Mechanical Circulatory Support in Pediatric Myocarditis: Support Strategies and Outcomes in a Nationally Representative Cohort.

Background: Myocarditis is a common cause of pediatric heart failure which may require mechanical circulatory support (MCS). The purpose of this study is to describe MCS strategies used in a nationwide cohort of pediatric patients with myocarditis, identify trends over time, and compare outcomes between MCS strategies. Methods: This study utilized the Kids' Inpatient Database (KID), a national sample of administrative discharge data. KID admissions from 2003-2016 were queried using ICD-9/10 codes to identify those with a diagnosis of myocarditis. MCS outcomes were compared using logistic regression. Results: Of 5,661 admissions for myocarditis, MCS was used in 424 (7.5%), comprised of extracorporeal membrane oxygenation (ECMO) in 312 (73.6%), including 32 (10.2%) instances of extracorporeal cardiopulmonary resuscitation (ECPR), temporary ventricular assist devices (tVAD) in 28 (6.6%), durable VAD (dVAD) in 42 (9.9%) and combination MCS in 42 (9.9%). MCS use increased over time (p=0.031), but MCS strategies did not significantly change. Mortality was high in the MCS group (28.3%). There was no difference in odds of death in the VAD only or combination MCS group compared to the non-ECPR ECMO group (p=0.07 and p=0.65, respectively). Conclusion: MCS is used in 1 in 13 pediatric myocarditis cases, and MCS use is increasing over time with ECMO remaining the most frequently used modality. Mortality remains high in patients that receive MCS but does not differ between those receiving VAD or combination MCS as compared to non-ECPR ECMO on unadjusted analysis. Further prospective analysis is required to evaluate the relative effectiveness of MCS modalities in this disease.

Combining In Vitro and In Silico New Approach Methods to Investigate Type 3 Iodothyronine Deiodinase Chemical Inhibition Across Species.

New approach methodologies (NAMs) are being developed to reduce and replace vertebrate animal testing in support of ecotoxicology and risk assessment. The US Environmental Protection Agency's Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) bioinformatic tool was used to evaluate amino acid sequence conservation of the type 3 iodothyronine deiodinase (DIO3) enzyme across species to demonstrate NAM applications for understanding effects of chemical interactions with a specific protein target. Existing literature was used to identify critical amino acids for thyroid hormone binding and interaction with a reducing cofactor. The SeqAPASS tool identifies whether known critical amino acids involved in ligand binding are exact, partial, or not matches across species compared with a template species based on molecular weight and side chain classification. This evaluation guided the design of variant proteins representing critical amino acid substitutions found in various species. Site-directed mutagenesis of the wild-type (WT) human DIO3 gene sequence was used to create six variant proteins expressed in cell culture, which were then tested in vitro for chemical inhibition. Significant differences in in vitro median inhibitory concentration results were observed among variants for potential competitive inhibitors. A molecular model representing the WT human DIO3 was constructed using Molecular Operating Environment (MOE) software and mutated in silico to create the six variants. The MOE Site Finder tool identified the proposed catalytic and cofactor sites and potential alternative binding sites. Virtual docking did not provide affinity scores with sufficient resolution to rank the potency of the chemical inhibitors. Chemical characteristics, function and location of substituted amino acids, and complexities of the protein target are important considerations in developing NAMs to evaluate chemical susceptibility across species. Environ Toxicol Chem 2023;42:1032-1048. © 2023 University of Wisconsin-Madison. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

A Narrative Review of Multiinstitutional Data Registries of Pediatric Congenital Heart Disease in Pediatric Cardiac Anesthesia and Critical Care Medicine.

Congenital heart disease (CHD) is one of the most common birth anomalies. While the care of children with CHD has improved over recent decades, children with CHD who undergo general anesthesia remain at increased risk for morbidity and mortality. Electronic health record systems have enabled institutions to combine data on the management and outcomes of children with CHD in multicenter registries. The application of descriptive analytics methods to these data can improve clinicians' understanding and care of children with CHD. This narrative review covers efforts to leverage multicenter data registries relevant to pediatric cardiac anesthesia and critical care to improve the care of children with CHD.

Exposure Prioritization (Ex Priori): A Screening-Level High-Throughput Chemical Prioritization Tool.

To estimate potential chemical risk, tools are needed to prioritize potential exposures for chemicals with minimal data. Consumer product exposures are a key pathway, and variability in consumer use patterns is an important factor. We designed Ex Priori, a flexible dashboard-type screening-level exposure model, to rapidly visualize exposure rankings from consumer product use. Ex Priori is Excel-based. Currently, it is parameterized for seven routes of exposure for 1108 chemicals present in 228 consumer product types. It includes toxicokinetics considerations to estimate body burden. It includes a simple framework for rapid modeling of broad changes in consumer use patterns by product category. Ex Priori rapidly models changes in consumer user patterns during the COVID-19 pandemic and instantly shows resulting changes in chemical exposure rankings by body burden. Sensitivity analysis indicates that the model is sensitive to the air emissions rate of chemicals from products. Ex Priori's simple dashboard facilitates dynamic exploration of the effects of varying consumer product use patterns on prioritization of chemicals based on potential exposures. Ex Priori can be a useful modeling and visualization tool to both novice and experienced exposure modelers and complement more computationally intensive population-based exposure models.

Deadenylase-dependent mRNA decay of GDF15 and FGF21 orchestrates food intake and energy expenditure.

Hepatokines, secretory proteins from the liver, mediate inter-organ communication to maintain a metabolic balance between food intake and energy expenditure. However, molecular mechanisms by which hepatokine levels are rapidly adjusted following stimuli are largely unknown. Here, we unravel how CNOT6L deadenylase switches off hepatokine expression after responding to stimuli (e.g., exercise and food) to orchestrate energy intake and expenditure. Mechanistically, CNOT6L inhibition stabilizes hepatic Gdf15 and Fgf21 mRNAs, increasing corresponding serum protein levels. The resulting upregulation of GDF15 stimulates the hindbrain to suppress appetite, while increased FGF21 affects the liver and adipose tissues to induce energy expenditure and lipid consumption. Despite the potential of hepatokines to treat metabolic disorders, their administration therapies have been challenging. Using small-molecule screening, we identified a CNOT6L inhibitor enhancing GDF15 and FGF21 hepatokine levels, which dramatically improves diet-induced metabolic syndrome. Our discovery, therefore, lays the foundation for an unprecedented strategy to treat metabolic syndrome.

Early prediction of clinical deterioration using data-driven machine-learning modeling of electronic health records.

OBJECTIVES: To develop and evaluate a high-dimensional, data-driven model to identify patients at high risk of clinical deterioration from routinely collected electronic health record (EHR) data. MATERIALS AND METHODS: In this single-center, retrospective cohort study, 488 patients with single-ventricle and shunt-dependent congenital heart disease <6 months old were admitted to the cardiac intensive care unit before stage 2 palliation between 2014 and 2019. Using machine-learning techniques, we developed the Intensive care Warning Index (I-WIN), which systematically assessed 1028 regularly collected EHR variables (vital signs, medications, laboratory tests, and diagnoses) to identify patients in the cardiac intensive care unit at elevated risk of clinical deterioration. An ensemble of 5 extreme gradient boosting models was developed and validated on 203 cases (130 emergent endotracheal intubations, 34 cardiac arrests requiring cardiopulmonary resuscitation, 10 extracorporeal membrane oxygenation cannulations, and 29 cardiac arrests requiring cardiopulmonary resuscitation onto extracorporeal membrane oxygenation) and 378 control periods from 446 patients. RESULTS: At 4 hours before deterioration, the model achieved an area under the receiver operating characteristic curve of 0.92 (95% confidence interval, 0.84-0.98), 0.881 sensitivity, 0.776 positive predictive value, 0.862 specificity, and 0.571 Brier skill score. Performance remained high at 8 hours before deterioration with 0.815 (0.688-0.921) area under the receiver operating characteristic curve. CONCLUSIONS: I-WIN accurately predicted deterioration events in critically-ill infants with high-risk congenital heart disease up to 8 hours before deterioration, potentially allowing clinicians to target interventions. We propose a paradigm shift from conventional expert consensus-based selection of risk factors to a data-driven, machine-learning methodology for risk prediction. With the increased availability of data capture in EHRs, I-WIN can be extended to broader applications in data-rich environments in critical care.

Use of a Risk Analytic Algorithm to Inform Weaning From Vasoactive Medication in Patients Following Pediatric Cardiac Surgery.

OBJECTIVES: Advanced clinical decision support tools, such as real-time risk analytic algorithms, show promise in assisting clinicians in making more efficient and precise decisions. These algorithms, which calculate the likelihood of a given underlying physiology or future event, have predominantly been used to identify the risk of impending clinical decompensation. There may be broader clinical applications of these models. Using the inadequate delivery of oxygen index, a U.S. Food and Drug Administration-approved risk analytic algorithm predicting the likelihood of low cardiac output state, the primary objective was to evaluate the association of inadequate delivery of oxygen index with success or failure of weaning vasoactive support in postoperative cardiac surgery patients. DESIGN: Multicenter retrospective cohort study. SETTING: Three pediatric cardiac ICUs at tertiary academic children's hospitals. PATIENTS: Infants and children greater than 2 kg and less than 12 years following cardiac surgery, who required vasoactive infusions for greater than 6 hours in the postoperative period. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Postoperative patients were identified who successfully weaned off initial vasoactive infusions (n = 2,645) versus those who failed vasoactive wean (required reinitiation of vasoactive, required mechanical circulatory support, renal replacement therapy, suffered cardiac arrest, or died) (n = 516). Inadequate delivery of oxygen index for final 6 hours of vasoactive wean was captured. Inadequate delivery of oxygen index was significantly elevated in patients with failed versus successful weans (inadequate delivery of oxygen index 11.6 [sd 19.0] vs 6.4 [sd 12.6]; p < 0.001). Mean 6-hour inadequate delivery of oxygen index greater than 50 had strongest association with failed vasoactive wean (adjusted odds ratio, 4.0; 95% CI, 2.5-6.6). In patients who failed wean, reinitiation of vasoactive support was associated with concomitant fall in inadequate delivery of oxygen index (11.1 [sd 18] vs 8.9 [sd 16]; p = 0.007). CONCLUSIONS: During the de-escalation phase of postoperative cardiac ICU management, elevation of the real-time risk analytic model, inadequate delivery of oxygen index, was associated with failure to wean off vasoactive infusions. Future studies should prospectively evaluate utility of risk analytic models as clinical decision support tools in de-escalation practices in critically ill patients.

A Systematic Review of Published Physiologically-based Kinetic Models and an Assessment of their Chemical Space Coverage.

Across multiple sectors, including food, cosmetics and pharmaceutical industries, there is a need to predict the potential effects of xenobiotics. These effects are determined by the intrinsic ability of the substance, or its derivatives, to interact with the biological system, and its concentration-time profile at the target site. Physiologically-based kinetic (PBK) models can predict organ-level concentration-time profiles, however, the models are time and resource intensive to generate de novo. Read-across is an approach used to reduce or replace animal testing, wherein information from a data-rich chemical is used to make predictions for a data-poor chemical. The recent increase in published PBK models presents the opportunity to use a read-across approach for PBK modelling, that is, to use PBK model information from one chemical to inform the development or evaluation of a PBK model for a similar chemical. Essential to this process, is identifying the chemicals for which a PBK model already exists. Herein, the results of a systematic review of existing PBK models, compliant with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) format, are presented. Model information, including species, sex, life-stage, route of administration, software platform used and the availability of model equations, was captured for 7541 PBK models. Chemical information (identifiers and physico-chemical properties) has also been recorded for 1150 unique chemicals associated with these models. This PBK model data set has been made readily accessible, as a Microsoft Excel® spreadsheet, providing a valuable resource for those developing, using or evaluating PBK models in industry, academia and the regulatory sectors.

Discovery of GSK251: A Highly Potent, Highly Selective, Orally Bioavailable Inhibitor of PI3Kδ with a Novel Binding Mode.

Optimization of a previously reported lead series of PI3Kδ inhibitors with a novel binding mode led to the identification of a clinical candidate compound 31 (GSK251). Removal of an embedded Ames-positive heteroaromatic amine by reversing a sulfonamide followed by locating an interaction with Trp760 led to a highly selective compound 9. Further optimization to avoid glutathione trapping, to enhance potency and selectivity, and to optimize an oral pharmacokinetic profile led to the discovery of compound 31 (GSK215) that had a low predicted daily dose (45 mg, b.i.d) and a rat toxicity profile suitable for further development.

High-throughput PBTK models for in vitro to in vivo extrapolation.

INTRODUCTION: Toxicity data are unavailable for many thousands of chemicals in commerce and the environment. Therefore, risk assessors need to rapidly screen these chemicals for potential risk to public health. High-throughput screening (HTS) for in vitro bioactivity, when used with high-throughput toxicokinetic (HTTK) data and models, allows characterization of these thousands of chemicals. AREAS COVERED: This review covers generic physiologically based toxicokinetic (PBTK) models and high-throughput PBTK modeling for in vitro-in vivo extrapolation (IVIVE) of HTS data. We focus on 'httk', a public, open-source set of computational modeling tools and in vitro toxicokinetic (TK) data. EXPERT OPINION: HTTK benefits chemical risk assessors with its ability to support rapid chemical screening/prioritization, perform IVIVE, and provide provisional TK modeling for large numbers of chemicals using only limited chemical-specific data. Although generic TK model design can increase prediction uncertainty, these models provide offsetting benefits by increasing model implementation accuracy. Also, public distribution of the models and data enhances reproducibility. For the httk package, the modular and open-source design can enable the tool to be used and continuously improved by a broad user community in support of the critical need for high-throughput chemical prioritization and rapid dose estimation to facilitate rapid hazard assessments.

Lumbar Discectomy and Reoperation Among Workers' Compensation Cases in Florida and New York: Are Treatment Trends Similar to Other Payer Types?

OBJECTIVE: The aim of this study was to better understand current treatment trends and revision rates for lumbar disc herniation (LDH) in the workers' compensation (WC) population compared with other payer types. METHODS: This was a retrospective analysis of outpatient claims data from Florida and New York during 2014 to 2016. RESULTS: WC patients were less likely to undergo discectomy in Florida (15% vs 19%; P < 0.001) and New York (10% vs 15%; P < 0.001). The odds of WC patients undergoing revision discectomy were 1.5 times greater than patients covered by private payers or all other non-WC payers (P = 0.002). CONCLUSIONS: WC patients undergo discectomy significantly less often than non-WC counterparts, which may be related to a higher risk of reoperation. New evidence-based treatments, such as annular repair, may be critical to advancing care in this unique population.

Acute coronary artery obstruction following surgical repair of congenital heart disease.

OBJECTIVES: Acute coronary artery obstruction is a rare complication of congenital heart disease surgery but imposes a high burden of morbidity and mortality. Previous case series have described episodes in specific congenital heart lesions or surgical repairs but have not examined the complication in all-comers to congenital heart surgery. We hypothesize that shorter time from a clinically recognized postoperative sentinel event suggestive of coronary ischemia to diagnosis of coronary obstruction is associated with improved clinical outcomes. METHODS: This was a single-center, retrospective review of patients diagnosed with acute coronary artery obstruction by angiography following surgical repair of congenital heart disease between January 2000 and June 2016. RESULTS: In total, 34 patients were identified. The most common procedures associated with coronary artery obstruction were the Norwood procedure, arterial switch operation, and aortic valve repair/replacement. In total, 79% required mechanical circulatory support, 41% died, and 27% were listed for heart transplant. Patients who died or were listed for heart transplant had longer median sentinel-event-to-cardiac-catheterization time (28 [6-168] hours vs 10 [3-56] hours, P = .001), and longer median sentinel-event-to-intervention time (32 [11-350] hours vs 13 [5-59] hours, P = .003). Patients with hypoplastic left heart syndrome were at greater risk of death or transplant listing (odds ratio, 9.23, P = .03). CONCLUSIONS: Time from clinically relevant postoperative sentinel event to diagnosis of coronary artery obstruction by angiography was associated with transplant-listing-free survival. Clinicians should maintain a high index of suspicion for coronary obstruction and consider early catheterization and coronary angiography for patients in whom post-operative coronary compromise is suspected.

A Narrative Review of Analytics in Pediatric Cardiac Anesthesia and Critical Care Medicine.

Congenital heart disease (CHD) is one of the most common birth anomalies, and the care of children with CHD has improved over the past 4 decades. However, children with CHD who undergo general anesthesia remain at increased risk for morbidity and mortality. The proliferation of electronic health record systems and sophisticated patient monitors affords the opportunity to capture and analyze large amounts of CHD patient data, and the application of novel, effective analytics methods to these data can enable clinicians to enhance their care of pediatric CHD patients. This narrative review covers recent efforts to leverage analytics in pediatric cardiac anesthesia and critical care to improve the care of children with CHD.

An Unexpected Fish Bite.

We report a case of a 22-month-old boy who suffered a piranha bite, amputating his fourth distal phalanx. His finger was irrigated and closed with a flap under sedation, but we could find little evidence during a brief literature search of what prophylactic antibiotics, if any, to provide. Because reports of infections from piranha bites are lacking, we examined studies evaluating oral flora from fish, as well as flora cultured from aquariums. In conclusion, if infection is present or prophylaxis is strongly desired, a broad spectrum oral antibiotic that covers Gram-positive and Gram-negative bacteria including Pseudomonas and Aeromonas species, such as ciprofloxacin, would be recommended; however, there is little evidence to suggest that prophylactic antibiotics provide benefit over appropriate wound management.

Developing a Physiologically-Based Pharmacokinetic Model Knowledgebase in Support of Provisional Model Construction.

Developing physiologically-based pharmacokinetic (PBPK) models for chemicals can be resource-intensive, as neither chemical-specific parameters nor in vivo pharmacokinetic data are easily available for model construction. Previously developed, well-parameterized, and thoroughly-vetted models can be a great resource for the construction of models pertaining to new chemicals. A PBPK knowledgebase was compiled and developed from existing PBPK-related articles and used to develop new models. From 2,039 PBPK-related articles published between 1977 and 2013, 307 unique chemicals were identified for use as the basis of our knowledgebase. Keywords related to species, gender, developmental stages, and organs were analyzed from the articles within the PBPK knowledgebase. A correlation matrix of the 307 chemicals in the PBPK knowledgebase was calculated based on pharmacokinetic-relevant molecular descriptors. Chemicals in the PBPK knowledgebase were ranked based on their correlation toward ethylbenzene and gefitinib. Next, multiple chemicals were selected to represent exact matches, close analogues, or non-analogues of the target case study chemicals. Parameters, equations, or experimental data relevant to existing models for these chemicals and their analogues were used to construct new models, and model predictions were compared to observed values. This compiled knowledgebase provides a chemical structure-based approach for identifying PBPK models relevant to other chemical entities. Using suitable correlation metrics, we demonstrated that models of chemical analogues in the PBPK knowledgebase can guide the construction of PBPK models for other chemicals.

Computational Exposure Science: An Emerging Discipline to Support 21st-Century Risk Assessment.

BACKGROUND: Computational exposure science represents a frontier of environmental science that is emerging and quickly evolving. OBJECTIVES: In this commentary, we define this burgeoning discipline, describe a framework for implementation, and review some key ongoing research elements that are advancing the science with respect to exposure to chemicals in consumer products. DISCUSSION: The fundamental elements of computational exposure science include the development of reliable, computationally efficient predictive exposure models; the identification, acquisition, and application of data to support and evaluate these models; and generation of improved methods for extrapolating across chemicals. We describe our efforts in each of these areas and provide examples that demonstrate both progress and potential. CONCLUSIONS: Computational exposure science, linked with comparable efforts in toxicology, is ushering in a new era of risk assessment that greatly expands our ability to evaluate chemical safety and sustainability and to protect public health. CITATION: Egeghy PP, Sheldon LS, Isaacs KK, Özkaynak H, Goldsmith M-R, Wambaugh JF, Judson RS, Buckley TJ. 2016. Computational exposure science: an emerging discipline to support 21st-century risk assessment. Environ Health Perspect 124:697-702; http://dx.doi.org/10.1289/ehp.1509748.

A Workflow to Investigate Exposure and Pharmacokinetic Influences on High-Throughput in Vitro Chemical Screening Based on Adverse Outcome Pathways.

BACKGROUND: Adverse outcome pathways (AOPs) link adverse effects in individuals or populations to a molecular initiating event (MIE) that can be quantified using in vitro methods. Practical application of AOPs in chemical-specific risk assessment requires incorporation of knowledge on exposure, along with absorption, distribution, metabolism, and excretion (ADME) properties of chemicals. OBJECTIVES: We developed a conceptual workflow to examine exposure and ADME properties in relation to an MIE. The utility of this workflow was evaluated using a previously established AOP, acetylcholinesterase (AChE) inhibition. METHODS: Thirty chemicals found to inhibit human AChE in the ToxCast™ assay were examined with respect to their exposure, absorption potential, and ability to cross the blood-brain barrier (BBB). Structures of active chemicals were compared against structures of 1,029 inactive chemicals to detect possible parent compounds that might have active metabolites. RESULTS: Application of the workflow screened 10 "low-priority" chemicals of 30 active chemicals. Fifty-two of the 1,029 inactive chemicals exhibited a similarity threshold of ≥ 75% with their nearest active neighbors. Of these 52 compounds, 30 were excluded due to poor absorption or distribution. The remaining 22 compounds may inhibit AChE in vivo either directly or as a result of metabolic activation. CONCLUSIONS: The incorporation of exposure and ADME properties into the conceptual workflow eliminated 10 "low-priority" chemicals that may otherwise have undergone additional, resource-consuming analyses. Our workflow also increased confidence in interpretation of in vitro results by identifying possible "false negatives." CITATION: Phillips MB, Leonard JA, Grulke CM, Chang DT, Edwards SW, Brooks R, Goldsmith MR, El-Masri H, Tan YM. 2016. A workflow to investigate exposure and pharmacokinetic influences on high-throughput in vitro chemical screening based on adverse outcome pathways. Environ Health Perspect 124:53-60; http://dx.doi.org/10.1289/ehp.1409450.

Characterization and prediction of chemical functions and weight fractions in consumer products.

Assessing exposures from the thousands of chemicals in commerce requires quantitative information on the chemical constituents of consumer products. Unfortunately, gaps in available composition data prevent assessment of exposure to chemicals in many products. Here we propose filling these gaps via consideration of chemical functional role. We obtained function information for thousands of chemicals from public sources and used a clustering algorithm to assign chemicals into 35 harmonized function categories (e.g., plasticizers, antimicrobials, solvents). We combined these functions with weight fraction data for 4115 personal care products (PCPs) to characterize the composition of 66 different product categories (e.g., shampoos). We analyzed the combined weight fraction/function dataset using machine learning techniques to develop quantitative structure property relationship (QSPR) classifier models for 22 functions and for weight fraction, based on chemical-specific descriptors (including chemical properties). We applied these classifier models to a library of 10196 data-poor chemicals. Our predictions of chemical function and composition will inform exposure-based screening of chemicals in PCPs for combination with hazard data in risk-based evaluation frameworks. As new information becomes available, this approach can be applied to other classes of products and the chemicals they contain in order to provide essential consumer product data for use in exposure-based chemical prioritization.

Exploring consumer exposure pathways and patterns of use for chemicals in the environment.

Humans are exposed to thousands of chemicals in the workplace, home, and via air, water, food, and soil. A major challenge in estimating chemical exposures is to understand which chemicals are present in these media and microenvironments. Here we describe the Chemical/Product Categories Database (CPCat), a new, publically available (http://actor.epa.gov/cpcat) database of information on chemicals mapped to "use categories" describing the usage or function of the chemical. CPCat was created by combining multiple and diverse sources of data on consumer- and industrial-process based chemical uses from regulatory agencies, manufacturers, and retailers in various countries. The database uses a controlled vocabulary of 833 terms and a novel nomenclature to capture and streamline descriptors of chemical use for 43,596 chemicals from the various sources. Examples of potential applications of CPCat are provided, including identifying chemicals to which children may be exposed and to support prioritization of chemicals for toxicity screening. CPCat is expected to be a valuable resource for regulators, risk assessors, and exposure scientists to identify potential sources of human exposures and exposure pathways, particularly for use in high-throughput chemical exposure assessment.

SHEDS-HT: an integrated probabilistic exposure model for prioritizing exposures to chemicals with near-field and dietary sources.

United States Environmental Protection Agency (USEPA) researchers are developing a strategy for high-throughput (HT) exposure-based prioritization of chemicals under the ExpoCast program. These novel modeling approaches for evaluating chemicals based on their potential for biologically relevant human exposures will inform toxicity testing and prioritization for chemical risk assessment. Based on probabilistic methods and algorithms developed for The Stochastic Human Exposure and Dose Simulation Model for Multimedia, Multipathway Chemicals (SHEDS-MM), a new mechanistic modeling approach has been developed to accommodate high-throughput (HT) assessment of exposure potential. In this SHEDS-HT model, the residential and dietary modules of SHEDS-MM have been operationally modified to reduce the user burden, input data demands, and run times of the higher-tier model, while maintaining critical features and inputs that influence exposure. The model has been implemented in R; the modeling framework links chemicals to consumer product categories or food groups (and thus exposure scenarios) to predict HT exposures and intake doses. Initially, SHEDS-HT has been applied to 2507 organic chemicals associated with consumer products and agricultural pesticides. These evaluations employ data from recent USEPA efforts to characterize usage (prevalence, frequency, and magnitude), chemical composition, and exposure scenarios for a wide range of consumer products. In modeling indirect exposures from near-field sources, SHEDS-HT employs a fugacity-based module to estimate concentrations in indoor environmental media. The concentration estimates, along with relevant exposure factors and human activity data, are then used by the model to rapidly generate probabilistic population distributions of near-field indirect exposures via dermal, nondietary ingestion, and inhalation pathways. Pathway-specific estimates of near-field direct exposures from consumer products are also modeled. Population dietary exposures for a variety of chemicals found in foods are combined with the corresponding chemical-specific near-field exposure predictions to produce aggregate population exposure estimates. The estimated intake dose rates (mg/kg/day) for the 2507 chemical case-study spanned 13 orders of magnitude. SHEDS-HT successfully reproduced the pathway-specific exposure results of the higher-tier SHEDS-MM for a case-study pesticide and produced median intake doses significantly correlated (p<0.0001, R2=0.39) with medians inferred using biomonitoring data for 39 chemicals from the National Health and Nutrition Examination Survey (NHANES). Based on the favorable performance of SHEDS-HT with respect to these initial evaluations, we believe this new tool will be useful for HT prediction of chemical exposure potential.