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Pharmacovigilance plays a crucial role in ensuring the safety of pharmaceutical products. It involves the systematic monitoring of adverse events and the detection of potential safety concerns related to drugs. Manual literature screening for pharmacovigilance related articles is a labor-intensive and time-consuming task, requiring streamlined solutions to cope with the continuous growth of literature. The primary objective of this study is to assess the performance of Large Language Models (LLMs) in automating literature screening for pharmacovigilance, aiming to enhance the process by identifying relevant articles more effectively. This study represents a novel application of LLMs including OpenAI's GPT-3.5, GPT-4, and Anthropic's Claude2, in the field of pharmacovigilance, evaluating their ability to categorize medical publications as relevant or irrelevant for safety signal reviews. Our analysis encompassed N-shot learning, chain-of-thought reasoning, and evaluating metrics, with a focus on factors impacting accuracy. The findings highlight the promising potential of LLMs in literature screening, achieving a reproducibility of 93%, sensitivity of 97%, and specificity of 67% showcasing notable strengths in terms of reproducibility and sensitivity, although with moderate specificity. Notably, performance improved when models were provided examples consisting of abstracts, labels, and corresponding reasoning explanations. Moreover, our exploration identified several potential contributing factors influencing prediction outcomes. These factors encompassed the choice of key words and prompts, the balance of the examples, and variations in reasoning explanations. By configuring advanced LLMs for efficient screening of extensive literature databases, this study underscores the transformative potential of these models in drug safety monitoring. Furthermore, these insights gained from this study can inform the development of automated systems for pharmacovigilance, contributing to the ongoing efforts to ensure the safety and efficacy of pharmacovigilance products. [ABSTRACT FROM AUTHOR]

In silico toxicogenomics methods are resource- and time-efficient approaches for inferring chemical–protein–disease associations with potential mechanism information for exploring toxicological effects. However, current in silico toxicogenomics systems make inferences based on only chemical–protein interactions without considering tissue-specific gene/protein expressions. As a result, inferred diseases could be overpredicted with false positives. In this work, six tissue-specific expression datasets of genes and proteins were collected from the Expression Atlas. Genes were then categorized into high, medium, and low expression levels in a tissue- and dataset-specific manner. Subsequently, the tissue-specific expression datasets were incorporated into the chemical–protein–disease inference process of our ChemDIS system by filtering out relatively low-expressed genes. By incorporating tissue-specific gene/protein expression data, the enrichment rate for chemical–disease inference was largely improved with up to 62.26% improvement. A case study of melamine showed the ability of the proposed method to identify more specific disease terms that are consistent with the literature. A user-friendly user interface was implemented in the ChemDIS system. The methodology is expected to be useful for chemical–disease inference and can be implemented for other in silico toxicogenomics tools. [ABSTRACT FROM AUTHOR]

It is challenging to estimate the health risks posed by exposure to particulate pollutants due to limited information for chemicals bound to particles in toxicity databases. This study predicted the cancer risks posed by the vapor-phase and fine particulate matter (PM2.5)-bound polycyclic aromatic hydrocarbon (PAH) emissions during diesel combustion in an industrialized city in southern Taiwan by using two different approaches. In the first approach, the cancer risks were estimated based on the vapor-phase and PM2.5-bound PAH concentrations in the exhausts, while the observations of the vapor-phase and PM2.5-bound samples in the in vitro cell toxicity tests, including Ames test, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, and tumor necrosis factor-alpha (TNF-alpha) detection were employed to modify the risks posed by the PM2.5-bound emission in the second approach. The results show that the vapor-phase concentrations of total PAH (30,944-118,565 ng/m3) and benzo(a)pyrene toxic equivalence quotient (BaP-TEQ; 8.2–69.1 ng/m3) were significantly higher than the PM2.5-bound concentrations (the total concentration and BaP-TEQ were 11.9–45.0 and 0.27–12.94 ng/m3, respectively), resulting in the dominance of the vapor-phase risk (71–98%) over the total risk (8.92 × 10−9–1.03 × 10−7). With the modification of the cell test results, the total risks were increased to 1.62 × 10−8–1.33 × 10−7. The ratios of the PM2.5-bound risk to the total risk in different conditions have been increased by 2–26 times (from 2–29% to 45–61%), suggesting the mixture effect of PM-bound pollutants on health risk assessment. [ABSTRACT FROM AUTHOR]