Return to search

Desenvolvimento de modelos de QSAR e análise quimioinformática da sensibilização e permeabilidade da pele / Development of QSAR models and cheminformatics analysis of skin sensitization and permeability

Submitted by Erika Demachki (erikademachki@gmail.com) on 2014-09-05T20:11:20Z
No. of bitstreams: 2
Alves, Vinicius de Medeiros - 2014.pdf: 3082084 bytes, checksum: da4838d5fe24841429f43de84204d98a (MD5)
license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2014-09-05T20:11:20Z (GMT). No. of bitstreams: 2
Alves, Vinicius de Medeiros - 2014.pdf: 3082084 bytes, checksum: da4838d5fe24841429f43de84204d98a (MD5)
license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)
Previous issue date: 2014-03-17 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few reports analyzing the relationships between their molecular structure and the sensitization potential including the connection to skin permeability, which is widely considered to be mechanistically implicated in sensitization. In this study, we have compiled, curated, and integrated the largest publicly available datasets related to chemically-induced skin sensitization and skin permeability. Unexpectedly, no correlation between sensitization and permeability has been found. Predictive QSAR models have been developed and validated for both skin sensitization and skin permeability using a standardized workflow fully compliant with the OECD guidelines. The classification accuracies of QSAR models discriminating sensitizers from non-sensitizers were 0.68-0.88 when evaluated on several external validation sets. When compared to the predictions generated by the OECD QSAR Toolbox skin sensitization module, our models had significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate and Negative Predicted Rate as well as Correct Classification Rate. We have also developed QSAR models of skin permeability measured quantitatively. Cross-species correlation between human and rodent permeability data was found to be low (r²=0.44); thus, skin permeability models were developed using human data only and their external accuracy was q²ext = 0.87 (for 62% of external compounds found within the model applicability domain). Skin sensitization models have been employed to identify putative chemical hazards in the Scorecard database of possible skin or sense organ toxicants that should be regarded as primary candidates for the experimental validation. / A exposição repetida a agentes químicos pode induzir a sensibilização da pele em indivíduos inerentemente suscetíveis e desencadear uma resposta imunológica exacerbada. Apesar de muitos compostos químicos estarem implicados na sensibilização da pele, existem poucos estudos analisando as relações entre a estrutura molecular e o potencial sensibilizador desses compostos, incluindo a conexão com a permeabilidade pela pele, a qual é referida como sendo primordial para o processo de sensibilização. Neste estudo foram compilados, integrados e preparados os maiores conjuntos de dados disponíveis publicamente relacionados tanto com a sensibilização da pele quanto à permeabilidade. Inesperadamente, não se encontrou correlação entre essas duas propriedades. Modelos de QSAR robustos e preditivos foram gerados e validados para ambas as propriedades usando um fluxo de trabalho totalmente complacente com as recomendações da OECD. As taxas de acerto dos modelos discriminaram estruturas sensibilizadoras de não sensibilizadoras com uma taxa de 0,68-0,88 de sucesso, quando avaliadas em vários conjuntos de validação externa. Quando comparados com o módulo de sensibilização da pele implementado na ferramenta QSAR Toolbox da OECD, os modelos tiveram baixa cobertura do espaço químico, mas precisão preditiva mais elevada para os mesmos conjuntos de compostos externos avaliados pelo valor de preditividade positiva e valor de preditividade negativa assim como pela acurácia balanceada. O coeficiente de correlação cruzada entre os dados de permeabilidade da pele humana e de roedores apresentou-se baixo (r²=0,44); assim, apenas o conjunto de dados de pele humana foi considerado para geração de modelos de permeabilidade, que apresentaram precisão externa de q²ext = 0,87 (para 62% dos compostos dentro do domínio de aplicabilidade). Modelos de sensibilização da pele foram empregados para identificação de toxicantes putativos no banco de dados de possíveis agentes toxicantes da Scorecard que podem ser considerados como candidatos para validação experimental.

Identiferoai:union.ndltd.org:IBICT/oai:repositorio.bc.ufg.br:tde/3028
Date17 March 2014
CreatorsAlves, Vinícius de Medeiros
ContributorsAndrade, Carolina Horta, Andrade, Carolina Horta, Ferreira, Elizabeth Igne, Camargo, Ademir J.
PublisherUniversidade Federal de Goiás, Programa de Pós-graduação em Ciências Farmacêuticas (FF), UFG, Brasil, Faculdade Farmácia - FF (RG)
Source SetsIBICT Brazilian ETDs
LanguagePortuguese
Detected LanguagePortuguese
Typeinfo:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis
Formatapplication/pdf
Sourcereponame:Biblioteca Digital de Teses e Dissertações da UFG, instname:Universidade Federal de Goiás, instacron:UFG
Rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/, info:eu-repo/semantics/openAccess
Relation824936988196152412, 600, 600, 600, 600, 6010281161524209375, 6216025074656932336, 2075167498588264571, ABRAHAM, M. H.; CHADHA, H. S.; MARTINS, F.; MITCHELL, R. C.; BRADBURY, M. W.; GRATTON, J. A. Hydrogen bonding part 46: a review of the correlation and prediction of transport properties by an lfer method: physicochemical properties, brain penetration and skin permeability. Pesticide Science, v. 55, n. 1, p. 78–88, 26 jan. 1999. ADEUSI, S. Pharmaceutical R&D: an organizational design approach to enhancing productivity. [s.l.] Massachusetts Institute of Technology, 2011. ADLER, S.; BASKETTER, D.; CRETON, S.; PELKONEN, O.; VAN BENTHEM, J.; ZUANG, V.; ANDERSEN, K. E.; ANGERS-LOUSTAU, A.; APTULA, A.; BAL-PRICE, A.; BENFENATI, E.; BERNAUER, U.; BESSEMS, J.; BOIS, F. Y.; BOOBIS, A.; BRANDON, E.; BREMER, S.; BROSCHARD, T.; CASATI, S.; COECKE, S.; CORVI, R.; CRONIN, M.; DASTON, G.; DEKANT, W.; FELTER, S.; GRIGNARD, E.; GUNDERT-REMY, U.; HEINONEN, T.; KIMBER, I.; KLEINJANS, J.; KOMULAINEN, H.; KREILING, R.; KREYSA, J.; LEITE, S. B.; LOIZOU, G.; MAXWELL, G.; MAZZATORTA, P.; MUNN, S.; PFUHLER, S.; PHRAKONKHAM, P.; PIERSMA, A.; POTH, A.; PRIETO, P.; REPETTO, G.; ROGIERS, V.; SCHOETERS, G.; SCHWARZ, M.; SERAFIMOVA, R.; TÄHTI, H.; TESTAI, E.; VAN DELFT, J.; VAN LOVEREN, H.; VINKEN, M.; WORTH, A.; ZALDIVAR, J.-M. Alternative (non-animal) methods for cosmetics testing: current status and future prospects-2010. Archives of toxicology, v. 85, n. 5, p. 367–485, maio 2011. AEBY, P.; ASHIKAGA, T.; BESSOU-TOUYA, S.; SCHEPKY, A.; GERBERICK, F.; KERN, P.; MARREC-FAIRLEY, M.; MAXWELL, G.; OVIGNE, J.-M.; SAKAGUCHI, H.; REISINGER, K.; TAILHARDAT, M.; MARTINOZZI-TEISSIER, S.; WINKLER, P. Identifying and characterizing chemical skin sensitizers without animal testing: Colipa’s research and method development program. Toxicology in vitro : an international journal published in association with BIBRA, v. 24, n. 6, p. 1465–73, set. 2010. ANDERSON, S. E.; SIEGEL, P. D.; MEADE, B. J. The LLNA: A brief review of recent advances and limitations. Journal of allergy, v. 2011, p. 424203, jan. 2011. ANDRICOPULO, A. D.; SALUM, L. B.; ABRAHAM, D. J. Structure-based drug design strategies in medicinal chemistry. Current topics in medicinal chemistry, v. 9, n. 9, p. 771–90, jan. 2009. ANKLEY, G. T.; BENNETT, R. S.; ERICKSON, R. J.; HOFF, D. J.; HORNUNG, M. W.; JOHNSON, R. D.; MOUNT, D. R.; NICHOLS, J. W.; RUSSOM, C. L.; SCHMIEDER, P. K.; SERRRANO, J. A.; TIETGE, J. E.; VILLENEUVE, D. L. Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environmental toxicology and chemistry / SETAC, v. 29, n. 3, p. 730–41, mar. 2010. ARTEMENKO, A. G.; MURATOV, E. N.; KUZ’MIN, V. E.; MURATOV, N. N.; VARLAMOVA, E. V; KUZ’MINA, A. V; GORB, L. G.; GOLIUS, A.; HILL, F. C.; LESZCZYNSKI, J.; TROPSHA, A. QSAR analysis of the toxicity of nitroaromatics in Tetrahymena pyriformis: structural factors and possible modes of action. SAR and QSAR in environmental research, v. 22, n. 5-6, p. 575–601, 2011. ASHBY, J.; BASKETTER, D. A.; PATON, D.; KIMBER, I. Structure activity relationships in skin sensitization using the murine local lymph node assay. Toxicology, v. 103, n. 3, p. 177–94, 10 dez. 1995. BAKER, M. Fragment-based lead discovery grows up. Nature reviews. Drug discovery, v. 12, n. January, p. 5–7, 2013. BARRATT, M. D. Quantitative structure-activity relationships for skin permeability. Toxicology in vitro : an international journal published in association with BIBRA, v. 9, n. 1, p. 27–37, fev. 1995. BASKETTER, D. A.; EVANS, P.; FIELDER, R. J.; GERBERICK, G. F.; DEARMAN, R. J.; KIMBER, I. Local lymph node assay - validation, conduct and use in practice. Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association, v. 40, n. 5, p. 593–8, maio 2002. BASKETTER, D. A.; ROBERTS, D. W.; CRONIN, M.; SCHOLES, E. W. The value of the local lymph node assay in quantitative structure-activity investigations. Contact dermatitis, v. 27, n. 3, p. 137–42, set. 1992. BASKETTER, D.; PEASE, C.; KASTING, G.; KIMBER, I.; CASATI, S.; CRONIN, M.; DIEMBECK, W.; GERBERICK, F.; HADGRAFT, J.; HARTUNG, T.; MARTY, J.-P.; NIKOLAIDIS, E.; PATLEWICZ, G.; ROBERTS, D.; ROGGEN, E.; ROVIDA, C.; VAN DE SANDT, J. Skin sensitisation and epidermal disposition: the relevance of epidermal disposition for sensitisation hazard identification and risk assessment. The report and recommendations of ECVAM workshop 59. Alternatives to laboratory animals : ATLA, v. 35, n. 1, p. 137–54, mar. 2007. BAXTER, K.; HORN, E.; GAL-EDD, N.; ZONNO, K.; O’LEARY, J.; TERRY, P. F.; TERRY, S. F. An end to the myth: there is no drug development pipeline. Science translational medicine, v. 5, n. 171, p. 171cm1, 6 fev. 2013. BELFIELD, G. P.; DELANEY, S. J. The impact of molecular biology on drug discovery. Biochemical Society transactions, v. 34, n. Pt 2, p. 313–6, abr. 2006. BERMAN, H. M.; KLEYWEGT, G. J.; NAKAMURA, H.; MARKLEY, J. L. How community has shaped the Protein Data Bank. Structure (London, England : 1993), v. 21, n. 9, p. 1485–91, 3 set. 2013. BLEICHER, K. H.; BÖHM, H.-J.; MÜLLER, K.; ALANINE, A. I. Hit and lead generation: beyond high-throughput screening. Nature reviews. Drug discovery, v. 2, n. 5, p. 369–78, maio 2003. BOS, J. D.; MEINARDI, M. M. The 500 Dalton rule for the skin penetration of chemical compounds and drugs. Experimental dermatology, v. 9, n. 3, p. 165–9, jun. 2000. BREIMAN, L. E. O. Random Forests. Machine Learning, v. 45, p. 5–32, 2001. BREIMAN, L.; FRIEDMAN, J. H.; OLSHEN, R. A.; STONE, C. J. Classification and Regression Trees. Belmont: Wadsworth Publishing, 1984. p. 358 BUEHLER, E. V. Delayed contact hypersensitivity in the guinea pig. Archives of dermatology, v. 91, p. 171–7, fev. 1965. CAPDEVILLE, R.; BUCHDUNGER, E.; ZIMMERMANN, J.; MATTER, A. Glivec (STI571, imatinib), a rationally developed, targeted anticancer drug. Nature reviews. Drug discovery, v. 1, n. 7, p. 493–502, jul. 2002. CAPORUSCIO, F.; TAFI, A. Pharmacophore modelling: a forty year old approach and its modern synergies. Current medicinal chemistry, v. 18, n. 17, p. 2543–53, jan. 2011. CHAST, F. A history of drug discovery. In: WERMUTH, C. (Ed.). The practice of Medicinal Chemistry. 3. ed. [s.l.] Elsevier, 2008. p. 3–62. CHAUDHRY, Q.; PICLIN, N.; COTTERILL, J.; PINTORE, M.; PRICE, N. R.; CHRÉTIEN, J. R.; RONCAGLIONI, A. Global QSAR models of skin sensitisers for regulatory purposes. Chemistry central journal, v. 4 Suppl 1, n. Suppl 1, p. S5, jan. 2010. CHAUHAN, P.; SHAKYA, M. Role of physicochemical properties in the estimation of skin permeability: in vitro data assessment by Partial Least-Squares Regression. SAR and QSAR in environmental research, v. 21, n. 5-6, p. 481–94, jul. 2010. CHEN, L. L.; LIAN, G. G.; HAN, L. L. Prediction of human skin permeability using artificial neural network (ANN) modeling. Acta pharmacologica Sinica, v. 28, n. 4, p. 591–600, abr. 2007. CHEN, L.; LIAN, G.; HAN, L. Modeling transdermal permeation. Part I. Predicting skin permeability of both hydrophobic and hydrophilic solutes. AIChE journal, v. 56, n. 5, p. 1136–1146, 2010. CHENG, T.; LI, Q.; ZHOU, Z.; WANG, Y.; BRYANT, S. H. Structure-based virtual screening for drug discovery: a problem-centric review. The AAPS journal, v. 14, n. 1, p. 133–41, mar. 2012. CHENG, Z.; ZHANG, Y.; ZHOU, C.; ZHANG, W.; GAO, S. Classification of skin sensitizers on the basis of their effective concentration 3 values by using adaptive boosting method. International journal of digital content technology and its applications, v. 4, n. 2, p. 109–121, 30 abr. 2010. 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? Journal of medicinal chemistry, v. Epub ahead, 6 jan. 2014. CHUPRINA, A.; LUKIN, O.; DEMOISEAUX, R.; BUZKO, A.; SHIVANYUK, A. Drug- and lead-likeness, target class, and molecular diversity analysis of 7.9 million commercially available organic compounds provided by 29 suppliers. Journal of chemical information and modeling, v. 50, n. 4, p. 470–479, 26 abr. 2010. CLARK, D. E. What has virtual screening ever done for drug discovery? Expert opinion on drug discovery, v. 3, n. 8, p. 841–51, ago. 2008. COI, A.; BIANUCCI, A. M. Combining structure- and ligand-based approaches for studies of interactions between different conformations of the hERG K(+) channel pore and known ligands. Journal of molecular graphics & modelling, v. 46, p. 93–104, nov. 2013. CONSONNI, V.; TODESCHINI, R. Molecular descriptors. In: PUZYN, T.; LESZCZYNSKI, J.; CRONIN, M. T. (Eds.). Recent Advances in QSAR Studies. Dordrecht: Springer, 2010. p. 29–. CRAMER, R. D. The inevitable QSAR renaissance. Journal of computer-aided molecular design, v. 26, n. 1, p. 35–8, jan. 2012. CRAMER, R. D.; PATTERSON, D. E.; BUNCE, J. D. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. Journal of the American Chemical Society, v. 110, n. 18, p. 5959–67, 1 ago. 1988. CRONIN, M. T. Quantitative structure-activity relationships (QSARs) -- aplications and methodology. In: PUZYN, T.; LESZCZYNSKI, J.; CRONIN, M. T. (Eds.). Recent Advances in QSAR Studies. Dordrecht: Springer, 2010. p. 3–11. CRONIN, M. T.; BASKETTER, D. A. Multivariate QSAR analysis of a skin sensitization database. SAR and QSAR in environmental research, v. 2, n. 3, p. 159–79, jan. 1994. CRONIN, M. T.; DEARDEN, J. C.; MOSS, G. P.; MURRAY-DICKSON, G. Investigation of the mechanism of flux across human skin in vitro by quantitative structure-permeability relationships. European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences, v. 7, n. 4, p. 325–30, mar. 1999. DEARDEN, J. C.; CRONIN, M. T. D.; KAISER, K. L. E. How not to develop a quantitative structure-activity or structure-property relationship (QSAR/QSPR). SAR and QSAR in environmental research, v. 20, n. 3-4, p. 241–66, jan. 2009. DEARMAN, R. J.; BETTS, C. J.; FARR, C.; MCLAUGHLIN, J.; BERDASCO, N.; WIENCH, K.; KIMBER, I. Comparative analysis of skin sensitization potency of acrylates (methyl acrylate, ethyl acrylate, butyl acrylate, and ethylhexyl acrylate) using the local lymph node assay. Contact dermatitis, v. 57, n. 4, p. 242–7, out. 2007. DEARMAN, R. J.; WRIGHT, Z. M.; BASKETTER, D. A.; RYAN, C. A.; GERBERICK, G. F.; KIMBER, I. The suitability of hexyl cinnamic aldehyde as a calibrant for the murine local lymph node assay. Contact dermatitis, v. 44, n. 6, p. 357–61, jun. 2001. DEBNATH, A. K. Quantitative structure-activity relationship (QSAR) paradigm--Hansch era to new millennium. Mini reviews in medicinal chemistry, v. 1, n. 2, p. 187–95, jul. 2001. DEGIM, I. T.; PUGH, W. J.; HADGRAFT, J. Skin permeability data: anomalous results. International journal of pharmaceutics, v. 170, n. 1, p. 129–133, ago. 1998. DEVILLERS, J. A neural network SAR model for allergic contact dermatitis. Toxicology methods, v. 10, p. 181–193, 2000. DICKEL, H.; KUSS, O.; SCHMIDT, A.; KRETZ, J.; DIEPGEN, T. L. Importance of irritant contact dermatitis in occupational skin disease. American journal of clinical dermatology, v. 3, n. 4, p. 283–9, jan. 2002. DOMINGOS, P. A few useful things to know about machine learning. Communications of the ACM, v. 55, n. 10, p. 78, 1 out. 2012. DOWNS, G. M.; BARNARD, J. M. Clustering Methods and Their Uses in Computational Chemistry. In: LIPKOWITZ, K. B.; BOYD, D. B. (Eds.). Reviews in Computational Chemistry. Hoboken: John Wiley & Sons, Inc., 2003. v. 18p. 1–40. DREWS, J. Case histories, magic bullets and the state of drug discovery. Nature Reviews Drug Discovery, v. 5, n. August, p. 635–640, 2006. EGEGHY, P. P.; JUDSON, R.; GANGWAL, S.; MOSHER, S.; SMITH, D.; VAIL, J.; HUBAL, E. A. C. The exposure data landscape for manufactured chemicals. The Science of the total environment, v. 414, p. 159–66, 1 jan. 2012. ESTRADA, E.; PATLEWICZ, G.; CHAMBERLAIN, M.; BASKETTER, D.; LARBEY, S. Computer-aided knowledge generation for understanding skin sensitization mechanisms: the TOPS-MODE approach. Chemical research in toxicology, v. 16, n. 10, p. 1226–35, out. 2003. EUROPEAN UNION. Directive 2003/15/EC of the European Parliament and of the Council of 27 February 2003 amending Council Directive 76/768/EEC on the approximation of the laws of the Member States relating to cosmetic productsOfficial journal of the european union, 2003. EUROPEAN UNION. Regulation (EC) No 1907/2006. Official journal of the European Union, n. L 136, p. 3–280, 2007. EUROPEAN UNION. Directive 2010/63/EU. Official Journal of the European Union, n. L 276, p. 33–79, 2010. FDA. New Drugs at FDA: CDER’s New Molecular Entities and New Therapeutic Biological Products of 2013. Disponível em: <http://www.fda.gov/drugs/developmentapprovalprocess/druginnovation/default.htm>. Acesso em: 2 jan. 2013. FEDOROWICZ, A.; SINGH, H.; SODERHOLM, S.; DEMCHUK, E. Structure-activity models for contact sensitization. Chemical research in toxicology, v. 18, n. 6, p. 954–69, jun. 2005. FEDOROWICZ, A.; ZHENG, L.; SINGH, H.; DEMCHUK, E. QSAR study of skin sensitization using local lymph node assay data. International journal of molecular sciences, v. 5, n. 2, p. 56–66, 30 jan. 2004. FLYNN, G. L. Physicochemical determinants of skin absorption. In: GERRITY, T. R.; HENRY, C. J. (Eds.). Principles of Route-to-Route Extrapolation for Risk assessment. New York, NY: Elsevier, 1990. p. 93–117. FOURCHES, D.; MURATOV, E.; TROPSHA, A. Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. Journal of chemical information and modeling, v. 50, n. 7, p. 1189–204, 26 jul. 2010. FREE, S. M.; WILSON, J. W. A Mathematical Contribution to Structure-Activity Studies. Journal of Medicinal Chemistry, v. 7, n. 4, p. 395–399, jul. 1964. FRIEDMAN, L. M.; FURBERG, C. D.; DEMETS, D. L. Fundamentals of Clinical Trials. 4. ed. New York, NY: Springer, 2010. p. 400 FUJITA, T.; IWASA, J.; HANSCH, C. A New Substituent Constant, π, Derived from Partition Coefficients. Journal of the American Chemical Society, v. 86, n. 23, p. 5175–5180, dez. 1964. GANELLIN, C. R. Robin Ganellin gives his views on medicinal chemistry and drug discovery. Interview by Stephen L. Carney. Drug discovery today, v. 9, n. 4, p. 158–60, 15 fev. 2004. GAVAGHAN, C. L.; ARNBY, C. H.; BLOMBERG, N.; STRANDLUND, G.; BOYER, S. Development, interpretation and temporal evaluation of a global QSAR of hERG electrophysiology screening data. Journal of computer-aided molecular design, v. 21, n. 4, p. 189–206, abr. 2007. GERBERICK, G. F.; RYAN, C. A.; KERN, P. S.; SCHLATTER, H. Compilation of historical local lymph node data for evaluation of skin sensitization alternative methods. Dermatitis, v. 16, n. 4, p. 157–202, 2005. GERBERICK, G. F.; TROUTMAN, J. A; FOERTSCH, L. M.; VASSALLO, J. D.; QUIJANO, M.; DOBSON, R. L. M.; GOEBEL, C.; LEPOITTEVIN, J.-P. Investigation of peptide reactivity of pro-hapten skin sensitizers using a peroxidase-peroxide oxidation system. Toxicological sciences : an official journal of the Society of Toxicology, v. 112, n. 1, p. 164–74, nov. 2009. GERBERICK, G. F.; VASSALLO, J. D.; BAILEY, R. E.; CHANEY, J. G.; MORRALL, S. W.; LEPOITTEVIN, J.-P. Development of a peptide reactivity assay for screening contact allergens. Toxicological sciences : an official journal of the Society of Toxicology, v. 81, n. 2, p. 332–43, out. 2004. GLEESON, M. P. Generation of a set of simple, interpretable ADMET rules of thumb. Journal of medicinal chemistry, v. 51, n. 4, p. 817–34, 28 fev. 2008. GLEESON, M. P.; MODI, S.; BENDER, A.; ROBINSON, R. L. M.; KIRCHMAIR, J.; PROMKATKAEW, M.; HANNONGBUA, S.; GLEN, R. C. The challenges involved in modeling toxicity data in silico: a review. Current pharmaceutical design, v. 18, n. 9, p. 1266–91, jan. 2012. GOLBRAIKH, A.; TROPSHA, A. Beware of q2! Journal of molecular graphics & modelling, v. 20, n. 4, p. 269–76, jan. 2002. GOLLA, S.; MADIHALLY, S.; ROBINSON, R. L.; GASEM, K. A. M. Quantitative structure-property relationship modeling of skin sensitization: a quantitative prediction. Toxicology in vitro : an international journal published in association with BIBRA, v. 23, n. 3, p. 454–65, abr. 2009. GRAMATICA, P. Principles of QSAR models validation: internal and external. QSAR & Combinatorial Science, v. 26, n. 5, p. 694–701, maio 2007. GRANDJEAN, P.; BERLIN, A.; GILBERT, M.; PENNING, W. Preventing percutaneous absorption of industrial chemicals: the “skin” denotation. American journal of industrial medicine, v. 14, n. 1, p. 97–107, jan. 1988. GUHA, R.; VAN DRIE, J. H. Structure-activity landscape index: identifying and quantifying activity cliffs. Journal of chemical information and modeling, v. 48, n. 3, p. 646–58, mar. 2008. GUIDO, R. V. C. Planejamento de inibidores da enzima gliceraldeído-3-fosfato desidrogenase de Trypanosoma cruzi: biologia estrutural e química medicinal. [s.l.] Universidade de São Paulo, 2008. GUNTURI, S. B.; THEERTHALA, S. S.; PATEL, N. K.; BAHL, J.; NARAYANAN, R. Prediction of skin sensitization potential using D-optimal design and GA-kNN classification methods. SAR and QSAR in environmental research, v. 21, n. 3-4, p. 305–35, abr. 2010. HAMMETT, L. P. The Effect of Structure upon the Reactions of Organic Compounds. Benzene Derivatives. Journal of the American Chemical Society, v. 59, n. 1, p. 96–103, jan. 1937. HANEKE, K. E.; TICE, R. R.; CARSON, B. L.; MARGOLIN, B. H.; STOKES, W. S. ICCVAM evaluation of the murine local lymph node assay. Data analyses completed by the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods. Regulatory toxicology and pharmacology : RTP, v. 34, n. 3, p. 274–86, dez. 2001. HANSCH, C. Quantitative approach to biochemical structure-activity relationships. Accounts of Chemical Research, v. 2, n. 8, p. 232–239, ago. 1969. HANSCH, C.; FUJITA, T. p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure. Journal of the American Chemical Society, v. 86, n. 8, p. 1616–1626, abr. 1964. HANSCH, C.; MALONEY, P. P.; FUJITA, T.; MUIR, R. M. Correlation of Biological Activity of Phenoxyacetic Acids with Hammett Substituent Constants and Partition Coefficients. Nature, v. 194, n. 4824, p. 178–180, 14 abr. 1962. HÄRMARK, L.; VAN GROOTHEEST, A. C. Pharmacovigilance: methods, recent developments and future perspectives. European journal of clinical pharmacology, v. 64, n. 8, p. 743–52, ago. 2008. HENNINO, A.; VOCANSON, M.; CHAVAGNAC, C.; SAINT-MEZARD, P.; DUBOIS, B.; KAISERLIAN, D.; NICOLAS, J. Update on the pathophysiology with special emphasis on CD8 effector T cells and CD4 regulatory T cells. Anais brasileiros de dermatologia, v. 80, n. 4, p. 335–347, ago. 2005. HOPFINGER, A. J.; WANG, S.; TOKARSKI, J. S.; JIN, B.; ALBUQUERQUE, M.; MADHAV, P. J.; DURAISWAMI, C. Construction of 3D-QSAR Models Using the 4D-QSAR Analysis Formalism. Journal of the American Chemical Society, v. 119, n. 43, p. 10509–10524, out. 1997. HOPFINGER, A. J. A QSAR investigation of dihydrofolate reductase inhibition by Baker triazines based upon molecular shape analysis. Journal of the American Chemical Society, v. 102, n. 24, p. 7196–7206, set. 1980. HOSTÝNEK, J. J.; MAGEE, P. S. Modelling in vivo human skin absorption. Quantitative structure-activity relationships, v. 16, n. 6, p. 473–479, 1997a. HOSTÝNEK, J. J.; MAGEE, P. S. Fragrance allergens: Classification and ranking by QSAR. Toxicology in vitro : an international journal published in association with BIBRA, v. 11, n. 4, p. 377–84, ago. 1997b. HOSTÝNEK, J. J.; MAGEE, P. S.; MAIBACH, H. I. QSAR predictive of contact allergy: scope and limitations. Current problems in dermatology, v. 25, p. 18–27, jan. 1996. HUGHES, J. P.; REES, S.; KALINDJIAN, S. B.; PHILPOTT, K. L. Principles of early drug discovery. British journal of pharmacology, v. 162, n. 6, p. 1239–49, mar. 2011. HUTT, P. B. The Historical Development of Animal Toxicity Testing. Disponível em: <http://nrs.harvard.edu/urn-3:HUL.InstRepos:8889439>. ICCVAM. The reduced murine local lymph node assay: an alternative test method using fewer animals to assess the allergic contact dermatitis potential of chemicals and products. Disponível em: <http://ntp.niehs.nih.gov/iccvam/docs/immunotox_docs/LLNA-LD/TMER.pdf>. Acesso em: 20 fev. 2012. ICCVAM; NICEATM. The murine local lymph node assay: a test method for assessing the allergic contact dermatitis potential of chemicals/compounds. Disponível em: <http://iccvam.niehs.nih.gov/docs/immunotox_docs/llna/llnarep.pdf>. Acesso em: 8 jun. 2012. IMMING, P. Medicinal Chemistry: definitions and objectives, drug activity phases, drug classification systems. In: WERMUTH, C. (Ed.). The practice of Medicinal Chemistry. 3. ed. [s.l.] Elsevier, 2008. p. 63–72. JAWORSKA, J.; DANCIK, Y.; KERN, P.; GERBERICK, F.; NATSCH, A. Bayesian integrated testing strategy to assess skin sensitization potency: from theory to practice. Journal of applied toxicology : JAT, v. 33, n. 11, p. 1353–64, 14 maio 2013. JAWORSKA, J.; HAROL, A.; KERN, P. S.; GERBERICK, G. F. Integrating non-animal test information into an adaptive testing strategy - skin sensitization proof of concept case. Altex, v. 28, n. 3, p. 211–25, jan. 2011. JOHANSEN, J. D.; FROSCH, P. J.; MENNÉ, T. Allergic contact dermatitis in humans: experimental and quantitative aspects. In: JOHANSEN, J. D.; FROSCH, P. J.; LEPOITTEVIN, J.-P. (Eds.). Contact Dermatitis. Berlin: Springer, 2011. p. 241–251. JOHANSSON, H.; LINDSTEDT, M.; ALBREKT, A.-S.; BORREBAECK, C. A. K. A genomic biomarker signature can predict skin sensitizers using a cell-based in vitro alternative to animal tests. BMC genomics, v. 12, n. 1, p. 399, jan. 2011. JOHNSON, M. E.; BLANKSCHTEIN, D.; LANGER, R. Permeation of steroids through human skin. Journal of pharmaceutical sciences, v. 84, n. 9, p. 1144–6, set. 1995. JOHNSON, M. E.; BLANKSCHTEIN, D.; LANGER, R. Evaluation of solute permeation through the stratum corneum: lateral bilayer diffusion as the primary transport mechanism. Journal of pharmaceutical sciences, v. 86, n. 10, p. 1162–72, out. 1997. KAR, S.; ROY, K. How far can virtual screening take us in drug discovery? Expert opinion on drug discovery, v. 8, n. 3, p. 245–61, mar. 2013. KARLBERG, A.-T.; BERGSTRÖM, M. A.; BÖRJE, A.; LUTHMAN, K.; NILSSON, J. L. G. Allergic contact dermatitis - formation, structural requirements, and reactivity of skin sensitizers. Chemical research in toxicology, v. 21, n. 1, p. 53–69, jan. 2008. KEEGEL, T.; MOYLE, M.; DHARMAGE, S.; FROWEN, K.; NIXON, R. The epidemiology of occupational contact dermatitis (1990-2007): a systematic review. International journal of dermatology, v. 48, n. 6, p. 571–8, jun. 2009. KERN, P. S.; GERBERICK, G. F.; 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 : contact, atopic, occupational, drug : official journal of the American Contact Dermatitis Society, North American Contact Dermatitis Group, v. 21, n. 1, p. 8–32, fev. 2010. KESSEL, M. The problems with today’s pharmaceutical business--an outsider's view. Nature biotechnology, v. 29, n. 1, p. 27–33, jan. 2011. KIELHORN, J.; MELCHING-KOLLMUS, S.; MANGELSDORF, I. The Dermal Absorption. Hanover: Fraunhofer Institute Toxicology and Experimental Medicine, 2005. p. 90–91 KIMBER, I.; BASKETTER, D. A.; GERBERICK, G. F.; DEARMAN, R. J. Allergic contact dermatitis. International immunopharmacology, v. 2, n. 2-3, p. 201–11, fev. 2002. KIMBER, I.; BASKETTER, D. A.; GERBERICK, G. F.; RYAN, C. A.; DEARMAN, R. J. Chemical allergy: translating biology into hazard characterization. Toxicological sciences : an official journal of the Society of Toxicology, v. 120(S1), p. S238–S268, mar. 2011. KIMBER, I.; MITCHELL, J. A.; GRIFFIN, A. C. Development of a murine local lymph node assay for the determination of sensitizing potential. Food and Chemical Toxicology, v. 24, n. 6-7, p. 585–586, jun. 1986. KIRCHNER, L. A.; MOODY, R. P.; DOYLE, E.; BOSE, B.; JEFFRY, J.; CHU, I. The prediction of skin permeability by using physicochemical data. ATLA, v. 25, p. 359–370, 1997. KIRKPATRICK, P. An audience with... Chris Lipinski. Nature Reviews Drug Discovery, v. 11, p. 900–901, 2012. KLEBE, G.; ABRAHAM, U.; MIETZNER, T. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. Journal of medicinal chemistry, v. 37, n. 24, p. 4130–46, nov. 1994. KNUDSEN, T. B.; KLEINSTREUER, N. C. Disruption of embryonic vascular development in predictive toxicology. Birth defects research. Part C, Embryo today : reviews, v. 93, n. 4, p. 312–23, dez. 2011. KUBINYI, H. Quantitative structure-activity relationships. V. A simple simple algorithm for Fujita-Ban and Free-Wilson analyses. Arzneimittel-Forschung, v. 27, n. 4, p. 750–8, jan. 1977. KUBINYI, H. Nonlinear dependence of biological activity on hydrophobic character: the bilinear model. Il Farmaco; edizione scientifica, v. 34, n. 3, p. 248–76, mar. 1979. KUZ’MIN, V. E.; ARTEMENKO, A G.; MURATOV, E. N. Hierarchical QSAR technology based on the Simplex representation of molecular structure. Journal of computer-aided molecular design, v. 22, n. 6-7, p. 403–21, 2008. KUZ’MIN, V. E.; MURATOV, E. N.; ARTEMENKO, A. G.; VARLAMOVA, E. V.; GORB, L.; WANG, J.; LESZCZYNSKI, J. Consensus QSAR modeling of phosphor-containing chiral AChE inhibitors. QSAR & combinatorial science, v. 28, n. 6-7, p. 664–677, jul. 2009. LEPOITTEVIN, J. Contact Dermatitis. 5. ed. Berlin: Springer Berlin Heidelberg, 2011. p. 91–110 LI, J. W.-H.; VEDERAS, J. C. Drug discovery and natural products: end of an era or an endless frontier? Science (New York, N.Y.), v. 325, n. 5937, p. 161–5, 10 jul. 2009. LI, S.; FEDOROWICZ, A.; SINGH, H.; SODERHOLM, S. C. Application of the random forest method in studies of local lymph node assay based skin sensitization data. Journal of chemical information and modeling, v. 45, n. 4, p. 952–64, 2005. LI, Y.; TSENG, Y. J.; PAN, D.; LIU, J.; KERN, P. S.; GERBERICK, G. F.; HOPFINGER, A. J. 4D-fingerprint categorical QSAR models for skin sensitization based on the classification of local lymph node assay measures. Chemical research in toxicology, v. 20, n. 1, p. 114–28, jan. 2007. LIEN, E. J.; GAO, H. QSAR analysis of skin permeability of various drugs in man as compared to in vivo and in vitro studies in rodents. Pharmaceutical research, v. 12, n. 4, p. 583–7, maio 1995. LIPINSKI, C. A; LOMBARDO, F.; DOMINY, B. W.; FEENEY, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3. Advanced Drug Delivery Reviews, v. 23, n. 1-3, p. 3–26, 1 mar. 1997. LIPINSKI, C.; HOPKINS, A. Navigating chemical space for biology and medicine. Nature, v. 432, n. 7019, p. 855–61, 16 dez. 2004. LOMBARDINO, J. G.; LOWE, J. A. The role of the medicinal chemist in drug discovery--then and now. Nature reviews. Drug discovery, v. 3, n. 10, p. 853–62, out. 2004. LOW, Y.; UEHARA, T.; MINOWA, Y.; YAMADA, H.; OHNO, Y.; URUSHIDANI, T.; SEDYKH, A.; MURATOV, E.; KUZ’MIN, V.; FOURCHES, D.; ZHU, H.; RUSYN, I.; TROPSHA, A. Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches. Chemical research in toxicology, v. 24, n. 8, p. 1251–62, 15 ago. 2011. LU, J.; ZHENG, M.; WANG, Y.; SHEN, Q.; LUO, X.; JIANG, H.; CHEN, K. Fragment-based prediction of skin sensitization using recursive partitioning. Journal of computer-aided molecular design, v. 25, n. 9, p. 885–93, set. 2011. MAGEE, P. S.; HOSTÝNEK, J. J.; MAIBACH, H. I.; FRANCISCO, S. A classification model for allergic contact dermatitis. QSAR & combinatorial science, v. 13, n. 1, p. 22–33, jun. 1994. MAGGIORA, G. M. On outliers and activity cliffs--why QSAR often disappoints. Journal of chemical information and modeling, v. 46, n. 4, p. 1535, 2006. MAGGIORA, G. M. The reductionist paradox: are the laws of chemistry and physics sufficient for the discovery of new drugs? Journal of computer-aided molecular design, v. 25, n. 8, p. 699–708, ago. 2011. MAGNUSSON, B.; KLIGMAN, A. M. The identification of contact allergens by animal assay. The guinea pig maximization test. The Journal of investigative dermatology, v. 52, n. 3, p. 268–76, mar. 1969. MAGNUSSON, B. M.; ANISSIMOV, Y. G.; CROSS, S. E.; ROBERTS, M. S. Molecular size as the main determinant of solute maximum flux across the skin. The Journal of investigative dermatology, v. 122, n. 4, p. 993–9, abr. 2004. MAGNUSSON, B. M.; PUGH, W. J.; ROBERTS, M. S. Simple rules defining the potential of compounds for transdermal delivery or toxicity. Pharmaceutical research, v. 21, n. 6, p. 1047–54, jun. 2004. MCGEE, P. Clinical trials on the move. Drug discovery & development, v. 9, n. 6, p. 16–22, 2006. MEDINA-FRANCO, J. L.; GIULIANOTTI, M. A; WELMAKER, G. S.; HOUGHTEN, R. A. Shifting from the single to the multitarget paradigm in drug discovery. Drug discovery today, v. 18, n. 9-10, p. 495–501, maio 2013. MELVILLE, J. L.; BURKE, E. K.; HIRST, J. D. Machine learning in virtual screening. Combinatorial chemistry & high throughput screening, v. 12, n. 4, p. 332–43, maio 2009. MENDOZA, L. Phase 0 clinical trials will overcome stagnation of anticancer drug development? Klinická onkologie : casopis Ceské a Slovenské onkologické spolecnosti, v. 24, n. 2, p. 143–5, jan. 2011. MERCIER, D. Clustering large datasets. Electronic review - Linacre College, 2003. MILLER, M. D.; YOURTEE, D. M.; GLAROS, A. G.; CHAPPELOW, C. C.; EICK, J. D.; HOLDER, A. J. Quantum mechanical structure-activity relationship analyses for skin sensitization. Journal of chemical information and modeling, v. 45, n. 4, p. 924–9, 2005. MODA, T. L.; TORRES, L. G.; CARRARA, A. E.; ANDRICOPULO, A. D. PK/DB: database for pharmacokinetic properties and predictive in silico ADME models. Bioinformatics (Oxford, England), v. 24, n. 19, p. 2270–1, 1 out. 2008. MOSS, G. P.; CRONIN, M. T. D. Quantitative structure-permeability relationships for percutaneous absorption: re-analysis of steroid data. International journal of pharmaceutics, v. 238, n. 1-2, p. 105–9, 15 maio 2002. MOSS, G. P.; SUN, Y.; WILKINSON, S. C.; DAVEY, N.; ADAMS, R.; MARTIN, G. P.; PRAPOPOPOLOU, M.; BROWN, M. B. The application and limitations of mathematical modelling in the prediction of permeability across mammalian skin and polydimethylsiloxane membranes. The Journal of pharmacy and pharmacology, v. 63, n. 11, p. 1411–27, nov. 2011. MUIR, D. C. G.; HOWARD, P. H. Are there other persistent organic pollutants? A challenge for environmental chemists. Environmental science & technology, v. 40, n. 23, p. 7157–66, 1 dez. 2006. MURATOV, E. N.; ARTEMENKO, A. G.; VARLAMOVA, E. V; POLISCHUK, P. G.; LOZITSKY, V. P.; FEDCHUK, A. S.; LOZITSKA, R. L.; GRIDINA, T. L.; KOROLEVA, L. S.; SIL’NIKOV, V. N.; GALABOV, A. S.; MAKAROV, V. A.; RIABOVA, O. B.; WUTZLER, P.; SCHMIDTKE, M.; KUZ’MIN, V. E. Per aspera ad astra: application of Simplex QSAR approach in antiviral research. Future medicinal chemistry, v. 2, n. 7, p. 1205–26, jul. 2010. NATURE. End of the Lipitor era.

Page generated in 0.0048 seconds