.35 h, and the terminal elimination half-life (t_1/2) was less than 4 h. Moreover, the compound preparation could prolong the elimination half-life and in vivo residence time of the components. The absolute bioavailabilities of chlorogenic acid, puerarin, isoferulic acid, caffeic acid, and ferulic acid were 0.84%, 1.50%, 45.17%, 23.82%, and 32.97%, respectively, while their relative bioavailabilities were all relatively high (ranging from 82% to 94%).

The established analytical method for plasma samples is rapid, stable, and accurate. Yinqiao Qingre tablets can be rapidly absorbed into the blood after oral administration, with isoferulic acid showing the highest plasma exposure and absolute bioavailability. The compatibility of the compound preparation is beneficial for the full exertion of therapeutic effects, and has little impact on the absolute bioavailability of the 5 components.

To identify and analyze disproportionality signals of drug-related thyroid adverse reactions, summarize potentially high-risk drugs, and provide references for clinical medication safety.

Reports of thyroid adverse events from the US Food and Drug Administration Adverse Event Reporting System (FAERS) between January 2004 and December 2024 were retrieved. Disproportionality analysis was used to assess drug-thyroid adverse event associations, with further evaluation of demographics, time to onset, and patient outcomes.

Among 1 261 drugs, 80 exhibited signals of thyroid adverse reactions, including 39 antineoplastic agents (48.75%), 7 anti-infectives (8.75%), 6 cardiovascular drugs (7.5%), and 9 neurologic agents (11.25%). Amiodarone (2 537 cases), pembrolizumab (1 953 cases), and nivolumab (1 748 cases) were the most frequently reported agents. The strongest thyroid signal intensities (by ROR) were observed for biotin (ROR=164.57), amiodarone (ROR=61.51), bexarotene (ROR=46.85), and benzophenylamine (ROR=35.26). Females showed higher susceptibility (female-to-male ratio: 1∶2.33), particularly in individuals aged 18~64 years (18 352 cases, 59.12%). Thyroid abnormalities associated with antineoplastic drugs had a shorter time to onset. Higher proportions of fatal outcomes were associated with digoxin, sorafenib, pembrolizumab, and nivolumab.

Numerous drugs are associated with the risk of thyroid dysfunction, warranting vigilant monitoring. Females, especially those aged 18~64, represent a high-risk population for frug-related thyroid abnormalities. For antineoplastic agents, the first 60 days of treatment constitute a critical monitoring period; long-term medications demand regular surveillance.

To explore post-marketing adverse drug events (ADE) of avacopan and explore the risk factors for severe ADE, providing a reference for clinical safe and rational drug use.

Retrieved adverse event report information associated with avacopan from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database were extracted, covering a total of 12 quarters from the fourth quarter of 2021 to the third quarter of 2024. Suspicious AEs were screened using the reporting odds ratio (ROR), proportional reporting ratio (PRR), bayesian confidence propagation neural network (BCPNN), multiple item empirical bayesian gamma poisson shrinker (MGPS) based on disproportionality analysis. Univariate and multivariate logistic regression analyses were conducted on severe ADE to explore the risk factors associated with avacopan-induced severe ADE according to their severity.

A total of 2 886 ADE reports associated with avacopan were obtained, including 1 489 reports of severe AEs. Using the disproportionality analysis methods, a total of 69 positive ADE signals were identified for avacopan, involving 14 systems and organs. The ADE associated with avacopan were sorted by frequency of occurrence, with hospitalisation (272 cases) having the highest number of reports. By signal strength, antineutrophil cytoplasmic antibody increased showed the strongest signal (ROR=290.83, 95%CI=114.09~741.35). The results of the Logistic regression analysis showed that gender (OR=1.27, 95%CI=1.02~1.58, P<0.05), age (65~80 years: OR=2.20, 95%CI=1.61~2.99, P<0.001；≥80 years: OR=4.61, 95%CI=2.89~7.34, P<0.001) and multiple medications (<5 prescribed drugs: OR=1.32, 95%CI=1.05~1.66, P<0.05; ≥5 prescribed drugs: OR=12.22, 95%CI=6.83~21.87, P<0.001) were the influencing factors for occurrence of severe ADE associated with avacopan.

Clinicians and pharmacists are advised topay greater attention to severe and new ADE signals not included in drug labeling. They should enhance management and pharmaceutical care for elderly, female, and polypharmacy patients to prevent serious ADE and ensure the patient medication safety.