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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

National User Resource for Biological Accelerator Mass Spectrometry

The National User Resource for Biological Accelerator Mass Spectrometry (User Resource) will provide isotopic analysis (primarily radiocarbon or 14C) by accelerator mass spectrometry (AMS) for NIH- funded researchers across the United States and will be the only User Resource of its type in the United States. The User Resource will provide measurement capability and expertise to a research community that requires highly sensitive, quantitative isotope analyses. Since commissioning a new accelerator mass spectrometer in June 2014, we have measured over 4000 samples a year for collaborators and service users. The User Resource will enable us to continue to meet these research needs, as well as provide for new users whose research programs would benefit from AMS as a measurement tool. The User Resource’s forte will be ultra-high sensitivity quantitation of radiocarbon and selected other radioisotopes for research studies where isotopes are required. Radioisotope labeling studies have been and will continue to be an important tool for addressing many complex biomedical science problems. AMS is a specialized and unique type of mass spectrometry that provides absolute quantitation of radiocarbon and other relevant radioisotopes with extreme sensitivity, having limits of detection in real samples on the order of a few attomol/mg of sample at measurement precisions of ~3%. It is the only instrumental method capable of quantifying radioisotope-labeled agents routinely in real-world samples with such precision and sensitivity. The sensitivity of AMS allows for the quantification of radiolabeled metabolites in extremely complex matrices of cells and organisms at very low concentrations and in small samples. AMS allows studies to be conducted without perturbing metabolism leading to more relevant quantification of metabolic rates and pathways. In addition, it enables quantification of pharmacokinetic and metabolic properties of toxicants at environmentally relevant concentrations in model systems as well as the ability to quantify pharmacokinetics and other molecular endpoints directly in humans. Such quantitative assessments can 1) improve risk assessment for toxicants, 2) address safety and efficacy considerations for therapeutic entities, 3) deepen understanding of xenobiotic and intermediary metabolism, 4) help understand the interactions between critical molecular pathways, and 5) improve efforts to model and predict various metabolic and biological states. These capabilities have been applied in a number of areas including research in carcinogenesis, toxicology, nutrition, pharmacology/drug development and basic biological science. As a NIGMS National Resource the National User Resource for Biological Accelerator Mass Spectrometry will help NIH funded scientists achieve a deeper understanding of the etiology of human health concerns by (1) enabling the quantification of pharmacokinetics and other molecular endpoints directly in humans; (2) offering the ability to conduct quantitative studies using biologics such as proteins or lipids, and thereby reducing the amount of radioisotope usage in biomedical labs; and (3) enabling more relevant studies of metabolic pathways in health and disease through the use of much lower, more biologically-relevant, concentrations of metabolic substrates in cells and intact organisms. Such studies support NIGMS’s basic biomedical research areas that contribute to the understanding of fundamental cellular and physiological principles and enable research supported by the Biophysics, Biomedical Technology, and Computational Biosciences (BBCB); Genetics and Molecular, Cellular, and Developmental Biology (GMCDB); Pharmacology, Physiology, Biological Chemistry (PPBC) and Training, Workforce Development, and Diversity (TWD) Divisions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

National User Resource for Biological Accelerator Mass Spectrometry (Final Report)

The National User Resource for Biological Accelerator Mass Spectrometry (User Resource) will provide isotopic analysis (primarily radiocarbon or 14C) by accelerator mass spectrometry (AMS) for NIH- funded researchers across the United States and will be the only User Resource of its type in the United States. The User Resource will provide measurement capability and expertise to a research community that requires highly sensitive, quantitative isotope analyses. Since commissioning a new accelerator mass spectrometer in June 2014, we have measured over 4000 samples a year for collaborators and service users. The User Resource will enable us to continue to meet these research needs, as well as provide for new users whose research programs would benefit from AMS as a measurement tool. The User Resource’s forte will be ultra-high sensitivity quantitation of radiocarbon and selected other radioisotopes for research studies where isotopes are required. Radioisotope labeling studies have been and will continue to be an important tool for addressing many complex biomedical science problems. AMS is a specialized and unique type of mass spectrometry that provides absolute quantitation of radiocarbon and other relevant radioisotopes with extreme sensitivity, having limits of detection in real samples on the order of a few attomol/mg of sample at measurement precisions of ~3%. It is the only instrumental method capable of quantifying radioisotope-labeled agents routinely in real-world samples with such precision and sensitivity. The sensitivity of AMS allows for the quantification of radiolabeled metabolites in extremely complex matrices of cells and organisms at very low concentrations and in small samples. AMS allows studies to be conducted without perturbing metabolism leading to more relevant quantification of metabolic rates and pathways. In addition, it enables quantification of pharmacokinetic and metabolic properties of toxicants at environmentally relevant concentrations in model systems as well as the ability to quantify pharmacokinetics and other molecular endpoints directly in humans. Such quantitative assessments can 1) improve risk assessment for toxicants, 2) address safety and efficacy considerations for therapeutic entities, 3) deepen understanding of xenobiotic and intermediary metabolism, 4) help understand the interactions between critical molecular pathways, and 5) improve efforts to model and predict various metabolic and biological states. These capabilities have been applied in a number of areas including research in carcinogenesis, toxicology, nutrition, pharmacology/drug development and basic biological science. As a NIGMS National Resource the National User Resource for Biological Accelerator Mass Spectrometry will help NIH funded scientists achieve a deeper understanding of the etiology of human health concerns by (1) enabling the quantification of pharmacokinetics and other molecular endpoints directly in humans; (2) offering the ability to conduct quantitative studies using biologics such as proteins or lipids, and thereby reducing the amount of radioisotope usage in biomedical labs; and (3) enabling more relevant studies of metabolic pathways in health and disease through the use of much lower, more biologically-relevant, concentrations of metabolic substrates in cells and intact organisms. Such studies support NIGMS’s basic biomedical research areas that contribute to the understanding of fundamental cellular and physiological principles and enable research supported by the Biophysics, Biomedical Technology, and Computational Biosciences (BBCB); Genetics and Molecular, Cellular, and Developmental Biology (GMCDB); Pharmacology, Physiology, Biological Chemistry (PPBC) and Training, Workforce Development, and Diversity (TWD) Divisions. Over the next five years, our goals are to: 1. Improve the efficiency of operation for AMS measurements through installation of new interfaces to our AMS systems, technical modifications to improve gas accepting ion source efficiency and upgrading our data analysis software for improved ease of use and data reporting. 2. Increase the accessibility and visibility of ultra-sensitive 14C measurements for the biomedical research community by training of new investigators and expanding our national user base. 3. Provide high throughput, ultra-sensitive 14C analysis for the NIGMS and NIH user community.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Toxicology and Biodegradability of Blendstocks for Mixing Controlled Compression Ignition Combustion: Literature Review of Available Data

The goal of the U.S. Department of Energy’s Co-Optima project is to develop an understanding of potential biomass-derived blendstocks for use in the transportation sector. This report documents the estimated environmental and toxicological impact of potential bioblendstocks for mixing controlled compression ignition (MCCI) engines. The candidate molecules were: 2-nonanol; n-undecane; 2,6,10-trimethyldodecane; 5-ethyl-4-propylnonane; hexylhexanoate; methyldecanoate; dibutoxymethane; 4-butoxyheptane; dipentylether; renewable diesel; and soy biodiesel. To provide a robust comparison, diesel surrogate molecules were also considered: α-methylnaphthalene; decahydronaphthalene; 2,2,4,4,6,8,8-heptamethylnonane; n-butylcyclohexane; n-hexadecane; tetralin; and n-dodecylbenzene. The intent of this work was not to provide absolute answers and remove any molecules from consideration, but to provide more robust data for each molecule that can be used in subsequent evaluations. Little literature data was available for many of the molecules, both potential blendstocks and diesel surrogates, so a quantitative structural activity relationship (QSAR) approach was used to provide input into the relevant models. This assessment included an evaluation of compartment partitioning in the event of an accidental chemical spill, fate and transport indicators, estimated biodegradability, and human and environmental toxicology. The higher molecular weight and longer hydrocarbon chain lengths of these MCCI molecules reduce water solubility and mobility. Generally, the oxygenated molecules improved biodegradability metrics. The hydrocarbons farnesane and 5-Et-4-PrNonane are not predicted to be biodegradable. Human acute toxicity was found to be slightly to non-toxic for the MCCI blendstocks and the diesel surrogates. 2-Nonanol, undecane, MeDecanoate, dibutoxymethane, and dipentylether were likely developmental toxicants. Although poorly water soluble, all the compounds, whether Co-Optima blendstocks or diesel surrogates, showed significant toxicity to D. magna. The oxygenated blendstocks were less likely to bioaccumulate than the non-oxygenated blendstocks, which behaved similar to the diesel surrogates. Overall, this predicted analysis showed that the MCCI blendstocks are very similar to their diesel counterparts. No significant showstoppers were found that would indicate concern moving forward with additional research into these potential fuels. The results presented were based solely on predictive models and serve to provide information going forward for continued evaluation of these molecules.

09 BIOMASS FUELS↗
DOE OSTI2024

Statistically-driven Experimental Design to Improve Reference-free Quantification of Small Molecules by Liquid Chromatography-Mass Spectrometry

Non-targeted analysis of small molecules and metabolites in unknown, complex samples using liquid chromatography-tandem mass spectrometry remains challenging. One of the main bottlenecks is the extensive unannotated regions of metabolomics mass spectrometry data, resulting in knowledge gaps. Small molecule annotation in mass spectrometry data has conventionally relied on reference standards and libraries for compound identification and confirmation, which can constrain compound identification to those molecules already known, thus limiting the ability to discover new knowledge and new markers. Retention time prediction can facilitate and expedite unknown compound identification in non-targeted analysis of complex metabolomics samples. Additionally, accurate retention time predictions can also inform sample mixture design for LC-MS/MS analyses. However, current machine learning-based methods for retention time prediction are typically developed for specific chromatographic platforms and are not generalizable across scales. And while technologies and methods to improve reference-free metabolite identification for more comprehensive annotation of unknowns has received much attention, development of the same for quantitation without reference standards has been much more limited, despite its importance in toxicological, environmental, food safety, forensics, and clinical applications. We believe that a reference-free quantitation strategy that exploits mass spectrometry data already collected for reference-free identification can provide much more insight on unknowns, and move the metabolomics field for more complete unknowns characterization. As such, we pursue two efforts to improve upon current state-of-the-art methods in non-targeted analysis: (1) machine learning-based retention time prediction and (2) statistical design of experiments framework for reference-free quantitation. In this work, we develop and demonstrate (1) a generalizable retention time prediction capability across chromatographic conditions and scales, and (2) a statistical design-based framework for response factor contribution elucidation and reference-free quantitation. Evaluation of our retention time prediction model, PrediToR, showed approximately 24% improvement over current models, and we observed approximately 10X improvement in concentration estimation accuracy from our statistical design-based response factor model over a primarily ionization efficiency-based model. We expect that future efforts to improve upon these new capabilities will further advance non-targeted analysis of small molecules towards truly reference-free metabolomics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

In Silico Guidance for In Vitro Androgen and Glucocorticoid Receptor ToxCast Assays

Molecular initiating events (MIEs) are key events in adverse outcome pathways (AOPs) that link molecular chemistry to target biology. As they are based in chemistry, these interactions are excellent targets for computational chemistry approaches to in silico modelling. In this work, we aim to link ligand chemical structure to MIEs for androgen receptor (AR) and glucocorticoid receptor (GR) binding using ToxCast data. This has been done using an automated computational algorithm to perform maximal common substructure searches on chemical binders for each target from the ToxCast dataset. The models developed show a high level of accuracy, correctly assigning 87.20% of AR binders and 96.81% of GR binders in a 25% test set using holdout cross-validation. The 2D structural alerts developed can be used as in silico models to predict these MIEs, and as guidance for in vitro ToxCast assays to confirm hits. These models can target such experimental work, reducing the number of assays to be performed to gain required toxicological insight. Development of these models has also allowed some structural alerts to be identified as predictors for agonist or antagonist behavior at the receptor target. This work represents a first step in using computational methods to guide and target experimental approaches.

Allen, Timothy H.↗
DOE OSTI2023

Dose Response, Dosimetric, and Metabolic Evaluations of Replacement PFAS Perfluoro-(2,5,8-trimethyl-3,6,9-trioxadodecanoic) Acid (HFPO-TeA)

Few studies are available on the environmental and toxicological effects of perfluoroalkyl ether carboxylic acids (PFECAs), such as GenX, which are replacing legacy PFAS in manufacturing processes. To collect initial data on the toxicity and toxicokinetics of a longer-chain PFECA, male and female Sprague Dawley rats were exposed to perfluoro-(2,5,8-trimethyl-3,6,9-trioxadodecanoic) acid (HFPO-TeA) by oral gavage for five days over multiple dose levels (0.3–335.2 mg/kg/day). Clinically, we observed mortality at doses >17 mg/kg/day and body weight changes at doses ≤ 17 mg/kg/day. For the 17 mg/kg/day dose level, T3 and T4 thyroid hormone concentrations were significantly decreased (p < 0.05) from controls and HFPO-TeA plasma concentrations were significantly different between sexes. Non-targeted analysis of plasma and in vitro hepatocyte assay extractions revealed the presence of another GenX oligomer, perfluoro-(2,5-dimethyl-3,6-dioxanonanoic) acid (HFPO-TA). In vitro to in vivo extrapolation (IVIVE) parameterized with in vitro toxicokinetic data predicted steady-state blood concentrations that were within seven-fold of those observed in the in vivo study, demonstrating reasonable predictivity. The evidence of thyroid hormone dysregulation, sex-based differences in clinical results and dosimetry, and IVIVE predictions presented here suggest that the replacement PFECA HFPO-TeA induces a complex and toxic exposure response in rodents.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Structure–activity relationship-based chemical classification of highly imbalanced Tox21 datasets

Abstract The specificity of toxicant-target biomolecule interactions lends to the very imbalanced nature of many toxicity datasets, causing poor performance in Structure–Activity Relationship (SAR)-based chemical classification. Undersampling and oversampling are representative techniques for handling such an imbalance challenge. However, removing inactive chemical compound instances from the majority class using an undersampling technique can result in information loss, whereas increasing active toxicant instances in the minority class by interpolation tends to introduce artificial minority instances that often cross into the majority class space, giving rise to class overlapping and a higher false prediction rate. In this study, in order to improve the prediction accuracy of imbalanced learning, we employed SMOTEENN, a combination of Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) algorithms, to oversample the minority class by creating synthetic samples, followed by cleaning the mislabeled instances. We chose the highly imbalanced Tox21 dataset, which consisted of 12 in vitro bioassays for > 10,000 chemicals that were distributed unevenly between binary classes. With Random Forest (RF) as the base classifier and bagging as the ensemble strategy, we applied four hybrid learning methods, i.e., RF without imbalance handling (RF), RF with Random Undersampling (RUS), RF with SMOTE (SMO), and RF with SMOTEENN (SMN). The performance of the four learning methods was compared using nine evaluation metrics, among which F 1 score, Matthews correlation coefficient and Brier score provided a more consistent assessment of the overall performance across the 12 datasets. The Friedman’s aligned ranks test and the subsequent Bergmann-Hommel post hoc test showed that SMN significantly outperformed the other three methods. We also found that a strong negative correlation existed between the prediction accuracy and the imbalance ratio (IR), which is defined as the number of inactive compounds divided by the number of active compounds. SMN became less effective when IR exceeded a certain threshold (e.g., > 28). The ability to separate the few active compounds from the vast amounts of inactive ones is of great importance in computational toxicology. This work demonstrates that the performance of SAR-based, imbalanced chemical toxicity classification can be significantly improved through the use of data rebalancing.

Idakwo, Gabriel↗
DOE OSTI2022

Transcriptional pathways linked to fetal and maternal hepatic dysfunction caused by gestational exposure to perfluorooctanoic acid (PFOA) or hexafluoropropylene oxide-dimer acid (HFPO-DA or GenX) in CD-1 mice

Per- and polyfluoroalkyl substances (PFAS) comprise a diverse class of chemicals used in industrial processes, consumer products, and fire-fighting foams which have become environmental pollutants of concern due to their persistence, ubiquity, and associations with adverse human health outcomes, including in pregnant persons and their offspring. Multiple PFAS are associated with adverse liver outcomes in adult humans and toxicological models, but effects on the developing liver are not fully described. Here we performed transcriptomic analyses in the mouse to investigate the molecular mechanisms of hepatic toxicity in the dam and its fetus after exposure to two different PFAS, perfluorooctanoic acid (PFOA) and its replacement, hexafluoropropylene oxide-dimer acid (HFPO-DA, known as GenX). Pregnant CD-1 mice were exposed via oral gavage from embryonic day (E) 1.5-17.5 to PFOA (0, 1, or 5 mg/kg-d) or GenX (0, 2, or 10 mg/kg-d). Maternal and fetal liver RNA was isolated (N = 5 per dose/group) and the transcriptome analyzed by Affymetrix Array. Differentially expressed genes (DEG) and differentially enriched pathways (DEP) were obtained. DEG patterns were similar in maternal liver for 5 mg/kg PFOA, 2 mg/kg GenX, and 10 mg/kg GenX (R2: 0.46-0.66). DEG patterns were similar across all 4 dose groups in fetal liver (R2: 0.59-0.81). There were more DEGs in fetal liver compared to maternal liver at the low doses for both PFOA (fetal = 69, maternal = 8) and GenX (fetal = 154, maternal = 93). Upregulated DEPs identified across all groups included Fatty Acid Metabolism, Peroxisome, Oxidative Phosphorylation, Adipogenesis, and Bile Acid Metabolism. Transcriptome-phenotype correlation analyses demonstrated > 1000 maternal liver DEGs were significantly correlated with maternal relative liver weight (R 2 >0.92). These findings show shared biological pathways of liver toxicity for PFOA and GenX in maternal and fetal livers in CD-1 mice. The limited overlap in specific DEGs between the dam and fetus suggests the developing liver responds differently than the adult liver to these chemical stressors. This work helps define mechanisms of hepatic toxicity of two structurally unique PFAS and may help predict latent consequences of developmental exposure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Predicting oncology drug-induced cardiotoxicity with donor-specific iPSC-CMs—a proof-of-concept study with doxorubicin

Abstract Many oncology drugs have been found to induce cardiotoxicity in a subset of patients, which significantly limits their clinical use and impedes the benefit of lifesaving anticancer treatments. Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) carry donor-specific genetic information and have been proposed for exploring the interindividual difference in oncology drug-induced cardiotoxicity. Herein, we evaluated the inter- and intraindividual variability of iPSC-CM-related assays and presented a proof of concept to prospectively predict doxorubicin (DOX)-induced cardiotoxicity (DIC) using donor-specific iPSC-CMs. Our findings demonstrated that donor-specific iPSC-CMs exhibited greater line-to-line variability than the intraindividual variability in impedance cytotoxicity and transcriptome assays. The variable and dose-dependent cytotoxic responses of iPSC-CMs resembled those observed in clinical practice and largely replicated the reported mechanisms. By categorizing iPSC-CMs into resistant and sensitive cell lines based on their time- and concentration-related phenotypic responses to DOX, we found that the sensitivity of donor-specific iPSC-CMs to DOX may predict in vivo DIC risk. Furthermore, we identified a differentially expressed gene, DND microRNA-mediated repression inhibitor 1 (DND1), between the DOX-resistant and DOX-sensitive iPSC-CMs. Our results support the utilization of donor-specific iPSC-CMs in assessing interindividual differences in DIC. Further studies will encompass a large panel of donor-specific iPSC-CMs to identify potential novel molecular and genetic biomarkers for predicting DOX and other oncology drug-induced cardiotoxicity.

Toxicology↗
DOE OSTI2024

Signature analysis of high-throughput transcriptomics screening data for mechanistic inference and chemical grouping

Abstract High-throughput transcriptomics (HTTr) uses gene expression profiling to characterize the biological activity of chemicals in in vitro cell-based test systems. As an extension of a previous study testing 44 chemicals, HTTr was used to screen an additional 1,751 unique chemicals from the EPA’s ToxCast collection in MCF7 cells using 8 concentrations and an exposure duration of 6 h. We hypothesized that concentration-response modeling of signature scores could be used to identify putative molecular targets and cluster chemicals with similar bioactivity. Clustering and enrichment analyses were conducted based on signature catalog annotations and ToxPrint chemotypes to facilitate molecular target prediction and grouping of chemicals with similar bioactivity profiles. Enrichment analysis based on signature catalog annotation identified known mechanisms of action (MeOAs) associated with well-studied chemicals and generated putative MeOAs for other active chemicals. Chemicals with predicted MeOAs included those targeting estrogen receptor (ER), glucocorticoid receptor (GR), retinoic acid receptor (RAR), the NRF2/KEAP/ARE pathway, AP-1 activation, and others. Using reference chemicals for ER modulation, the study demonstrated that HTTr in MCF7 cells was able to stratify chemicals in terms of agonist potency, distinguish ER agonists from antagonists, and cluster chemicals with similar activities as predicted by the ToxCast ER Pathway model. Uniform manifold approximation and projection (UMAP) embedding of signature-level results identified novel ER modulators with no ToxCast ER Pathway model predictions. Finally, UMAP combined with ToxPrint chemotype enrichment was used to explore the biological activity of structurally related chemicals. The study demonstrates that HTTr can be used to inform chemical risk assessment by determining in vitro points of departure, predicting chemicals’ MeOA and grouping chemicals with similar bioactivity profiles.

Toxicology↗
DOE OSTI2022

Effects of Serum and Compound Preparation Methods on Delayed Repolarization Evaluation With Human iPSC-CMs

Abstract Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have been widely used in the Comprehensive in vitro Proarrhythmia Assay (CiPA). The notable difference of the electrophysiological (EP) responses of hiPSC-CMs in serum and serum-free media (SFM) is puzzling and may impact regulatory decision-making on the cardiac safety of candidate drugs in inducing QT prolongation and torsade de pointes (TdP). In this study, we compared the EP responses of hiPSC-CMs to 10 CiPA compounds and moxifloxacin in serum and SFM; explained the potential reason behind the different EP responses—abiotic compound loss to plastic tubes/plates of hydrophobic compounds prepared in SFM; and investigated the impact of compound preparation methods on drug bioavailability in exposure media, which affects the TdP risk prediction of drugs tested in serum-containing and SFM. For assays to be conducted in SFM, awareness of abiotic compound loss of hydrophobic compounds in serum-free preparations is critical for delay repolarization evaluation and data extrapolation from in vitro to in vivo.

Toxicology↗
DOE OSTI2025

A physiologically based pharmacokinetic (PBPK) model to align dosimetry of the isobutyl metabolic series in rats and humans

Here, we developed a physiologically based pharmacokinetic (PBPK) model in rats and humans for the isobutyl metabolic series including isobutyl acetate, isobutanol, isobutyraldehyde, and isobutyric acid. Chemical manufactures routinely use these compounds as solvents, for chemical synthesis, as potential biofuels, de-icing fluids, and additives for food and/or fragrance in consumer products. Human exposure to isobutyl compounds can occur through inhalation or oral routes. We previously developed a PBPK model for the propyl metabolic series and utilized it as a framework to create the isobutyl PBPK model due to the chemical similarities between the two series. To support model development, we measured in vitro metabolism of isobutyl acetate in rat and human blood and liver S9 fractions. Compared to rats, humans demonstrated faster isobutyl acetate hydrolysis in liver S9 fractions, while the hydrolysis rates in blood were similar between the two species. We used concentrations of isobutyl compounds measured in air and blood from rats exposed to isobutyl acetate and isobutanol as well as other published data to further parameterize the model. Following exposure to either isobutyl acetate or isobutanol, we observed isobutanol concentrations highest among the isobutyl compounds in the blood of rats. Overall, the model accurately predicts measured time course concentrations of isobutyl acetate, isobutanol, and isobutyric acid in available data in rats and humans. Sensitivity analyses identified alveolar ventilation rates, isobutyl metabolism rates, and cardiac output as the most sensitive parameters affecting concentrations of isobutyl compounds in blood. The isobutyl PBPK model enables comparisons of internal dose metrics across various isobutyl compound exposures and species and allows for calculation of equivalent external exposures that result in the same dose metric. Regulators can employ this PBPK model to predict and align internal dose metrics of isobutyl compounds for risk assessment purposes.

2-methyl-1-propanol↗
NASA NTRS2024

Spacecraft Maximum Allowable Concentrations for Airborne Contaminants: Revision C

The enclosed table lists official Spacecraft Maximum Allowable Concentrations (SMACs) for selected airborne contaminants. They are based upon experiments conducted at standard pressure and oxygen environments and may or may not be applicable to altered atmospheres. The values listed in this summary document are applicable only to ambient conditions at standard temperature and oxygen (14.7 psi and 21% O2 at 25°C). These are guideline values set by the National Aeronautics and Space Administration (NASA)/Johnson Space Center (JSC) Toxicology Group in cooperation with the National Research Council Committee on Toxicology (NRCCOT), through publication in the peer- reviewed scientific literature, or NASA Technical Memoranda. Based on documented guidance (NRC, 1992; NRC, 2016), NASA has established SMACs for 58 chemical compounds that are particularly relevant to atmospheric contamination of the International Space Station (ISS) and targets of Exploration. Some long-term limits (1000-days) have also been established to support manned deep-space exploration. Summaries of these SMACs are presented in tabular form as part of this publication. This document provides a tabular summary of values that have been previously established based on guidelines established by NASA and the National Research Council’s Committee on Toxicology and vetted and published through the NRCCOT and/or non-NASA scientific literature and NASA Technical Memoranda. Short‐term (1‐ and 24‐hour) SMACs apply to off‐nominal situations, such as accidental releases aboard a spacecraft. These limits permit risk of minor, reversible effects, such as mild mucosal irritation. In contrast, the long‐term SMACs are set to fully protect healthy crewmembers from adverse effects resulting from continuous exposure to specific air pollutants for up to 1000 days. Because allergic reactions or chemical idiosyncrasy to certain airborne pollutants are very difficult to predict, crewmembers with allergies or unusual sensitivity to trace pollutants may not be afforded complete protection, even when long‐term SMACs are not exceeded.

SMACs↗
DOE OSTI2020

Patterns of Trace Element Accumulation in Waterfowl Restricted to Impoundments Holding Coal Combustion Waste

Waterfowl are often exposed to and readily accumulate anthropogenic contaminants when foraging in polluted environments. Settling impoundments containing coal combustion waste (CCW) enriched in trace elements such as arsenic (As), selenium (Se), and mercury (Hg) are often used by free-ranging migratory and resident waterfowl and represent potential sources for contaminant uptake. To assess accumulation of CCW contaminants, we experimentally restricted waterfowl to a CCW-contaminated impoundment and quantified trace element burdens in blood, muscle, and liver tissues over known periods of exposure (between 3 and 92 d). From these data we developed models 1) to predict elemental bioaccumulation with increased exposure time, and 2) to predict muscle/liver burdens based on concentrations in blood as a nondestructive sampling method. Although Hg and As did not bioaccumulate in our waterfowl, we observed an increase in Se concentrations in muscle, liver, and blood tissues over the duration of our experiment. Furthermore, we found that blood may be used as an effective nondestructive sampling alternative to predict muscle and liver tissue concentrations in birds contaminated with Se and As through dietary exposure. These data provide unique insights into accumulation rates of contaminants for waterfowl utilizing habitats contaminated with CCW and demonstrate the efficacy of nonlethal sampling of waterfowl to quantify contaminant exposure. Environ Toxicol Chem 2020;39:1052–1059.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2000

Comparative Study in Laboratory Rats to Validate Sperm Quality Methods and Endpoints

Abstract The Naval Health Research Center, Detachment (Toxicology) performs toxicity studies in laboratory animals to characterize the risk of exposure to chemicals of Navy interest. Research was conducted at the Toxicology Detachment at WPAFB, OH in collaboration with Wright State University, Department of Biological Sciences for the validation of new bioassay methods for evaluating reproductive toxicity. The Hamilton Thorne sperm analyzer was used to evaluate sperm damage produced by exposure to a known testicular toxic agent, methoxyacetic acid and by inhalation exposure to JP-8 and JP-5 in laboratory rats. Sperm quality parameters were evaluated (sperm concentration, motility, and morphology) to provide evidence of sperm damage. The Hamilton Thorne sperm analyzer utilizes a DNA specific fluorescent stain (similar to flow cytometry) and digitized optical computer analysis to detect sperm cell damage. The computer assisted sperm analysis (CASA) is a more rapid, robust, predictive and sensitive method for characterizing reproductive toxicity. The results presented in this poster report validation information showing exposure to methoxyacetic acid causes reproductive toxicity and inhalation exposure to JP-8 and JP-5 had no significant effects. The CASA method detects early changes that result in reproductive deficits and these data will be used in a continuing program to characterize the toxicity of chemicals, and combinations of chemicals, of military interest to formulate permissible exposure limits.

Price, W. A.↗