[en] Allergic contact dermatitis is increasingly of interest for the hazard characterization of chemicals. in vivo animal testing is usually adopted but in silico approaches are becoming the new frontier due to their swiftness and economic efficiency. Indeed, in silico models can rationalise the experimental outcomes besides having predictive ability. The aim of the present work was to explore the electrophilic chemical behaviour responsible for allergic contact dermatitis using quantitative QSAR regression models. Eight models were proposed, using an experimental LLNA dataset of 366 chemicals. Each model is unique to encode a type of electrophilic reactivity domain. The models were obtained using autocorrelation, electro-topological and atom centered fragment based on two-dimensional descriptors, which incorporated the electronic and stereochemical features of substances interacting with skin proteins to induce skin cell proliferation. Finally, simple steps were proposed to integrate the eight models for the application on the test chemicals.
Disciplines :
Chemistry
Author, co-author :
Chayawan; Laboratory of Environmental Chemistry and Toxicology, Environmental Health Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
Selvestrel, Gianluca; Laboratory of Environmental Chemistry and Toxicology, Environmental Health Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy. Electronic address: gianluca.selvestrel@marionegri.it
Baderna, Diego; Laboratory of Environmental Chemistry and Toxicology, Environmental Health Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
Toma, Cosimo; Laboratory of Environmental Chemistry and Toxicology, Environmental Health Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
Caballero Alfonso, Ana Yisel; Laboratory of Environmental Chemistry and Toxicology, Environmental Health Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
Gamba, Alessio ; Université de Liège - ULiège > GIGA > GIGA In silico medecine - Biomechanics Research Unit ; Laboratory of Environmental Chemistry and Toxicology, Environmental Health Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
Benfenati, Emilio; Laboratory of Environmental Chemistry and Toxicology, Environmental Health Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
Language :
English
Title :
Skin sensitization quantitative QSAR models based on mechanistic structural alerts.
The authors are grateful for the contribution of the project European LIFE VERMEER (LIFE16 ENV/IT/000167) for financial support. The authors would like to thank Chemaxon ( http://www.chemaxon.com ) for the academic license of the Marvin suite used to display and characterize chemical structures. The authors are grateful to Prof. P. Gramatica for providing QSARINS software. The authors would like to thank Prof. Kunal Roy for access to tools available at https://dtclab.webs.com/software-tools and are thankful to Prof. M.T.D. Cronin for providing the SMARTS strings used in the present work.
Alves, V.M., Muratov, E., Fourches, D., Strickland, J., Kleinstreuer, N., Andrade, C.H., Tropsha, A., Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds. Toxicol. Appl. Pharmacol. 284 (2015), 262–272, 10.1016/j.taap.2014.12.014.
Alves, V.M., Capuzzi, S.J., Muratov, E.N., Braga, R.C., Thornton, T.E., Fourches, D., Strickland, J., Kleinstreuer, N., Andrade, C.H., Tropsha, A., QSAR models of human data can enrich or replace LLNA testing for human skin sensitization. Green Chem. 18 (2016), 6501–6515, 10.1039/c6gc01836j.
Ayers, P.W., The physical basis of the hard/soft acid/base principle. Faraday Discuss. 135 (2007), 161–190, 10.1039/B606877D.
Basketter, D.A., Kimber, I., Allergic contact dermatitis: is the reactive chemistry of skin sensitizers the whole story?. Contact Derm. 68 (2013), 244–245, 10.1111/cod.12052.
Basketter, D.A., Smith Pease, C.K., Patlewicz, G.Y., Contact allergy: the local lymph node assay for the prediction of hazard and risk. Clin. Exp. Dermatol., 2003, 10.1046/j.1365-2230.2003.01247.x.
Bonchev, D., Trinajstić, N., Information theory, distance matrix, and molecular branching. J. Chem. Phys. 67 (1977), 4517–4533, 10.1063/1.434593.
Chayawan, Vikas, Externally predictive single-descriptor based QSPRs forphysico-chemical properties of polychlorinated-naphthalenes: e0xploring relationships of logSW, logKOA, and logKOWwithelectron-correlation. J. Hazard. Mater. 296 (2015), 68–81, 10.1016/j.jhazmat.2015.04.028.
Cherkasov, A., Muratov, E.N., Fourches, D., Varnek, A., Baskin, I.I., Cronin, M., Dearden, J., Gramatica, P., Martin, Y.C., Todeschini, R., Consonni, V., Kuz'Min, V.E., Cramer, R., Benigni, R., Yang, C., Rathman, J., Terfloth, L., Gasteiger, J., Richard, A., Tropsha, A., QSAR modeling: where have you been? Where are you going to?. J. Med. Chem. 57 (2014), 4977–5010, 10.1021/jm4004285.
Chirico, N., Gramatica, P., Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. J. Chem. Inf. Model. 51 (2011), 2320–2335, 10.1021/ci200211n.
Chirico, N., Gramatica, P., Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection. J. Chem. Inf. Model. 52 (2012), 2044–2058, 10.1021/ci300084j.
Consonni, V., Todeschini, R., New spectral indices for molecule description. Match 60 (2008), 3–14.
Consonni, V., Ballabio, D., Todeschini, R., Comments on the definition of the q 2 parameter for QSAR validation. J. Chem. Inf. Model. 49 (2009), 1669–1678, 10.1021/ci900115y.
Dearman, R.J., Basketter, D.A., Kimber, I., Local lymph node assay: use in hazard and risk assessment. J. Appl. Toxicol. 19 (1999), 299–306, 10.1002/(SICI)1099-1263(199909/10)19:5<299::AID-JAT591>3.0.CO;2-C.
Edwards, D.A., Langer, R., A linear theory of transdermal transport phenomena. J. Pharm. Sci., 1994, 10.1002/jps.2600830925.
Enoch, S.J., Roberts, D.W., Predicting skin sensitization potency for michael acceptors in the LLNA using quantum mechanics calculations. Chem. Res. Toxicol. 26 (2013), 767–774, 10.1021/tx4000655.
Enoch, S.J., Madden, J.C., Cronin, M.T.D., Identification of mechanisms of toxic action for skin sensitisation using a SMARTS pattern based approach. SAR QSAR Environ. Res. 19 (2008), 555–578, 10.1080/10629360802348985.
Eriksson, L., Jaworska, J., Worth, A.P., Cronin, M.T.D., McDowell, R.M., Gramatica, P., Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ. Health Perspect., 2003, 10.1289/ehp.5758.
Fitzpatrick, J.M., Roberts, D.W., Patlewicz, G., An evaluation of selected (Q)SARs/expert systems for predicting skin sensitisation potential. SAR QSAR Environ. Res. 29 (2018), 439–468, 10.1080/1062936X.2018.1455223.
Galvez, J., Garcia, R., Salabert, M.T., Soler, R., Charge indexes. New topological descriptors. J. Chem. Inj Comput. Sci. 34 (1994), 520–525, 10.1021/ci00019a008.
Gleeson, D., Gleeson, M.P., Theoretical studies to estimate the skin sensitization potential of chemicals of the Schiff base domain. Int. J. Quantum Chem. 120 (2020), 1–13, 10.1002/qua.26218.
Gleeson, D., M.P.G, Theoretical studies to estimate the skin sensitization potential of chemicals of the Schiff base domain. Int. J. Quantum Chem., 120, 2020, e26218, 10.1002/qua.26218.
Gramatica, P., Principles of QSAR models validation: internal and external. QSAR Comb. Sci. 26 (2007), 694–701, 10.1002/qsar.200610151.
Gramatica, P., Chirico, N., Papa, E., Cassani, S., Kovarich, S., QSARINS: a new software for the development, analysis, and validation of QSAR MLR models. J. Comput. Chem. 34 (2013), 2121–2132, 10.1002/jcc.23361.
Kern, P.S., Gerberick, F.G., Ryan, C.A., Kimber, I., Aptula, A., Basketter, D.A., Local lymph node data for the evaluation of skin sensitization alternatives: a second compilation. Dermatitis 21 (2010), 8–32, 10.2310/6620.2009.09038.
Levine, I., Quantum Chemistry, 6, 2009, Pearson Prentice Hall, Upper Saddle River, NJ, USA 2009.
Mansouri, K., Ringsted, T., Ballabio, D., Todeschini, R., Consonni, V., Quantitative structure-activity relationship models for ready biodegradability of chemicals. J. Chem. Inf. Model. 53 (2013), 867–878, 10.1021/ci4000213.
Martin, T.M., Harten, P., Young, D.M., Muratov, E.N., Golbraikh, A., Zhu, H., Tropsha, A., Does rational selection of training and test sets improve the outcome of QSAR modeling?. J. Chem. Inf. Model. 52 (2012), 2570–2578, 10.1021/ci300338w.
Morales Helguera, A., Combes, R.D., Pérez González, M., Natália, M., Cordeiro, D.S., Applications of 2D descriptors in drug design: a DRAGON tale. Curr. Top. Med. Chem., 2008.
Moss, R.A., Adventures in reactive intermediate chemistry: a perspective and retrospective. J. Org. Chem. 82 (2017), 2307–2318.
Nandy, A., Kar, S., Roy, K., Linear discriminant analysis for skin sensitisation potential of diverse organic chemicals. Mol. Simul. 39 (2013), 432–441, 10.1080/08927022.2012.738421.
Nandy, A., Kar, S., Roy, K., Development of classification-and regression-based QSAR models and in silico screening of skin sensitisation potential of diverse organic chemicals. Mol. Simul. 40 (2014), 261–274, 10.1080/08927022.2013.801076.
Ojha, P.K., Mitra, I., Das, R.N., Roy, K., Further exploring rm2 metrics for validation of QSPR models. Chemometr. Intell. Lab. Syst. 107 (2011), 194–205, 10.1016/j.chemolab.2011.03.011.
Park, H.-S., Jun, C.H., A simple and fast algorithm for K-medoids clustering. Expert Syst. Appl. 36 (2009), 3336–3341, 10.1016/j.eswa.2008.01.039.
Pathakoti, K., Ming-Ju Huang, M.-J., John D. Watts, J.D., He, X., Hwang, H.-M., Using experimental data of Escherichia coli to develop a QSAR model for predicting the photo-induced cytotoxicity of metal oxide nanoparticles. J. Photochem. Photobiol. B, Biol. 130 (2017), 234–240, 10.1016/j.jphotobiol.2013.11.023.
Promkatkaew, M., Gleeson, D., Hannongbua, S., Gleeson, M.P., Skin sensitization prediction using quantum chemical calculations: a theoretical model for the SNAr domain. Chem. Res. Toxicol. 27 (2014), 51–60, 10.1021/tx400323e.
Roberts, D.W., Estimating skin sensitization potency from a single dose LLNA. Regul. Toxicol. Pharmacol. 71 (2015), 437–443, 10.1016/j.yrtph.2015.01.009.
Roberts, D.W., Natsch, A., High throughput kinetic profiling approach for covalent binding to peptides: application to skin sensitization potency of Michael acceptor electrophiles. Chem. Res. Toxicol. 22 (2009), 592–603, 10.1021/tx800431x.
Roberts, D.W., Patlewicz, G., Mechanism based structure-activity relationships for skin sensitisation–the carbonyl group domain. SAR QSAR Environ. Res. 13 (2002), 145–152, 10.1080/10629360290002244.
Roberts, D.W., Aptula, A.O., Patlewicz, G., Mechanistic applicability domains for non-animal based prediction of toxicological endpoints. QSAR analysis of the Schiff base applicability domain for skin sensitization. Chem. Res. Toxicol. 19 (2006), 1228–1233, 10.1021/tx060102o.
Roberts, D.W., Aptula, A.O., Patlewicz, G., Electrophilic chemistry related to skin sensitization. Reaction mechanistic applicability domain classification for a published data set of 106 chemicals tested in the mouse local lymph node assay. Chem. Res. Toxicol. 20 (2007), 44–60, 10.1021/tx060121y.
Roberts, D.W., Patlewicz, G., Dimitrov, S.D., Low, L.K., Aptula, A.O., Kern, P.S., Dimitrova, G.D., Comber, M.I.H., Phillips, R.D., Niemelä, J., Madsen, C., Wedebye, E.B., Bailey, P.T., Mekenyan, O.G., TIMES-SS - A mechanistic evaluation of an external validation study using reaction chemistry principles. Chem. Res. Toxicol. 20 (2007), 1321–1330, 10.1021/tx700169w.
Roberts, Api, A.M., Aptula, A.O., Chemical applicability domain of the Local Lymph Node Assay (LLNA) for skin sensitisation potency. Part 2. The biological variability of the murine Local Lymph Node Assay (LLNA) for skin sensitisation. Regul. Toxicol. Pharmacol. 80 (2016), 255–259, 10.1016/j.yrtph.2016.07.013.
Roberts, D.W., Aptula, A., Api, A.M., Structure–Potency relationships for epoxides in allergic contact dermatitis. Chem. Res. Toxicol. 30 (2017), 524–531, 10.1021/acs.chemrestox.6b00241.
Rose, K., Hall, L.H., E-state modeling of fish toxicity independent of 3D structure information. SAR QSAR Environ. Res. 14 (2003), 113–129, 10.1080/1062936031000073144.
Schneider, G., Neidhart, W., Giller, T., Schmid, G., Scaffold-hopping by topological pharmacophore search: a contribution to virtual screening. Angew. Chem. Int. Ed. 38 (1999), 2894–2895, 10.1002/(SICI)1521-3773(19991004)38:19<2894::AID-ANIE2894>3.0.CO;2-F.
Selvestrel, G., Robino, F., Baderna, D., Manganelli, S., Asturiol, D., Manganaro, A., Russo, M.Z., Lavado, G., Toma, C., Roncaglioni, A., Benfenati, E., SpheraCosmolife: a new tool for the risk assessment of cosmetic products. ALTEX - Altern. Anim. Exp., 2021, 10.14573/altex.2010221.
Steinbeck, C., Han, Y., Kuhn, S., Horlacher, O., Luttmann, E., Willighagen, E., The chemistry development kit (CDK): an open-source java library for chemo- and bioinformatics. J. Chem. Inf. Comput. Sci. 43 (2003), 493–500, 10.1021/ci025584y.
Todeschini, Roberto, Consonni, Viviana, Handbook of Molecular Descriptors. 2000, WILEY-VCH, Verlag GmbH, D-69469 Weinheim (Federal Republic of Germany).
Todeschini, R., Consonni, V., A.M, The k correlation index: theory development and its application in chemometrics. Chemometr. Intell. Lab. Syst. 46 (1999), 13–29, 10.1016/S0169-7439(98)00124-5.
Toropov, A.A., Toropova, A.P., The unreliability of the reliability criteria in the estimation of QSAR for skin sensitivity: a pun or a reliable law?. Toxicol. Lett. 340 (2021), 133–140, 10.1016/j.toxlet.2021.01.015.
Toropova, A.P., Toropov, A.A., Hybrid optimal descriptors as a tool to predict skin sensitization in accordance to OECD principles. Toxicol. Lett. 275 (2017), 57–66, 10.1016/j.toxlet.2017.03.023.
Toropova, A.P., Toropov, A.A., Hybrid optimal descriptors as a tool to predict skin sensitization in accordance to OECD principles. Toxicol. Lett. 275 (2017), 57–66, 10.1016/j.toxlet.2017.03.023.
Vikas, Reenu, Chayawan, Does electron-correlation has any role in the quantitative structure–activity relationships?. J. Mol. Graph. Model. 42 (2013), 7–16, 10.1016/j.jmgm.2013.02.005.
Votano, J.R., Parham, M., Hall, L.M., Hall, L.H., Kier, L.B., Oloff, S., Tropsha, A., QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation. J. Med. Chem. 49 (2006), 7169–7181, 10.1021/jm051245v.
Wang, Y., Li, Y., Ding, J., Jiang, Z., Chang, Y., Estimation of bioconcentration factors using molecular electro-topological state and flexibility. SAR QSAR Environ. Res. 19 (2008), 375–395, 10.1080/10629360802085058.
Wang, C.-C., Lin, Y.-C., Wang, S.-S., Shih, C., Lin, Y.-H., Tung, C.-W., SkinSensDB: a curated database for skin sensitization assays. J. Cheminform., 9, 2017, 5, 10.1186/s13321-017-0194-2.
Wilhelm, K.-P., Zhai, H., Maibach, H.I., Basketter, D.A., Kimber, I., Dearman, R.J., Ryan, C.A., Gerberick, G.F., The local lymph node assay. Dermatotoxicology, 2012, 296–305, 10.3109/9781841848570.039.
Wilm, A., Kühnl, J., Kirchmair, J., Computational approaches for skin sensitization prediction. Crit. Rev. Toxicol. 48 (2018), 738–760, 10.1080/10408444.2018.1528207.
Wilm, A., Stork, C., Bauer, C., Schepky, A., Kühnl, J., Kirchmair, J., Skin doctor: machine learning models for skin sensitization prediction that provide estimates and indicators of prediction reliability. Int. J. Mol. Sci., 20, 2019, 10.3390/ijms20194833.
Wilm, A., Norinder, U., Agea, M.I., De Bruyn Kops, C., Stork, C., Kühnl, J., Kirchmair, J., Skin doctor CP: conformal prediction of the skin sensitization potential of small organic molecules. Chem. Res. Toxicol. 34 (2021), 330–344, 10.1021/acs.chemrestox.0c00253.
