An interdisciplinary framework for derivation of occupational exposure limits.

Protecting the health and safety of workers in industrial operations is a top priority. One of the resources used in industry to ensure worker safety is the occupational exposure limit (OEL). OELs are derived from the assessment and interpretation of empirical data from animal and/or human studies. There are various guidelines for the derivation and implementation of OELs globally, with a range of stakeholders (including regulatory bodies, governmental agencies, expert groups and others). The purpose of this manuscript is to supplement existing guidance with learnings from a multidisciplinary team approach within an industry setting. The framework we present is similar in construct to other risk assessment frameworks and includes: (1) problem formulation, (2) literature review, (3) weight of evidence considerations, (4) point of departure selection/derivation, (5) application of assessment factors, and the final step, (6) derivation of the OEL. Within each step are descriptions and examples to consider when incorporating data from various disciplines such as toxicology, epidemiology, and exposure science. This manuscript describes a technical framework by which available data relevant for occupational exposures is compiled, analyzed, and utilized to inform safety threshold derivation applicable to OELs.

Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach.

Traditional experimental testing to identify endocrine disruptors that enhance estrogenic signaling relies on expensive and labor-intensive experiments. We sought to design a knowledge-based deep neural network (k-DNN) approach to reveal and organize public high-throughput screening data for compounds with nuclear estrogen receptor α and ß (ERα and ERß) binding potentials. The target activity was rodent uterotrophic bioactivity driven by ERα/ERß activations. After training, the resultant network successfully inferred critical relationships among ERα/ERß target bioassays, shown as weights of 6521 edges between 1071 neurons. The resultant network uses an adverse outcome pathway (AOP) framework to mimic the signaling pathway initiated by ERα and identify compounds that mimic endogenous estrogens (i.e., estrogen mimetics). The k-DNN can predict estrogen mimetics by activating neurons representing several events in the ERα/ERß signaling pathway. Therefore, this virtual pathway model, starting from a compound's chemistry initiating ERα activation and ending with rodent uterotrophic bioactivity, can efficiently and accurately prioritize new estrogen mimetics (AUC = 0.864-0.927). This k-DNN method is a potential universal computational toxicology strategy to utilize public high-throughput screening data to characterize hazards and prioritize potentially toxic compounds.

Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches.

As defined by the World Health Organization, an endocrine disruptor is an exogenous substance or mixture that alters function(s) of the endocrine system and consequently causes adverse health effects in an intact organism, its progeny, or (sub)populations. Traditional experimental testing regimens to identify toxicants that induce endocrine disruption can be expensive and time-consuming. Computational modeling has emerged as a promising and cost-effective alternative method for screening and prioritizing potentially endocrine-active compounds. The efficient identification of suitable chemical descriptors and machine-learning algorithms, including deep learning, is a considerable challenge for computational toxicology studies. Here, we sought to apply classic machine-learning algorithms and deep-learning approaches to a panel of over 7500 compounds tested against 18 Toxicity Forecaster assays related to nuclear estrogen receptor (ERα and ERß) activity. Three binary fingerprints (Extended Connectivity FingerPrints, Functional Connectivity FingerPrints, and Molecular ACCess System) were used as chemical descriptors in this study. Each descriptor was combined with four machine-learning and two deep- learning (normal and multitask neural networks) approaches to construct models for all 18 ER assays. The resulting model performance was evaluated using the area under the receiver- operating curve (AUC) values obtained from a fivefold cross-validation procedure. The results showed that individual models have AUC values that range from 0.56 to 0.86. External validation was conducted using two additional sets of compounds (n = 592 and n = 966) with established interactions with nuclear ER demonstrated through experimentation. An agonist, antagonist, or binding score was determined for each compound by averaging its predicted probabilities in relevant assay models as an external validation, yielding AUC values ranging from 0.63 to 0.91. The results suggest that multitask neural networks offer advantages when modeling mechanistically related endpoints. Consensus predictions based on the average values of individual models remain the best modeling strategy for computational toxicity evaluations.

Modulating inhibitors of transthyretin fibrillogenesis via sulfation: polychlorinated biphenyl sulfates as models.

Small molecules that bind with high affinity to thyroxine (T4) binding sites on transthyretin (TTR) kinetically stabilize the protein's tetrameric structure, thereby efficiently decreasing the rate of tetramer dissociation in TTR related amyloidoses. Current research efforts aim to optimize the amyloid inhibiting properties of known inhibitors, such as derivatives of biphenyls, dibenzofurans and benzooxazoles, by chemical modification. In order to test the hypothesis that sulfate group substituents can improve the efficiencies of such inhibitors, we evaluated the potential of six polychlorinated biphenyl sulfates to inhibit TTR amyloid fibril formation in vitro. In addition, we determined their binding orientations and molecular interactions within the T4 binding site by molecular docking simulations. Utilizing this combined experimental and computational approach, we demonstrated that sulfation significantly improves the amyloid inhibiting properties as compared to both parent and hydroxylated PCBs. Importantly, several PCB sulfates were of equal or higher potency than some of the most effective previously described inhibitors.

Sulfated metabolites of polychlorinated biphenyls are high-affinity ligands for the thyroid hormone transport protein transthyretin.

BACKGROUND: The displacement of l-thyroxine (T4) from binding sites on transthyretin (TTR) is considered a significant contributing mechanism in polychlorinated biphenyl (PCB)-induced thyroid disruption. Previous research has discovered hydroxylated PCB metabolites (OH-PCBs) as high-affinity ligands for TTR, but the binding potential of conjugated PCB metabolites such as PCB sulfates has not been explored. OBJECTIVES: We evaluated the binding of five lower-chlorinated PCB sulfates to human TTR and compared their binding characteristics to those determined for their OH-PCB precursors and for T4. METHODS: We used fluorescence probe displacement studies and molecular docking simulations to characterize the binding of PCB sulfates to TTR. The stability of PCB sulfates and the reversibility of these interactions were characterized by HPLC analysis of PCB sulfates after their binding to TTR. The ability of OH-PCBs to serve as substrates for human cytosolic sulfotransferase 1A1 (hSULT1A1) was assessed by OH-PCB-dependent formation of adenosine-3',5'-diphosphate, an end product of the sulfation reaction. RESULTS: All five PCB sulfates were able to bind to the high-affinity binding site of TTR with equilibrium dissociation constants (Kd values) in the low nanomolar range (4.8-16.8 nM), similar to that observed for T4 (4.7 nM). Docking simulations provided corroborating evidence for these binding interactions and indicated multiple high-affinity modes of binding. All OH-PCB precursors for these sulfates were found to be substrates for hSULT1A1. CONCLUSIONS: Our findings show that PCB sulfates are high-affinity ligands for human TTR and therefore indicate, for the first time, a potential relevance for these metabolites in PCB-induced thyroid disruption.