The need for strategic development of safety sciences

Busquet, François; Hartung, Thomas

doi:10.14573/altex.1701031

Cited by 15 publications

(11 citation statements)

References 54 publications

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“…Regulatory acceptance is generally considered more likely if the NAM has “relevance,” which means that it can be clearly linked to the regulator's operationalized protection goal, and “reliability,” which can be enhanced by ensuring technical guidance documents are available to regulators. But the adoption of a NAM does not depend solely on its attributes because features of the adopting system, including organizational and institutional factors, also need to be addressed in order to realize a shift toward greater use of NAMs in chemical risk assessment (NTP 2004; ECCC 2016; Busquet and Hartung 2017; ICCVAM 2018; SCESEHD et al 2018; Tickner et al 2018). Furthermore, previous surveys concerning the perceptions of NAMs within the toxicology community have provided a portrait of perceived drivers and obstacles, and demographics tied to the use and level of knowledge about NAMs (Vachon et al 2017; Zaunbrecher et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting the Perception of New Approach Methodologies (NAMs) in the Ecotoxicology Community

Mondou

Hickey

Rahman

et al. 2020

Integr Envir Assess & Manag

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Given current legislative mandates to assess the safety of thousands of chemicals and the slow pace at which conventional testing proceeds, there is a need to accelerate chemical risk assessment. Governments and businesses are increasingly interested in new approach methodologies (NAMs) that promise to reduce costs and delays. We explore 5 sociological factors within the ecotoxicology community that can influence the perception of NAMs: 1) professional profile (educational cohort, employer), 2) internal science communication within professional forums, 3) concern for “error cost,” 4) collaboration across stakeholders, and 5) fundamental beliefs regarding toxicology. We conducted an online survey (n = 171; 2018) asking participants about their experiences and perspectives at events of the Society of Environmental Toxicology and Chemistry (SETAC) to assess 1) how NAMs are discussed compared to conventional testing and 2) how respondents perceive their viability. We developed ordered logistic regression (OLR) models to understand the influence of exploratory variables (cohort, core views on toxicology, frequency of collaboration) on respondents' evaluation of the viability of different NAMs. Our results showed that 1) NAMs were more likely than conventional methods to be challenged in forum discussions, which may be fueled by concerns for error costs in regulatory decision making; 2) perceptions of the viability of NAMs tended to follow a “pattern of familiarity,” whereby respondents that were more knowledgeable about a test method tended to find it more viable; 3) respondents who agreed with the Paracelsus maxim had a greater likelihood of finding conventional testing viable; and 4) the more a respondent reported collaborating with industry on alternative testing strategies, the more likely she or he was to report that NAMs were less viable. These results suggest that there are professional and organizational barriers to greater acceptance of NAMs that can be addressed through a social learning process within the professional community. Integr Environ Assess Manag 2020;16:269–281. © 2020 SETAC

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Section: Introductionmentioning

confidence: 99%

Factors Affecting the Perception of New Approach Methodologies (NAMs) in the Ecotoxicology Community

Mondou

Hickey

Rahman

et al. 2020

Integr Envir Assess & Manag

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“…Interestingly, these large databases also impact on the derivation of thresholds of toxicological concern (TTC) ( Hartung, 2017b ; van Ravenzwaay et al , 2017 ), which might synergize with the in silico approach of a RASAR: the RASAR would prioritize substances from the hazard properties side, while TTC bring in the relevant exposure for the respective toxicological space. This could be an integral part for the strategic development of a new safety sciences paradigm ( Busquet and Hartung, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility

Luechtefeld

Marsh²,

Rowlands

et al. 2018

Toxicological Sciences

241

212

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Earlier we created a chemical hazard database via natural language processing of dossiers submitted to the European Chemical Agency with approximately 10 000 chemicals. We identified repeat OECD guideline tests to establish reproducibility of acute oral and dermal toxicity, eye and skin irritation, mutagenicity and skin sensitization. Based on 350–700+ chemicals each, the probability that an OECD guideline animal test would output the same result in a repeat test was 78%–96% (sensitivity 50%–87%). An expanded database with more than 866 000 chemical properties/hazards was used as training data and to model health hazards and chemical properties. The constructed models automate and extend the read-across method of chemical classification. The novel models called RASARs (read-across structure activity relationship) use binary fingerprints and Jaccard distance to define chemical similarity. A large chemical similarity adjacency matrix is constructed from this similarity metric and is used to derive feature vectors for supervised learning. We show results on 9 health hazards from 2 kinds of RASARs—“Simple” and “Data Fusion”. The “Simple” RASAR seeks to duplicate the traditional read-across method, predicting hazard from chemical analogs with known hazard data. The “Data Fusion” RASAR extends this concept by creating large feature vectors from all available property data rather than only the modeled hazard. Simple RASAR models tested in cross-validation achieve 70%–80% balanced accuracies with constraints on tested compounds. Cross validation of data fusion RASARs show balanced accuracies in the 80%–95% range across 9 health hazards with no constraints on tested compounds.

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“…The free concentration (C f ), also called the unbound concentration (C u = C f ), can be derived from the nominal concentration (C nom ) by multiplying C nom by the fraction unbound (f u ), i.e., the of a potential toxicant via the daily diet. All large-scale NAMbased projects, e.g., SEURAT1, Tox21 and EU-ToxRisk (Gocht et al, 2015;Berggren et al, 2017;Wetmore et al, 2014;Sipes et al, 2017;Siméon et al, 2020;Escher et al, 2019;Krebs et al, 2020;Daneshian et al, 2016;Busquet and Hartung, 2017;Judson et al, 2014;Cote et al, 2016;Kavlock et al, 2018;Graepel et al, 2019) have incorporated an IVIVE procedure to convert free concentrations from NAM to a corresponding external dose. Very sophisticated software has been developed for such purposes.…”

Section: Predicting the Free Compound Fraction (F U ) In Vitromentioning

confidence: 99%

Chemical concentrations in cell culture compartments (C5) – free concentrations

Kisitu

Hollert

Fisher

et al. 2020

ALTEX

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In biological systems (cell culture media, cells, body fluids), drugs/toxicants are usually not freely dissolved but partially bound to biomolecules; only a fraction of the chemical is free/unbound (fu). To predict pharmacological effects and toxicity, it is important that the fu of the drug is known. As the differences between free and nominal concentrations are determined by test system parameters (e.g., the protein and lipid content, and the type of surface material), comparison of nominal concentrations between two different new approach methods (NAM) may lead to faulty conclusions. The same problem exists when in vitro concentrations are compared to those in human subjects. Therefore, the respective fu of a chemical in a test system needs to be determined for in vitro-to-in vivo extrapolations (IVIVE). Besides direct measurements, prediction models can help to obtain fu. Here we describe a simplified approach to approximate fu and provide background information on the underlying assumptions. Comparative predictions and measurements of fu of various drugs are shown to exemplify the approach. Basic input data, like protein and lipid concentrations, are also provided. Beyond such test systems data, the only required chemical-specific inputs are the lipophilicity of the candidate drug and its ionization state, as determined by the dissociation constants of its acidic or basic groups. This overview is intended to be used by any lab scientist without specific toxicokinetics training to obtain an estimate of fu in a given cell culture medium.

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The need for strategic development of safety sciences

Cited by 15 publications

References 54 publications

Factors Affecting the Perception of New Approach Methodologies (NAMs) in the Ecotoxicology Community

Factors Affecting the Perception of New Approach Methodologies (NAMs) in the Ecotoxicology Community

Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility

Chemical concentrations in cell culture compartments (C5) – free concentrations

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