The Environmental Protection Agency (EPA) provides access to information on a variety of topics related to the environment and strives to inform citizens of health risks. The EPA also has an extensive library network that consists of 26 libraries throughout the United States, which provide access to a plethora of information to EPA employees, scientists, and researchers. The EPA implemented a reorganization project to digitize their materials so they would be more accessible to a wider range of users, but this plan was drastically accelerated when the EPA was threatened with a budget cut. It chose to close and reduce the hours and services of some of their libraries. As a result, the agency was accused of denying users the “right to know” by making information unavailable, not providing an adequate strategic plan, and discarding vital materials. This case study explores the background of the digitization project, the practices implemented, and the critiques of the project.

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The Environmental Protection Agency

Neurodevelopmental disabilities, including autism, attention-defi cit hyperactivity disorder, dyslexia, and other cognitive impairments, aff ect millions of children worldwide, and some diagnoses seem to be increasing in frequency. Industrial chemicals that injure the developing brain are among the known causes for this rise in prevalence. In 2006, we did a systematic review and identifi ed fi ve industrial chemicals as developmental neurotoxicants: lead, methylmercury, polychlorinated biphenyls, arsenic, and toluene. Since 2006, epidemiological studies have documented six additional developmental neurotoxicants—manganese, fl uoride, chlorpyrifos, dichlorodiphenyltrichloroethane, tetrachloroethylene, and the polybrominated diphenyl ethers. We postulate that even more neurotoxicants remain undiscovered. To control the pandemic of developmental neurotoxicity, we propose a global prevention strategy. Untested chemicals should not be presumed to be safe to brain development, and chemicals in existing use and all new chemicals must therefore be tested for developmental neurotoxicity. To coordinate these eff orts and to accelerate translation of science into prevention, we propose the urgent formation of a new international clearinghouse.

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Neurobehavioural effects of developmental toxicity

Due to practical and ethical concerns associated with human experimentation, animal models have been essential in cancer research. However, the average rate of successful translation from animal models to clinical cancer trials is less than 8%. Animal models are limited in their ability to mimic the extremely complex process of human carcinogenesis, physiology and progression. Therefore the safety and efficacy identified in animal studies is generally not translated to human trials. Animal models can serve as an important source of in vivo information, but alternative translational approaches have emerged that may eventually replace the link between in vitro studies and clinical applications. This review summarizes the current state of animal model translation to clinical practice, and offers some explanations for the general lack of success in this process. In addition, some alternative strategies to the classic in vivo approach are discussed.

Review Article Lost in translation: animal models and clinical trials in cancer treatment

The Tox21 Data Challenge has been the largest effort of the scientific community to compare computational methods for toxicity prediction. This challenge comprised 12,000 environmental chemicals and drugs which were measured for 12 different toxic effects by specifically designed assays. We participated in this challenge to assess the performance of Deep Learning in computational toxicity prediction. Deep Learning has already revolutionized image processing, speech recognition, and language understanding but has not yet been applied to computational toxicity. Deep Learning is founded on novel algorithms and architectures for artificial neural networks together with the recent availability of very fast computers and massive datasets. It discovers multiple levels of distributed representations of the input, with higher levels representing more abstract concepts. We hypothesized that the construction of a hierarchy of chemical features gives Deep Learning the edge over other toxicity prediction methods. Furthermore, Deep Learning naturally enables multi-task learning, that is, learning of all toxic effects in one neural network and thereby learning of highly informative chemical features. In order to utilize Deep Learning for toxicity prediction, we have developed the DeepTox pipeline. First, DeepTox normalizes the chemical representations of the compounds. Then it computes a large number of chemical descriptors that are used as input to machine learning methods. In its next step, DeepTox trains models, evaluates them, and combines the best of them to ensembles. Finally, DeepTox predicts the toxicity of new compounds. In the Tox21 Data Challenge, DeepTox had the highest performance of all computational methods winning the grand challenge, the nuclear receptor panel, the stress response panel, and six single assays (teams ``Bioinf@JKU''). We found that Deep Learning excelled in toxicity prediction and outperformed many other computational approaches like naive Bayes, support vector machines, and random forests.

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DeepTox: Toxicity Prediction using Deep Learning

This is the advice of a committee of leading American experts, chaired by Harvey Fineberg from the Harvard School of Public Health. It represents the fifth report in a series, commissioned by the US National Research Council, that considers how society can understand and cope with decisions about risks. The Committee's remit was to provide advice on risk characterisation, defined by the Research Council as the translation of `the information in a risk assessment ... into a form usable by a risk manager, individual decision maker, or the public'. In this book, the Committee has responded to the challenge clearly and authoritatively, beginning with a profound re-definition of `risk characterisation' that forms the basis for all that follows. In the view of the Committee, risk characterisation is a process. It is a process which starts before quantitative analysis of the risks, because it includes defining what risks to assess, and how most appropriately to assess them. It is an iterative process, in which assumptions are challenged and re-worked, and new information may be incorporated. It is a process in which qualitative judgements contribute to the fuller understanding of the problem; where quantitative scientific estimates, although important, contribute only a part. Most fundamentally, it is a process which, in a democratic society, needs to involve all those affected by the perceived problem and consequent decision. In the words of the Committee: `Experience shows that analyses, no matter how thorough, that do not address the decision-relevant questions, use reasonable assumptions, and meaningfully include the key affected parties can result in huge expenses and long delays and jeopardise the quality of understanding and the acceptability of the final decisions.' In other words, until or unless we expand our understanding of risk characterisation to include the process of defining the assessment itself, we are unlikely to make progress in gaining public acceptance for major decisions on health or environmental issues. For those without sufficient time to read the book, the ten-page summary provides a succinct overview of the Committee's advice, complete with bullet points and emboldened key phrases. However, the main body of the book (and particularly Appendix A, which discusses a number of case studies) is well worth scanning for its well-reasoned and well-structured discussion of the issues and the suggested way forward. Nor is the Committee lost in an `academic ivory tower'. It recognises that involvement of all those with vested interests cannot ensure a rapid or consensus solution or preclude some groups `dropping out' and choosing the route of litigation. It also recognises that allowing a `voice' for a wide range of interest groups can be time consuming and difficult to manage. However, the Committee argues: `While we are sensitive to concerns about cost and delay, we note that huge costs and delays have sometimes resulted when a risk situation was inadequately diagnosed, a problem misformulated, key interested and affected parties did not participate, or analysis proceeded unintegrated with deliberation. We believe that following [our] principles can reduce delays and costs as much as or more than it increases them.' So what are the Committee's principles? Getting the science right - any quantitative science that is undertaken must be of the highest standards. Getting the right science - this ensures that all the relevant risks are considered. Getting the right participation - this ensures that all those affected have a `voice' in the process. Getting the participation right - this ensures that the process is responsive to the needs of all the participants. Developing an accurate, balanced and informative synthesis - this should include a balanced understanding of the uncertainties in current knowledge, encompassing ignorance and indeterminacy as well as more quantifiable uncertainties. Again, in the words of the Committee: `These criteria are related. To be decision-relevant, risk characterisation must be accurate, balanced and informative. This requires getting the science right and getting the right science. Participation helps ask the right questions of the science, check the plausibility of assumptions, and ensure that any synthesis is both balanced and informative.' In order to set up an appropriate risk characterisation process, the Committee recommends that those responsible for it should `begin by developing a provisional diagnosis of the decision situation' in order to identify potential participants, allocate resources and structure the process. However, in doing so, they should `treat the diagnosis as tentative and remain open to change, always keeping in mind that their goal is a process that leads to a useful and credible risk characterization'. The Committee also stresses the need for those responsible for the process to `develop the capability to cope with attempts by some interested and affected parties to delay decision, and to develop a range of strategies for reaching closure'. This is likely to require the development of new skills and may require organisational changes `to improve communication across sub-units and to allow for the flexibility and judgement necessary to match the process to decision'. This balanced, reasoned and authoritative book is, in my opinion, a `must for all those involved in informing societal decision on risks.

Understanding Risk - Informing Decisions in a Democratic Society

With the release of the landmark report Toxicity Testing in the 21st Century: A Vision and a Strategy, the U.S. National Academy of Sciences, in 2007, precipitated a major change in the way toxicity testing is conducted. It envisions increased efficiency in toxicity testing and decreased animal usage by transitioning from current expensive and lengthy in vivo testing with qualitative endpoints to in vitro toxicity pathway assays on human cells or cell lines using robotic high-throughput screening with mechanistic quantitative parameters. Risk assessment in the exposed human population would focus on avoiding significant perturbations in these toxicity pathways. Computational systems biology models would be implemented to determine the dose-response models of perturbations of pathway function. Extrapolation of in vitro results to in vivo human blood and tissue concentrations would be based on pharmacokinetic models for the given exposure condition. This practice would enhance human relevance of test results, and would cover several test agents, compared to traditional toxicological testing strategies. As all the tools that are necessary to implement the vision are currently available or in an advanced stage of development, the key prerequisites to achieving this paradigm shift are a commitment to change in the scientific community, which could be facilitated by a broad discussion of the vision, and obtaining necessary resources to enhance current knowledge of pathway perturbations and pathway assays in humans and to implement computational systems biology models. Implementation of these strategies would result in a new toxicity testing paradigm firmly based on human biology.

https://www.wellbeingintlstudiesrepository.org/cgi/viewcontent.cgi?article=1109&context=acwp_arte

Reid D. Patterson

Papers

Toxicity testing in the 21st century: a vision and a strategy