Quantifying the benefits of using read-across and in silico techniques to fulfill hazard data requirements for chemical categories

Stanton, Kathleen; Kruszewski, Francis H.

doi:10.1016/j.yrtph.2016.09.004

Cited by 32 publications

(26 citation statements)

References 16 publications

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“…In addition, about 75% of European Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) dossiers submitted between 2010 and 2013 contain read-across for at least one endpoint (Ball et al, 2016). The read-across method is endorsed by other regulatory authorities worldwide including the Research Institute for Fragrance Materials, the International Council of Chemical Associations (ICCA) and the U.S. Environmental Protection Agency's High Production Volume (HPV) Challenge Program (Bishop et al, 2012;Stanton and Kruszewski, 2016).…”

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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“…It can also be used for regulatory purposes: a properly used model can predict whether a new chemical can be harmful to people and the environment even without the use of experimental techniques, so the registration of a chemical can be denied or sought, or new safety instructions can be deferred. 91,118 Many literature examples 36,[119][120][121][122][123][124][125][126] have shown how these methods are associated with regulatory issues. Mechanism of action experimental determination is time-consuming and expensive.…”

Section: Resultsmentioning

confidence: 99%

In silico Risk Assessment Studies of New Psychoactive Substances Derived from Amphetamines and Cathinones

Melo¹,

Martins²,

Rodrigues³

et al. 2020

J. Braz. Chem. Soc.

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The amount and variety of new psychoactive substances (NPS) are expanding, and there are difficulties in assessing their risks. In this regard, in silico methods are potentially useful to predict NPS properties faster and at a lower cost. In this work a quantitative structure-activity relationship (QSAR) model was used to verify the risk of drugs derived from amphetamines and cathinones. A dataset of 26 derivatives with in vitro affinity for norepinephrine transporter (NET) was selected. To ensure reproducibility of the results, only geometric molecular descriptors (AM1 (Austin model 1) level) obtained from the platform ChemDes and ordered predictors selection (OPS) were used. The model presents good internal statistics (n = 23; coefficient of determination (R 2) = 0.914). The small number of samples was divided into seven training sets (n = 17) and seven test sets (n = 6). The average R 2 pred = 0.754 showed that the model has good predictive capacity. Based on the tests, this model can accurately predict the risk range of three previously selected derivatives: methedrone (low), ethcathinone (medium), and methamphetamine (high), even when only data referring to NET are employed. We used these data to create a simple free program in Java that focuses on the risk assessment of recreational drugs belonging to this class of compounds.

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“…(See further information on the OECD's work on "omics" technologies in Section 5.8.) Read-across and similar approaches can fill data for requirements for chemical categories as well as eliminate the need for many animal tests (Stanton and Kruszewski, 2016).…”

Section: Enhancing Hazard Assessment Methodsmentioning

confidence: 99%

“…The same methods used to estimate costs and cost savings above as a result of MAD and (Q)SARs/read-across can be applied to estimate the number of animals used for testing. Based on this approach, this report suggests a substantial reduction (56%) in the number of test animals that are needed ( Stanton and Kruszewski (2016) found that through the use of read-across and (Q)SAR techniques to fill data gaps for 261 chemical substances, 100 000 to 150 000 test animals were not needed, and USD 50 million to USD 70 million in testing costs were avoided.…”

Section: Reducing Testing and Repeat Testing For New Industrial Chemimentioning

confidence: 99%

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Quantifying the benefits of using read-across and in silico techniques to fulfill hazard data requirements for chemical categories

Cited by 32 publications

References 16 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

In silico Risk Assessment Studies of New Psychoactive Substances Derived from Amphetamines and Cathinones

Responses from the survey of governments and industry

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