Expanding Predictive Capacities in Toxicology: Insights from Hackathon-Enhanced Data and Model Aggregation.

In the realm of predictive toxicology for small molecules, the applicability domain of QSAR models is often limited by the coverage of the chemical space in the training set. Consequently, classical models fail to provide reliable predictions for wide classes of molecules. However, the emergence of innovative data collection methods such as intensive hackathons have promise to quickly expand the available chemical space for model construction. Combined with algorithmic refinement methods, these tools can address the challenges of toxicity prediction, enhancing both the robustness and applicability of the corresponding models. This study aimed to investigate the roles of gradient boosting and strategic data aggregation in enhancing the predictivity ability of models for the toxicity of small organic molecules. We focused on evaluating the impact of incorporating fragment features and expanding the chemical space, facilitated by a comprehensive dataset procured in an open hackathon. We used gradient boosting techniques, accounting for critical features such as the structural fragments or functional groups often associated with manifestations of toxicity.

Assessing the impact of a new medical toxicology service on the treatment of paracetamol overdose at a large tertiary care hospital.

BACKGROUND: Paracetamol overdose is the most common cause of acute liver failure in the United States. Administration of acetylcysteine is the standard of care for this intoxication. Laboratory values and clinical criteria are used to guide treatment duration, but decision-making is nuanced and often complex and difficult. The purpose of this study was to evaluate the effect of the introduction of a medical toxicology service on the rate of errors in the management of paracetamol overdose. METHODS: This was a single center, retrospective, cohort evaluation. Patients with suspected paracetamol overdose were divided into two groups: those attending in the 1 year period before and those in the 1 year after the introduction of the medical toxicology service. The primary outcome was the frequency of deviations from the established management of paracetamol intoxication, using international guidelines as a reference. RESULTS: Fifty-four patients were eligible for the study (20 pre-toxicology-service, 34 post-toxicology-service). The frequency of incorrect therapeutic decisions was significantly lower in the post-toxicology service implementation versus the pre-implementation group (P = 0.005). DISCUSSION: Our study suggests that a medical toxicology service reduces the incidence of management errors, including the number of missed acetylcysteine doses in patients with paracetamol overdose. The limitations include the retrospective study design and that the study was conducted at a single center, which may limit generalizability. CONCLUSIONS: The implementation of a medical toxicology service was associated with a decrease in the number of errors in the management of paracetamol overdose.

Feasibility and evaluation of high-fidelity simulation education for acute clinical toxicology.

To prepare medical students appropriately for the management of toxicological emergencies, we have developed a simulation-based medical education (SBME) training in acute clinical toxicology. Our aim is to report on the feasibility, evaluation and lessons learned of this training. Since 2019, each year approximately 180 fifth-year medical students are invited to participate in the SBME training. The training consists of an interactive lecture and two SBME stations. For each station, a team of students had to perform the primary assessment and management of an intoxicated patient. After the training, the students completed a questionnaire about their experiences and confidence in clinical toxicology. Overall, the vast majority of students agreed that the training provided a fun, interactive and stimulating way to teach about clinical toxicology. Additionally, they felt more confident regarding their skills in this area. Our pilot study shows that SBME training was well-evaluated and feasible over a longer period.

A problem formulation framework for the application of in silico toxicology methods in chemical risk assessment.

The first step in the hazard or risk assessment of chemicals should be to formulate the problem through a systematic and iterative process aimed at identifying and defining factors critical to the assessment. However, no general agreement exists on what components an in silico toxicology problem formulation (PF) should include. The present work aims to develop a PF framework relevant to the application of in silico models for chemical toxicity prediction. We modified and applied a PF framework from the general risk assessment literature to peer reviewed papers describing PFs associated with in silico toxicology models. Important gaps between the general risk assessment literature and the analyzed PF literature associated with in silico toxicology methods were identified. While the former emphasizes the need for PFs to address higher-level conceptual questions, the latter does not. There is also little consistency in the latter regarding the PF components addressed, reinforcing the need for a PF framework that enable users of in silico toxicology models to answer the central conceptual questions aimed at defining components critical to the model application. Using the developed framework, we highlight potential areas of uncertainty manifestation in in silico toxicology PF in instances where particular components are missing or implicitly described. The framework represents the next step in standardizing in silico toxicology PF component. The framework can also be used to improve the understanding of how uncertainty is apparent in an in silico toxicology PF, thus facilitating ways to address uncertainty.

Exploring rabbit as a nonrodent species for general toxicology studies.

To avoid adverse events in humans, toxicity studies in nonclinical species have been the foundation of safety evaluation in the pharmaceutical industry. However, it is recognized that working with animals in research is a privilege, and conscientious use should always respect the 3Rs: replacement, reduction, and refinement. In the wake of the shortages in routine nonrodent species and considering that nonanimal methods are not yet sufficiently mature, the value of the rabbit as a nonrodent species is worth exploring. Historically used in vaccine, cosmetic, and medical device testing, the rabbit is seldom used today as a second species in pharmaceutical development, except for embryo-fetal development studies, ophthalmic therapeutics, some medical devices and implants, and vaccines. Although several factors affect the decision of species selection, including pharmacological relevance, pharmacokinetics, and ADME considerations, there are no perfect animal models. In this forum article, we bring together experts from veterinary medicine, industry, contract research organizations, and government to explore the pros and cons, residual concerns, and data gaps regarding the use of the rabbit for general toxicity testing.

Una Acción Civil (1998): la ley detrás del daño corporal, los defectos congénitos y la leucemia / A Civil Action (1998): the law behind bodily harm, birth defects and leukemia

Acción Civil es una película fundamental para comprender cómo la contaminación del ambiente influye en la salud humana, pero también el contexto biopsicosocial, ético, legítimo y legal en torno a la problemática de ejercer el derecho a un ambiente saludable. Muestra cómo la contaminación afecta a las comunidades, la responsabilidad que muchas empresas intentan eludir frente a sus acciones en perjuicio de la vida humana y ambiente y cómo la comunidad y los medios de comunicación pueden trabajar juntos para abordar estas cuestiones y tomar medidas para prevenir y tratar estos problemas. En la docencia universitaria de Ciencias de la Salud, el filme se constituye en un recurso pedagógico para ilustrar y concientizar sobre la problemática de la contaminación del agua potable y ambiente en general en la salud humana y especialmente durante la gestación. El visionado del filme por otro lado estimula el pensamiento crítico, divergente, analítico, reflexivo, emocional y el aprendizaje significativo, al tiempo que permite integrar conocimientos de las asignaturas que cursan y cultura general en una experiencia inmersiva. Asimismo, permite integrar aspectos legales que pocas veces son abordados en la carrera como una transversal educativa que contribuye al acervo de cultura general del estudiante.(AU)

A Civil Action is a fundamental film to understand how environmental contamination influences human health, but also the biopsychosocial, ethical, legitimate and legal context around the problem of exercising the right to a healthy environment. It shows how pollution affects communities, the responsibility that many companies try to evade due to their actions in detriment of human life and the environment, and how the community and the media can work together to address these issues and take measures to prevent and treat these problems. In the university teaching of Health Sciences, the film becomes a pedagogical resource to illustrate and raise awareness about the problem of drinking water contamination and the environment in general in human health and especially during pregnancy. Viewing the film on the other hand stimulates critical, divergent, analytical, reflective, emotional thinking and significant learning, while allowing the integration of knowledge of the subjects they are studying and general culture in an immersive experience. Likewise, it allows the integration of legal aspects that are rarely addressed in the career as an educational transversal that contributes to the heritage of the student's general culture.(AU)

The probable future of toxicology - probabilistic risk assessment.

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 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.

Probabilistic risk assessment, initially from engineering, is applied in toxicology to understand chemical-related hazards and their consequences. In toxicology, uncertainties abound ­ unclear molecular events, varied proposed outcomes, and population-level assessments for issues like neurodevelopmental disorders. Establishing links between chemical exposures and diseases, especially rare events like birth defects, often demands extensive studies. Existing methods struggle with subtle effects or those affecting specific groups. Future risk assessments must address developmental disease origins, presenting challenges beyond current capabilities. The intricate nature of many toxicological processes, lack of consensus on mechanisms and outcomes, and the need for nuanced population-level assessments highlight the complexities in understanding and quantifying risks associated with chemical exposures in the field of toxicology.

Quantifying the Benefits of Imputation over QSAR Methods in Toxicology Data Modeling.

Imputation machine learning (ML) surpasses traditional approaches in modeling toxicity data. The method was tested on an open-source data set comprising approximately 2500 ingredients with limited in vitro and in vivo data obtained from the OECD QSAR Toolbox. By leveraging the relationships between different toxicological end points, imputation extracts more valuable information from each data point compared to well-established single end point methods, such as ML-based Quantitative Structure Activity Relationship (QSAR) approaches, providing a final improvement of up to around 0.2 in the coefficient of determination. A significant aspect of this methodology is its resilience to the inclusion of extraneous chemical or experimental data. While additional data typically introduces a considerable level of noise and can hinder performance of single end point QSAR modeling, imputation models remain unaffected. This implies a reduction in the need for laborious manual preprocessing tasks such as feature selection, thereby making data preparation for ML analysis more efficient. This successful test, conducted on open-source data, validates the efficacy of imputation approaches in toxicity data analysis. This work opens the way for applying similar methods to other types of sparse toxicological data matrices, and so we discuss the development of regulatory authority guidelines to accept imputation models, a key aspect for the wider adoption of these methods.