Guidance on use of alternative methods for testing in the safety assessment of cosmetics and quasi-drug

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1 Asian Congress 2016, Nov. 15, 2016, Karatsu, Saga, Japan Guidance on use of alternative methods for testing in the safety assessment of cosmetics and quasi-drug Hajime Kojima, NIHS, et al. 1

2 Table 1. Test methods for safety evaluation of cosmetic ingredients defined by each cosmetic industry association. JCIA safety evaluation guidance (2015) PCPC safety evaluation guideline Cosmetic Europe, COLIPA (2014) guideline(2004) Single dose toxicity Oral toxicity Acute toxicity (oral or inhalation) Repeat dose toxicity Dermal toxicity Sub-chronic toxicity (oral or inhalation) - Inhalation toxicity Primary skin irritation Primary dermal irritation Dermal irritation Cumulative skin irritation - - Skin sensitization Dermal sensitization Skin sensitization Phtotoxicity Phtotoxicity& Photoallegy Phtotoxicity Photosensitization Ocular irritation Eye irritation Eye irritation Genotoxicity Genotoxicity Mutagenicity Human patch test Controlled use studies in human Human data - - Toxicokinetics - Mucous membrane irritation Mucous membrane irritation Skin absorption Skin absorption Dermal absorption Reproductive and developmental toxicity Reproductive and developmental toxicity Reproductive toxicity, Carcinogenecity, Additional genotoxicity 2

3 Table 2. Test methods for safety evaluation of ingredients for regulatory use Quasi-drug safety evaluation guidance (2008) SCCS safety evaluation guidance (2012) Special cosmetic safety evaluation guideline Functional cosmetic safety evaluation guideline Acute toxicity Acute toxicity Acute toxicity Repeat dose toxicity (Subchronic, Chronic) Reproductive toxicity Repeat dose toxicity Reproductive toxicity Skin irritation Corrosivity & irritation Acute skin irritation Primary skin irritation - Cumulative skin irritation - Skin sensitization Skin sensitization Skin sensitization Skin sensitization Phtoto-induced toxicity Phtotoxicity Photo sensitization Photo sensitization Photo sensitization Mucous membrane irritation Acute eye irritation Eye or mucous membrane irritation Genotoxicity Mutagenicity/genotoxicity Genotoxicity Human data Human patch test Human patch test Controlled use studies in human Human repeat insult patch test ADME Toxicokinetics Carcinogenecity Dermal/percutaneous absorption Carcinogenecity 3

4 Regulatory issue on alternative methods According to the MHLW notification, Japan in 2011, JaCVAM (Japanese Center for the Validation of Alternative Methods) decided to accelerate new in vitro testing methods to take advantage of this opportunity to strongly impact testing throughout Japan. Therefore, the members are coordinating the Guidance on the use of alternative test methods in safety assessment of cosmetics and quasi-drugs since The members have consists on dermatologists, delegates of cosmetic companies, the technical officers of PMDA (Pharmaceuticals and Medical Devices Agency) and specialists of NIHS (National Institute of Health Sciences) and this member is on-going to make drafting the guidance based on the OECD test guideline and/or JaCVAM evaluation document per each alternative test method. 4

5 Members for the guidance Classification Name Affiliation Dermatologist Tokio Nakada Showa University, Fujigaoka Hospital, Japan Akiko Yagami Fujita Health University School of Medicine, Japan Regulator Yukiko Hoshino Pharmaceuticals and Medical Devices Agency, Naofumi Iizuka Japan Takatoshi Nakamura Shinichi Sekizawa Kazutoshi Shinoda Mio Yagi Researcher Kenji Sugibayashi Faculty of Pharmaceutical Sciences, Josai University, Hiroaki Todo Japan Yoshiaki Ikarashi Hajime Kojima National Institute of Health Sciences, Japan Industry Daisuke Araki Japan Cosmetic Industry Association, Japan Hitoshi Sakaguchi Hitoshi Sasa Mariko Sugiyama 5

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7 On-going draft guidance Complete of public comment on in vitro skin absorption Approach to a guidance on non-animal testings for skin sensitization Start discussion for Short Time Exposure assay for alternative to eye irritation testing 7

8 Approach to a guidance on non-animal testings for skin sensitization 8

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10 ENV/JM/HA(2016)11 Annex 1 Case study Purpose 1 An Adverse Outcome Pathway-based "2 out of 3" integrated testing strategy approach to skin hazard identification (BASF) Hazard identification 2 Sequential Testing Strategy (STS) for hazard identification of skin sensitisers (RIVM) Hazard identification 3 A non-testing Pipeline approach for skin sensitisation (G. Patlewicz) Hazard identification 4 Stacking meta-model for skin sensitisation hazard identification (L'Oréal) Hazard identification 5 Integrated decision strategy for skin sensitisation hazard (ICCVAM) Hazard identification 6 Consensus of classification trees for skin sensitisation hazard prediction (EC- JRC) Hazard identification 7 Sensitizer potency prediction based on Key event 1 + 2: Combination of kinetic peptide reactivity data and KeratinoSens data (Givaudan) Potency prediction 8 The artificial neural network model for predicting LLNA EC3 (Shiseido) Potency prediction 9 Bayesian Network DIP (BN-ITS-3) for hazard and potency identification of skin sensitizers (P&G) Potency prediction 10 Sequential testing strategy (STS) for sensitising potency classification based on in chemico and in vitro data (Kao Corporation) Potency prediction 11 Integrated testing strategy (ITS) for sensitising potency classification based on in silico, in chemico, and in vitro data (Kao Corporation) Potency prediction 12 DIP for skin allergy risk assessment (SARA) (Unilever) Potency prediction 10

11 Artificial Neural Network (ANN) analysis... axon synapses dendrite In vitro test data h-clat DPRA-Cys DPRA-Lys KeratinoSens Other biomarkers Input layer Hidden layer In vivo test data LLNA EC3 EC3 values for LLNA positive chemicals For LLNA negative chemicals, 101% was used as EC3 value (converted to mm/cm 2 unit). Output layer 11

12 Log(Predicted LLNA EC3 (um/cm 2 ) ) Correlation between LLNA and the ANN prediction R= 0.87(N=134) - RMS error= After 10-fold cross-validation Log(Published LLNA EC3 (um/cm 2 )) *Root mean square (RMS) error = ((measured value - predicted value) 2 ) / number of data This ANN model showed a good correlation between predicted values and actual values. Therefore, our ANN model can contribute to building a new QRA evaluation system which is using no animals. 12

Kao STS & ITS Kao Sequential (Tiered) Testing Strategy (STS) DPRA (KE1) and h-clat (KE3) Predicting the three potency classes not classified, weak (EC3 1%), strong (EC3 < 1%) Kao Integrated

13 Kao STS & ITS Kao Sequential (Tiered) Testing Strategy (STS) DPRA (KE1) and h-clat (KE3) Predicting the three potency classes not classified, weak (EC3 1%), strong (EC3 < 1%) Kao Integrated (Score-based) Testing Strategy (ITS) DPRA, Derek (KE1) and h-clat (KE3) Predicting the three potency classes not classified, weak (EC3 1%), strong (EC3 < 1%) Takenouchi et al Total score of 0-1: not classified, of 2-6: weak, of 7: strong 13

14 Kao STS & ITS compared to LLNA data Excluding negative results of chemicals with logkow>3.5 Kao STS (n=139) Hazard accuracy:81% (10 FN, 17 FP) Potency accuracy : 69% (20 under, 23 over) Kao STS (n=129) Hazard accuracy:85% (2 FN, 17 FP) Potency accuracy : 73% Kao ITS (n=139) Hazard accuracy:84% (11 FN, 11 FP) Potency accuracy : 71% (18 under, 12 over) Kao ITS (n=128) Hazard accuracy:89% (3 FN, 11 FP) Potency accuracy : 74% Takenouchi et al

3 out of 3 for Bottom-up 3 out of 3 for Bottom-up could be used as one of testing strategies to identify non-sensitizers as part of bottom-up approach DPRA Negative KeratinoSens Negative h-clat

15 3 out of 3 for Bottom-up 3 out of 3 for Bottom-up could be used as one of testing strategies to identify non-sensitizers as part of bottom-up approach DPRA Negative KeratinoSens Negative h-clat Negative Nonsensitizer vs human data vs LLNA Sensitivity (%) Specificity (%) Accuracy (%) Sensitivity (%) Specificity (%) Accuracy (%) N LLNA out of 3 for bottom-up Analyzed using data from Urbisch et al Inconsistent results of in vitro assays would require additional information to support the conclusion. 3 out of 3 for Bottom-up is not the only approach. Additional testing strategies to identify non-sensitizers are also under discussion. 15

16 Summary The members of guidance team have also contributed to the establishment of a guidance for skin sensitization based on Test Guidelines (TGs) and guidance documents in OECD. 16

17 Thank you for your attention 17

May 24, 2018 OpenTox Asia

May 24, 2018 OpenTox Asia Development of an artificial neural network model for risk assessment of skin sensitization using multiple in vitro sensitization tests and in silico parameter Morihiko Hirota