Thyroid disruptions by xenobiotics have been associated with a broad spectrum of severe adverse human health effects, such as impaired brain development and metabolic syndrome. Ingestion of indoor dust and contact with industrial chemicals are two significant human exposure routes of thyroid hormone disrupting chemicals (THDCs), raising serious concerns for human health. However, it is a laborious and costly process to identify THDCs using conventional experimental methods, due to the number of chemicals in commerce and the varieties of potential disruption mechanisms. In this thesis, we are aimed at in silico identification of novel THDCs targeting transthyretin (TTR) and thyroid hormone receptor (THR) among dust contaminants and commonly used industrial chemicals. In vitro assays were used to validate the in silico prediction results. Co-crystallization and molecular dynamics (MD) simulations were applied to reveal binding modes of THDCs at the studied biological targets and to explain their intermolecular recognition. The main findings presented in this thesis are: 1. Over 144 environmental pollutants have been confirmed as TTR-binders in vitro and these cover a wide range of environmental pollutants and show distinct chemical profiles including a large group of halogenated aromatic compounds and a second group of per- and polyfluoroalkyl substances. (Paper I) 2. In total 485 organic contaminants have been reported to be detected in household dust. The developed QSAR classification model predicted 7.6% of these dust contaminants and 53.1% of their metabolites as potential TTR-binders, which emphasizes the importance of metabolic bioactivation. After in vitro validation, four novel TTR binders with IC50 ≤ 10 µM were identified, i.e. perfluoroheptanesulfonic acid, 2,4,2',4'-tetrahydroxybenzophenone (BP2), 2,4,5-trichlorophenoxyacetic acid, and 3,5,6-trichloro-2-pyridinol. (Paper II) 3. The development of a robust structure-based virtual screening (VS) protocol resulted in the prediction of 31 dust contaminants as potential binders to THRβ1 including musk compounds, PFASs, and bisphenol A derivatives. The in vitro experiments confirmed four compounds as weak binders to THRβ1, i.e. 2,4,5-trichlorophenoxyacetic acid, bisphenol A (3-chloro-2-hydroxypropyl) (2,3-dihydroxypropyl) ether, 2,4,2',4'-tetrahydroxybenzophenone, and 2,4-dichlorophenoxyacetic acid. (Paper III) 4. We revealed the binding conformations of perfluorooctanesulfonic acid, perfluorooctanoic acid, and BP2 in the thyroxine binding sites (TBSs) of TTR by co-crystallizing TTR with the three compounds. A VS protocol was developed based on the TTR complex structures that predicted 192 industrial chemicals as potential binders to TTR. Seven novel TTR binders were confirmed by in vitro experiments including clonixin, 2,6-dinitro-p-cresol (DNPC), triclopyr, fluroxypyr, bisphenol S, picloram, and mesotrione. We further co-crystallized TTR with PBS, clonixin, DNPC, and triclopyr, and their complex structures showed that the compounds bind in the TBSs as proposed by the VS protocol. In summary, 13 indoor dust contaminants and industrial chemicals were identified as THDCs using a combination of in silico and in vitro approaches. To the best of our knowledge, none of these compounds has previously been reported to bind to TTR or THR. The identifications of these THDCs improve our understanding on the structure-activity relationships of THDCs. The crystal structures of TTR-THDC complexes and the information on THDC-Target intermolecular interactions provide a better understanding on the mechanism-of-actions behind thyroid disruption. The dataset compiled and in silico methods developed serve as a basis for identification of more diverse THDCs in the future and a tool for guiding de novo design of safer replacements.

Orientador: Márcia Miguel Castro Ferreira / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Química / Made available in DSpace on 2018-08-25T11:39:21Z (GMT). No. of bitstreams: 1 Martins_JoaoPauloAtaide_D.pdf: 3637503 bytes, checksum: 5fe52d182b4f300eb103baf168ad75ab (MD5) Previous issue date: 2013 / Resumo: O planejamento de fármacos com o auxílio do computador é uma área de pesquisa de extrema importância em química e áreas correlatas. O conjunto de ferramentas disponíveis para tal fim consiste, dentre outras, em programas para geração de descritores e construção e validação de modelos matemáticos em QSAR (do inglês, Quantitative Structure-Activity Relationship). Com o objetivo de tornar esse estudo mais acessível para a comunidade científica, novas metodologias e programas para geração de descritores e construção e validação de modelos QSAR foram desenvolvidos nessa tese. Uma nova metodologia de QSAR 4D, conhecida com LQTA-QSAR, foi desenvolvida com o objetivo de gerar descritores espaciais levando em conta os perfis de amostragem conformacional das moléculas em estudo obtidos a partir de simulações de dinâmica molecular. A geração desses perfis é feita com o software livre GROMACS e os descritores são gerados a partir de um novo software desenvolvido nesse trabalho, chamado de LQTAgrid. Os resultados obtidos com essa metodologia foram validados comparando-os com resultados obtidos para conjuntos de dados disponíveis na literatura. Um outro software de fácil uso, e que engloba as principais ferramentas de construção e validação de modelos em QSAR, foi desenvolvido e chamado de QSAR modeling. Esse software implementa o método de seleção de variáveis OPS, desenvolvido em nosso laboratório, e utiliza PLS (do inglês Partial Least Squares) como método de regressão. A escolha do algoritmo PLS implementado no programa foi feita com base em um estudo sobre o desempenho e a precisão no erro de validação dos principais algoritmos PLS disponíveis na literatura. Além disso, o programa QSAR modeling foi utilizado em um estudo de QSAR 2D para um conjunto de 20 flavonóides com atividade anti-mutagênica contra 3-nitrofluoranteno (3-NFA) / Abstract: Computer aided drug design is an important research field in chemistry and related areas. The available tools used in such studies involve software to generate molecular descriptors and to build and validate mathematical models in QSAR (Quantitative Structure-Activity Relationship). A new set of methodologies and software to generate molecular descriptors and to build and validate QSAR models were developed aiming to make these kind of studies more accessible to scientific community. A new 4DQSAR methodology, known as LQTA-QSAR, was developed with the purpose to generate spatial descriptors taking into account conformational ensemble profile obtained from molecular dynamics simulations. The generation of these profiles is performed by free software GROMACS and the descriptors are generated by a new software developed in this work, called LQTAgrid. The results obtained with this methodology were validated comparing them with results available in literature. Another user friendly software, which contains some of the most important tools used to build and validate QSAR models was developed and called QSAR modeling. This software implements the OPS variable selection algorithm, developed in our laboratory, and uses PLS (Partial Least Squares) as regression method. The choice of PLS algorithm implemented in the program was performed by a study about the performance and validation precision error involving the most important PLS algorithms available in literature. Further, QSAR modeling was used in a 2D QSAR study with 20 flavonoid derivatives with antimutagenic activity against 3-nitrofluoranthene (3-NFA) / Doutorado / Físico-Química / Doutor em Ciências

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.