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All sources NASA NTRS DOE OSTI

DOE OSTI2023

Review of machine learning and deep learning models for toxicity prediction

The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.

Research & Experimental Medicine↗

DOE OSTI2024

Aryl hydrocarbon receptor-dependent toxicity by retene requires metabolic competence

Polycyclic aromatic hydrocarbons (PAHs) are a class of organic compounds frequently detected in the environment with widely varying toxicities. Many PAHs activate the aryl hydrocarbon receptor (AHR), inducing the expression of a battery of genes, including xenobiotic metabolizing enzymes like cytochrome P450s (CYPs); however, not all PAHs act via this mechanism. We screened several parent and substituted PAHs in in vitro AHR activation assays to classify their unique activity. Retene (1-methyl-7-isopropylphenanthrene) displays Ahr2-dependent teratogenicity in zebrafish, but did not activate human AHR or zebrafish Ahr2, suggesting a retene metabolite activates Ahr2 in zebrafish to induce developmental toxicity. To investigate the role of metabolism in retene toxicity, studies were performed to determine the functional role of cyp1a, cyp1b1, and the microbiome in retene toxicity, identify the zebrafish window of susceptibility, and measure retene uptake, loss, and metabolite formation in vivo. Cyp1a-null fish were generated using CRISPR-Cas9. Cyp1a-null fish showed increased sensitivity to retene toxicity, whereas Cyp1b1-null fish were less susceptible, and microbiome elimination had no significant effect. Zebrafish required exposure to retene between 24 and 48 hours post fertilization (hpf) to exhibit toxicity. After static exposure, retene concentrations in zebrafish embryos increased until 24 hpf, peaked between 24 and 36 hpf, and decreased rapidly thereafter. We detected retene metabolites at 36 and 48 hpf, indicating metabolic onset preceding toxicity. This study highlights the value of combining molecular and systems biology approaches with mechanistic and predictive toxicology to interrogate the role of biotransformation in AHR-dependent toxicity.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1992

Occupational safety considerations with hydrazine

Hydrazine is a reducing agent that is most commonly used as a propellant and as an oxygen scavenger in boilers. Hydrazine is extremely irritating and has been demonstrated to produce both acute and chronic toxicity. As a result, the established permissible inhalation exposure limits are very low, and respiration protection is required whenever vapors are present. Liquid hydrazine penetrates the skin and produces a chemical burn; therefore, some protective measures must also be taken to protect the skin from liquid contact. Often, however, a cumbersome, whole-body protective suit is worn to protect against skin contact with vapor as well. To what extent it is actually necessary to protect skin from vapor penetration had not previously been demonstrated. In an attempt to answer this question, we conducted a study with rats to compare the dermal penetration of hydrazine vapor with inhalation. Pharmacokinetic modeling was used to compare body burdens resulting from these different routes of exposure. The analysis concluded that the vapor concentration during a skin-only exposure would have to be at least 200 times higher than that during inhalation to achieve the same body burden. This type of estimation illustrates the use of predictive toxicology in occupational exposures.

Clewell, Harvey J., III↗

DOE OSTI2021

CATMoS: Collaborative Acute Toxicity Modeling Suite

Background: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. Objectives: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different endpoints: LD50 value, U.S. Environmental Protection Agency hazard categories, Globally Harmonized System for Classification and Labelling hazard categories, very toxic chemicals (LD50 =50 mg/kg), and non-toxic chemicals (LD50 >2000 mg/kg). Methods: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. Results: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared to in vivo results. Discussion: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for over 800,000 chemicals have been made available via the NTP’s Integrated Chemical Environment. The models are also implemented in a free, standalone open-source tool, OPERA, which allows predictions of new and untested chemicals to be made.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

NASA NTRS2001

Radiation transport modeling and assessment to better predict radiation exposure, dose, and toxicological effects to human organs on long duration space flights

NASA is very interested in improving its ability to monitor and forecast the radiation levels that pose a health risk to space-walking astronauts as they construct the International Space Station and astronauts that will participate in long-term and deep-space missions. Human exploratory missions to the moon and Mars within the next quarter century, will expose crews to transient radiation from solar particle events which include high-energy galactic cosmic rays and high-energy protons. Because the radiation levels in space are high and solar activity is presently unpredictable, adequate shielding is needed to minimize the deleterious health effects of exposure to radiation. Today, numerous models have been developed and used to predict radiation exposure. Such a model is the Space Environment Information Systems (SPENVIS) modeling program, developed by the Belgian Institute for Space Aeronautics. SPENVIS, which has been assessed to be an excellent tool in characterizing the radiation environment for microelectronics and investigating orbital debris, is being evaluated for its usefulness with determining the dose and dose-equivalent for human exposure. Thus far. the calculations for dose-depth relations under varying shielding conditions have been in agreement with calculations done using HZETRN and PDOSE, which are well-known and widely used models for characterizing the environments for human exploratory missions. There is disagreement when assessing the impact of secondary radiation particles since SPENVIS does a crude estimation of the secondary radiation particles when calculating LET versus Flux. SPENVIS was used to model dose-depth relations for the blood-forming organs. Radiation sickness and cancer are life-threatening consequences resulting from radiation exposure. In space. exposure to radiation generally includes all of the critical organs. Biological and toxicological impacts have been included for discussion along with alternative risk mitigation methods--shielding and anti-carcinogens. c 2001. Elsevier Science Ltd. All rights reserved.

STS-91 Shuttle Project↗

NASA NTRS2000

Radiation Transport Modeling and Assessment to Better Predict Radiation Exposure, Dose, and Toxicological Effects to Human Organs on Long Duration Space Flights

NASA's long-range plans include possible human exploratory missions to the moon and Mars within the next quarter century. Such missions beyond low Earth orbit will expose crews to transient radiation from solar particle events which include high-energy galactic cosmic rays and high-energy protons. Because the radiation levels in space are high and the missions long, adequate shielding is needed to minimize the deleterious health effects of exposure to radiation. The focus of this study is radiation exposure to the blood-forming organs of the NASA astronauts. NASA/JSC developed the Phantom Torso Experiment for Organ Dose Measurements which housed active and passive dosimeters that would monitor and record absorbed radiation levels at vital organ locations. This experiment was conducted during the STS-9 I mission in May '98 and provided the necessary space radiation data for correlation to results obtained from the current analytical models used to predict exposure to the blood-forming organs. Numerous models (i.e., BRYNTRN and HZETRN) have been developed and used to predict radiation exposure. However, new models are continually being developed and evaluated. The Space Environment Information Systems (SPENVIS) modeling program, developed by the Belgian Institute for Space Aeronomy, is to be used and evaluated as a part of the research activity. It is the intent of this research effort to compare the modeled data to the findings from the STS-9 I mission; assess the accuracy and efficiency of this model; and to determine its usefulness for predicting radiation exposure and developing better guidelines for shielding requirements for long duration manned missions.

Denkins, Pamela↗

DOE OSTI2023

Connecting suborganismal data to bioenergetic processes: killifish embryos exposed to a dioxin-like compound

A core challenge for ecological risk assessment is to integrate molecular responses into a chain of causality to organismal or population level outcomes. Bioenergetic theory may be a useful approach for integrating suborganismal responses to predict organismal level responses that influence population dynamics. In this work, we describe a novel application of Dynamic Energy Budget (DEB), theory in the context of a toxicity framework (Adverse Outcome Pathways, AOP) to make quantitative predictions of chemical exposures to individuals, starting from suborganismal data. We use early life stage exposure of Fundulus heteroclitus to dioxin-like chemicals (DLCs) and connect AOP Key Events (KEs) to DEB processes through “damage” that is produced at a rate proportional to the internal toxicant concentration. We use transcriptomic data of fish embryos exposed to DLCs to translate molecular indicators of damage into changes in DEB parameters (damage increases somatic maintenance costs) and use DEB models to predict sublethal and lethal effects of young fish. By changing a small subset of model parameters, we predict the evolved tolerance to DLCs in some wild F. heteroclitus populations, a data set not used in model parameterization. The differences in model parameters points to reduced sensitivity and altered damage repair dynamics as contributing to this evolved resistance. Our methodology has potential extrapolation to untested chemicals of ecological concern.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2024

Contact-dependent growth inhibition (CDI) systems deploy a large family of polymorphic ionophoric toxins for inter-bacterial competition

Contact-dependent growth inhibition (CDI) is a widespread form of inter-bacterial competition mediated by CdiA effector proteins. CdiA is presented on the inhibitor cell surface and delivers its toxic C-terminal region (CdiA-CT) into neighboring bacteria upon contact. Inhibitor cells also produce CdiI immunity proteins, which neutralize CdiA-CT toxins to prevent auto-inhibition. Here, we describe a diverse group of CDI ionophore toxins that dissipate the transmembrane potential in target bacteria. These CdiA-CT toxins are composed of two distinct domains based on AlphaFold2 modeling. The C-terminal ionophore domains are all predicted to form five-helix bundles capable of spanning the cell membrane. The N-terminal "entry" domains are variable in structure and appear to hijack different integral membrane proteins to promote toxin assembly into the lipid bilayer. The CDI ionophores deployed by E. coli isolates partition into six major groups based on their entry domain structures. Comparative sequence analyses led to the identification of receptor proteins for ionophore toxins from groups 1 & 3 (AcrB), group 2 (SecY) and groups 4 (YciB). Using forward genetic approaches, we identify novel receptors for the group 5 and 6 ionophores. Group 5 exploits homologous putrescine import proteins encoded by puuP and plaP, and group 6 toxins recognize di/tripeptide transporters encoded by paralogous dtpA and dtpB genes. Finally, we find that the ionophore domains exhibit significant intra-group sequence variation, particularly at positions that are predicted to interact with CdiI. Accordingly, the corresponding immunity proteins are also highly polymorphic, typically sharing only ~30% sequence identity with members of the same group. Competition experiments confirm that the immunity proteins are specific for their cognate ionophores and provide no protection against other toxins from the same group. The specificity of this protein interaction network provides a mechanism for self/nonself discrimination between E. coli isolates.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2000

Development and Application of Computational/In Vitro Toxicological Methods for Chemical Hazard Risk Reduction of New Materials for Advanced Weapon Systems

The development of quantitative structure-activity relationship (QSAR) is essential for reducing the chemical hazards of new weapon systems. The current collaboration between HEST (toxicology research and testing), MLPJ (computational chemistry) and PRS (computational chemistry, new propellant synthesis) is focusing R&D efforts on basic research goals that will rapidly transition to useful products for propellant development. Computational methods are being investigated that will assist in forecasting cellular toxicological end-points. Models developed from these chemical structure-toxicity relationships are useful for the prediction of the toxicological endpoints of new related compounds. Research is focusing on the evaluation tools to be used for the discovery of such relationships and the development of models of the mechanisms of action. Combinations of computational chemistry techniques, in vitro toxicity methods, and statistical correlations, will be employed to develop and explore potential predictive relationships; results for series of molecular systems that demonstrate the viability of this approach are reported. A number of hydrazine salts have been synthesized for evaluation. Computational chemistry methods are being used to elucidate the mechanism of action of these salts. Toxicity endpoints such as viability (LDH) and changes in enzyme activity (glutahoione peroxidase and catalase) are being experimentally measured as indicators of cellular damage. Extrapolation from computational/in vitro studies to human toxicity, is the ultimate goal. The product of this program will be a predictive tool to assist in the development of new, less toxic propellants.

Frazier, John M.↗

DOE OSTI2026

Transplatformer: translating toxicogenomic profiles between generations of platforms

Background Transcriptomic profiling technologies have advanced the analysis of biological and toxicological responses. However, substantial differences in probe design, dynamic range, gene coverage, and preprocessing pipelines across platforms introduce artifacts that limit cross-study integration and hinder the reuse of historical datasets. We aim to develop computational methods for accurate cross-platform translation to maximize the value of legacy resources. Results We present TransPlatformer a deep learning framework for translating gene expression profiles across heterogeneous toxicogenomics platforms. TransPlatformer employs a novel attention-based architecture to map high-dimensional fold-change vectors from legacy microarray technologies to current platforms. Models are trained and evaluated using DrugMatrix, spanning three technological generations. We investigate mixed-tissue, single-tissue, and cross-tissue training paradigms and benchmark performance against multilayer perceptron and matrix-completion baselines. In mixed-tissue training, TransPlatformer achieves a greater than 50% reduction in mean absolute error (0.043 vs. 0.09) and nearly doubles Pearson correlation ( ≈ 0.71 vs. 0.37) relative to baseline methods. Importantly, TransPlatformer preserves rare but biologically meaningful over- and under-expressed signals, with mean absolute error below 0.22. Single-tissue models yield further improvements for well-represented organs, such as a 10% reduction in liver mean absolute error, while underscoring the need for data augmentation strategies in low-sample tissues.ra Conclusions TransPlatformer provides an effective and scalable computational solution for cross-platform transcriptomic translation. By enabling biologically faithful harmonization of gene expression data, the proposed approach facilitates the reuse of legacy toxicogenomics datasets, enhances downstream biomarker discovery, and supports more reproducible predictive modeling in toxicology.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2009

Space Toxicology: Environmental Health Considerations during Spaceflight Operations and Potential Paths for Research

Space Toxicology is a specialized discipline for spaceflight, space habitation and occupation of celestial bodies including planets, moons and asteroids [1]. Astronaut explorers face unique challenges to their health while working and living with limited resources for rescue and medical care during space operation. At its core the practice of space toxicology to identify, assess and predict potential chemical contaminants and limit the astronaut s exposure to these environmental factors in order to protect crew health. Space toxicologists are also charged with setting safe exposure limits that will protect the astronaut against a multitude of chemical exposures, in a physiologically altered state. In order to maintain sustained occupation in space, toxicological risks are gauged and managed within the context of isolation, continual exposures, reuse of air and water, limited rescue options, and the necessary use of highly toxic compounds required for propulsion. As the space program move towards human presence and exploration other celestial bodies in situ toxicological risks, such as inhalation of unusual and/or reactive mineral dusts must also be analyzed and controlled. Placing humans for long-term presence in space creates several problems and challenges to the long-term health of the crew, such as bone-loss and immunological challenges and has spurred research into acute, chronic and episodic exposure of the pulmonary system to mineral dusts [2]. NASA has demonstrated that lunar soil contains several types of reactive dusts, including an extremely fine respirable component. In order to protect astronaut health, NASA is now investigating the toxicity of this unique class of dusts. Understanding how these reactive components behave "biochemically" in a moisture-rich pulmonary environment will aid in determining how toxic these particles are to humans. The data obtained from toxicological examination of lunar dusts will determine the human risk criteria for lunar dust exposure and produce a lunar health standard.

Khan-Mayberry, Noreen N.↗

DOE OSTI2021

Total Blood Mercury Predicts Methylmercury Exposure in Fish and Shellfish Consumers

Abstract Many studies evaluating methylmercury (MeHg) toxicity rely on whole blood total mercury (THg) measurements to estimate MeHg exposure. However, whole blood THg includes other forms of mercury (Hg), such as inorganic Hg, which have different exposure sources and toxicological effects than MeHg. Therefore, estimating the whole blood MeHg/THg ratio is critical to predicting MeHg exposure and, subsequently, efforts to establish an exposure–response relationship for use in risk assessment. A large, representative dataset (National Health and Nutrition Examination Survey (NHANES) 2011–2016) was used to determine the whole blood MeHg/THg ratio among (a) self-reported fish and shellfish consumers, ≥ 15 years of age (the “full adult” population ( N = 5268 training dataset; N = 2336 test dataset)) and (b) female fish and shellfish consumers, 15–44 years of age (the “women of reproductive age” population ( N = 1285 training dataset; N = 560 test dataset)). Unadjusted and adjusted linear and spline models with direct measurements for both THg and MeHg were evaluated. The mean whole blood MeHg/THg ratio was 0.75 (95% confidence interval (CI): 0.74, 0.75). This ratio was significantly higher among those with higher THg concentrations. All models exhibited excellent fit (adjusted R 2 from 0.957 to 0.982). Performance was slightly improved in spline versus linear models. For the full adult population and women of reproductive age, the unadjusted spline model predicted whole blood MeHg concentrations of 5.65 µg/L and 5.55 µg/L, respectively, when the THg concentration was 5.80 µg/L. These results suggest that whole blood THg is a good predictor of whole blood MeHg among fish and shellfish consumers.

Wells, Ellen M.↗

NASA NTRS1985

Statement of contamination problem

Space station contamination information is given. There are five major areas in which there are task requirements: (1) potential contaminants need to be identified, i.e., both the sources and types of contaminants, (2) the scope and magnitude of contaminant effects need to be determined, i.e., toxicological effects, microbacteriological effects and impurities, (3) mathematical models for predictive methods need to be developed. (4) state-of-the-art and advanced technologies for monitoring contaminants and for methods of decontamination need to be identified, and (5) automated monitoring and control systems need to be designed.

Hoffler, W.↗

NASA NTRS2014

Protecting Astronaut Health at First Entry into Vehicles Visiting the international Space Station: Insights from Whole-Module Offgas Testing

NASA has accumulated considerable experience in offgas testing of whole modules prior to their docking with the International Space Station (ISS). Since 1998, the Space Toxicology Office has performed offgas testing of the Lab module, both MPLM modules, US Airlock, Node 1, Node 2, Node 3, ATV1, HTV1, and three commercial vehicles. The goal of these tests is twofold: first, to protect the crew from adverse health effects of accumulated volatile pollutants when they first enter the module on orbit, and secondly, to determine the additional pollutant load that the ISS air revitalization systems must handle. In order to predict the amount of accumulated pollutants, the module is sealed for at least 1/5th the worst‐case time interval that could occur between the last clean air purge and final hatch closure on the ground and the crew's first entry on orbit. This time can range from a few days to a few months. Typically, triplicate samples are taken at pre‐planned times throughout the test. Samples are then analyzed by gas chromatography and mass spectrometry, and the rate of accumulation of pollutants is then extrapolated over time. The analytical values are indexed against 7‐day spacecraft maximum allowable concentrations (SMACs) to provide a prediction of the total toxicity value (T‐value) at the time of first entry. This T‐value and the toxicological effects of specific pollutants that contribute most to the overall toxicity are then used to guide first entry operations. Finally, results are compared to first entry samples collected on orbit to determine the predictive ability of the ground‐based offgas test.

Meyers, Valerie↗

DOE OSTI2023

Conotoxin Prediction: New Features to Increase Prediction Accuracy

Conotoxins are toxic, disulfide-bond-rich peptides from cone snail venom that target a wide range of receptors and ion channels with multiple pathophysiological effects. Conotoxins have extraordinary potential for medical therapeutics that include cancer, microbial infections, epilepsy, autoimmune diseases, neurological conditions, and cardiovascular disorders. Despite the potential for these compounds in novel therapeutic treatment development, the process of identifying and characterizing the toxicities of conotoxins is difficult, costly, and time-consuming. This challenge requires a series of diverse, complex, and labor-intensive biological, toxicological, and analytical techniques for effective characterization. While recent attempts, using machine learning based solely on primary amino acid sequences to predict biological toxins (e.g., conotoxins and animal venoms), have improved toxin identification, these methods are limited due to peptide conformational flexibility and the high frequency of cysteines present in toxin sequences. This results in an enumerable set of disulfide-bridged foldamers with different conformations of the same primary amino acid sequence that affect function and toxicity levels. Consequently, a given peptide may be toxic when its cysteine residues form a particular disulfide-bond pattern, while alternative bonding patterns (isoforms) or its reduced form (free cysteines with no disulfide bridges) may have little or no toxicological effects. Similarly, the same disulfide-bond pattern may be possible for other peptide sequences and result in different conformations that all exhibit varying toxicities to the same receptor or to different receptors. We present here new features, when combined with primary sequence features to train machine learning algorithms to predict conotoxins, that significantly increase prediction accuracy.

collisional cross section↗

DOE OSTI2023

Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances

The market for illicit drugs has been reshaped by the emergence of more than 1100 new psychoactive substances (NPS) over the past decade, posing a major challenge to the forensic and toxicological laboratories tasked with detecting and identifying them. Tandem mass spectrometry (MS/MS) is the primary method used to screen for NPS within seized materials or biological samples. The most contemporary workflows necessitate labor-intensive and expensive MS/MS reference standards, which may not be available for recently emerged NPS on the illicit market. Here, we present NPS-MS, a deep learning method capable of accurately predicting the MS/MS spectra of known and hypothesized NPS from their chemical structures alone. NPS-MS is trained by transfer learning from a generic MS/MS prediction model on a large data set of MS/MS spectra. We show that this approach enables a more accurate identification of NPS from experimentally acquired MS/MS spectra than any existing method. We demonstrate the application of NPS-MS to identify a novel derivative of phencyclidine (PCP) within an unknown powder seized in Denmark without the use of any reference standards. We anticipate that NPS-MS will allow forensic laboratories to identify more rapidly both known and newly emerging NPS. NPS-MS is available as a web server at https://nps-ms.ca/, which provides MS/MS spectra prediction capabilities for given NPS compounds. Additionally, it offers MS/MS spectra identification against a vast database comprising approximately 8.7 million predicted NPS compounds from DarkNPS and 24.5 million predicted ESI-QToF-MS/MS spectra for these compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

National User Resource for Biological Accelerator Mass Spectrometry Annual Report

59 BASIC BIOLOGICAL SCIENCES↗

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 ↗