IN SILICO METHODS : PRINCIPLE AND PERFORMANCES

C Piroird • A Del Bufalo • S Ringeissen • J Eilstein • L Baux • M Thomas • H Nocairi • C Gomes • N Ade • S Teissier • P Berthe • JR MeunierL’Oréal Research Life Sciences Department, 93601 Aulnay-Sous-Bois, Cedex, France.

INDIVIDUAL PREDICTIVE CAPACITIES OF IN SILICO AND IN VITRO METHODSFOR THE ALTERNATIVE ASSESSMENT OF SKIN SENSITIZATION: A COMPARATIVE STUDY ON A COMMON CHEMICAL SET

INTRODUCTIONSkin sensitization is a delayed type allergy consisting of a cellular immune reaction to small molecular weight chemicals (hapten). So far, animal test methods such as the LLNA are used to predict the skin sensitization potential of unknown chemicals. In line with the 3Rs concept, ranges of in silico and in vitroalternative methods have been developed. While in silico methods are based on structure activity relationships, in vitro assays model the early events of the skin sensitization process: chemical reactivity assays (DPRA, GSH assay) reflect the haptenation mechanism, Nrf-2 based assays (Keratinosens, AREc42) analyze the induction of the cellular antioxidant pathway and DC-based assays (MUSST, hCLAT) measure DC maturation markers (CD86, CD54). These assays were generally shown to have good predictive values for the hazard identification of skin sensitizers, but the correlation studies reported, differ in the nature of the reference that was used (animal or clinical references) as well as in the number of data analyzed. In the present study, we show the data of 3 in silico methods (Derek, TIMES-SS and ToxTree) and 3 in vitro assays (DPRA, MUSST, Nrf-2 reporter assay from Invitrogen) on a common chemical set composed of 165 compounds. We analyze the individual performances of each method for the hazard identification of sensitizers.

DESCRIPTION OF THE DATA SETFor this study we worked on a set of 165 ingredients with in vivo LLNA data. This set was composed of (Figure 1A): 68 non sensitizers and 97 sensitizers 143 mono-substances and 22 mixtures 93 public references and 72 L’Oreal raw materials

Moreover, the set was composed of a large range of different cosmetic classes (fragrances, preservatives, actives, surfactants…), the most represented class being the one of the dyes (Figure 1B).

Figure 1: Presentation of data set• Compositon of the set in terms of sensitizers (S) versus non sensitizers (NS), mono-ingredients versus mixtures and internal molecules versus public references.• Distribution among cosmetic ingredients’ classes.

Three models were used : 1. Toxtree “Skin Sensitization Alerts” decision tree (IdeaConsult) which relies on a Reaction Mechanistic Domains classification (Aptula and Roberts, 2006). 2. DerekforWindows “Skin sensitization” alerts (Lhasa Ltd) which is an expert knowledge system comprising sets of structural rules (Marchant et al., 2008).3. Times-SS (OASIS- LMC) – This is an hybrid expert system combining a simulator of skin metabolism, structural alerts and 3D-QSARs (Patlewicz et al., 2007).The rules for coding outcomes of each model are as follows (Fig 2). The predictivity varies between 73% and 92% with generally less specificity than sensitivity. TIMES is the most accurate but its domain of applicability is the most restricted (Fig 3). Each model has its specificity and will ideally not be used similarly in an integrated testing strategy (ITS) (e.g. Toxtree classification is foreseen as a clustering method to be used upfront of an ITS).

Figure 2: Classification rules

CONCLUSIONIn conclusion, all the tested assays show comparable good predictive values on a common set of ingredients and are generally more sensitive than specific. Further analysis using physical/chemical characteristics will define the applicability domain of each test. Among the different tests, the rate of predicted ingredients is variable and as observed on Fig 7, the “Inconclusive” cases (white boxes) are different for each test. Moreover, only 60/165 ingredients (36%) are similarly predicted by all the tests (all boxes green or all boxes red). This underlines the added value of combining the different tests in an integrated testing strategy to increase the rate of predicted molecules. Besides, combining the different tests can also increase the general predictive performances and the index of confidence for a given prediction. For instance, for 58 ingredients with identical in silico/in vitro conclusions, the prediction is the same as the one made by the LLNA. For the 2 cases that show different conclusions than in the LLNA, one is the isopropyl myristate which is known to be a false positive in the LLNA (Gerberick et al., 2005) and the other example is a dye, predicted sensitizer by all the in silico/in vitro tests, negative in LLNA but positive in the guinea pig Magnusson and Kligmann test. Altogether, when all the in silico/in vitro methods give the same conclusions, the final prediction is highly accurate. For the remaining 105 cases where the conclusions of the in silico/in vitro methods are different, it is necessary to identify the most appropriate statistical tools/methodology to obtain the best prediction model (see poster 753/613).

REFERENCES• Ade,N., Leon,F., Pallardy,M., Peiffer,J.L., Kerdine-Romer,S., Tissier,M.H., Bonnet,P.A., Fabre,I. and Ourlin,J.C. (2009) HMOX1 and NQO1 genes are upregulated in response to contact sensitizers in dendritic cells and THP-1 cell line: role of the Keap1/Nrf2 pathway Toxicol. Sci., 107, 451-460.• Aptula,A.O. and Roberts,D.W. (2006) Mechanistic applicability domains for nonanimal-based prediction of toxicological end points: general principles and application to reactive toxicity. Chem. Res. Toxicol., 19, 1097-1105.• Emter,R., Ellis,G. and Natsch,A. (2010) Performance of a novel keratinocyte-based reporter cell line to screen skin sensitizers in vitro. Toxicol. Appl. Pharmacol., 245, 281-290.• Gerberick,G.F., Ryan,C.A., Kern,P.S., Schlatter,H., Dearman,R.J., Kimber,I., Patlewicz,G.Y. and Basketter,D.A. (2005) Compilation of historical local lymph node data for evaluation of skin sensitization alternative methods. Dermatitis, 16,157-202.• Marchant,C.A., Briggs,K.A. and Long,A. (2008) In silico tools for sharing data and knowledge on toxicity and metabolism: derek for windows, meteor, and vitic. Toxicol. Mech. Methods, 18, 177-187.• Natsch,A., Caroline,B., Leslie,F., Frank,G., Kimberly,N., Allison,H., Heather,I., Robert,L., Stefan,O., Hendrik,R., Andreas,S. and Roger,E. (2011) The intra- and inter-laboratory reproducibility and predictivity of the KeratinoSens assay to predict skin sensitizers in vitro: results of a ring-study in five laboratories. Toxicol. In Vitro, 25, 733-744.• Natsch,A. and Emter,R. (2008) Skin sensitizers induce antioxidant response element dependent genes: application to the in vitro testing of the sensitization potential of chemicals. Toxicol. Sci., 102, 110-119.• Patlewicz,G., Dimitrov,S.D., Low,L.K., Kern,P.S., Dimitrova,G.D., Comber,M.I., Aptula,A.O., Phillips,R.D., Niemela,J., Madsen,C., Wedebye,E.B., Roberts,D.W., Bailey,P.T. and Mekenyan,O.G. (2007) TIMES-SS--a promising tool for the assessment of skin sensitization hazard. A characterization with respect to the OECD validation principles for (Q)SARs and an external evaluation for predictivity. Regul. Toxicol. Pharmacol., 48, 225-239.

Figure 7: In silico/in vitro profile of the 165 chemicalsThe table represents the prediction of the 7 in silico/in vitro tests (columns)

for the 165 molecules (lines). The green code stands for “non sensitizer”,red for “sensitizer” and the white for “Inconclusive”

DPRA: PRINCIPLE AND PERFORMANCES

There are a variety of characteristics that determine whether a chemical can be considered as a contact sensitizer including its ability to react with nucleophilic residues of proteins. The “Direct Peptide Reactivity Assay” (DPRA) allows to measure this ability and is used to screen for skin sensitizer. DPRA consists of two synthetic peptides containing nucleophilic residues such as cysteine or lysine which are incubated with tested compounds. After a 24 hour reaction period with one of each synthetic peptide, the samples are analyzed by HPLC using UV detection to monitor the depletion of peptide following reaction. A decision tree based on both peptides’ depletion is used to classify the molecule into reactivity categories. In this assay, “inconclusive” classification was issued for 22 chemicals when interfering factors linked to the detection method used (UV) or to the solubility were observed. The Cooper’s statistics show a good overall predictivity for the DPRA (84% accuracy) with 89% sensitivity and 76% specificity.

Nrf-2 ASSAY: PRINCIPLE AND PERFORMANCES

Due to their electrophilic reactivity, sensitizers have shown their ability to activate the Nrf-2 trans-cription factor and the transcription of the ARE (Antioxydant Responsive Element) dependent genes (Ade et al., 2009; Natsch and Emter, 2008). Based on this property, cellular assays that enable to assess the activation of Nrf-2 have been developed (Natsch et al., 2011). We here used the “CellSensor ARE-bla HepG2 » assay distributed by Invitrogen which consists in a hepatocytic reporter cell line, the HepG2 that has been steadily transfected with the gene coding for the enzyme β-lactamase under the control of the Nrf-2 promoter. In this assay, cells are exposed to a concentration range of chemicals in a 384-well plate and cultured for 18h. The activation of Nrf-2 is then assessed by fluorescence. In this assay a molecule is predicted as a sensitizer based on the level of induction of Nrf-2 compared to the positive reference (tert-butylhydroquinone) and on the effective dose that al-lows the induction. Specific cut-off values on those two parameters were determined by statistical analysis. With these criteria, 88% of the chemical set can be predicted. On these molecules, the Cooper’s statistics show quite good overall predictivity for the Nrf-2 assay (74% accuracy) with 76% sensitivity and 72% specificity. Those performances are however weaker than those published by Natsch et al. (2010) in the KeratinoSens assay on a different set of molecules (Emter et al., 2010).

Figure 5: Nrf-2 (Reporter assay from Invitrogen)

MUSST: PRINCIPLE AND PERFORMANCES

One of the early events of sensitization is the activation of dendritic cells by the hapten recognized as a danger signal by the immune system. The “Myeloïd U937 Skin Sensitization Test” (MUSST) is designed to model this part of the sensitization process using the human monocytic cell line U937 as dendritic cell surrogates and the co-stimulatory molecule CD86 as the measured marker of cell activation.The MUSST is thus based on flow cytometry assessment of CD86 upregulation on U937 cells after exposure to chemicals. Practically, the U937 cells are exposed to a concentration range of chemi-cals in a 96-well plate and cultured for 48 h. In this assay, an ingredient is predicted as sensitizer if it induces a dose-dependent increase of the CD86 expression over 150% of the vehicle control at non toxic doses. In agreement with the MUSST prediction model (ECVAM pre-validation ongoing), an “inconclu-sive” classification was issued for 62 chemicals when interfering factors were observed such as cy-totoxicity issues, colour interference or solubility. Complementary test methods were developed to address these limitations (apoptosis, CD86 mRNA, Episkin U937 co-culture). A MUSST classi-fication was determined for the other 103 chemicals. The MUSST correctly classified 28 / 42 non sensitizers and 52 / 61 sensitizers. The Cooper’s statistics show a good overall predictivity (78% accuracy) with 85% sensitivity and 67% specificity.

Figure 4: DPRA (Direct Peptide Reactivity Assay)

Figure 6: MUSST (Myeloid U937 Skin Sensitization Test)Figure 3: In silico methods