Environmental exposures are ubiquitous and play a significant, and sometimes understated, role in public health as they can lead to the development of various chronic and infectious diseases. In an ideal world, there would be sufficient experimental data to determine the health effects of exposure to priority environmental contaminants. However, this is not the case, as emerging chemicals are continuously added to this list, furthering the data gaps. Recently, simulation science has evolved and can provide appropriate solutions using a multitude of computational methods and tools. In its quest to protect communities across the country from environmental health threats, ATSDR employs a variety of simulation science tools such as Physiologically Based Pharmacokinetic (PBPK) modeling, Quantitative Structure-Activity Relationship (QSAR) modeling, and benchmark dose (BMD) modeling, among others. ATSDR's use of such tools has enabled the agency to evaluate exposures in a timely, efficient, and effective manner. ATSDR's work in simulation science has also had a notable impact beyond the agency, as evidenced by external researchers' widespread appraisal and adaptation of the agency's methodology. ATSDR continues to advance simulation science tools and their applications by collaborating with researchers within and outside the agency, including other federal/state agencies, NGOs, the private sector, and academia.

Substances of unknown or variable composition, complex reaction products, and biological materials (UVCBs) are commonly found in the environment. However, assessing their human toxicological risk is challenging due to their variable composition and many constituents. Metal naphthenate salts are one such category of UVCBs that are the reaction products of naphthenic acids with metals to form complex mixtures. Metal naphthenates are often found or used in household and industrial materials with potential for human exposure, but very few of these materials have been evaluated for causing human health hazards. Herein, we evaluate metal naphthenates using predictions derived from read-across and quantitative structure-activity/property relationship (QSAR/QSPR) models. Accordingly, we first built a computational chemistry library by enumerating the structures of naphthenic acids and derived 11,850 QSAR-acceptable structures; then, we used open and commercial in silico tools on these structures to predict a set of physicochemical properties and toxicity endpoints. We then compared the QSAR/QSPR predictions with available experimental data on naphthenic acids to provide a more complete picture of the contributions of the components to the toxicity profiles of metal naphthenate mixtures. The available systematic acute oral toxicity values (LD(50)) and QSAR LD(50) predictions of all the naphthenic acid components indicated low concern for toxicity. The point of departure predictions for chronic repeated dose toxicity for the naphthenic acid components using QSAR models developed from studies on rats ranged from 25 to 50 mg/kg/day. These values are in good agreement with findings from studies on copper and zinc naphthenates, which had no observed adverse effect levels of 30 and 118 mg/kg/day, respectively. Hence, this study demonstrates how published in silico approaches can be used to identify the potential components of metal naphthenates for further testing, inform groupings of UVCBs such as naphthenates, as well as fill the data gaps using read-across and QSAR models to inform risk assessment.

For short-term chemical inhalation exposures to hazardous chemicals, the incidence of a health effect in biological testing usually conforms to a general linear model with a probit link function dependent on inhalant concentration C and the duration of exposure t. The National Academy's Acute Exposure Guideline Levels (AEGLs) Committee relies on these models when establishing AEGLs. Threshold concentrations at AEGL durations are established by the toxic load equation C(n) x t=constant, which toxic load exponent n (TLE or n-value) directly follows from the bivariate probit model. When multiple probit datasets are available, the AEGL Committee routinely pools studies' incidence data. Such meta-analytical models are valid only when the pooled data are homogeneous, with similar sensitivities and equivalent responses to exposure concentrations and durations. In the present study, the homogeneity of datasets meta-analyzed by the AEGL Committee was examined, finding that 70% of datasets pooled by the AEGL Committee are heterogeneous. In these instances, data pooling leads to a statistically invalid model and TLE estimate, potentially resulting in under- or over-estimated inhalation guidance levels. When data pooling is inappropriate, other meta-analysis options include categorical regression, fixed and random effects models, or even designation of a key study based on scientific judgement. In the present work, options of TLE meta-analysis are summarized in a decision tree contingent on statistical testing.

OBJECTIVE: Dimethyl sulfide (DMS, CAS 75-18-3) is an industrial chemical. It is both an irritant and neurotoxicant that may be life-threatening because of accidental release. The effects of DMS on public health and associated public health response depend on the exposure concentration and duration. However, currently, public health advisory information exists for only a 1 h exposure duration, developed by the American Industrial Hygiene Association (AIHA). In the present work, the AIHA-reviewed data were computationally extrapolated to other common short-term durations. METHODS: The extrapolation was carried out using the toxic load equation, C(n) x t = TL, where C and t are exposure concentration and duration, TL is toxic load, and n is a chemical-specific toxic load exponent derived in the present work using probit meta-analysis. The developed threshold levels were vetted against the AIHA database of clinical and animal health effects induced by DMS. RESULTS: Tier-1 levels were derived based on human exposures that resulted in an easily detectable odor, because DMS is known to have a disagreeable odor that may cause nausea. Tier-2 levels were derived from the lower 95% confidence bounds on a benchmark concentration that caused 10% incidence (BMCL10) of coma in rats during a 15 min inhalation exposure to DMS. Tier-3 levels were based on a BMCL05 for mortality in rats. CONCLUSION: Emergency responders and health assessors may consider these computationally derived threshold levels as a supplement to traditional chemical risk assessment procedures in instances where AIHA developed public health advisory levels do not exist.

Cellular proteins are essential for human immunodeficiency virus type 1 (HIV-1) replication and may serve as viable new targets for treating infection. Using gene trap insertional mutagenesis, a high-throughput approach based on random inactivation of cellular genes, candidate genes were found that limit virus replication when mutated. Disrupted genes (N=87) conferring resistance to lytic infection with several viruses were queried for an affect on HIV-1 replication by utilizing small interfering RNA (siRNA) screens in TZM-bl cells. Several genes regulating diverse pathways were found to be required for HIV-1 replication, including DHX8, DNAJA1, GTF2E1, GTF2E2, HAP1, KALRN, UBA3, UBE2E3, and VMP1. Candidate genes were independently tested in primary human macrophages, toxicity assays, and/or Tat-dependent beta-galactosidase reporter assays. Bioinformatics analyses indicated that several host factors present in this study participate in canonical pathways and functional processes implicated in prior genome-wide studies. However, the genes presented in this study did not share identity with those found previously. Novel antiviral targets identified in this study should open new avenues for mechanistic investigation.