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Recent Science Inventory records from the EPA

Up to 100 Science Inventory records released or updated since midnight 02/22/2018

Published: Fri, 23 Feb 2018 01:12:10 GMT

Copyright: Public Domain

The Challenges of PFAS Remediation

Thu, 22 Feb 2018 21:36:10 GMT

Many military bases and their surrounding communities are impacted by contamination with per- and polyfluoroalkyl substances (PFAS) from Aqueous Film-Forming Foams (AFFFs). Soil sorption technologies provide a promising solution to immobilize PFAS in the soil and prevent groundwater and drinking water contamination. This article is the result of a collaborative effort between Battelle and the U.S. EPA’s review of the most promising technologies.

Per and polyfluoroalkyl substances in carpet and landfills

Thu, 22 Feb 2018 20:53:06 GMT

This will be a talk to audiences comprising members of industries that consume PFAS and members of the public interested in possible effects of exposure to them.

Marginal abatement cost curve for NOx incorporating controls, renewable electricity, energy efficiency and fuel switching

Thu, 22 Feb 2018 19:02:08 GMT

A marginal abatement cost curve (MACC) traces out the relationship between the quantity of pollution abated and the marginal cost of abating each additional unit. In the context of air quality management, MACCs typically are developed by sorting end-of-pipe controls by their relative cost-effectiveness. Other potentially important abatement measures, such as renewable electricity, energy efficiency, and fuel switching (RE/EE/FS), are often not incorporated into MACCs as it is difficult to quantify their costs and abatement potential. In this paper, a U.S. energy system model is used to develop a MACC for nitrogen oxides (NOx) that incorporates both end-of-pipe controls and these additional measures. The MACC is decomposed by sector and region, and the relative cost-effectiveness of RE/EE/FS and end-of-pipe controls are compared. RE/EE/FS are shown to produce considerable emission reductions after end-of-pipe controls have been exhausted. Furthermore, some RE/EE/FS are shown to be cost-competitive with end-of-pipe controls.

Estimating intermittent individual spawning behavior via disaggregating group data

Thu, 22 Feb 2018 17:10:36 GMT

In order to understand fish biology and reproduction it is important to know the fecundity patterns of individual fish, as frequently established by recording the output of mixed-sex groups of fish in a laboratory setting. However, for understanding individual reproductive health and modeling purposes it is important to estimate individual fecundity from group fecundity. We created a multistage method that disaggregates group level data into estimates for individual level clutch size and spawning interval distributions. The first stage of the method develops estimates of the daily spawning probability of fish. Daily spawning probabilities are then used to calculate the log-likelihood of candidate distributions of clutch size. Selecting the best candidate distribution for clutch size allows for a Monte Carlo resampling of annotations of the original data which state how many sh spawned on which day. We verify this disaggregation technique by combining data from fathead minnow pairs, and checking that the disaggregation method reproduced the original clutch sizes and spawning intervals. This method will allow scientists to estimate individual clutch size and spawning interval distributions from group spawning data without specialized or elaborate experimental designs.

Harvesting the Promise of AOPs: An assessment and recommendations

Thu, 22 Feb 2018 16:56:46 GMT

The adverse outcome pathway (AOP) concept was developed specifically to serve as a knowledge assembly and communication tool to facilitate the transparent translation of mechanistic information into outcomes meaningful to the regulatory assessment of chemicals. However, as the AOP framework has evolved, it has become recognized that the potential stakeholder community for the concept and associated knowledge is broader than scientists and regulators directly involved in chemical safety assessment. For example, the tractability of AOP-based approaches for addressing biomedical challenges has become increasingly evident.

Environmental effects of ozone depletion, UV radiation and interactions with climate change: UNEP Environmental Effects Assessment Panel, update 2017

Thu, 22 Feb 2018 14:39:05 GMT

The Environmental Effects Assessment Panel (EEAP) is one of three Panels of experts that inform the Parties to the Montreal Protocol. The EEAP focuses on the effects of UV radiation on human health, terrestrial and aquatic ecosystems, air quality, and materials, as well as on the interactive effects of UV radiation and global climate change. When considering the effects of climate change, it has become clear that processes resulting in changes in stratospheric ozone are more complex than previously held. Because of the Montreal Protocol, there are now indications of the beginnings of a recovery of stratospheric ozone, although the time required to reach levels like those before the 1960s is still uncertain, particularly as the effects of stratospheric ozone on climate change and vice versa, are not yet fully understood. Some regions will likely receive enhanced levels of UV radiation, while other areas will likely experience a reduction in UV radiation as ozone- and climate-driven changes affect the amounts of UV radiation reaching the Earth's surface. Like the other Panels, the EEAP produces detailed Quadrennial Reports every four years; the most recent was published as a series of seven papers in 2015 (Photochem. Photobiol. Sci., 2015, 14, 1–184). In the years in between, the EEAP produces less detailed and shorter Update Reports of recent and relevant scientific findings. The most recent of these was for 2016 (Photochem. Photobiol. Sci., 2017, 16, 107–145). The present 2017 Update Report assesses some of the highlights and new insights about the interactive nature of the direct and indirect effects of UV radiation, atmospheric processes, and climate change. A full 2018 Quadrennial Assessment, will be made available in 2018/2019

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity (SOT)

Thu, 22 Feb 2018 14:23:38 GMT

In order to protect human health from chemicals that can mimic natural hormones, the U. S. Congress mandated the U.S. EPA to screen chemicals for their potential to be endocrine disruptors through the Endocrine Disruptor Screening Program (EDSP). However, the number of chemicals to which humans are exposed is too large (tens of thousands) to be accommodated by the EDSP Tier 1 battery, so combinations of in vitro high-throughput screening (HTS) assays and computational models are being developed to help prioritize chemicals for more detailed testing. Previously, CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) demonstrated the effectiveness of combining many QSAR models trained on HTS data to prioritize a large chemical list for estrogen receptor activity. The limitations of single models were overcome by combining all models built by the consortium into consensus predictions. CoMPARA is a larger scale collaboration between 35 international groups, following the steps of CERAPP to model androgen receptor activity using a common training set of 1746 compounds provided by U.S. EPA. Eleven HTS ToxCast/Tox21 in vitro assays were integrated into a computational network model to detect true AR activity. Bootstrap uncertainty quantification was used to remove potential false positives/negatives. Reference chemicals (158) from the literature were used to validate the model, which showed 95.2% and 97.5% balanced accuracies for AR agonists and antagonists respectively. A library of ~80k bioactivities, representing ~11k chemicals curated from PubChem literature data using ScrubChem tools was integrated with CoMPARA’s consensus predictions that combined several structure-based and QSAR modeling approaches. The results of this project will be used to prioritize a large set of more than 50k chemicals for further testing over the next phases of ToxCast/Tox21, among other projects. This work does not reflect the official policy of any federal agency.

Transcriptome profiling to identify ATRA-responsive genes in human iPSC-derived endoderm for high-throughput point of departure analysis (SOT Annual Meeting)

Thu, 22 Feb 2018 14:19:46 GMT

Toxicological tipping points occur at chemical concentrations that overwhelm a cell’s adaptive response leading to permanent effects. We focused on retinoid signaling in differentiating endoderm to identify developmental pathways for tipping point analysis. Human induced pluripotent stem cell (iPSC)-derived endodermal cells (Allele) were exposed to 0.1% DMSO (vehicle-control) or five concentrations (0.001 - 10 µM) of all-trans retinoic acid (ATRA) renewed daily for 8 days (Vala Sciences). Total RNA samples were collected at 6 hours (h), 4 days (d), and 8 d and prepared for RNA sequencing (Cofactor Genomics). Log2(RPKM+1) values were used for differential gene expression analysis (ANOVA) and filtered using a conservative false discovery rate (FDR) of 2 or <-2). The resulting list comprised 6033 genes, including Foxa2, an endoderm-specific biomarker for which protein expression was confirmed via high-content imaging. Based on Foxa2 expression, differentiation occurred between 6 h and 4 d in the vehicle-control samples. ATRA significantly reduced this marker in a concentration-dependent manner in the 0.1 - 10 µM range. Pathway analysis of the 6033 gene list resulted in 8 pathways with enrichment scores above 9 (p-values < 0.0001). These pathways included protein digestion and absorption, which foreshadows differentiation of endodermal tissues. Stage-specific expression of ATRA-responsive genes returned two highly enriched pathways associated with 4 d ATRA exposure that were driven predominantly by increased expression of 29 genes encoding histones. This effect followed a concentration-response with undifferentiated controls between 0.1 and 1 µM. Overall, gene expression effects were most consistently observed following 1 µM exposure, which elicited time-dependent effects on the expression of Foxa2, Wnt6, Shh, and Hand1, genes known for their role in development. Taken together, iPSC-derived endoderm is a suitable human cell-based platform for high-throughput screening to identify toxicological tipping points in a differentiating system. The genes occurring within the identified pathways are being used for targeted, transcriptomic tipping point analysis of ToxCast chemical libraries. This work was conducted under EPA contract EPD13054 but does not represent US EPA policy.

Elucidating an Adverse Outcome Pathway of Microcephaly for use in Computational Toxicology (SOT Annual Meeting)

Thu, 22 Feb 2018 14:14:37 GMT

While evidence now supports a causal link between maternal Zika viral infection and microcephaly, genetic errors and chemical stressors may also precipitate this malformation through disruption of neuroprogenitor cell (NPC) proliferation, migration and differentiation in the early developing brain. Here, we present an Adverse Outcome Pathway (AOP) framework for microcephaly that can be used to help unravel the complexity of its pathogenesis. Publically available databases were used in conjunction with U.S. EPA developed tools to isolate relevant genes and pathways associated with this clinical diagnosis. The Mammalian Phenotype Browser database contains 85 gene associations for the phenotype ‘microcephaly’ (MP:0000433). Since reductions in cortical surface area and ventricular dilations are main features of the microcephalic brain, we searched for these features in prenatal developmental toxicity studies from EPA’s ToxRefDB database. This query identified 75 chemicals that caused either reductions in fetal brain size/mass (40) or dilated brain ventricles/hydrocephaly (39). Since the minimal overlap in chemicals (4) that elicited both pathologies suggests that mechanistically diverse pathways converge on these apical endpoints. Lastly, a high-throughput literature mining tool was built to query PubMed for references and construct a MicrocephalyConnections knowledgebase of relevant information for gene, chemical, or viral effects on development. The knowledgebase was used to elucidate an AOP for primary autosomal microcephaly (MCPH) that links evidence for molecular/subcellular changes in microcephalin (MCHP1) and abnormal spindle-like microcephaly (ASPM) function to cellular/tissue level changes leading to hypocellularity of the embryonic ventricular zone. This putative AOP pinpoints mitotic spindle orientation as a key determinant of NPC pool size at the onset of neurogenesis, whereby the logistical dynamics of microtubule assembly in the centrosome are synchronized to the cell cycle. Ongoing research is investigating the extent to which an AOP for MCPH1-microcephaly co-opts a network for chemical- or viral- induced microcephaly by modeling ToxCast data and literature associated with the 75 ToxRefDB chemicals that invoke fetal brain pathologies. (This abstract does not necessarily reflect EPA policy).

Using Chemical Structure Information to Predict In Vitro Pharmacokinetic Parameters (SOT)

Thu, 22 Feb 2018 13:50:25 GMT

Toxicokinetic data are key for relating exposure and internal dose when building in vitro-based risk assessment models. However, conducting in vivo toxicokinetic studies has time and cost limitations, and in vitro toxicokinetic data is available only for a limited set of chemicals. Data gap filling techniques are commonly used to predict hazard in the absence of empirical data. The most established techniques are read-across and quantitative structure-activity relationships (QSARs). This study aims at utilizing both of these techniques to develop predictive models for two in vitro toxicokinetic parameters: fraction unbound (fub) in plasma and intrinsic clearance. The analysis relied on a dataset of ~7k chemicals with predicted exposure data, of which 974 chemicals had human in vitro fub in plasma and 540 chemicals had human in vitro intrinsic clearance data. Chemotyper and PubChem fingerprints, and PaDEL descriptors were used as chemical structural features. Unsupervised feature selection was performed to remove the features with less than 80% variance. Read-across and QSAR models were then developed as follows: (1) Read-across: unsupervised KMeans clustering was performed on training chemicals, and was used for predicting clusters for the test chemicals. Next, similarity-weighted read-across predictions were made for each of the test chemicals using analogs from the cluster within a threshold of similarity range. (2) QSAR: The continuous descriptors were normalized to have mean=0 and standard deviation=1. QSAR models were then developed using random forest and support vector machines. The models were evaluated using a 5-fold cross-validation scheme. The best read-across and QSAR model for fub (range: 0 - 1) in plasma had a cross-validated root-mean-squared-error of 0.24 and 0.25, respectively. The difference in parameter mean values between clusters, as measured with a T-test, suggest that fub in plasma is more structurally predictable than intrinsic clearance. Future work includes building predictive models for intrinsic clearance and incorporation of uncertainty in experimental data to quantify uncertainty in model predictions.

Combining In Vivo, In Vitro And Toxicokinetics Data In Readacross: A Case Study Using Caffeine (SOT)

Thu, 22 Feb 2018 13:46:59 GMT

Readacross can be used to fill data gaps in chemical safety assessment. Readacross starts with the identification and evaluation of source analogs, including assessment of the physicochemical and mechanistic similarity of source analogs. We describe an approach to quantitative readacross prediction of point of departure (POD) using physicochemical, in vitro and in vivo data, with caffeine as an example target. Thirteen analogs were identified on the basis of structural similarity. Only theophylline, theobromine, paraxanthine were associated with any in vitro or in vivo experimental data. Relevant physicochemical properties (e.g. density, MP, BP, etc.) were all within a 1 log unit. Mechanistic in vitro assay information was used to determine bioactivity similarity. Caffeine and its analogs were active in three assays related to blockade of adenosine receptor A1 and A2a, demonstrating mechanistic similarity. Caffeine was as or more potent in vitro than any of the source analogs. In vivo POD data, specifically lowest effect levels (LELs) were available for 2 of the analogs; theophylline and theobromine. The LELs from rat subchronic studies were 75 and 250 mg/kg/day respectively. The LELs in rat chronic studies were 7.5 and 250 mg/kg/day, respectively. The lowest LEL value, 7.5 mg/kg/day, was taken as the readacross POD, and provides a conservative prediction of the LEL for caffeine (100 and 49 mg/kg/day in subchronic and chronic studies). A highthroughput toxicokinetics (HTTK) model was used as an in vitro to in vivo extrapolation approach for adenosine receptor activity (at 1 uM) which was the most representative assay, and yielded a POD for bioactivity of 0.1 mg/kg/day, significantly below the in vivo readacross POD. This value includes both human population variability and uncertainty in the kinetics, which is one reason for the low value. Additionally, this is the predicted dose at which desired activity occurs, as opposed to a higher dose where toxicity would be seen. The HTTK model is helpful in extrapolating from in vitro data, and can help to define a “safe” range for a target chemical. The readacross predictions derived for caffeine provide a proof of principle of this approach for using structure, physicochemical and in vitro data to help select readacross analogs. This abstract does not necessarily represent U.S. EPA policy