Abstract
Aims: Given the current gaps of scientific knowledge and the need of efficient application of food
law, this paper makes an analysis of principles of European food law for the appropriateness of applying biological
activity Machine Learning prediction models to guarantee public safety.
Background: Cheminformatic methods are able to design and create predictive models with high rate of accuracy
saving time, costs and animal sacrifice. It has been applied on different disciplines including nanotechnology.
Objective: Given the current gaps of scientific knowledge and the need of efficient application of food law,
this paper makes an analysis of principles of European food law for the appropriateness of applying biological
activity Machine Learning prediction models to guarantee public safety.
Methods: A systematic study of the regulation and the incorporation of predictive models of biological activity
of nanomaterials was carried out through the analysis of the express nanotechnology regulation on foods,
applicable in European Union.
Results: It is concluded Machine Learning could improve the application of nanotechnology food regulation,
especially methods such as Perturbation Theory Machine Learning (PTML), given that it is aligned with principles
promoted by the standards of Organization for Economic Co-operation and Development, European
Union regulations and European Food Safety Authority.
Conclusion: To our best knowledge this is the first study focused on nanotechnology food regulation and it
can help to support technical European Food Safety Authority Opinions for complementary information.
Keywords:
Nanotechnology, Regulation, Toxicity, Safety, Cheminformatics, Machine learning.
Graphical Abstract
[2]
Fiedler, F.A.; Reynolds, G.H. Legal problems of nanotechnology: an overview. South. Calif. Interdiscip. Law J., 1993, 3, 593-630.
[4]
Bowman, D.M.; Hodge, G.A. A small matter of regulation: an international review of nanotechnology regulation. Columbia Sci. Technol. Law Rev., 2007, 8, 1-36.
[5]
Reynolds, G.H. Nanotechnology and regulatory policy: three futures. Harv. J. Law Technol., 2003, 17, 179-208.
[6]
Wejnert, J. Regulatory mechanisms for molecular nanotechnology. Jurimetrics, 2004, 44, 323-350.
[8]
Magnuson, B.A. In: Benefits and challenges of the application of nanotechnology to food; Technical proceedings of the 2007 nanotechnology and clean tech conference and trade show. , 2007; pp. 20-24.
[18]
Peter, K; Mar, G.; Terumi, M.; Hoseok, S.; Jihane, E.G. OECD council recommendation on the safety testing and assessment of manufacured nanomaterials. 2017.
[23]
EFSA. Re‐evaluation of titanium dioxide (E 171) as a food additive. EFSA J., 2016, 14, 1-83.
[24]
EFSA. Evaluation of di‐calcium malate, used as a novel food ingredient and as a source of calcium in foods for the general population, food supplements, total diet replacement for weight control and food for special medical purposes. EFSA J., 2018, 16, 1-16.
[25]
EFSA. Scientific opinion on the re‐evaluation of silver (E 174) as food additive. EFSA J., 2016, 14, 1-64.
[26]
EFSA. Re‐evaluation of calcium silicate (E 552), magnesium silicate (E 553a(i)), magnesium trisilicate (E 553a(ii)) and talc (E 553b) as food additives. EFSA J., 2018, 16, 1-50.
[27]
EFSA. Scientific Opinion on the re-evaluation of gold (E 175) as a food additive. EFSA J., 2016, 14, 1-43.
[28]
EFSA. Scientific Opinion on the re‐evaluation of vegetable carbon (E 153) as a food additive. EFSA J., 2012, 10, 1-34.
[33]
European Court. Case C 111/16, 2017.
[36]
European Court. Case C-58/10, 2011.
[37]
European Court. Case C-282/15, 2017.
[38]
European Court. Case C-333/08, 2010.
[39]
European Court. Case C 236/01, 2003.
[41]
OECD. Guidance document on the validation of (Quantitative)
structure-activity relationship [(Q)SAR] models. 2014.