Application of evidence-based methods to construct mechanism-driven chemical assessment frameworks

The workshop titled “Application of evidence-based methods to construct mechanism-driven chemical assessment frameworks” was co-organized by the Evidence-based Toxicology Collaboration and the European Food Safety Authority (EFSA) and hosted by EFSA at its headquarters in Parma, Italy on October 2 and 3, 2019. The goal was to explore integration of systematic review with mechanistic evidence evaluation. Participants were invited to work on concrete products to advance the exploration of how evidence-based approaches can support the development and application of adverse outcome pathways (AOP) in chemical risk assessment. The workshop discussions were centered around three related themes: 1) assessing certainty in AOPs, 2) literature-based AOP development, and 3) integrating certainty in AOPs and non-animal evidence into decision frameworks. Several challenges, mostly related to methodology, were identified and largely determined the workshop recommendations. The workshop recommendations included the comparison and potential alignment of processes used to develop AOP and systematic review methodology, including the translation of vocabulary of evidence-based methods to AOP and vice versa, the development and improvement of evidence mapping and text mining methods and tools, as well as a call for a fundamental change in chemical risk and uncertainty assessment methodology if to be conducted based on AOPs and new approach methodologies (NAM). The usefulness of evidence-based approaches for mechanism-based chemical risk assessments was stressed, particularly the potential contribution of the rigor and transparency inherent to such approaches in building stakeholders’ trust for implementation of NAM evidence and AOPs into chemical risk assessment

Separating chemotherapy-related developmental neurotoxicity from cytotoxicity in monolayer and neurosphere cultures of human fetal brain cells

Chemotherapy-induced neurotoxicity can reduce the quality of life of patients by affecting their intelligence, senses and mobility. Ten percent of safety-related late-stage clinical failures are due to neurological side effects. Animal models are poor in predicting human neurotoxicity due to interspecies differences and most in vitro assays cannot distinguish neurotoxicity from general cytotoxicity for chemotherapeutics. We developed in vitro assays capable of quantifying the paediatric neurotoxic potential for cytotoxic drugs. Mixed cultures of human fetal brain cells were differentiated in monolayers and as 3D-neurospheres in the presence of non-neurotoxic chemotherapeutics (etoposide, teniposide) or neurotoxicants (methylmercury). The cytotoxic potency towards dividing progenitors versus differentiated neurons and astrocytes was compared using: (1) immunohistochemistry staining and cell counts in monolayers; (2) through quantitative Western blots in neurospheres; and (3) neurosphere migration assays. Etoposide and teniposide, were 5–10 times less toxic to differentiated neurons compared to the mix of all cells in monolayer cultures. In contrast, the neurotoxicant methylmercury did not exhibit selectivity and killed all cells with the same potency. In 3D neurospheres, etoposide and teniposide were 24 to 10 times less active against neurons compared to all cells. These assays can be used prioritise drugs for local drug delivery to brain tumours

GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making

Objectives: The objective of the study is to present the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) conceptual approach to the assessment of certainty of evidence from modeling studies (i.e., certainty associated with model outputs). / Study Design and Setting: Expert consultations and an international multidisciplinary workshop informed development of a conceptual approach to assessing the certainty of evidence from models within the context of systematic reviews, health technology assessments, and health care decisions. The discussions also clarified selected concepts and terminology used in the GRADE approach and by the modeling community. Feedback from experts in a broad range of modeling and health care disciplines addressed the content validity of the approach. / Results: Workshop participants agreed that the domains determining the certainty of evidence previously identified in the GRADE approach (risk of bias, indirectness, inconsistency, imprecision, reporting bias, magnitude of an effect, dose–response relation, and the direction of residual confounding) also apply when assessing the certainty of evidence from models. The assessment depends on the nature of model inputs and the model itself and on whether one is evaluating evidence from a single model or multiple models. We propose a framework for selecting the best available evidence from models: 1) developing de novo, a model specific to the situation of interest, 2) identifying an existing model, the outputs of which provide the highest certainty evidence for the situation of interest, either “off-the-shelf” or after adaptation, and 3) using outputs from multiple models. We also present a summary of preferred terminology to facilitate communication among modeling and health care disciplines. / Conclusion: This conceptual GRADE approach provides a framework for using evidence from models in health decision-making and the assessment of certainty of evidence from a model or models. The GRADE Working Group and the modeling community are currently developing the detailed methods and related guidance for assessing specific domains determining the certainty of evidence from models across health care–related disciplines (e.g., therapeutic decision-making, toxicology, environmental health, and health economics)

Applying evidence-based methods to the development and use of adverse outcome pathways

The workshop “Application of evidence-based methods to construct mechanistic frameworks for the development and use of non-animal toxicity tests” was organized by the Evidence-based Toxicology Collaboration and hosted by the Grading of Recommendations Assessment, Development and Evaluation Working Group on June 12, 2019. The purpose of the workshop was to bring together international regulatory bodies, risk assessors, academic scientists, and industry to explore how systematic review methods and the adverse outcome pathway framework could be combined to develop and use mechanistic test methods for predicting the toxicity of chemical substances in an evidence-based manner. The meeting covered the history of biological frameworks, the way adverse outcome pathways are currently developed, the basic principles of systematic methodology, including systematic reviews and evidence maps, and assessment of certainty in models, and adverse outcome pathways in particular. Specific topics were discussed via case studies in small break-out groups. The group concluded that adverse outcome pathways provide an important framework to support mechanism-based assessment in environmental health. The process of their development has a few challenges that could be addressed with systematic methods and automation tools. Addressing these challenges will increase the transparency of the evidence behind adverse outcome pathways and the consistency with which they are defined; this in turn will increase their value for supporting public health decisions. It was suggested to explore the details of applying systematic methods to adverse outcome pathway development in a series of case studies and workshops

GRADE Guidelines 30: The GRADE Approach to Assessing the Certainty of Modelled Evidence - an Overview in the Context of Health Decision-making.

OBJECTIVES: To present the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) conceptual approach to the assessment of certainty of evidence from modelling studies (i.e. certainty associated with model outputs). STUDY DESIGN AND SETTING: Expert consultations and, an international multi-disciplinary workshop informed development of a conceptual approach to assessing the certainty of evidence from models within the context of systematic reviews, health technology assessments, and health care decisions. The discussions also clarified selected concepts and terminology used in the GRADE approach and by the modelling community. Feedback from experts in a broad range of modelling and health care disciplines addressed the content validity of the approach. RESULTS: Workshop participants agreed, that the domains determining the certainty of evidence previously identified in the GRADE approach (risk of bias, indirectness, inconsistency, imprecision, reporting bias, magnitude of an effect, dose-response relation, and the direction of residual confounding) also apply when of assessing the certainty of evidence from models. The assessment depends on the nature of model inputs and the model itself and on whether one is evaluating evidence from a single model or multiple models. We propose a framework for selecting the best available evidence from models: 1) developing de novo a model specific to the situation of interest, 2) identifying an existing model the outputs of which provide the highest certainty evidence for the situation of interest, either "off the shelf" or after adaptation, and 3) using outputs from multiple models. We also present a summary of preferred terminology to facilitate communication among modelling and health care disciplines. CONCLUSIONS: This conceptual GRADE approach provides a framework for using evidence from models in health decision making and the assessment of certainty of evidence from a model or models. The GRADE Working Group and the modelling community are currently developing the detailed methods and related guidance for assessing specific domains determining the certainty of evidence from models across health care-related disciplines (e.g. therapeutic decision-making, toxicology, environmental health, health economics)

COVID-19 through Adverse Outcome Pathways: Building networks to better understand the disease - 3rd CIAO AOP Design Workshop

ALTEX - Alternatives to Animal ExperimentationCopyright © 2022 the author(s). On April 28-29, 2021, 50 scientists from different fields of expertise met for the 3rd online CIAO workshop. The CIAO project “Modelling the Pathogenesis of COVID-19 using the Adverse Outcome Pathway (AOP) framework” aims at building a holistic assembly of the available scientific knowledge on COVID-19 using the AOP framework. An individual AOP depicts the disease progression from the initial contact with the SARS-CoV-2 virus through biological key events (KE) toward an adverse outcome such as respiratory distress, anosmia or multiorgan failure. Assembling the individual AOPs into a network highlights shared KEs as central biological nodes involved in multiple outcomes observed in COVID-19 patients. During the workshop, the KEs and AOPs established so far by the CIAO members were presented and posi­tioned on a timeline of the disease course. Modulating factors influencing the progression and severity of the disease were also addressed as well as factors beyond purely biological phenomena. CIAO relies on an interdisciplinary crowd­sourcing effort, therefore, approaches to expand the CIAO network by widening the crowd and reaching stakeholders were also discussed. To conclude the workshop, it was decided that the AOPs/KEs will be further consolidated, inte­grating virus variants and long COVID when relevant, while an outreach campaign will be launched to broaden the CIAO scientific crowd.The CIAO project is steered by the Joint Research Centre of the European Commission (EC-JRC), the Humane Society International (HSI), and the Physicians Committee for Responsible Medicine (PCRM). For Jorid Birkelund Sørli, the research is supported by FIKA, Focused Research Effort on Chemicals in the Working Environment from the Danish Government. For Daniel Jacobson, this work was supported by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC for the US Department of Energy (LOIS:10074) and the National Institutes of Health 3RF1AG053303-01S2

Reviewing the animal literature: how to describe and choose between different types of literature reviews

Before starting any (animal) research project, review of the existing literature is good practice. From both the scientific and the ethical perspective, high-quality literature reviews are essential. Literature reviews have many potential advantages besides synthesising the evidence for a research question. First, they can show if a proposed study has already been performed, preventing redundant research. Second, when planning new experiments, reviews can inform the experimental design, thereby increasing the reliability, relevance and efficiency of the study. Third, reviews may even answer research questions using already available data. Multiple definitions of the term literature review co-exist. In this paper, we describe the different steps in the review process, and the risks and benefits of using various methodologies in each step. We then suggest common terminology for different review types: narrative reviews, mapping reviews, scoping reviews, rapid reviews, systematic reviews and umbrella reviews. We recommend which review to select, depending on the research question and available resources. We believe that improved understanding of review methods and terminology will prevent ambiguity and increase appropriate interpretation of the conclusions of reviews

Method design and validation for the determination of uranium levels in human urine using high-resolution alpha spectrometry

Quantification of uranium in human urine is a valuable technique for assessing occupational and public exposure to uranium. A reliable method has been developed and validated in the ARPANSA Radiochemistry Laboratory by means of standard radiochemical separation and purification techniques and measurement using high-resolution alpha spectrometry. This method can be used to evaluate the levels of naturally occurring 234U, 235U and 238U in urine. Method design and validation is the process of defining an analytical requirement, and then confirming that the method under consideration has performance capabilities consistent with what the application requires. The method was designed to measure levels down to 2 mBq/day of total uranium, corresponding to approximately 1/100th of the annual committed effective dose of 20 mSv. Validation tests were developed to assess selectivity, accuracy, recovery and quantification of uncertainty. The radiochemical recovery of this method was measured using 232U tracer. The typical minimum detectable concentration for total uranium for 24-h urine samples is approximately 0.6 mBq/day or 0.019 &mu;g/day.<br /