One Health assessment of persistent organic chemicals and PFAS for consumption of restored anadromous fish.

BACKGROUND: Restoration efforts have led to the return of anadromous fish, potential source of food for the Penobscot Indian Nation, to the previously dammed Penobscot River, Maine. OBJECTIVE: U.S. Environmental Protection Agency (EPA), Penobscot Indian Nation's Department of Natural Resources (PINDNR), and Agency for Toxic Substances and Disease Registry (ATSDR), measured contaminants in six species of anadromous fish. Fish tissue concentrations were then used, along with exposure parameters, to evaluate potential human and aquatic-dependent wildlife risk. METHODS: PINDNR collected, filleted, froze, and shipped fish for analysis of polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), dioxins/furans, and per- and polyfluoroalkyl substances (PFAS). Contaminant levels were compared to reference doses (where possible) and wildlife values (WVs). RESULTS: Chemical concentrations ranged from 6.37 nanogram per gram (ng/g) wet weight (ww) in American Shad roe to 100 ng/g ww in Striped Bass for total PCBs; 0.851 ng/g ww in American Shad roe to 5.92 ng/g ww in large Rainbow Smelt for total PBDEs; and 0.037 ng/g ww in American Shad roe to 0.221 ng/g ww in Striped Bass for total dioxin/furans. PFAS concentrations ranged between 0.38 ng/g ww of PFBA in Alewife to 7.86 ng/g ww of PFUnA in Sea Lamprey. Dioxin/furans and PFOS levels indicated that there are potential human health risks. The WV for mink for total PCBs (72 ng/g) was exceeded in Striped Bass and the WV for Kestrel for PBDEs (8.7 ng/g) was exceeded in large Rainbow Smelt. Mammalian wildlife consuming Blueback Herring, Striped Bass, and Sea Lamprey may be at risk based on PFOS WVs from Canada. IMPACT: Anadromous fish returning to the Penobscot River potentially could represent the restoration of a major component of tribal traditional diet. However, information about contaminant levels in these fish is needed to guide the tribe about consumption safety. Analysis of select species of fish and risk calculations demonstrated the need for a protective approach to consumption for both humans and wildlife. This project demonstrates that wildlife can also be impacted by contamination of fish and their risks can be as great or greater than those of humans. A One Health approach addresses this discrepancy and will lead to a healthier ecosystem.

Determination of Cyanotoxins and Prymnesins in Water, Fish Tissue, and Other Matrices: A Review.

Harmful algal blooms (HABs) and their toxins are a significant and continuing threat to aquatic life in freshwater, estuarine, and coastal water ecosystems. Scientific understanding of the impacts of HABs on aquatic ecosystems has been hampered, in part, by limitations in the methodologies to measure cyanotoxins in complex matrices. This literature review discusses the methodologies currently used to measure the most commonly found freshwater cyanotoxins and prymnesins in various matrices and to assess their advantages and limitations. Identifying and quantifying cyanotoxins in surface waters, fish tissue, organs, and other matrices are crucial for risk assessment and for ensuring quality of food and water for consumption and recreational uses. This paper also summarizes currently available tissue extraction, preparation, and detection methods mentioned in previous studies that have quantified toxins in complex matrices. The structural diversity and complexity of many cyanobacterial and algal metabolites further impede accurate quantitation and structural confirmation for various cyanotoxins. Liquid chromatography-triple quadrupole mass spectrometer (LC-MS/MS) to enhance the sensitivity and selectivity of toxin analysis has become an essential tool for cyanotoxin detection and can potentially be used for the concurrent analysis of multiple toxins.

Risks from mercury in anadromous fish collected from Penobscot River, Maine.

Levels of total mercury were measured in tissue of six species of migratory fish (alewife, American shad, blueback herring, rainbow smelt, striped bass, and sea lamprey), and in roe of American shad for two consecutive years collected from the Penobscot River or its estuary. The resultant mercury levels were compared to reference doses as established in the U.S. Environmental Protection Agency (EPA) Integrated Risk Information System and wildlife values. Mercury concentrations ranged from 4 µg/kg ww in roe to 1040 µg/kg ww in sea lamprey. Sea lamprey contained the highest amounts of mercury for both seasons of sampling. Current health advisories are set at sufficient levels to protect fishers from harmful consumption of the fish for mercury alone, except for sea lamprey. Based upon published wildlife values for mink, otter, and eagle, consumption of rainbow smelt, striped bass, or sea lamprey poses a risk to mink; striped bass and sea lamprey to otter; and sea lamprey to eagle. For future consideration, the resultant data may serve as a reference point for both human health and wildlife risk assessments for the consumption of anadromous fish. U.S. EPA works with federally recognized Tribes across the nation greatly impacted by restrictions on sustenance fishing, to develop culturally sensitive risk assessments.

Developmental toxicity prediction.

Developmental toxicity may be estimated using commercial and noncommercial software that is already available in the market and/or literature, or models may be built from scratch using both commercial and noncommercial software packages. In this chapter, commonly available software programs that can predict the developmental toxicity of chemicals are described. In addition, a method for developing qualitative structure-activity relationship (SAR) models to predict the developmental toxicity of chemicals qualitatively (yes/no prediction) and quantitative structure-activity relationship (QSAR) models to predict quantitative estimates (e.g., LOAEL) of developmental toxicants is also described in this chapter. Additional information described in this chapter include methods to predict physicochemical properties of chemicals that can be used as descriptor variables in the model building process, statistical methods that be used to build QSAR models as well as methods to validate the models that are developed. Most of the methods described in this chapter can be used to develop models for health endpoints other than developmental toxicity as well.

Development of quantitative structure-activity relationship (QSAR) models to predict the carcinogenic potency of chemicals. II. Using oral slope factor as a measure of carcinogenic potency.

The overall risk associated with exposure to a chemical is determined by combining quantitative estimates of exposure to the chemical with their known health effects. For chemicals that cause carcinogenicity, oral slope factors (OSFs) and inhalation unit risks are used to quantitatively estimate the carcinogenic potency or the risk associated with exposure to the chemical by oral or inhalation route, respectively. Frequently, there is a lack of animal or human studies in the literature to determine OSFs. This study aims to circumvent this problem by developing quantitative structure-activity relationship (QSAR) models to predict the OSFs of chemicals. The OSFs of 70 chemicals based on male/female human, rat, and mouse bioassay data were obtained from the United States Environmental Protection Agency's Integrated Risk Information System (IRIS) database. A global QSAR model that considered all 70 chemicals as well as species and/or sex-specific QSARs were developed in this study. Study results indicate that the species and sex-specific QSARs (r(2)>0.8, q(2)>0.7) had a better predictive abilities than the global QSAR developed using data from all species and sexes (r(2)=0.77, q(2)=0.73). The QSARs developed in this study were externally validated, and demonstrated reasonable predictive abilities.

Development of quantitative structure-activity relationship (QSAR) models to predict the carcinogenic potency of chemicals I. Alternative toxicity measures as an estimator of carcinogenic potency.

Determining the carcinogenicity and carcinogenic potency of new chemicals is both a labor-intensive and time-consuming process. In order to expedite the screening process, there is a need to identify alternative toxicity measures that may be used as surrogates for carcinogenic potency. Alternative toxicity measures for carcinogenic potency currently being used in the literature include lethal dose (dose that kills 50% of a study population [LD(50)]), lowest-observed-adverse-effect-level (LOAEL) and maximum tolerated dose (MTD). The purpose of this study was to investigate the correlation between tumor dose (TD(50)) and three alternative toxicity measures as an estimator of carcinogenic potency. A second aim of this study was to develop a Classification and Regression Tree (CART) between TD(50) and estimated/experimental predictor variables to predict the carcinogenic potency of new chemicals. Rat TD(50)s of 590 structurally diverse chemicals were obtained from the Cancer Potency Database, and the three alternative toxicity measures considered in this study were estimated using TOPKAT, a toxicity estimation software. Though poor correlations were obtained between carcinogenic potency and the three alternative toxicity (both experimental and TOPKAT) measures for the CPDB chemicals, a CART developed using experimental data with no missing values as predictor variables provided reasonable estimates of TD(50) for nine chemicals that were part of an external validation set. However, if experimental values for the three alternative measures, mutagenicity and logP are not available in the literature, then either the CART developed using missing experimental values or estimated values may be used for making a prediction.

A hierarchical clustering methodology for the estimation of toxicity.

ABSTRACT A quantitative structure-activity relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural similarity is defined in terms of 2-D physicochemical descriptors (such as connectivity and E-state indices). A genetic algorithm-based technique is used to generate statistically valid QSAR models for each cluster (using the pool of descriptors described above). The toxicity for a given query compound is estimated using the weighted average of the predictions from the closest cluster from each step in the hierarchical clustering assuming that the compound is within the domain of applicability of the cluster. The hierarchical clustering methodology was tested using a Tetrahymena pyriformis acute toxicity data set containing 644 chemicals in the training set and with two prediction sets containing 339 and 110 chemicals. The results from the hierarchical clustering methodology were compared to the results from several different QSAR methodologies.