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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). [ABSTRACT FROM AUTHOR]

Cheminformatics is the research field that deals with information about chemical systems. This includes the chemical structure which is used in computational chemistry where quantum chemistry is too complex. The Chemistry Development Kit (CDK) was one of the first Open Science libraries in chemistry, co-founded in The Netherlands. The source code goes as far back as 1997 and has been maintained for more than 25 years. The CDK is used by many tools in drug discovery, computational toxicology, and bioinformatics. This project will develop improvements to the core library and update tools using the CDK to use the latest release. [ABSTRACT FROM AUTHOR]

Both because of the shortcomings of existing risk assessment methodologies, as well as newly available tools to predict hazard and risk with machine learning approaches, there has been an emerging emphasis on probabilistic risk assessment. Increasingly sophisticated artificial intelligence (AI) models can be applied to a plethora of exposure and hazard data to obtain not only predictions for particular endpoints but also to estimate the uncertainty of the risk assessment outcome. This provides the basis for a shift from deterministic to more probabilistic approaches but comes at the cost of an increased complexity of the process as it requires more resources and human expertise. There are still challenges to overcome before a probabilistic paradigm is fully embraced by regulators. Based on an earlier white paper (Maertens et al., 2022), a workshop discussed the prospects, challenges, and path forward for implementing such AI-based probabilistic hazard assessment. Moving forward, we will see the transition from categorized into probabilistic and dose-dependent hazard outcomes, the application of internal thresholds of toxicological concern for data-poor substances, the acknowledgement of user-friendly open-source software, a rise in the expertise of toxicologists required to understand and interpret artificial intelligence models, and the honest communication of uncertainty in risk assessment to the public. [ABSTRACT FROM AUTHOR]

The kinetic responses of leukocyte subsets to exercise and their recovery may serve as indicators of immunological resilience. These time-dependent responses were investigated in healthy young men using a bicycle ergometer test. Fifteen recreationally active male cyclists (20–35 years, VO2max 56.9 ± 3.9 mL kg−1 min−1) performed four exercise protocols with a 1 h duration in a cross-over design: at 70% of the maximal workload (Wmax) in a hydrated and a mildly dehydrated state, at 50% of the Wmax, and intermittently at 85/55% of the Wmax in blocks of 2 min. The numbers of lymphocytes, monocytes, neutrophils, eosinophils, basophils, thrombocytes, and NK cells (CD16 and CD56) were measured at different time points up to 24 h post-exercise. The total leukocyte counts and those of most subsets increased from the start of the exercise, peaking after 30–60 min of exercising. The neutrophil numbers, however, peaked 3 h post-exercise. The CD16brightCD56dim NK cells showed a 1.5-fold increase compared to the CD16brightCD56bright NK cells. Other than for MCP-1, no significant differences were found in the serum cytokine levels. Our results show that exercise intensity is reflected in different time-dependent changes in leukocyte subsets, which supports the concept that the exchange of immune cells between peripheral blood and tissues contributes to enhanced immune surveillance during strenuous exercise. [ABSTRACT FROM AUTHOR]

Aim: To investigate the effects of exercise on salivary concentrations of inflammatory markers by analyzing a panel of 25 inflammatory markers in subjects who had participated in bicycle ergometer tests varying in workload and hydration status. Methods: Fifteen healthy young men (20–35 years) had performed 4 different exercise protocols of 1 hour duration in a randomly assigned cross-over design, preceded by a rest protocol. Individual workloads depended on participant's pre-assessed individual maximum workload (Wmax): rest (protocol 1), 70% Wmax in hydrated (protocol 2) and dehydrated (protocol 3) state, 50% Wmax (protocol 4) and intermittent 85%/55% Wmax in 2 min blocks (protocol 5). Saliva samples were collected before (T0) and immediately after exercise (T1), and at several time points after exercise (2 hours (T3), 3 hours (T4), 6 hours (T5) and 24 hours (T6)). Secretory Leukocyte Protease Inhibitor (SLPI), Matrix Metallopeptidase-9 (MMP-9) and lactoferrin was analyzed using a commercial ELISA kit, a panel of 22 cytokines and chemokines were analyzed using a commercial multiplex immunoassay. Data was analyzed using a multilevel mixed linear model, with multiple test correction. Results: Among a panel of 25 inflammatory markers, SLPI concentrations were significantly elevated immediately after exercise in all protocols compared to rest and higher concentrations reflected the intensity of exercise and hydration status. MMP-9 showed a significant increase in the 70% Wmax dehydrated, 50% Wmax and intermittent protocols. Conclusions: Salivary concentrations of SLPI and MMP-9 seem associated with exercise intensity and hydration status and may offer non-invasive biomarkers to study (local) inflammatory responses to different exercise intensities in human studies. [ABSTRACT FROM AUTHOR]

Introduction: Strenuous physical stress induces a range of physiological responses, the extent depending, among others, on the nature and severity of the exercise, a person's training level and overall physical resilience. This principle can also be used in an experimental set-up by measuring time-dependent changes in biomarkers for physiological processes. In a previous report, we described the effects of workload delivered on a bicycle ergometer on intestinal functionality. As a follow-up, we here describe an analysis of the kinetics of various other biomarkers. Aim: To analyse the time-dependent changes of 34 markers for different metabolic and immunological processes, comparing four different exercise protocols and a rest protocol. Methods: After determining individual maximum workloads, 15 healthy male participants (20–35 years) started with a rest protocol and subsequently performed (in a cross-over design with 1-week wash-out) four exercise protocols of 1-h duration at different intensities: 70% W max in a hydrated and a mildly dehydrated state, 50% W max and intermittent 85/55% W max in blocks of 2 min. Perceived exertion was monitored using the Borg' Rating of Perceived Exertion scale. Blood samples were collected both before and during exercise, and at various timepoints up to 24 h afterward. Data was analyzed using a multilevel mixed linear model with multiple test correction. Results: Kinetic changes of various biomarkers were exercise-intensity-dependent. Biomarkers included parameters indicative of metabolic activity (e.g., creatinine, bicarbonate), immunological and hematological functionality (e.g., leukocytes, hemoglobin) and intestinal physiology (citrulline, intestinal fatty acid-binding protein, and zonulin). In general, responses to high intensity exercise of 70% W max and intermittent exercise i.e., 55/85% W max were more pronounced compared to exercise at 50% W max . Conclusion: High (70 and 55/85% W max ) and moderate (50% W max ) intensity exercise in a bicycle ergometer test produce different time-dependent changes in a broad range of parameters indicative of metabolic activity, immunological and hematological functionality and intestinal physiology. These parameters may be considered biomarkers of homeostatic resilience. Mild dehydration intensifies these time-related changes. Moderate intensity exercise of 50% W max shows sufficient physiological and immunological responses and can be employed to test the health condition of less fit individuals. [ABSTRACT FROM AUTHOR]

This study describes the international ring trial of the epidermal-equivalent (EE) sensitizer potency assay. This assay does not distinguish a sensitizer from a non-sensitizer, but may classify known skin sensitizers according to their potency. It assesses the chemical concentration resulting in 50% cytotoxicity (EE-EC50) or the 2-fold increase in IL-1α (IL-1α2x). Four laboratories received 13 coded sensitizers. Reproducible results were obtained in each laboratory. A binary prediction model, EC50 ≤7 mg/ml = weak to moderate sensitizer and EC50 <7 mg/ml = strong to extreme sensitizer had an accuracy of 77%. A superior EE (EC50 and IL-1α2x) correlation was observed to human in vivo DSA05 data compared to LLNA-EC3 data. Human in vivo NOEL and LLNA-EC3 data correlated to a similar extent to in vitro EE data. Our results indicate that this easily transferable EE potency assay is suitable for testing chemical allergens of unknown potencies and may now be ready for further validation, providing complementary potency information to other assays already undergoing validation for assessing skin sensitization potential. [ABSTRACT FROM AUTHOR]