Network Analysis for Signal Detection in Spontaneous Adverse Event Reporting Database: Application of Network Weighting Normalization to Characterize Cardiovascular Drug Safety

The Semmelweis Study: a longitudinal occupational cohort study within the framework of the Semmelweis Caring University Model Program for supporting healthy aging

The Semmelweis Study is a prospective occupational cohort study that seeks to enroll all employees of Semmelweis University (Budapest, Hungary) aged 25 years and older, with a population of 8866 people, 70.5% of whom are women. The study builds on the successful experiences of the Whitehall II study and aims to investigate the complex relationships between lifestyle, environmental, and occupational risk factors, and the development and progression of chronic age-associated diseases. An important goal of the Semmelweis Study is to identify groups of people who are aging unsuccessfully and therefore have an increased risk of developing age-associated diseases. To achieve this, the study takes a multidisciplinary approach, collecting economic, social, psychological, cognitive, health, and biological data. The Semmelweis Study comprises a baseline data collection with open healthcare data linkage, followed by repeated data collection waves every 5 years. Data are collected through computer-assisted self-completed questionnaires, followed by a physical health examination, physiological measurements, and the assessment of biomarkers. This article provides a comprehensive overview of the Semmelweis Study, including its origin, context, objectives, design, relevance, and expected contributions.

Safety analysis of therapeutic drugs for breast cancer patients and construction of a predictive model for serious adverse drug reactions

OBJECTIVES

To explore the characteristics of the occurrence of antineoplastic drug adverse reactions (ADRs) in breast cancer and to utilize a computerized tool to identify early warning indicators of potentially serious ADRs.

METHODS

We conducted descriptive statistical analyses of the demographic features, medication use characteristics, and clinical manifestations of suspected ADRs in ADR-exposed patients using data from the Shaanxi Provincial Adverse Drug Reaction Monitoring Center, China, from 2017 to 2021. Using disproportionality methods (reporting odds ratio, proportional reporting ratio, and comprehensive standard method), the relationship between drugs and ADRs was measured. Finally, a web-based clinical prediction model for serious ADRs based on binary logistic regression was developed to estimate individual event probabilities numerically.

RESULTS

We developed a new computer-mineable breast cancer-ADR system. In total, 1119 ADR reports were received between 2017 and 2021, with an increasing trend in the number. Antineoplastic medications of natural sources made up the greatest portion of the drug category (530, 38.10%) while targeted drugs' part increased with time. The medicine with the greatest number of ADR cases was docetaxel. Bone marrow failure was the most reported ADR. The disproportionality methods produced 19 signals of disproportionate reporting, two signals of disproportionate reporting were unknown ADRs. The occurrence of serious ADRs was shown to be substantially correlated with gender, platinum drugs, and blood and lymphatic system disorders. The clinical prediction model for serious ADRs had above-moderate discriminatory power (C-index was 0.775).

CONCLUSIONS

The number of ADRs to breast cancer antineoplastic drugs was constantly increasing, with docetaxel ranking first, with the majority of ADRs presenting as bone marrow suppression, nausea, and vomiting. Data mining identified 19 signals of disproportionate reporting.

Network-Based Methods and Their Applications in Drug Discovery

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.