Assessing author willingness to enter study information into structured data templates as part of the manuscript submission process: A pilot study.

Background: Environmental health and other researchers can benefit from automated or semi-automated summaries of data within published studies as summarizing study methods and results is time and resource intensive. Automated summaries can be designed to identify and extract details of interest pertaining to the study design, population, testing agent/intervention, or outcome (etc.). Much of the data reported across existing publications lack unified structure, standardization and machine-readable formats or may be presented in complex tables which serve as barriers that impede the development of automated data extraction methodologies.As full automation of data extraction seems unlikely soon, encouraging investigators to submit structured summaries of methods and results in standardized formats with meta-data tagging of content may be of value during the publication process. This would produce machine-readable content to facilitate automated data extraction, establish sharable data repositories, help make research data FAIR, and could improve reporting quality. Objectives: A pilot study was conducted to assess the feasibility of asking participants to summarize study methods and results using a structured, web-based data extraction model as a potential workflow that could be implemented during the manuscript submission process. Methods: Eight participants entered study details and data into the Health Assessment Workplace Collaborative (HAWC). Participants were surveyed after the extraction exercise to ascertain 1) whether this extraction exercise will impact their conducting and reporting of future research, 2) the ease of data extraction, including which fields were easiest and relatively more problematic to extract and 3) the amount of time taken to perform data extractions and other related tasks. Investigators then presented participants the potential benefits of providing structured data in the format they were extracting. After this, participants were surveyed about 1) their willingness to provide structured data during the publication process and 2) whether they felt the potential application of structured data entry approaches and their implementation during the journal submission process should continue to be further explored. Conclusions: Routine provision of structured data that summarizes key information from research studies could reduce the amount of effort required for reusing that data in the future, such as in systematic reviews or agency scientific assessments. Our pilot study suggests that directly asking authors to provide that data, via structured templates, may be a viable approach to achieving this: participants were willing to do so, and the overall process was not prohibitively arduous. We also found some support for the hypothesis that use of study templates may have halo benefits in improving the conduct and completeness of reporting of future research. While limitations in the generalizability of our findings mean that the conditions of success of templates cannot be assumed, further research into how such templates might be designed and implemented does seem to have enough chance of success that it ought to be undertaken.

Systematic Evidence Map for Over One Hundred and Fifty Per- and Polyfluoroalkyl Substances (PFAS).

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) are a large class of synthetic (man-made) chemicals widely used in consumer products and industrial processes. Thousands of distinct PFAS exist in commerce. The 2019 U.S. Environmental Protection Agency (U.S. EPA) Per- and Polyfluoroalkyl Substances (PFAS) Action Plan outlines a multiprogram national research plan to address the challenge of PFAS. One component of this strategy involves the use of systematic evidence map (SEM) approaches to characterize the evidence base for hundreds of PFAS. OBJECTIVE: SEM methods were used to summarize available epidemiological and animal bioassay evidence for a set of ∼150 PFAS that were prioritized in 2019 by the U.S. EPA's Center for Computational Toxicology and Exposure (CCTE) for in vitro toxicity and toxicokinetic assay testing. METHODS: Systematic review methods were used to identify and screen literature using manual review and machine-learning software. The Populations, Exposures, Comparators, and Outcomes (PECO) criteria were kept broad to identify mammalian animal bioassay and epidemiological studies that could inform human hazard identification. A variety of supplemental content was also tracked, including information on in vitro model systems; exposure measurement-only studies in humans; and absorption, distribution, metabolism, and excretion (ADME). Animal bioassay and epidemiology studies meeting PECO criteria were summarized with respect to study design, and health system(s) were assessed. Because animal bioassay studies with ≥21-d exposure duration (or reproductive/developmental study design) were most useful to CCTE analyses, these studies underwent study evaluation and detailed data extraction. All data extraction is publicly available online as interactive visuals with downloadable metadata. RESULTS: More than 40,000 studies were identified from scientific databases. Screening processes identified 44 animal and 148 epidemiology studies from the peer-reviewed literature and 95 animal and 50 epidemiology studies from gray literature that met PECO criteria. Epidemiological evidence (available for 15 PFAS) mostly assessed the reproductive, endocrine, developmental, metabolic, cardiovascular, and immune systems. Animal evidence (available for 40 PFAS) commonly assessed effects in the reproductive, developmental, urinary, immunological, and hepatic systems. Overall, 45 PFAS had evidence across animal and epidemiology data streams. DISCUSSION: Many of the ∼150 PFAS were data poor. Epidemiological and animal evidence were lacking for most of the PFAS included in our search. By disseminating this information, we hope to facilitate additional assessment work by providing the initial scoping literature survey and identifying key research needs. Future research on data-poor PFAS will help support a more complete understanding of the potential health effects from PFAS exposures. https://doi.org/10.1289/EHP10343.

ORSO (Online Resource for Social Omics): A data-driven social network connecting scientists to genomics datasets.

High-throughput sequencing has become ubiquitous in biomedical sciences. As new technologies emerge and sequencing costs decline, the diversity and volume of available data increases exponentially, and successfully navigating the data becomes more challenging. Though datasets are often hosted by public repositories, scientists must rely on inconsistent annotation to identify and interpret meaningful data. Moreover, the experimental heterogeneity and wide-ranging quality of high-throughput biological data means that even data with desired cell lines, tissue types, or molecular targets may not be readily interpretable or integrated. We have developed ORSO (Online Resource for Social Omics) as an easy-to-use web application to connect life scientists with genomics data. In ORSO, users interact within a data-driven social network, where they can favorite datasets and follow other users. In addition to more than 30,000 datasets hosted from major biomedical consortia, users may contribute their own data to ORSO, facilitating its discovery by other users. Leveraging user interactions, ORSO provides a novel recommendation system to automatically connect users with hosted data. In addition to social interactions, the recommendation system considers primary read coverage information and annotated metadata. Similarities used by the recommendation system are presented by ORSO in a graph display, allowing exploration of dataset associations. The topology of the network graph reflects established biology, with samples from related systems grouped together. We tested the recommendation system using an RNA-seq time course dataset from differentiation of embryonic stem cells to cardiomyocytes. The ORSO recommendation system correctly predicted early data point sources as embryonic stem cells and late data point sources as heart and muscle samples, resulting in recommendation of related datasets. By connecting scientists with relevant data, ORSO provides a critical new service that facilitates wide-ranging research interests.

Python BMDS: A Python interface library and web application for the canonical EPA dose-response modeling software.

Several primary sources of publicly available, quantitative dose-response data from traditional toxicology study designs relevant to predictive toxicology applications are now available, including the redeveloped U.S. Environmental Protection Agency's Toxicity Reference Database (ToxRefDB v2.0), the Health Assessment Workspace Collaborative (HAWC), and the National Toxicology Program's Chemical Program's Chemical Effects in Biological Systems (CEBS). These resources provide effect level information but modeling these data to a curve may be more informative for predictive toxicology applications. Benchmark Dose Software (BMDS) has been recognized broadly and used for regulatory applications at multiple agencies. However, the current BMDS software was not amenable to modeling large datasets. Herein we describe development and use of a Python package that implements a wrapper around BMDS, a software that requires manual input in the dose-response modeling process (i.e., best-fitting model-selection, reporting, and dose-dropping). In the Python BMDS, users can select the BMDS version, customize model recommendation logic, and export summaries of the resultant BMDS output. Further, using the Python interface, a web-based application programming interface (API) has been developed for easy integration into other software systems, pipelines, or databases. Software utility was demonstrated via modeling nearly 28,000 datasets in ToxRefDB v2.0, re-creation of an existing, published large-scale analysis, and demonstration of usage in software such as CEBS and HAWC. Python BMDS enables rapid-batch processing of dose-response datasets using a modeling software with broad acceptance in the toxicology community, thereby providing an important tool for leveraging the publicly available quantitative toxicology data in a reproducible manner.

A scoping review of the health and toxicological activity of bisphenol A (BPA) structural analogues and functional alternatives.

Recent studies report widespread usage or exposure to a variety of chemicals with structural or functional similarity to bisphenol A (BPA), referred to as BPA analogues or derivatives. These have been detected in foodstuffs, house dust, environmental samples, human urine or blood, and consumer products. Compared to BPA, relatively little is known about potential toxicity of these compounds. This scoping review aimed to summarize the human, animal, and mechanistic toxicity data for 24 BPA analogues of emerging interest to research and regulatory communities. PubMed was searched from March 1, 2015 to January 5, 2019 and combined with the results obtained from literature searches conducted through March 23, 2015, in The National Toxicology Program's Research Report 4 (NTP RR-04), "Biological Activity of Bisphenol A (BPA) Structural Analogues and Functional Alternatives". Study details are presented in interactive displays using Tableau Public. In total, 5748 records were screened for inclusion. One hundred sixty seven studies were included from NTP RR-04 and 175 studies were included from the updated literature search through January 2019. In total, there are 22, 117, and 221 human epidemiological, experimental animal, or in vitro studies included. The most frequently studied BPA analogues are bisphenol S (BPS), bisphenol F (4,4-BPF), and bisphenol AF (BPAF). Notable changes in the literature since 2015 include the growing body of human epidemiological studies and in vivo studies conducted in zebrafish. Numerous new endpoints were also evaluated across all three evidence streams including diabetes, obesity, and oxidative stress. However, few studies have addressed endpoints such as neurodevelopmental outcomes or impacts on the developing mammary or prostate glands, which are known to be susceptible to disruption by BPA. Further, there remains a critical need for better exposure information in order to prioritize experimental studies. Moving forward, researchers should also ensure that full dose responses are performed for all main effects in order to support hazard and risk characterization efforts. The evidence gathered here suggests that hazard and risk characterizations should expand beyond BPA in order to consider BPA structural and functional analogues.

Application of Benchmark Concentration (BMC) Analysis on Zebrafish Data: A New Perspective for Quantifying Toxicity in Alternative Animal Models.

Over the past decade, the zebrafish is increasingly being used as a model to screen for chemical-mediated toxicities including developmental toxicity (DT) and neurotoxicity (NT). One of the major challenges is lack of harmonization in data analysis approaches, thereby posing difficulty in comparing findings across laboratories. To address this, we sought to establish a unified data analysis strategy for both DT and NT data, by adopting the benchmark concentration (BMC) analysis. There are two critical aspects in the BMC analysis: having a toxicity endpoint amenable for BMC and selecting a proper benchmark response (BMR) for the endpoint. For the former, in addition to the typical endpoints in NT assay (eg, hyper/hypo- response quantified by distance moved), we also used endpoints that assess the differences in movement patterns between chemical-treated embryos and control embryos. For the latter, we standardized the selection of BMR, which is analogous to minimum activity threshold, based on intrinsic response variations in the endpoint. When comparing our BMC results with a traditionally used LOAEL method (lowest-observed-adverse-effect level), we found high active compound concordance (100% for DT vs 74% for NT); generally, the BMC was more sensitive than LOAEL (no. of BMC more sensitive/no. of concordant active compounds, 43/50 for DT vs 16/26 for NT). Using the BMC with standardized toxicity endpoints and an appropriate BMR, we may now have a unified data-analysis approach to comparing results across different zebrafish datasets, for a better understanding of strengths and challenges when using the zebrafish as a screening tool.

Screening for Developmental Neurotoxicity at the National Toxicology Program: The Future Is Here.

The National Toxicology Program (NTP) receives requests to evaluate chemicals with potential to cause adverse health effects, including developmental neurotoxicity (DNT). Some recent requests have included classes of chemicals such as flame retardants, polycyclic aromatic compounds, perfluoroalkyl substances, and bisphenol A analogs with approximately 20-50 compounds per class, many of which include commercial mixtures. However, all the compounds within a class cannot be tested using traditional DNT animal testing guideline studies due to resource and time limitations. Hence, a rapid and biologically relevant screening approach is required to prioritize compounds for further in vivo testing. Because neurodevelopment is a complex process involving multiple distinct cellular processes, one assay will unlikely address the complexity. Hence, the NTP sought to characterize a battery of in vitro and alternative animal assays to quantify chemical effects on a variety of neurodevelopmental processes. A culmination of this effort resulted in a NTP-hosted collaborative project with approximately 40 participants spanning across domains of academia, industry, government, and regulatory agencies; collaborators presented data on cell-based assays and alternative animal models that was generated using a targeted set of compounds provided by the NTP. The NTP analyzed the assay results using benchmark concentration (BMC) modeling to be able to compare results across the divergent assays. The results were shared with the contributing researchers on a private web application during the workshop, and are now publicly available. This article highlights the overview and goals of the project, and describes the NTP's approach in creating the chemical library, development of NTPs data analysis strategy, and the structure of the web application. Finally, we discuss key issues with emphasis on the utility of this approach, and knowledge gaps that need to be addressed for its use in regulatory decision making.

Software Tools to Facilitate Systematic Review Used for Cancer Hazard Identification.

Objective and systematic methods to search, review, and synthesize published studies are a fundamental aspect of carcinogen hazard classification. Systematic review is a historical strength of the International Agency for Research on Cancer (IARC) Monographs Program and the United States National Toxicology Program (NTP) Office of the Report on Carcinogens (RoC). Both organizations are tasked with evaluating peer-reviewed, published evidence to determine whether specific substances, exposure scenarios, or mixtures pose a cancer hazard to humans. This evidence synthesis is based on objective, transparent, published methods that call for extracting and interpreting data in a systematic manner from multiple domains, including a) human exposure, b) epidemiological evidence, c) evidence from experimental animals, and d) mechanistic evidence. The process involves multiple collaborators and requires an extensive literature search, review, and synthesis of the evidence. Several online tools have been implemented to facilitate these collaborative systematic review processes. Specifically, Health Assessment Workplace Collaborative (HAWC) and Table Builder are custom solutions designed to record and share the results of the systematic literature search, data extraction, and analyses. In addition, a content management system for web-based project management and document submission has been adopted to enable access to submitted drafts simultaneously by multiple co-authors and to facilitate their peer review and revision. These advancements in cancer hazard classification have applicability in multiple systematic review efforts. https://doi.org/10.1289/EHP4224.

Beyond the RfD: Broad Application of a Probabilistic Approach to Improve Chemical Dose-Response Assessments for Noncancer Effects.

BACKGROUND: The National Academies recommended risk assessments redefine the traditional noncancer Reference Dose (RfD) as a probabilistically derived risk-specific dose, a framework for which was recently developed by the World Health Organization (WHO). OBJECTIVES: Our aim was to assess the feasibility and implications of replacing traditional RfDs with probabilistic estimates of the human dose associated with an effect magnitude M and population incidence I (HDMI). METHODS: We created a comprehensive, curated database of RfDs derived from animal data and developed a standardized, automated, web-accessible probabilistic dose-response workflow implementing the WHO framework. RESULTS: We identified 1,464 RfDs and associated endpoints, representing 608 chemicals across many types of effects. Applying our standardized workflow resulted in 1,522 HDMI values. Traditional RfDs are generally within an order of magnitude of the HDMI lower confidence bound for I=1% and M values commonly used for benchmark doses. The greatest contributor to uncertainty was lack of benchmark dose estimates, followed by uncertainty in the extent of human variability. Exposure at the traditional RfD frequently implies an upper 95% confidence bound of several percent of the population affected. Whether such incidences are considered acceptable is likely to vary by chemical and risk context, especially given the wide range of severity of the associated effects, from clinical chemistry to mortality. CONCLUSIONS: Overall, replacing RfDs with HDMI estimates can provide a more consistent, scientifically rigorous, and transparent basis for risk management decisions, as well as support additional decision contexts such as economic benefit-cost analysis, risk-risk tradeoffs, life-cycle impact analysis, and emergency response. https://doi.org/10.1289/EHP3368.

Human and animal evidence of potential transgenerational inheritance of health effects: An evidence map and state-of-the-science evaluation.

BACKGROUND: An increasing number of reports suggest early life exposures result in adverse effects in offspring who were never directly exposed; this phenomenon is termed "transgenerational inheritance." Given concern for public health implications for potential effects of exposures transmitted to subsequent generations, it is critical to determine how widespread and robust this phenomenon is and to identify the range of exposures and possible outcomes. OBJECTIVES: This scoping report examines the evidence for transgenerational inheritance associated with exposure to a wide range of stressors in humans and animals to identify areas of consistency, uncertainty, data gaps, and to evaluate general risk of bias issues for the transgenerational study design. METHODS: A protocol was developed to collect and categorize the literature into a systematic evidence map for transgenerational inheritance by health effects, exposures, and evidence streams following the Office of Health Assessment and Translation (OHAT) approach for conducting literature-based health assessments. RESULTS: A PubMed search yielded 63,758 unique records from which 257 relevant studies were identified and categorized into a systematic evidence map by evidence streams (46 human and 211 animal), broad health effect categories, and exposures. Data extracted from the individual studies are available in the Health Assessment Workspace Collaborative (HAWC) program. There are relatively few bodies of evidence where multiple studies evaluated the same exposure and the same or similar outcomes. Studies evaluated for risk of bias generally had multiple issues in design or conduct. CONCLUSIONS: The evidence mapping illustrated that risk of bias, few studies, and heterogeneity in exposures and endpoints examined present serious limitations to available bodies of evidence for assessing transgenerational effects. Targeted research is suggested to addressed inconsistencies and risk of bias issues identified, and thereby establish more robust bodies of evidence to critically assess transgenerational effects - particularly by adding data on exposure-outcome pairs where there is some evidence (i.e., reproductive, metabolic, and neurological effects).

A Web-Based System for Bayesian Benchmark Dose Estimation.

BACKGROUND: Benchmark dose (BMD) modeling is an important step in human health risk assessment and is used as the default approach to identify the point of departure for risk assessment. A probabilistic framework for dose-response assessment has been proposed and advocated by various institutions and organizations; therefore, a reliable tool is needed to provide distributional estimates for BMD and other important quantities in dose-response assessment. OBJECTIVES: We developed an online system for Bayesian BMD (BBMD) estimation and compared results from this software with U.S. Environmental Protection Agency's (EPA's) Benchmark Dose Software (BMDS). METHODS: The system is built on a Bayesian framework featuring the application of Markov chain Monte Carlo (MCMC) sampling for model parameter estimation and BMD calculation, which makes the BBMD system fundamentally different from the currently prevailing BMD software packages. In addition to estimating the traditional BMDs for dichotomous and continuous data, the developed system is also capable of computing model-averaged BMD estimates. RESULTS: A total of 518 dichotomous and 108 continuous data sets extracted from the U.S. EPA's Integrated Risk Information System (IRIS) database (and similar databases) were used as testing data to compare the estimates from the BBMD and BMDS programs. The results suggest that the BBMD system may outperform the BMDS program in a number of aspects, including fewer failed BMD and BMDL calculations and estimates. CONCLUSIONS: The BBMD system is a useful alternative tool for estimating BMD with additional functionalities for BMD analysis based on most recent research. Most importantly, the BBMD has the potential to incorporate prior information to make dose-response modeling more reliable and can provide distributional estimates for important quantities in dose-response assessment, which greatly facilitates the current trend for probabilistic risk assessment. https://doi.org/10.1289/EHP1289.

An Intuitive Approach for Predicting Potential Human Health Risk with the Tox21 10k Library.

In vitro-in vivo extrapolation (IVIVE) analyses translating high-throughput screening (HTS) data to human relevance have been limited. This study represents the first report applying IVIVE approaches and exposure comparisons using the entirety of the Tox21 federal collaboration chemical screening data, incorporating assay response efficacy and quality of concentration-response fits, and providing quantitative anchoring to first address the likelihood of human in vivo interactions with Tox21 compounds. This likelihood was assessed using a maximum blood concentration to in vitro response ratio approach (Cmax/AC50), analogous to decision-making methods for clinical drug-drug interactions. Fraction unbound in plasma (fup) and intrinsic hepatic clearance (CLint) parameters were estimated in silico and incorporated in a three-compartment toxicokinetic (TK) model to first predict Cmax for in vivo corroboration using therapeutic scenarios. Toward lower exposure scenarios, 36 compounds of 3925 unique chemicals with curated activity in the HTS data using high-quality dose-response model fits and ≥40% efficacy gave "possible" human in vivo interaction likelihoods lower than median human exposures predicted in the United States Environmental Protection Agency's ExpoCast program. A publicly available web application has been designed to provide all Tox21-ToxCast dose-likelihood predictions. Overall, this approach provides an intuitive framework to relate in vitro toxicology data rapidly and quantitatively to exposures using either in vitro or in silico derived TK parameters and can be thought of as an important step toward estimating plausible biological interactions in a high-throughput risk-assessment framework.

ORIO (Online Resource for Integrative Omics): a web-based platform for rapid integration of next generation sequencing data.

Established and emerging next generation sequencing (NGS)-based technologies allow for genome-wide interrogation of diverse biological processes. However, accessibility of NGS data remains a problem, and few user-friendly resources exist for integrative analysis of NGS data from different sources and experimental techniques. Here, we present Online Resource for Integrative Omics (ORIO; https://orio.niehs.nih.gov/), a web-based resource with an intuitive user interface for rapid analysis and integration of NGS data. To use ORIO, the user specifies NGS data of interest along with a list of genomic coordinates. Genomic coordinates may be biologically relevant features from a variety of sources, such as ChIP-seq peaks for a given protein or transcription start sites from known gene models. ORIO first iteratively finds read coverage values at each genomic feature for each NGS dataset. Data are then integrated using clustering-based approaches, giving hierarchical relationships across NGS datasets and separating individual genomic features into groups. In focusing its analysis on read coverage, ORIO makes limited assumptions about the analyzed data; this allows the tool to be applied across data from a variety of experiments and techniques. Results from analysis are presented in dynamic displays alongside user-controlled statistical tests, supporting rapid statistical validation of observed results. We emphasize the versatility of ORIO through diverse examples, ranging from NGS data quality control to characterization of enhancer regions and integration of gene expression information. Easily accessible on a public web server, we anticipate wide use of ORIO in genome-wide investigations by life scientists.

Standardizing benchmark dose calculations to improve science-based decisions in human health assessments.

BACKGROUND: Benchmark dose (BMD) modeling computes the dose associated with a prespecified response level. While offering advantages over traditional points of departure (PODs), such as no-observed-adverse-effect-levels (NOAELs), BMD methods have lacked consistency and transparency in application, interpretation, and reporting in human health assessments of chemicals. OBJECTIVES: We aimed to apply a standardized process for conducting BMD modeling to reduce inconsistencies in model fitting and selection. METHODS: We evaluated 880 dose-response data sets for 352 environmental chemicals with existing human health assessments. We calculated benchmark doses and their lower limits [10% extra risk, or change in the mean equal to 1 SD (BMD/L10/1SD)] for each chemical in a standardized way with prespecified criteria for model fit acceptance. We identified study design features associated with acceptable model fits. RESULTS: We derived values for 255 (72%) of the chemicals. Batch-calculated BMD/L10/1SD values were significantly and highly correlated (R2 of 0.95 and 0.83, respectively, n = 42) with PODs previously used in human health assessments, with values similar to reported NOAELs. Specifically, the median ratio of BMDs10/1SD:NOAELs was 1.96, and the median ratio of BMDLs10/1SD:NOAELs was 0.89. We also observed a significant trend of increasing model viability with increasing number of dose groups. CONCLUSIONS: BMD/L10/1SD values can be calculated in a standardized way for use in health assessments on a large number of chemicals and critical effects. This facilitates the exploration of health effects across multiple studies of a given chemical or, when chemicals need to be compared, providing greater transparency and efficiency than current approaches.

Inflammatory pseudotumor of the spleen managed laparoscopically. Can preoperative imaging establish the diagnosis? Case report and literature review.

BACKGROUND: Inflammatory pseudotumor of the spleen, or inflammatory myofibroblastic tumor, is an uncommon condition easily mistaken for a malignant process. Patients may be asymptomatic or may present with nonspecific abdominal complaints. Mild constitutional symptoms have also been reported. The ability to diagnose this entity radiographically preoperatively may allow the uniform application of laparoscopy to manage this condition and spare patients open splenectomy. METHODS: Chart review and literature review. RESULTS: Inflammatory pseudotumor of the spleen was successfully managed laparoscopically. Preoperative evaluation suggested but could not confirm the diagnosis. CONCLUSIONS: A hypovascular solid splenic mass by Doppler ultrasound or CT scan suggests the diagnosis of inflammatory pseudotumor. Stellate central calcifications seen on CT scan make the diagnosis very likely. Laparoscopic splenectomy can be used for definitive diagnosis and symptomatic relief with minimal morbidity and mortality.