Review of Existing QSAR/QSPR Models Developed for Properties Used in Hazardous Chemicals Classification System

Quintero, Flor A.; Patel, Suhani J.; Muñoz, Felipe; Mannan, M. Sam

doi:10.1021/ie301079r

Cited by 58 publications

(42 citation statements)

References 83 publications

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“…To promote this approach in a regulatory context, the Organisation for Economic Co-operation and Development (OECD) proposed in 2004 that, for every predictive model, the authors should report (1) a defined end point; (2) an unambiguous algorithm; (3) a defined domain of applicability; (4) appropriate measures of goodness-of-fit, robustness, and predictivity; and, (5) whenever possible, a mechanistic interpretation. 1 Such a validation of QSPR procedures is clearly desirable in view of their empirical character and the possibility of chance correlations. Accordingly, it is expected that routine applications of procedures designed with the OECD principles in mind will lead to a large number of models compliant with these principles in the forthcoming years.…”

Section: Introductionmentioning

confidence: 99%

Physics-Based Modeling of Chemical Hazards in a Regulatory Framework: Comparison with Quantitative Structure–Property Relationship (QSPR) Methods for Impact Sensitivities

Mathieu

2016

Ind. Eng. Chem. Res.

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A semiempirical model based on simple physical assumptions is rigorously compared to a combination of two recent state-of-the-art quantitative structure−property relationship (QSPR) methods for impact sensitivities of nitroaliphatic and nitramine compounds. For most datasets considered, it yields slightly better predictions than QSPR schemes, which is noteworthy, considering the fact that it relies only on three adjustable parameters and an equation developed independently of the data. Further advantages of this approach include physical interpretability, enhanced robustness, and wider applicability. Therefore, there is no doubt that such physics-based models provide valuable alternatives to the purely empirical relationships usually employed in regulatory contexts, especially in situations where experimental data are scarce.

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Section: Introductionmentioning

confidence: 99%

Physics-Based Modeling of Chemical Hazards in a Regulatory Framework: Comparison with Quantitative Structure–Property Relationship (QSPR) Methods for Impact Sensitivities

Mathieu

2016

Ind. Eng. Chem. Res.

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“…5 Nowadays QSPR/QSAR is introduced as an acceptable tool in molecular design for different purposes [46,47]. For example in the current century QSAR is embedded in the pharmaceutical industry as an essential and inseparable tool from drug discovery and activity optimization during drug development process [37,[48][49][50][51].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…(39)(40)(41)(42)(43)(44)(45)(46)(47)(48). The terms which are utilized in these equations are defined bellow: …”

Section: Model Validationmentioning

confidence: 99%

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Chemometrics tools in QSAR/QSPR studies: A historical perspective

Yousefinejad

Hemmateenejad

2015

Chemometrics and Intelligent Laboratory Systems

121

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“…a) The heterogeneity of nanomaterial family: There is a need to develop separate models that are specific to nanomaterial types and thus properties 70 . Due to large number of nanomaterial types that can be engineered (and subsequently different mechanisms of toxicity), it has been suggested by Puzyn that individual classes of nanomaterials should be modelled separately 42 .…”

Section: Barrier 2: Validation Of the Modelmentioning

confidence: 99%

Nano(Q)SAR: Challenges, pitfalls and perspectives

et al. 2014

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Regulation for nanomaterial is urgently needed and the drive to adopt an intelligent testing strategy is evident. The intelligent testing strategy will not only be beneficial from a cost reduction point of view but will also mean the reduction of the moral and ethical concerns related to animal research. In the chemical and legislative world, such an approach is promoted by REACH and in particular the use of (Q)SAR as a tool for the purpose of categorisation. In addition to traditional compounds, (Q)SAR has also been applied to nanomaterials i.e. nano(Q)SAR, useful to correlate toxicological endpoints with physicochemical properties.Although (Q)SAR in chemicals is well established, nano(Q)SAR is still at an early stage of its development and its successful uptake is far from reality. The purpose of this paper is to identify some of the pitfalls and challenges associated with nano-(Q)SARs, in relation for its use to categorise nanomaterials. Our findings show clear gaps in the research framework that must be addressed if we are to have reliable predications from the use of such models. Three major types of barriers were identified: a) the need to improve quality of experimental data in which the models are being developed from in the first place, b) the need to have practical guidelines for the development of the nano(Q)SAR models, c) the need to standardise and harmonise activities for the purpose of regulation. Out of the three barriers, immediate attention is needed for a) as this underpins activities associated in b) and c). It should be noted that the usefulness of data in the context of nano-(Q)SAR modelling is not only about the quantity of data but also about the quality, consistency and accessibility of those data.

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Review of Existing QSAR/QSPR Models Developed for Properties Used in Hazardous Chemicals Classification System

Cited by 58 publications

References 83 publications

Physics-Based Modeling of Chemical Hazards in a Regulatory Framework: Comparison with Quantitative Structure–Property Relationship (QSPR) Methods for Impact Sensitivities

Physics-Based Modeling of Chemical Hazards in a Regulatory Framework: Comparison with Quantitative Structure–Property Relationship (QSPR) Methods for Impact Sensitivities

Chemometrics tools in QSAR/QSPR studies: A historical perspective

Nano(Q)SAR: Challenges, pitfalls and perspectives

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