A case study on the application of an expert-driven read-across approach in support of quantitative risk assessment of p,p’-dichlorodiphenyldichloroethane

Lizarraga, Lucina E.; Dean, Jeffry L.; Kaiser, Jonas; Wesselkamper, Scott C.; Lambert, Jason C.; Zhao, Qianqian

doi:10.1016/j.yrtph.2019.02.010

Cited by 11 publications

(2 citation statements)

References 88 publications

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“…However, these protocols are applicable only to a chemical with a simple structure for which analogues can be easily chosen (e.g., analogues with a different number of carbon atoms in the alkyl chain of n-alkanols). Furthermore, there are only a few reports of read-across studies that aimed to make quantitative predictions, such as NOAEL values (Lizarraga et al, 2019;Mellor et al, 2017;Schultz et al, 2017), especially where the data gap involves reproductive or developmental toxicity.…”

Section: Introductionmentioning

confidence: 99%

Integration of read-across and artificial neural network-based QSAR models for predicting systemic toxicity: A case study for valproic acid

et al. 2020

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We present a systematic, comprehensive and reproducible weight-of-evidence approach for predicting the no-observed-adverse-effect level (NOAEL) for systemic toxicity by using read-across and quantitative structure-activity relationship (QSAR) models to fill gaps in rat repeated-dose and developmental toxicity data. As a case study, we chose valproic acid, a developmental toxicant in humans and animals. High-quality in vivo oral rat repeated-dose and developmental toxicity data were available for five and nine analogues, respectively, and showed qualitative consistency, especially for developmental toxicity. Similarity between the target and analogues is readily defined computationally, and data uncertainties associated with the similarities in structural, physico-chemical and toxicological properties, including toxicophores, were low. Uncertainty associated with metabolic similarity is low-to-moderate, largely because the approach was limited to in silico prediction to enable systematic and objective data collection. Uncertainty associated with completeness of read-across was reduced by including in vitro and in silico metabolic data and expanding the experimental animal database. Taking the "worst-case" approach, the smallest NOAEL values among the analogs (i.e., 200 and 100 mg/kg/day for repeated-dose and developmental toxicity, respectively) were read-across to valproic acid. Our previous QSAR models predict repeated-dose NOAEL of 148 (males) and 228 (females) mg/kg/day, and developmental toxicity NOAEL of 390 mg/kg/day for valproic acid. Based on read-across and QSAR, the conservatively predicted NOAEL is 148 mg/kg/day for repeated-dose toxicity, and 100 mg/kg/day for developmental toxicity. Experimental values are 341 mg/kg/day and 100 mg/kg/day, respectively. The present approach appears promising for quantitative and qualitative in silico systemic toxicity prediction of untested chemicals.

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Section: Introductionmentioning

confidence: 99%

Integration of read-across and artificial neural network-based QSAR models for predicting systemic toxicity: A case study for valproic acid

et al. 2020

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“…Metabolism is a primary principle for identifying surrogate chemicals ( Wang et al, 2012 ). Read-across techniques are currently being used by the U.S. Environmental Protection Agency (EPA) to evaluate new chemicals under the Toxic Substances Control Act (TSCA) ( Patlewicz et al, 2019 ) and determine Provisional Peer-Reviewed Toxicity Values (PPRTVs) for contaminants at Superfund sites ( Wang et al, 2012 ; Lizarraga et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis

Boyce

Favela²,

Bonzo³

et al. 2023

Front. Toxicol.

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Understanding the metabolic fate of a xenobiotic substance can help inform its potential health risks and allow for the identification of signature metabolites associated with exposure. The need to characterize metabolites of poorly studied or novel substances has shifted exposure studies towards non-targeted analysis (NTA), which often aims to profile many compounds within a sample using high-resolution liquid-chromatography mass-spectrometry (LCMS). Here we evaluate the suitability of suspect screening analysis (SSA) liquid-chromatography mass-spectrometry to inform xenobiotic chemical metabolism. Given a lack of knowledge of true metabolites for most chemicals, predictive tools were used to generate potential metabolites as suspect screening lists to guide the identification of selected xenobiotic substances and their associated metabolites. Thirty-three substances were selected to represent a diverse array of pharmaceutical, agrochemical, and industrial chemicals from Environmental Protection Agency’s ToxCast chemical library. The compounds were incubated in a metabolically-active in vitro assay using primary hepatocytes and the resulting supernatant and lysate fractions were analyzed with high-resolution LCMS. Metabolites were simulated for each compound structure using software and then combined to serve as the suspect screening list. The exact masses of the predicted metabolites were then used to select LCMS features for fragmentation via tandem mass spectrometry (MS/MS). Of the starting chemicals, 12 were measured in at least one sample in either positive or negative ion mode and a subset of these were used to develop the analysis workflow. We implemented a screening level workflow for background subtraction and the incorporation of time-varying kinetics into the identification of likely metabolites. We used haloperidol as a case study to perform an in-depth analysis, which resulted in identifying five known metabolites and five molecular features that represent potential novel metabolites, two of which were assigned discrete structures based on in silico predictions. This workflow was applied to five additional test chemicals, and 15 molecular features were selected as either reported metabolites, predicted metabolites, or potential metabolites without a structural assignment. This study demonstrates that in some–but not all–cases, suspect screening analysis methods provide a means to rapidly identify and characterize metabolites of xenobiotic chemicals.

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Microbial degradation of organic pollutants using indigenous bacterial strains

Siddiqa

Faisal

2021

Handbook of Bioremediation

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A case study on the application of an expert-driven read-across approach in support of quantitative risk assessment of p,p’-dichlorodiphenyldichloroethane

Cited by 11 publications

References 88 publications

Integration of read-across and artificial neural network-based QSAR models for predicting systemic toxicity: A case study for valproic acid

Integration of read-across and artificial neural network-based QSAR models for predicting systemic toxicity: A case study for valproic acid

Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis

Microbial degradation of organic pollutants using indigenous bacterial strains

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