Engaging Virtual Learners: Moving Classroom as Organization Online

The global shift toward online learning and remote work necessitated the transition of a highly experiential course to a virtual environment, challenging the assumption that Classroom-as-Organization (CAO), a teaching methodology designed to foster student engagement, skill development and deep learning, is limited to face-to-face (F2F) delivery. This article explores the process of adapting CAO’s interactive and immersive elements for online platforms, addressing both the challenges and opportunities presented by this transition. By reviewing the CAO literature, which predominantly focuses on F2F applications, we reflect on the complexities of translating such a dynamic pedagogy to online learning and propose potential avenues for future research on the effectiveness of CAO in virtual settings

t4 Workshop Report: Integrated Testing Strategies (ITS) for Safety Assessment

Integrated testing strategies (ITS), as opposed to single definitive tests or fixed batteries of tests, are expected to efficiently combine different information sources in a quantifiable fashion to satisfy an information need, in this case for regulatory safety assessments. With increasing awareness of the limitations of each individual tool and the development of highly targeted tests and predictions, the need for combining pieces of evidence increases. The discussions that took place during this workshop, which brought together a group of experts coming from different related areas, illustrate the current state of the art of ITS, as well as promising developments and identifiable challenges. The case of skin sensitization was taken as an example to understand how possible ITS can be constructed, optimized and validated. This will require embracing and developing new concepts such as adverse outcome pathways (AOP), advanced statistical learning algorithms and machine learning, mechanistic validation and “Good ITS Practices”.JRC.I.5-Systems Toxicolog

A Perspective and a New Integrated Computational Strategy for Skin Sensitization Assessment

Traditionally, the skin sensitization potential of chemicals has been assessed using animal models. Due to growing ethical, political, and financial concerns, sustainable alternatives to animal testing need to be developed. As publicly available skin sensitization data continues to grow, computational approaches, such as alert-based systems, read-across, and QSAR models, are expected to reduce or replace animal testing for the prediction of human skin sensitization potential. Herein, we discuss current computational approaches to predicting skin sensitization and provide future perspectives of the field. As a proof-of-concept study, we have compiled the largest skin sensitization data set in the public domain and benchmarked several methods for building skin sensitization models. We propose a new comprehensive approach, which integrates multiple QSAR models developed with in vitro, in chemico, animal, and human data, and a Naive Bayes model for predicting human skin sensitization. Both the data sets and the KNIME implementation of the model allowing skin sensitization prediction for molecules of interest have been made freely available

Novel clinical phenotypes, drug categorization, and outcome prediction in drug-induced cholestasis: Analysis of a database of 432 patients developed by literature review and machine learning support

publishedVersio

The application of natural language processing for the extraction of mechanistic information in toxicology

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 1 CORRADI et al.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:• A proof-of-concept that NLP can support the extraction of information from text for modern toxicology.• 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)

The application of natural language processing for the extraction of mechanistic information in toxicology

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)

An AI supported case study applying in vitro studies using the ONTOX toolbox: Protocol for a probabilistic risk assessment of perfluoroctanoic acid (PFOA)

The project ‘Ontology-driven and artificial intelligence-based repeated dose toxicity testing of chemicals for next generation risk assessment’ (ONTOX) under the EU programme Horizon 2020 is running from 01.05.21 to 30.04.26 and is coordinated by Vrije Universiteit, Brussel, Belgium (project website, URL: ONTOX project). The vision of ONTOX is to provide a functional and sustainable solution for advancing human risk assessment of chemicals without the use of animals in line with the principles of 21st century toxicity testing and next generation risk assessment. ONTOX will perform a case study on probabilistic risk assessment (PRA) on the selected chemical perfluoroctanoic acid (PFOA). The exposure assessment will use already established methods from the newly published scoping review, “Accessible methods and tools to estimate chemical exposure in humans to support risk assessment: a systematic scoping review”, or custom-made methods using R. The hazard characterisation will use some published methods as a starting point which will be adjusted and combined to fit this case study. The hazard identification/characterisation will use data from published literature and on in vitro data produced in ONTOX. The whole ONTOX toolbox will be used in this risk assessment, such as physiological based kinetic (PBK) models, quantitative in vivo in vitro extrapolation (QIVIVE), physiological maps (PMs) and boolean models , and a large transformer-based AI model. This is a protocol for the case study on PFOA, which will provide a proof-of-principle of PRA using in vitro studies.publishedVersio

CATMoS: Collaborative Acute Toxicity Modeling Suite.

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495