Nano(Q)SAR: Challenges, pitfalls and perspectives

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Tantra, R ; Oksel, C ; Puzyn, T ; Wang, J ; Robinson, KN ; Wang, XZ ; Ma, CY ; Wilkins, T (2015)
  • Publisher: Taylor & Francis
  • Subject:
    mesheuropmc: skin and connective tissue diseases | body regions | fungi

Regulation for nanomaterials is urgently needed, and the drive to adopt an intelligent testing strategy is evident. Such a strategy will not only provide economic benefits but will also reduce moral and ethical concerns arising from animal testing. For regulatory purposes, such an approach is promoted by REACH, particularly the use of quantitative structure–activity relationships [(Q)SAR] as a tool for the categorisation of compounds according to their physicochemical and toxicological properties. In addition to compounds, (Q)SAR has also been applied to nanomaterials in the form of nano(Q)SAR. Although (Q)SAR in chemicals is well established, nano(Q)SAR is still in early stages of development and its successful uptake is far from reality. This article aims to identify some of the pitfalls and challenges associated with nano-(Q)SARs in relation to the categorisation of nanomaterials. Our findings show clear gaps in the research framework that must be addressed if we are to have reliable predictions from such models. Three major barriers were identified: the need to improve quality of experimental data in which the models are developed from, the need to have practical guidelines for the development of the nano(Q)SAR models and the need to standardise and harmonise activities for the purpose of regulation. Of these three, the first, i.e. the need to improve data quality requires immediate attention, as it underpins activities associated with the latter two. 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.
  • References (71)
    71 references, page 1 of 8

    (1) Roco, M. C.; Bainbridge, W. S.: Societal implications of nanoscience and nanotechnology: Maximizing human benefit. J. Nanopart. Res. 2005, 7, 1-13.

    (2) Tantra, R.; Shard, A.: We need answers. Nat. Nanotechnol. 2013, 8, 71-71.

    (3) Bajorath, J.: Chemoinformatics: concepts, methods, and tools for drug discovery; Springer, 2004; Vol. 275.

    (4) Duffus, J. H.; Nordberg, M.; Templeton, D. M.: Glossary of terms used in toxicology, (IUPAC recommendations 2007). Pure Appl. Chem. 2007, 79, 1153-1344.

    (5) Patlewicz, G. "Chemical Categories and Read Across," Joint Research Centre, (6) Lahl, U.; Gundert-Remy, U.: The use of (Q) SAR methods in the context of REACH. Toxicol. Mech. Methods 2008, 18, 149-158.

    (7) OECD: Guidance Document on the Validation of (Quantitative) StructureActivity Relationship Models. 2007.

    (8) OECD: Guidance on the Grouping of Chemicals. 2007.

    (9) van Leeuwen, C.; Vermeire, T.; Vermeire, T.: Risk assessment of chemicals: an introduction; Springer, 2007.

    (10) Burello, E.; Worth, A. P.: QSAR modeling of nanomaterials. Wiley Interdisciplinary Reviews: Nanomedicine and Nanobiotechnology 2011, 3, 298-306.

    (11) Dudek, A. Z.; Arodz, T.; Galvez, J.: Computational methods in developing quantitative structure-activity relationships (QSAR): a review. Comb. Chem. High Throughput Screening 2006, 9, 213-228.

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