Chemicals regulation and non-animal methods: displacing the gold standard.

Regulating industrial chemicals in foodstuffs and consumer products is a major aspect of protecting populations against health risks. Non-animal testing methods are an essential part of the radical change to the framework for toxicity testing that is long overdue in global economies. This paper discusses reasons why the drive to reduce animal testing for chemical safety testing is so difficult to achieve, as perceived by those who are closely involved in chemicals regulations in different capacities.

Wellcome's 2019 funding for PhD programme training: An experiment in enhancing research culture.

In September 2019, the results of Wellcome's call for proposals for PhD training programmes were announced. This funding call was notable for making contributions to positive research culture as important a selection criterion as scientific excellence in PhD training. Aware that it's intervention on enhancing research culture was experimental, Wellcome also commissioned work to reflect on its processes.

Enhancing research culture through PhD training: a systems approach to identifying leverage points for policy formation.

This article examines the role of PhD training programmes in identifying and implementing positive interventions in research culture in the biosciences. Using a data set consisting of transcripts from interviews and group discussions with 179 participants from 18 of the current 23 (78%) UK-based Wellcome-funded PhD programmes, we apply a systems theory methodology to the system of higher education and PhD training. Using system mapping as an investigative tool, this approach identifies points of leverage within the system where policy interventions might be best targeted to affect changes to research culture in the global higher education sector.

CIAO: a living experiment in interdisciplinary large-scale collaboration facilitated by the Adverse Outcome Pathway framework.

Introduction: The CIAO project was launched in Spring 2020 to address the need to make sense of the numerous and disparate data available on COVID-19 pathogenesis. Based on a crowdsourcing model of large-scale collaboration, the project has exploited the Adverse Outcome Pathway (AOP) knowledge management framework built to support chemical risk assessment driven by mechanistic understanding of the biological perturbations at the different organizational levels. Hence the AOPs might have real potential to integrate data produced through different approaches and from different disciplines as experienced in the context of COVID-19.

Why adverse outcome pathways need to be FAIR.

Adverse outcome pathways (AOPs) provide evidence for demonstrating and assessing causality between measurable toxicological mechanisms and human or environmental adverse effects. AOPs have gained increasing attention over the past decade and are believed to provide the necessary steppingstone for more effective risk assessment of chemicals and materials and moving beyond the need for animal testing. However, as with all types of data and knowledge today, AOPs need to be reusable by machines, i.

Professional expectations and patient expectations concerning the development of Artificial Intelligence (AI) for the early diagnosis of Pulmonary Hypertension (PH).

The expectations of professionals working on the development of healthcare Artificial Intelligence (AI) technologies and the patients who will be affected by them have received limited attention. This paper reports on a Foresight Workshop with professionals involved with pulmonary hypertension (PH) and a Focus Group with members of a PH patient group, to discuss expectations of AI development and implementation. We show that while professionals and patients had similar expectations of AI, with respect to the priority of early diagnosis; data risks of privacy and reuse; and responsibility, other expectations differed.

(De)troubling transparency: artificial intelligence (AI) for clinical applications.

Artificial intelligence (AI) and machine learning (ML) techniques occupy a prominent role in medical research in terms of the innovation and development of new technologies. However, while many perceive AI as a technology of promise and hope-one that is allowing for more early and accurate diagnosis-the acceptance of AI and ML technologies in hospitals remains low. A major reason for this is the lack of transparency associated with these technologies, in particular epistemic transparency, which results in AI disturbing or troubling established knowledge practices in clinical contexts.

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

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.

Plastic diagnostics: The remaking of disease and evidence in personalized medicine.

Politically authorized reports on personalized and precision medicine stress an urgent need for finer-grained disease categories and faster taxonomic revision, through integration of genomic and phenotypic data. Developing a data-driven taxonomy is, however, not as simple as it sounds. It is often assumed that an integrated data infrastructure is relatively easy to implement in countries that already have highly centralized and digitalized health care systems.

Harvesting the promise of AOPs: An assessment and recommendations.

The Adverse Outcome Pathway (AOP) concept is a knowledge assembly and communication tool to facilitate the transparent translation of mechanistic information into outcomes meaningful to the regulatory assessment of chemicals. The AOP framework and associated knowledgebases (KBs) have received significant attention and use in the regulatory toxicology community. However, it is increasingly apparent that the potential stakeholder community for the AOP concept and AOP KBs is broader than scientists and regulators directly involved in chemical safety assessment.

A social-technological epistemology of clinical decision-making as mediated by imaging.

In recent years there has been growing attention to the epistemology of clinical decision-making, but most studies have taken the individual physicians as the central object of analysis. In this paper we argue that knowing in current medical practice has an inherently social character and that imaging plays a mediating role in these practices. We have analyzed clinical decision-making within a medical expert team involved in diagnosis and treatment of patients with pulmonary hypertension (PH), a rare disease requiring multidisciplinary team involvement in diagnosis and management.

Variability in cardiac electrophysiology: Using experimentally-calibrated populations of models to move beyond the single virtual physiological human paradigm.

Physiological variability manifests itself via differences in physiological function between individuals of the same species, and has crucial implications in disease progression and treatment. Despite its importance, physiological variability has traditionally been ignored in experimental and computational investigations due to averaging over samples from multiple individuals. Recently, modelling frameworks have been devised for studying mechanisms underlying physiological variability in cardiac electrophysiology and pro-arrhythmic risk under a variety of conditions and for several animal species as well as human.

Human-based approaches to pharmacology and cardiology: an interdisciplinary and intersectorial workshop.

Both biomedical research and clinical practice rely on complex datasets for the physiological and genetic characterization of human hearts in health and disease. Given the complexity and variety of approaches and recordings, there is now growing recognition of the need to embed computational methods in cardiovascular medicine and science for analysis, integration and prediction. This paper describes a Workshop on Computational Cardiovascular Science that created an international, interdisciplinary and inter-sectorial forum to define the next steps for a human-based approach to disease supported by computational methodologies.

Validation and variability: dual challenges on the path from systems biology to systems medicine.

Systems biology is currently making a bid to show that it is able to make an important contribution to personalised or precision medicine. In order to do so, systems biologists need to find a way of tackling the pervasive variability of biological systems that is manifested in the medical domain as inter-subject variability. This need is simultaneously social and epistemic: social as systems biologists attempt to engage with the interests and concerns of clinicians and others in applied medical research; epistemic as they attempt to develop new strategies to cope with variability in the validation of the computational models typical of systems biology.

Bridging experiments, models and simulations: an integrative approach to validation in computational cardiac electrophysiology.

Computational models in physiology often integrate functional and structural information from a large range of spatiotemporal scales from the ionic to the whole organ level. Their sophistication raises both expectations and skepticism concerning how computational methods can improve our understanding of living organisms and also how they can reduce, replace, and refine animal experiments. A fundamental requirement to fulfill these expectations and achieve the full potential of computational physiology is a clear understanding of what models represent and how they can be validated.

Retaining volunteers in volunteer computing projects.

Volunteer computing projects (VCPs) have been set up by groups of scientists to recruit members of the public who are asked to donate spare capacity on their personal computers to the processing of scientific data or computationally intensive models. VCPs serve two purposes: to acquire significant computing capacity and to educate the public about science. A particular challenge for these scientists is the retention of volunteers as there is a very high drop-out rate.