Regulatory acceptance of in silico approaches for the safety assessment of cosmetic-related substances

Abstract In silico approaches are becoming increasingly important tools in toxicology. This is especially the case for the cosmetics industry, where growing numbers of regions are implementing bans on the in vivo testing of ingredients and products, resulting in an increased need to be able to generate safety data in vitro and/or in silico. ‘In silico’ data include results obtained from (quantitative) structure activity relationship ((Q)SAR) models, chemical categories, grouping, read-across and physiologically-based (pharmaco)kinetic (PB(P)K) models, and ‘big data’ analysis. There are general and more specific factors to be considered when using different in silico methods, many of which are described in the multitude of guidance documents available to support their use for regulatory purposes. There are particular challenges associated with the use of in silico approaches for cosmetic safety assessment, but with proper consideration and justification as to how they have been addressed, none should be insurmountable. Whilst in silico methods are currently predominantly used for internal rather than regulatory decision making, as confidence grows in their applicability and predictivity, this is likely to change. Many of the most commonly cited issues with the use of in silico approaches relate to the need for better documentation and justification as to why the method used was appropriate. It is also frequently stated that they should be used within a weight of evidence (WoE) approach and with all available data, rather than meeting regulatory requirements as a stand-alone method. Whilst these considerations are important, it should be remembered that they are equally applicable to data generated using in vitro and in vivo methods.

[1]  T Burgdorf,et al.  Workshop on acceleration of the validation and regulatory acceptance of alternative methods and implementation of testing strategies. , 2018, Toxicology in vitro : an international journal published in association with BIBRA.

[2]  Richard Cubberley,et al.  Applying the skin sensitisation adverse outcome pathway (AOP) to quantitative risk assessment. , 2014, Toxicology in vitro : an international journal published in association with BIBRA.

[3]  Fiona Sewell,et al.  The current status of exposure-driven approaches for chemical safety assessment: A cross-sector perspective. , 2017, Toxicology.

[4]  Gavin Maxwell,et al.  From pathways to people: applying the adverse outcome pathway (AOP) for skin sensitization to risk assessment. , 2013, ALTEX.

[5]  T W Schultz,et al.  A strategy for structuring and reporting a read-across prediction of toxicity. , 2015, Regulatory toxicology and pharmacology : RTP.

[6]  S. More,et al.  The principles and methods behind EFSA's Guidance on Uncertainty Analysis in Scientific Assessment , 2018, EFSA journal. European Food Safety Authority.

[7]  Nicholas Ball,et al.  The challenge of using read-across within the EU REACH regulatory framework; how much uncertainty is too much? Dipropylene glycol methyl ether acetate, an exemplary case study. , 2014, Regulatory toxicology and pharmacology : RTP.

[8]  K R Przybylak,et al.  Characterisation of data resources for in silico modelling: benchmark datasets for ADME properties , 2018, Expert opinion on drug metabolism & toxicology.

[9]  Fiona Sewell,et al.  Steps towards the international regulatory acceptance of non‐animal methodology in safety assessment , 2017, Regulatory toxicology and pharmacology : RTP.

[10]  George Papadatos,et al.  Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set , 2017, bioRxiv.

[11]  J. Dearden,et al.  QSAR modeling: where have you been? Where are you going to? , 2014, Journal of medicinal chemistry.

[12]  Kevin A Ford,et al.  Refinement, Reduction, and Replacement of Animal Toxicity Tests by Computational Methods. , 2016, ILAR journal.

[13]  A Worth,et al.  Application of physiologically-based toxicokinetic modelling in oral-to-dermal extrapolation of threshold doses of cosmetic ingredients. , 2014, Toxicology letters.

[14]  Carl Westmoreland,et al.  A future approach to measuring relative skin sensitising potency: a proposal , 2006, Journal of applied toxicology : JAT.

[15]  Igor I Baskin,et al.  Machine Learning Methods in Computational Toxicology. , 2018, Methods in molecular biology.

[16]  Weida Tong,et al.  The challenge of the application of 'omics technologies in chemicals risk assessment: Background and outlook. , 2017, Regulatory toxicology and pharmacology : RTP.

[17]  Anne Hersey,et al.  Legacy data sharing to improve drug safety assessment: the eTOX project , 2017, Nature Reviews Drug Discovery.

[18]  Maurice Whelan,et al.  How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology , 2016, Toxicological sciences : an official journal of the Society of Toxicology.

[19]  Fred A. Wright,et al.  A chemical–biological similarity-based grouping of complex substances as a prototype approach for evaluating chemical alternatives† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c6gc01147k Click here for additional data file. , 2016, Green chemistry : an international journal and green chemistry resource : GC.

[20]  Anthony Hardy,et al.  Guidance on Uncertainty Analysis in Scientific Assessments , 2018, EFSA journal. European Food Safety Authority.

[21]  Tudor I. Oprea,et al.  In silico toxicology protocols. , 2018, Regulatory toxicology and pharmacology : RTP.

[22]  Hao Zhu,et al.  t4 report*: Toward Good Read-Across Practice (GRAP) Guidance , 2016, ALTEX.

[23]  Jie Shen,et al.  Toward Good Read-Across Practice (GRAP) , 2016 .

[24]  P. Zhao,et al.  Report from the EMA workshop on qualification and reporting of physiologically based pharmacokinetic (PBPK) modeling and simulation , 2017, CPT: pharmacometrics & systems pharmacology.

[25]  M Cottin,et al.  Alternatives in cosmetics testing. , 1995, Toxicology in vitro : an international journal published in association with BIBRA.

[26]  Alexander Tropsha,et al.  Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides? , 2017, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.

[27]  Thomas Hartung,et al.  Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility , 2018, Toxicological sciences : an official journal of the Society of Toxicology.

[28]  S D Dimitrov,et al.  QSAR Toolbox – workflow and major functionalities* , 2016, SAR and QSAR in environmental research.

[29]  John Delaney,et al.  Modern agrochemical research: a missed opportunity for drug discovery? , 2006, Drug discovery today.

[30]  Takao Ashikaga,et al.  Evaluation of combinations of in vitro sensitization test descriptors for the artificial neural network‐based risk assessment model of skin sensitization , 2015, Journal of applied toxicology : JAT.

[31]  Bertrand Desprez,et al.  Non-animal methods to predict skin sensitization (I): the Cosmetics Europe database* , 2018, Critical reviews in toxicology.

[32]  Judy Strickland,et al.  Bayesian integrated testing strategy (ITS) for skin sensitization potency assessment: a decision support system for quantitative weight of evidence and adaptive testing strategy , 2015, Archives of Toxicology.

[33]  S. H. Bennekou,et al.  Scientific Opinion on the state of the art of Toxicokinetic/Toxicodynamic (TKTD) effect models for regulatory risk assessment of pesticides for aquatic organisms , 2018, EFSA journal. European Food Safety Authority.

[34]  Hao Zhu,et al.  Big Data in Chemical Toxicity Research: The Use of High-Throughput Screening Assays To Identify Potential Toxicants , 2014, Chemical research in toxicology.

[35]  Imran Shah,et al.  Predicting Organ Toxicity Using in Vitro Bioactivity Data and Chemical Structure. , 2017, Chemical research in toxicology.

[36]  Nigel Greene,et al.  Early toxicity screening strategies. , 2009, Current opinion in drug discovery & development.

[37]  Catherine Mahony,et al.  The future trajectory of adverse outcome pathways: a commentary , 2018, Archives of Toxicology.

[38]  Andrew Worth,et al.  Multiscale modelling approaches for assessing cosmetic ingredients safety. , 2017, Toxicology.

[39]  Harvey J Clewell,et al.  Quantitative in vitro to in vivo extrapolation of cell-based toxicity assay results , 2012, Critical reviews in toxicology.

[40]  Andrew Worth,et al.  Investigating the state of physiologically based kinetic modelling practices and challenges associated with gaining regulatory acceptance of model applications , 2017, Regulatory toxicology and pharmacology : RTP.