Background: Adverse outcome pathway (AOP) networks are versatile tools in toxicology and risk assessment that capture and visualize mechanisms driving toxicity originating from various data sources. They share a common structure consisting of a set of molecular initiating events and key events, connected by key event relationships, leading to the actual adverse outcome. AOP networks are to be considered living documents that should be frequently updated by feeding in new data. Such iterative optimization exercises are typically done manually, which not only is a time-consuming effort, but also bears the risk of overlooking critical data. The present study introduces a novel approach for AOP network optimization of a previously published AOP network on chemical-induced cholestasis using artificial intelligence to facilitate automated data collection followed by subsequent quantitative confidence assessment of molecular initiating events, key events, and key event relationships. Methods: Artificial intelligence-assisted data collection was performed by means of the free web platform Sysrev. Confidence levels of the tailored Bradford-Hill criteria were quantified for the purpose of weight-of-evidence assessment of the optimized AOP network. Scores were calculated for biological plausibility, empirical evidence, and essentiality, and were integrated into a total key event relationship confidence value. The optimized AOP network was visualized using Cytoscape with the node size representing the incidence of the key event and the edge size indicating the total confidence in the key event relationship. Results: This resulted in the identification of 38 and 135 unique key events and key event relationships, respectively. Transporter changes was the key event with the highest incidence, and formed the most confident key event relationship with the adverse outcome, cholestasis. Other important key events present in the AOP network include: nuclear receptor changes, intracellular bile acid accumulation, bile acid synthesis changes, oxidative stress, inflammation and apoptosis. Conclusions: This process led to the creation of an extensively informative AOP network focused on chemical-induced cholestasis. This optimized AOP network may serve as a mechanistic compass for the development of a battery of in vitro assays to reliably predict chemical-induced cholestatic injury.
To study the ways in which compounds can induce adverse effects, toxicologists have been constructing Adverse Outcome Pathways (AOPs). An AOP can be considered as a pragmatic tool to capture and visualize mechanisms underlying different types of toxicity inflicted by any kind of stressor, and describes the interactions between key entities that lead to the adverse outcome on multiple biological levels of organization. The construction or optimization of an AOP is a labor intensive process, which currently depends on the manual search, collection, reviewing and synthesis of available scientific literature. This process could however be largely facilitated using Natural Language Processing (NLP) to extract information contained in scientific literature in a systematic, objective, and rapid manner that would lead to greater accuracy and reproducibility. This would support researchers to invest their expertise in the substantive assessment of the AOPs by replacing the time spent on evidence gathering by a critical review of the data extracted by NLP. As case examples, we selected two frequent adversities observed in the liver: namely, cholestasis and steatosis denoting accumulation of bile and lipid, respectively. We used deep learning language models to recognize entities of interest in text and establish causal relationships between them. We demonstrate how an NLP pipeline combining Named Entity Recognition and a simple rules-based relationship extraction model helps screen compounds related to liver adversities in the literature, but also extract mechanistic information for how such adversities develop, from the molecular to the organismal level. Finally, we provide some perspectives opened by the recent progress in Large Language Models and how these could be used in the future. We propose this work brings two main contributions: 1) a proof-of-concept that NLP can support the extraction of information from text for modern toxicology and 2) a template open-source model for recognition of toxicological entities and extraction of their relationships. All resources are openly accessible via GitHub (https://github.com/ontox-project/en-tox).
BackgroundCritically ill patients are subject to severe skeletal muscle wasting during intensive care unit (ICU) stay, resulting in impaired short- and long-term functional outcomes and health-related quality of life. Increased protein provision may improve functional outcomes in ICU patients by attenuating skeletal muscle breakdown. Supporting evidence is limited however and results in great variety in recommended protein targets.MethodsThe PRECISe trial is an investigator-initiated, bi-national, multi-center, quadruple-blinded randomized controlled trial with a parallel group design. In 935 patients, we will compare provision of isocaloric enteral nutrition with either a standard or high protein content, providing 1.3 or 2.0 g of protein/kg/day, respectively, when fed on target. All unplanned ICU admissions with initiation of invasive mechanical ventilation within 24 h of admission and an expected stay on ventilator support of at least 3 days are eligible. The study is designed to assess the effect of the intervention on functional recovery at 1, 3, and 6 months following ICU admission, including health-related quality of life, measures of muscle strength, physical function, and mental health. The primary endpoint of the trial is health-related quality of life as measured by the Euro-QoL-5D-5-level questionnaire Health Utility Score. Overall between-group differences will be assessed over the three time points using linear mixed-effects models.DiscussionThe PRECISe trial will evaluate the effect of protein on functional recovery including both patient-centered and muscle-related outcomes.Trial registrationClinicalTrials.gov Identifier: NCT04633421. Registered on November 18, 2020. First patient in (FPI) on November 19, 2020. Expected last patient last visit (LPLV) in October 2023.
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