Desenvolvimento de modelos de QSAR para identificação de substratos e inibidores de CYP3A4
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFG |
Texto Completo: | http://repositorio.bc.ufg.br/tede/handle/tede/5266 |
Resumo: | The discovery and development of drugs consist of a complex process, requiring the integration of various strategic areas such as knowledge, innovation, technology, management and high investments in Research, Development and Innovation (RD&I). No drug can be approved for use in humans without first go through extensive studies aimed at ensuring its effectiveness and safety. On the other hand, a drug that inhibits the activity of a metabolic enzyme cytochrome P450 family (CYP450) can affect the pharmacokinetics of other drugs, resulting in drug-drug interactions (DDIs), which potentially lead to side effects and toxic effects. The main oxidative enzymes responsible for drug metabolism have as main representatives CYP450 superfamily, wherein the CYP3A4 isoform is the most important because it is responsible for metabolizing approximately 50% of the drugs on the market. Several computational methods have been developed as a strategy to predict human metabolism in the early stages of research and development of drugs. In silico models of metabolism have advantages such as faster, lower cost and ease of operation when compared to traditional models in vitro and in vivo. The work aimed mainly at the development of Quantitative Relations between models chemical structure and activity / property (QSAR / QSPR) robust and predictive, to identify CYP3A4 substrates and inhibitors. To this were collected, integrated and prepared larger data sets available in the literature substrates and inhibitors of CYP3A4. Several QSAR models were generated and validated for both properties using a workflow that contemplated carefully the recommendations of the Organization for Economic Co-operation Development (OECD). The combination of different descriptors and machine learning methods have led to obtain robust and predictive QSAR models, with correct classification rate (CCR) ranging from 0.65 to 0.83 and 0.69 to 0.89 of coverage, showing a statistically significant values for classification of compounds with high accuracy whether or not substrates of CYP3A4 substrates. The binary Morgan RFgenerated model to classify compounds inhibitors and non-inhibitors also proved highly robust and predictive with sensitivity values of 0.77 and accuracy of 0.76, and the Morgan-RF model multiclass obtained values of 0.68 sensitivity and 0.69 for accuracy. The map of predicted probability proved useful as it could encode major structural fragments to classify compounds inhibitors or not CYP3A4 inhibitors. In conclusion, have been developed and validated many QSAR to predict the interaction with the CYP450 enzyme that may be useful in the early stages of the development of new drugs. The next step is the online availability of the models obtained in LabMol server (http://labmol.farmacia.ufg.br). |
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Andrade, Carolina Hortahttp://lattes.cnpq.br/2018317447324228Andrade, Carolina HortaSilva, Vinícius Barreto daOliveira, Valéria dehttp://lattes.cnpq.br/0500342953672978Silva, Flávia Cristina da2016-02-23T13:26:06Z2015-02-26SILVA, F. C. Desenvolvimento de modelos de QSAR para identificação de substratos e inibidores de CYP3A4. 2015. 90 f. Dissertação (Mestrado em Ciências Farmacêuticas) - Universidade Federal de Goiás, Goiânia, 2015.http://repositorio.bc.ufg.br/tede/handle/tede/5266The discovery and development of drugs consist of a complex process, requiring the integration of various strategic areas such as knowledge, innovation, technology, management and high investments in Research, Development and Innovation (RD&I). No drug can be approved for use in humans without first go through extensive studies aimed at ensuring its effectiveness and safety. On the other hand, a drug that inhibits the activity of a metabolic enzyme cytochrome P450 family (CYP450) can affect the pharmacokinetics of other drugs, resulting in drug-drug interactions (DDIs), which potentially lead to side effects and toxic effects. The main oxidative enzymes responsible for drug metabolism have as main representatives CYP450 superfamily, wherein the CYP3A4 isoform is the most important because it is responsible for metabolizing approximately 50% of the drugs on the market. Several computational methods have been developed as a strategy to predict human metabolism in the early stages of research and development of drugs. In silico models of metabolism have advantages such as faster, lower cost and ease of operation when compared to traditional models in vitro and in vivo. The work aimed mainly at the development of Quantitative Relations between models chemical structure and activity / property (QSAR / QSPR) robust and predictive, to identify CYP3A4 substrates and inhibitors. To this were collected, integrated and prepared larger data sets available in the literature substrates and inhibitors of CYP3A4. Several QSAR models were generated and validated for both properties using a workflow that contemplated carefully the recommendations of the Organization for Economic Co-operation Development (OECD). The combination of different descriptors and machine learning methods have led to obtain robust and predictive QSAR models, with correct classification rate (CCR) ranging from 0.65 to 0.83 and 0.69 to 0.89 of coverage, showing a statistically significant values for classification of compounds with high accuracy whether or not substrates of CYP3A4 substrates. The binary Morgan RFgenerated model to classify compounds inhibitors and non-inhibitors also proved highly robust and predictive with sensitivity values of 0.77 and accuracy of 0.76, and the Morgan-RF model multiclass obtained values of 0.68 sensitivity and 0.69 for accuracy. The map of predicted probability proved useful as it could encode major structural fragments to classify compounds inhibitors or not CYP3A4 inhibitors. In conclusion, have been developed and validated many QSAR to predict the interaction with the CYP450 enzyme that may be useful in the early stages of the development of new drugs. The next step is the online availability of the models obtained in LabMol server (http://labmol.farmacia.ufg.br).A descoberta e o desenvolvimento de fármacos consistem um processo complexo, sendo necessária a integração de várias áreas estratégicas como conhecimento, inovação, tecnologia, gerenciamento e altos investimentos em Pesquisa, Desenvolvimento e Inovação (PD&I). Nenhum fármaco pode ser aprovado para uso em humanos sem que antes passe por extensivos estudos que visem garantir sua eficácia e segurança. Um fármaco que inibe a atividade metabólica de uma enzima da família citocromo P450 (CYP450), pode afetar a farmacocinética de outros fármacos, resultando em interações fármaco-fármaco (DDIs), que podem conduzir potencialmente a efeitos colaterais e tóxicos. As principais enzimas oxidativas responsáveis pelo metabolismo de fármacos possuem como principais representantes a superfamília CYP450, em que a isoforma CYP3A4 é a mais importante, pois é responsável por metabolizar aproximadamente 50 % dos fármacos disponíveis no mercado. Diversos métodos computacionais têm sido desenvolvidos como estratégia para predizer o metabolismo humano nos primeiros estágios de pesquisa e desenvolvimento de fármacos. Modelos in silico do metabolismo apresentam vantagens como maior rapidez, menor custo e maior facilidade de operação, quando comparados aos modelos tradicionais in vitro e in vivo. O trabalho teve como objetivo central o desenvolvimento de modelos de Relações Quantitativas entre estrutura química e atividade/propriedade (QSAR/QSPR) robustos e preditivos, visando identificar substratos e inibidores de CYP3A4. Para isso, foram compilados, integrados e preparados os maiores conjuntos de dados disponíveis na literatura de substratos e inibidores de CYP3A4. Vários modelos de QSAR foram gerados e validados para ambas as propriedades usando um fluxo de trabalho que contemplou criteriosamente as recomendações da Organization for Economic Co-operation Development (OECD). A combinação de diferentes descritores e métodos de aprendizado de máquina levaram a obtenção de modelos QSAR robustos e consistentes, com taxa de classificação correta (CCR) que variam entre 0,65-0,83 e cobertura de 0,69-0,89,demonstrando valores estatisticamente significativos para classificação com alta precisão de compostos em substratos ou não substratos de CYP3A4. O modelo Morgan-RF binário gerado para classificar compostos em inibidores e não inibidores se mostraram também altamente robusto e preditivo com valores de sensibilidade de 0,77 e acurácia de 0,76, e o modelo Morgan-RF multiclasse obteve valores de 0,68 para sensibilidade e 0,69 para acurácia. O mapa de probabilidade predita se mostrou útil, pois conseguiu codificar fragmentos estruturais importantes para classificar compostos em inibidores ou não inibidores de CYP3A4. Como conclusões foram desenvolvidos e validados diversos modelos de QSAR para prever a interação com a enzima CYP450 que podem ser úteis nos estágios iniciais do desenvolvimento de novos fármacos. O próximo passo será a disponibilização online dos modelos obtidos no servidor do LabMol (http://labmol.farmacia.ufg.br).Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2016-02-23T13:23:39Z No. of bitstreams: 2 Dissertação - Flávia Cristina da Silva - 2015.pdf: 1680001 bytes, checksum: 3d5116e971ca03d4464ee6b64ab4a913 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-02-23T13:26:06Z (GMT) No. of bitstreams: 2 Dissertação - Flávia Cristina da Silva - 2015.pdf: 1680001 bytes, checksum: 3d5116e971ca03d4464ee6b64ab4a913 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)Made available in DSpace on 2016-02-23T13:26:06Z (GMT). No. of bitstreams: 2 Dissertação - Flávia Cristina da Silva - 2015.pdf: 1680001 bytes, checksum: 3d5116e971ca03d4464ee6b64ab4a913 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2015-02-26application/pdfporUniversidade Federal de GoiásPrograma de Pós-graduação em Ciências Farmacêuticas (FF)UFGBrasilFaculdade Farmácia - FF (RG)http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessQSARIn silicoMetabolismo de fármacosSubstratoInibidorCYP3A4QSARIn silicoDrug metabolismSubstrateInhibitorCYP3A4CIENCIAS BIOLOGICAS::FARMACOLOGIADesenvolvimento de modelos de QSAR para identificação de substratos e inibidores de CYP3A4Development of QSAR models for identification of CYP3A4 inhibitors and substratesinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis8249369881961524126006006006010281161524209375700814650651154363reponame:Repositório Institucional da UFGinstname:Universidade Federal de Goiás (UFG)instacron:UFGLICENSElicense.txtlicense.txttext/plain; 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dc.title.por.fl_str_mv |
Desenvolvimento de modelos de QSAR para identificação de substratos e inibidores de CYP3A4 |
dc.title.alternative.eng.fl_str_mv |
Development of QSAR models for identification of CYP3A4 inhibitors and substrates |
title |
Desenvolvimento de modelos de QSAR para identificação de substratos e inibidores de CYP3A4 |
spellingShingle |
Desenvolvimento de modelos de QSAR para identificação de substratos e inibidores de CYP3A4 Silva, Flávia Cristina da QSAR In silico Metabolismo de fármacos Substrato Inibidor CYP3A4 QSAR In silico Drug metabolism Substrate Inhibitor CYP3A4 CIENCIAS BIOLOGICAS::FARMACOLOGIA |
title_short |
Desenvolvimento de modelos de QSAR para identificação de substratos e inibidores de CYP3A4 |
title_full |
Desenvolvimento de modelos de QSAR para identificação de substratos e inibidores de CYP3A4 |
title_fullStr |
Desenvolvimento de modelos de QSAR para identificação de substratos e inibidores de CYP3A4 |
title_full_unstemmed |
Desenvolvimento de modelos de QSAR para identificação de substratos e inibidores de CYP3A4 |
title_sort |
Desenvolvimento de modelos de QSAR para identificação de substratos e inibidores de CYP3A4 |
author |
Silva, Flávia Cristina da |
author_facet |
Silva, Flávia Cristina da |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Andrade, Carolina Horta |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/2018317447324228 |
dc.contributor.referee1.fl_str_mv |
Andrade, Carolina Horta |
dc.contributor.referee2.fl_str_mv |
Silva, Vinícius Barreto da |
dc.contributor.referee3.fl_str_mv |
Oliveira, Valéria de |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/0500342953672978 |
dc.contributor.author.fl_str_mv |
Silva, Flávia Cristina da |
contributor_str_mv |
Andrade, Carolina Horta Andrade, Carolina Horta Silva, Vinícius Barreto da Oliveira, Valéria de |
dc.subject.por.fl_str_mv |
QSAR In silico Metabolismo de fármacos Substrato Inibidor CYP3A4 |
topic |
QSAR In silico Metabolismo de fármacos Substrato Inibidor CYP3A4 QSAR In silico Drug metabolism Substrate Inhibitor CYP3A4 CIENCIAS BIOLOGICAS::FARMACOLOGIA |
dc.subject.eng.fl_str_mv |
QSAR In silico Drug metabolism Substrate Inhibitor CYP3A4 |
dc.subject.cnpq.fl_str_mv |
CIENCIAS BIOLOGICAS::FARMACOLOGIA |
description |
The discovery and development of drugs consist of a complex process, requiring the integration of various strategic areas such as knowledge, innovation, technology, management and high investments in Research, Development and Innovation (RD&I). No drug can be approved for use in humans without first go through extensive studies aimed at ensuring its effectiveness and safety. On the other hand, a drug that inhibits the activity of a metabolic enzyme cytochrome P450 family (CYP450) can affect the pharmacokinetics of other drugs, resulting in drug-drug interactions (DDIs), which potentially lead to side effects and toxic effects. The main oxidative enzymes responsible for drug metabolism have as main representatives CYP450 superfamily, wherein the CYP3A4 isoform is the most important because it is responsible for metabolizing approximately 50% of the drugs on the market. Several computational methods have been developed as a strategy to predict human metabolism in the early stages of research and development of drugs. In silico models of metabolism have advantages such as faster, lower cost and ease of operation when compared to traditional models in vitro and in vivo. The work aimed mainly at the development of Quantitative Relations between models chemical structure and activity / property (QSAR / QSPR) robust and predictive, to identify CYP3A4 substrates and inhibitors. To this were collected, integrated and prepared larger data sets available in the literature substrates and inhibitors of CYP3A4. Several QSAR models were generated and validated for both properties using a workflow that contemplated carefully the recommendations of the Organization for Economic Co-operation Development (OECD). The combination of different descriptors and machine learning methods have led to obtain robust and predictive QSAR models, with correct classification rate (CCR) ranging from 0.65 to 0.83 and 0.69 to 0.89 of coverage, showing a statistically significant values for classification of compounds with high accuracy whether or not substrates of CYP3A4 substrates. The binary Morgan RFgenerated model to classify compounds inhibitors and non-inhibitors also proved highly robust and predictive with sensitivity values of 0.77 and accuracy of 0.76, and the Morgan-RF model multiclass obtained values of 0.68 sensitivity and 0.69 for accuracy. The map of predicted probability proved useful as it could encode major structural fragments to classify compounds inhibitors or not CYP3A4 inhibitors. In conclusion, have been developed and validated many QSAR to predict the interaction with the CYP450 enzyme that may be useful in the early stages of the development of new drugs. The next step is the online availability of the models obtained in LabMol server (http://labmol.farmacia.ufg.br). |
publishDate |
2015 |
dc.date.issued.fl_str_mv |
2015-02-26 |
dc.date.accessioned.fl_str_mv |
2016-02-23T13:26:06Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
SILVA, F. C. Desenvolvimento de modelos de QSAR para identificação de substratos e inibidores de CYP3A4. 2015. 90 f. Dissertação (Mestrado em Ciências Farmacêuticas) - Universidade Federal de Goiás, Goiânia, 2015. |
dc.identifier.uri.fl_str_mv |
http://repositorio.bc.ufg.br/tede/handle/tede/5266 |
identifier_str_mv |
SILVA, F. C. Desenvolvimento de modelos de QSAR para identificação de substratos e inibidores de CYP3A4. 2015. 90 f. Dissertação (Mestrado em Ciências Farmacêuticas) - Universidade Federal de Goiás, Goiânia, 2015. |
url |
http://repositorio.bc.ufg.br/tede/handle/tede/5266 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.program.fl_str_mv |
824936988196152412 |
dc.relation.confidence.fl_str_mv |
600 600 600 |
dc.relation.department.fl_str_mv |
6010281161524209375 |
dc.relation.cnpq.fl_str_mv |
700814650651154363 |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Goiás |
dc.publisher.program.fl_str_mv |
Programa de Pós-graduação em Ciências Farmacêuticas (FF) |
dc.publisher.initials.fl_str_mv |
UFG |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Faculdade Farmácia - FF (RG) |
publisher.none.fl_str_mv |
Universidade Federal de Goiás |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFG instname:Universidade Federal de Goiás (UFG) instacron:UFG |
instname_str |
Universidade Federal de Goiás (UFG) |
instacron_str |
UFG |
institution |
UFG |
reponame_str |
Repositório Institucional da UFG |
collection |
Repositório Institucional da UFG |
bitstream.url.fl_str_mv |
http://repositorio.bc.ufg.br/tede/bitstreams/c53668e0-fd50-4c1f-999e-37a496623a58/download http://repositorio.bc.ufg.br/tede/bitstreams/c18b77ba-0041-49b0-980f-5c92ac69c755/download http://repositorio.bc.ufg.br/tede/bitstreams/6b3d4d61-7231-4a0d-acce-f1dddc56bcdf/download http://repositorio.bc.ufg.br/tede/bitstreams/89a56b8c-135c-4873-8c06-197487c250b0/download http://repositorio.bc.ufg.br/tede/bitstreams/c8b83034-c1d8-4b16-b58e-78bdba253769/download |
bitstream.checksum.fl_str_mv |
bd3efa91386c1718a7f26a329fdcb468 4afdbb8c545fd630ea7db775da747b2f ef48816a10f2d45f2e2fee2f478e2faf 9da0b6dfac957114c6a7714714b86306 3d5116e971ca03d4464ee6b64ab4a913 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositório Institucional da UFG - Universidade Federal de Goiás (UFG) |
repository.mail.fl_str_mv |
tasesdissertacoes.bc@ufg.br |
_version_ |
1798044434454020096 |