Potential Mechanism by which Eriodictyol Protects against Doxorubicininduced Cardiotoxicity based on Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation.

BACKGROUND: The clinical use of doxorubicin (DOX), an anthracycline antibiotic with broad-spectrum applications against various malignant tumors, is limited by doxorubicininduced cardiotoxicity (DIC). Eriodictyol (ERD) has shown cardioprotective effects, but the mechanism of its protective effect on DIC remains unknown. AIMS: This study aimed to explore the potential mechanisms by which ERD confers protection against DIC. METHODS: ERD and DIC targets were identified from the TCMSP, PharmMaper, SwissTargetPrediction, TargetNet, BATMAN, GeneCards, and PharmGKB databases. Differential gene expression data between DIC and normal tissues were extracted from the GEO database. A protein‒ protein interaction (PPI) network of the intersecting ERD-DIC targets was constructed using the STRING platform and visualized with Cytoscape 3.10.0 software. Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for ERD-DIC cross-targets were conducted. Validation included molecular docking with AutoDock Tools software and molecular dynamics simulations with Gromacs 2019.6 software. RESULTS: Network pharmacology analysis revealed 43 intersecting ERD-DIC targets, including 6 key targets. GO functional enrichment analysis indicated that the intersecting targets were enriched in 550 biological processes, 45 cell components, and 41 molecular functions. KEGG pathway enrichment analysis identified 114 enriched signaling pathways. Molecular docking revealed a strong binding affinity between ERD and 6 key targets, as well as multiple targets within the ROS pathway. Molecular dynamics simulations indicated that ERD has favorable binding with 3 crucial targets. CONCLUSION: The systematic network pharmacology analysis suggests that ERD may mitigate DIC through multiple targets and pathways, with the ROS pathway potentially playing a crucial role. These findings provide a reference for foundational research and clinical applications of ERD in treating DIC.

The Potential Mechanism of Eriodictyol in Treating Alzheimer's Disease: A Study on Computer-assisted Investigational Strategies.

BACKGROUND: At present, drug development for treating Alzheimer's disease (AD) is still highly challenging. Eriodictyol (ERD) has shown great potential in treating AD, but its molecular mechanism is unknown. OBJECTIVE: We aimed to explore the potential targets and mechanisms of ERD in the treatment of AD through network pharmacology, molecular docking, and molecular dynamics simulations. METHODS: ERD-related targets were predicted based on the CTD, SEA, PharmMapper, Swiss TargetPrediction, and ETCM databases, and AD-related targets were predicted through the TTD, OMIM, DrugBank, GeneCards, Disgenet, and PharmGKB databases. Protein-protein interaction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomics analyses (KEGG) were used to analyse the potential targets and key pathways of the anti-AD effect of ERD. Subsequently, potential DEGs affected by AD were analysed using the AlzData database, and their relationships with ERD were evaluated through molecular docking and molecular dynamics simulations. RESULTS: A total of 198 ERD-related targets, 3716 AD-related targets, and 122 intersecting targets were identified. GO annotation analysis revealed 1497 biological processes, 78 cellular components, and 132 molecular functions of 15 core targets. KEGG enrichment analysis identified 168 signalling pathways. We ultimately identified 9 DEGs associated with AD through analysis of the AlzData data. Molecular docking results showed good affinity between the selected targets and ERD, with PTGS2, HSP90AA1, and BCL2. The interactions were confirmed by molecular dynamics simulations. CONCLUSION: ERD exerts anti-AD effects through multiple targets, pathways, and levels, providing a theoretical foundation and valuable reference for the development of ERD as a natural anti-AD drug.

The Potential Mechanism of Liujunzi Decoction in the Treatment of Breast Cancer based on Network Pharmacology and Molecular Docking Technology.

BACKGROUND: Liujunzi Decoction (LJZD) is a potential clinical treatment for Breast Cancer (BC), but the active ingredients and mechanisms underlying its effectiveness remain unclear. OBJECTIVE: The study aimed to investigate the target gene of LJZD compatibility and the possible mechanism of action in the treatment of breast cancer by using network pharmacology and molecular docking. METHODS: Based on TCMSP, ETCM, and BATMAN database searching and screening to obtain the ingredients of LJZD, the related targets were obtained. Breast cancer-related targets were collected through GEO, Geencards, OMIM, and other databases, and drug-disease Venn diagrams were drawn by R. The PPI network map was constructed by using Cytoscape. The intersecting targets were imported into the STRING database, and the core targets were analyzed and screened. The intersected targets were analyzed by the DAVID database for GO and KEGG enrichment. AutoDock Vina and Gromacs were used for molecular docking and simulation of the core targets and active ingredients. RESULTS: 126 active ingredients of LJZD were obtained; 241 targets related to breast cancer were sought after screening, and 180 intersection targets were identified through Venn diagram analysis. The core targets were FOS and ESR1. KEGG enrichment analysis mainly involved PI3K/Akt, MAPK, and other signaling pathways. CONCLUSION: This study has explored the possible targets and signaling pathways of LJZD in treating breast cancer through network pharmacology and bioinformatics analysis. Molecular docking and simulation have further validated the potential mechanism of action of LJZD in breast cancer treatment, providing essential experimental data for future studies.

Drug-Induced Acute Pancreatitis: A Real-World Pharmacovigilance Study Using the FDA Adverse Event Reporting System Database.

Timely identification and discontinuation of culprit-drug is the cornerstone of clinical management of drug-induced acute pancreatitis (AP), but the comprehensive landscape of AP culprit-drugs is still lacking. To provide the current overview of AP culprit-drugs to guide clinical practice, we reviewed the adverse event (AE) reports associated with AP in the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database from 2004 to 2022, and summarized a potential AP culprit-drug list and its corresponding AE report quantity proportion. The disproportionality analysis was used to detect adverse drug reaction (ADR) signals for each drug in the drug list, and the ADR signal distribution was integrated to show the risk characteristic of drugs according to the ADR signal detection results. In the FAERS database, a total of 62,206 AE reports were AP-related, in which 1,175 drugs were reported as culprit-drug. On the whole, metformin was the drug with the greatest number of AE reports, followed by quetiapine, liraglutide, exenatide, and sitagliptin. Drugs used in diabetes was the drug class with the greatest number of AE reports, followed by immunosuppressants, psycholeptics, drugs for acid-related disorders, and analgesics. In disproportionality analysis, 595 drugs showed potential AP risk, whereas 580 drugs did not show any positive ADR signal. According to the positive-negative distribution of the ADR signal for drug classes, the drug class with the greatest number of positive drugs was antineoplastic agents. In this study, we provided the current comprehensive landscape of AP culprit-drugs from the pharmacovigilance perspective, which can provide reference information for clinical practice.

Real-World Data in Pharmacovigilance Database Provides a New Perspective for Understanding the Risk of Clostridium difficile Infection Associated with Antibacterial Drug Exposure.

Antibacterial drug exposure (ADE) is a well-known potential risk factor for Clostridium difficile infection (CDI), but it remains controversial which certain antibacterial drugs are associated with the highest risk of CDI occurrence. To summarize CDI risk associated with ADE, we reviewed the CDI reports related to ADE in the FDA Adverse Event Reporting System database and conducted disproportionality analysis to detect adverse reaction (ADR) signals of CDI for antibacterial drugs. A total of 8063 CDI reports associated with ADE were identified, which involved 73 antibacterial drugs. Metronidazole was the drug with the greatest number of reports, followed by vancomycin, ciprofloxacin, clindamycin and amoxicillin. In disproportionality analysis, metronidazole had the highest positive ADR signal strength, followed by vancomycin, cefpodoxime, ertapenem and clindamycin. Among the 73 antibacterial drugs, 58 showed at least one positive ADR signal, and ceftriaxone was the drug with the highest total number of positive signals. Our study provided a real-world overview of CDI risk for AED from a pharmacovigilance perspective and showed risk characteristics for different antibacterial drugs by integrating its positive-negative signal distribution. Meanwhile, our study showed that the CDI risk of metronidazole and vancomycin may be underestimated, and it deserves further attention and investigation.

Severe cutaneous adverse reactions to drugs: A real-world pharmacovigilance study using the FDA Adverse Event Reporting System database.

Background: Sound drug safety information is important to optimize patient management, but the widely recognized comprehensive landscape of culprit-drugs that cause severe cutaneous adverse reactions (SCARs) is currently lacking. Objective: The main aim of the study is to provide a comprehensive landscape of culprit-drugs for SCARs to guide clinical practice. Methods: We analyzed reports associated with SCARs in the FDA Adverse Event Reporting System database between 1 January 2004 and 31 December 2021 and compiled a list of drugs with potentially serious skin toxicity. According to this list, we summarized the reporting proportions of different drugs and drug classes and conducted disproportionality analysis for all the drugs. In addition, the risk characteristic of SCARs due to different drugs and drug classes was summarized by the positive-negative distribution based on the results of the disproportionality analysis. Results: A total of 77,789 reports in the FDA Adverse Event Reporting System database were considered SCAR-related, of which lamotrigine (6.2%) was the most reported single drug followed by acetaminophen (5.8%) and allopurinol (5.8%) and antibacterials (20.6%) was the most reported drug class followed by antiepileptics (16.7%) and antineoplastics (11.3%). A total of 1,219 drugs were reported as culprit-drugs causing SCARs in those reports, and the largest number of drugs belonged to antineoplastics. In disproportionality analysis, 776 drugs showed at least one positive pharmacovigilance signal. Drugs with the most positive signals were lamotrigine, acetaminophen, furosemide, and sulfamethoxazole/trimethoprim. Conclusion: Our study provided a real-world overview of SCARs to drugs, and the investigation of SCAR positive-negative distribution across different drugs revealed its risk characteristics, which may help optimize patient management.

Drug-induced QT prolongation and torsade de pointes: a real-world pharmacovigilance study using the FDA Adverse Event Reporting System database.

Introduction: Drug-induced QT prolongation and (or) Torsade de Pointes (TdP) is a well-known serious adverse reaction (ADR) for some drugs, but the widely recognized comprehensive landscape of culprit-drug of QT prolongation and TdP is currently lacking. Aim: To identify the top drugs reported in association with QT prolongation and TdP and provide information for clinical practice. Method: We reviewed the reports related to QT prolongation and TdP in the FDA Adverse Event Reporting System (FAERS) database from January 1, 2004 to December 31, 2022, and summarized a potential causative drug list accordingly. Based on this drug list, the most frequently reported causative drugs and drug classes of QT prolongation and TdP were counted, and the disproportionality analysis for all the drugs was conducted to in detect ADR signal. Furthermore, according to the positive-negative distribution of ADR signal, we integrated the risk characteristic of QT prolongation and TdP in different drugs and drug class. Results: A total of 42,713 reports in FAERS database were considered to be associated with QT prolongation and TdP from 2004 to 2022, in which 1,088 drugs were reported as potential culprit-drugs, and the largest number of drugs belonged to antineoplastics. On the whole, furosemide was the most frequently reported drugs followed by acetylsalicylic acid, quetiapine, citalopram, metoprolol. In terms of drug classes, psycholeptics was the most frequently reported drug classes followed by psychoanaleptics, analgesics, beta blocking agents, drugs for acid related disorders. In disproportionality analysis, 612 drugs showed at least one positive ADR signals, while citalopram, ondansetron, escitalopram, loperamide, and promethazine were the drug with the maximum number of positive ADR signals. However, the positive-negative distribution of ADR signals between different drug classes showed great differences, representing the overall risk difference of different drug classes. Conclusion: Our study provided a real-world overview of QT prolongation and TdP to drugs, and the presentation of the potential culprit-drug list, the proportion of reports, the detection results of ADR signals, and the distribution characteristics of ADR signals may help understand the safety profile of drugs and optimize clinical practice.