Determination of “fitness-for-purpose” of quantitative structure-activity relationship (QSAR) models to predict (eco-)toxicological endpoints for regulatory use

Belfield, Samuel J.; Enoch, Steven J.; Firman, James W.; Madden, Judith C.; Schultz, T.W.; Cronin, Mark T.D.

doi:10.1016/j.yrtph.2021.104956

Cited by 13 publications

(9 citation statements)

References 40 publications

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“…Thus, if ECHA wanted to confirm QSAR predictions or research any QSAR evidence based on the predictions, not only would they be unable to find this information in the AoA, other applicants who share information may replicate the same error, further lowering the quality of AoA. At the same time, approaches to addressing uncertainty need to be inclusive of forward-thinking regulatory use of QSARs not necessarily addressed in the OECD principles in addition to initiatives around state-of-the-art A.I.-based QSAR models [ 72 , 73 ].…”

Section: Discussionmentioning

confidence: 99%

Multi-Strategy Assessment of Different Uses of QSAR under REACH Analysis of Alternatives to Advance Information Transparency

Chinen

Malloy

2022

IJERPH

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Under the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) analysis of alternatives (AoA) process, quantitative structure–activity relationship (QSAR) models play an important role in expanding information gathering and organizing frameworks. Increasingly recognized as an alternative to testing under registration. QSARs have become a relevant tool in bridging data gaps and supporting weight of evidence (WoE) when assessing alternative substances. Additionally, QSARs are growing in importance in integrated testing strategies (ITS).For example, the REACH ITS framework for specific endpoints directs registrants to consider non-testing results, including QSAR predictions, when deciding if further animal testing is needed. Despite the raised profile of QSARs in these frameworks, a gap exists in the evaluation of QSAR use and QSAR documentation under authorization. An assessment of the different uses (e.g., WoE and ITS) in which QSAR predictions play a role in evidence gathering and organizing remains unaddressed for AoA. This study approached the disparity in information for QSAR predictions by conducting a substantive review of 24 AoA through May 2017, which contained higher-tier endpoints under REACH. Understanding the manner in which applicants manage QSAR prediction information in AoA and assessing their potential within ITS will be valuable in promoting regulatory use of QSARs and building out future platforms in the face of rapidly evolving technology while advancing information transparency.

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Section: Discussionmentioning

confidence: 99%

Multi-Strategy Assessment of Different Uses of QSAR under REACH Analysis of Alternatives to Advance Information Transparency

Chinen

Malloy

2022

IJERPH

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“…Cronin et al, [15] recently developed a set of criteria that enabled the full assessment of QSAR models from conception to application, facilitating all aspects of uncertainty to be defined and scored. This was further expanded upon by Belfield et al [4], where it was demonstrated that the criteria could also be employed to determine fitness-for-purpose. Although these criteria have been developed in order to account for all potential usages of QSAR, completion of the present literature review has elucidated further areas of consideration specifically relevant to construction of QSAR models for prediction of mixture effects.…”

Section: Uncertainty Criteria and Assessment For Mixture Studiesmentioning

confidence: 99%

A review of quantitative structure-activity relationship modelling approaches to predict the toxicity of mixtures

Belfield

Firman²,

Enoch³

et al. 2023

Computational Toxicology

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“…The purpose of these analyses was not to conclude that a particular approach should, or should not, be used, but to assist in the validation process, identify aspects of a model that may be associated with significant levels of uncertainty and determine the overall confidence that may be assigned to a model. This approach to understanding uncertainty provides the opportunity to determine the type and level of confidence required for a predictive toxicology approach to be “fit-for-purpose” ( Belfield et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

A scheme to evaluate structural alerts to predict toxicity – Assessing confidence by characterising uncertainties

Cronin

Bauer²,

Bonnell³

et al. 2022

Regulatory Toxicology and Pharmacology

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Determination of “fitness-for-purpose” of quantitative structure-activity relationship (QSAR) models to predict (eco-)toxicological endpoints for regulatory use

Cited by 13 publications

References 40 publications

Multi-Strategy Assessment of Different Uses of QSAR under REACH Analysis of Alternatives to Advance Information Transparency

Multi-Strategy Assessment of Different Uses of QSAR under REACH Analysis of Alternatives to Advance Information Transparency

A review of quantitative structure-activity relationship modelling approaches to predict the toxicity of mixtures

A scheme to evaluate structural alerts to predict toxicity – Assessing confidence by characterising uncertainties

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