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.

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BackgroundCervical Cancer (CC) is the number one cancer among women in sub-Saharan Africa. Although CC is preventable, most women in developing countries do not have access to screening.MethodsThis cross-sectional study was conducted to determine the prevalence and risk factors for cervical lesions using visual inspection with acetic acid (VIA) among 112 HIV positive and 161 negative women aged 18–69 years.ResultsThe presence of cervical lesions was greater among HIV positive (22.9%) than HIV negative women (5.7%; p < 0.0001). In logistic models, the risk of cervical lesions among HIV positive women was 5.24 times higher when adjusted by age (OR 5.24, CI 2.31–11.88), and 4.06 times higher in a full model (OR 4.06, CI 1.61–10.25), than among HIV negative women. In the age-adjusted model women who had ≥2 lifetime sexual partners were 3 times more likely (OR 3.00, CI 1.02–8.85) to have cervical lesions compared to women with one lifetime partner and the odds of cervical lesions among women with a history of STIs were 2.16 greater (OR 2.16, CI 1.04–4.50) than among women with no previous STI. In the fully adjusted model women who had a previous cervical exam were 2.5 times more likely (OR 2.53, CI 1.06–6.05) to have cervical lesions than women who had not.ConclusionsThe high prevalence of HIV infection and the strong association between HIV and cervical lesions highlight the need for substantial scale-up of cervical screening to decrease the rate of CC in Swaziland.

HighlightsFunctional role of thousands of chemicals is analyzed.These data are combined with chemical weight fractions in personal care products.Empirical compositions for products are developed based on function.Classifier models for function and weight fraction are built.These methods can fill data gaps for consumer product exposure models.

BackgroundAdverse 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.ObjectivesWe 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.MethodsThirty 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.ResultsApplication 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.ConclusionsThe 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.”CitationPhillips 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

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.

HIV/AIDS remains one of the leading causes of death among children under 5 years in Swaziland. Although it has been shown that early initiation of infants and children diagnosed with HIV on antiretroviral therapy (ART) significantly reduces mortality, many children do not initiate ART until at the later stages of disease. This study was designed to collect qualitative data from mothers and caregivers of HIV-positive children to identify the barriers to ART initiation. Focus group discussion (FGD) sessions were conducted in siSwati between July and September 2014 among caregivers of children 2–18 months in Swaziland who did or did-not initiate ART between January 2011 and December 2012 after HIV DNA PCR-positive diagnosis of the infants. Denial, guilt, lack of knowledge, TB/HIV co-infection, HIV-related stigma, lack of money, and distance to clinics were reported by the participants as barriers to ART initiation. The findings further revealed that non-initiation on ART was not linked to a negative perception of the treatment. Findings suggest a need to improve sensitivity among health care workers as well as education and counselling services that will facilitate the ART initiation process.

Background Construction workers are among the segments of the US population that were hit hardest by the opioid prescription and overdose deaths in the past decades. Factors that underlie opioid use in construction workers have been compartmentalized and isolated in existing studies of opioid use and opioid overdose, but they ignore the overall context of their use. This study examines prescription opioid use and its association with a variety of occupational and nonoccupational factors in construction workers in the United States. Methods Data from the 2011‐2017 Medical Expenditure Panel Survey (n = 7994) were analyzed. The prevalence of prescribed opioid use and the association with occupational and nonoccupational characteristics among construction workers were examined in four multiple logistic regression models. Results The odds of prescription opioid use for workers with occupational injuries was more than triple that of their noninjured counterparts when demographics and occupational factors were controlled (odds ratio = 3.38, 95% confidence interval: 2.38‐4.81). Odds of prescription opioid use were higher in older construction workers, workers who were white, non‐Hispanic, working part‐time, and in poorer health, while Hispanic workers and those without health insurance were much less likely to report prescription opioid use. Conclusions Prescription opioid use among construction workers encompasses both occupational and nonoccupational factors. As an insight into opioid use among construction workers becomes clearer, effectively responding to the opioid crisis remains a challenge.