Publicação
Prediction of the in vitro intrinsic clearance determined in suspensions of human hepatocytes by using artificial neural networks
| Resumo: | Use of in vitro suspensions of human hepatocytes is currently accepted as one of the most promising tools for prediction of metabolic clearance in new drugs. The possibility of creating computational models based on this data may potentiate the early selection process of new drugs. We present an artificial neural network for modelling human hepatocyte intrinsic clearances (CL(int)) based only on calculated molecular descriptors. In vitro CL(int) data obtained in human hepatocytes suspensions was divided into a train group of 71 drugs for network optimization and a test group of another 18 drugs for early-stop and internal validation resulting in correlations of 0.953 and 0.804 for the train and test group respectively. The model applicability was tested with 112 drugs by comparing the in silica predicted CL(int) with the in vivo CL(int) estimated by the "well-stirred" model based on the in vivo hepatic clearance (CL(H)). Acceptable correlations were observed with r values of 0.508 and 63% of drugs within a 10-fold difference when considering blood binding in acidic drugs only. This model may be a valuable tool for prediction and simulation in the drug development process, allowing the in silico estimation of the human in vivo hepatic clearance. (C) 2009 Elsevier B.V. All rights reserved. |
|---|---|
| Autores principais: | Paixão, Paulo |
| Outros Autores: | Gouveia, Luís F.; Morais, José A. G. |
| Assunto: | Human hepatic clearance In vitro intrinsic clearance Human hepatocytes suspension In silico prediction Artificial neural network |
| Ano: | 2010 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório da Universidade de Lisboa |
| _version_ | 1866811150321057792 |
|---|---|
| author | Paixão, Paulo |
| author2 | Gouveia, Luís F. Morais, José A. G. |
| author2_role | author author |
| author_facet | Paixão, Paulo Gouveia, Luís F. Morais, José A. G. |
| author_role | author |
| contributor_name_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Paixão, Paulo\"},{\"Person.name\":\"Gouveia, Luís F.\"},{\"Person.name\":\"Morais, José A. G.\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| datacite.creators.creator.creatorName.fl_str_mv | Paixão, Paulo Gouveia, Luís F. Morais, José A. G. |
| datacite.date.Accepted.fl_str_mv | 2010-03-18T00:00:00Z |
| datacite.date.available.fl_str_mv | 2013-05-28T11:12:15Z |
| datacite.date.embargoed.fl_str_mv | 2013-05-28T11:12:15Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| datacite.subjects.subject.fl_str_mv | Human hepatic clearance In vitro intrinsic clearance Human hepatocytes suspension In silico prediction Artificial neural network |
| datacite.titles.title.fl_str_mv | Prediction of the in vitro intrinsic clearance determined in suspensions of human hepatocytes by using artificial neural networks |
| dc.contributor.none.fl_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| dc.creator.none.fl_str_mv | Paixão, Paulo Gouveia, Luís F. Morais, José A. G. |
| dc.date.Accepted.fl_str_mv | 2010-03-18T00:00:00Z |
| dc.date.available.fl_str_mv | 2013-05-28T11:12:15Z |
| dc.date.embargoed.fl_str_mv | 2013-05-28T11:12:15Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://dx.doi.org/10.1016/j.ejps.2009.12.007 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| dc.subject.none.fl_str_mv | Human hepatic clearance In vitro intrinsic clearance Human hepatocytes suspension In silico prediction Artificial neural network |
| dc.title.fl_str_mv | Prediction of the in vitro intrinsic clearance determined in suspensions of human hepatocytes by using artificial neural networks |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | Use of in vitro suspensions of human hepatocytes is currently accepted as one of the most promising tools for prediction of metabolic clearance in new drugs. The possibility of creating computational models based on this data may potentiate the early selection process of new drugs. We present an artificial neural network for modelling human hepatocyte intrinsic clearances (CL(int)) based only on calculated molecular descriptors. In vitro CL(int) data obtained in human hepatocytes suspensions was divided into a train group of 71 drugs for network optimization and a test group of another 18 drugs for early-stop and internal validation resulting in correlations of 0.953 and 0.804 for the train and test group respectively. The model applicability was tested with 112 drugs by comparing the in silica predicted CL(int) with the in vivo CL(int) estimated by the "well-stirred" model based on the in vivo hepatic clearance (CL(H)). Acceptable correlations were observed with r values of 0.508 and 63% of drugs within a 10-fold difference when considering blood binding in acidic drugs only. This model may be a valuable tool for prediction and simulation in the drug development process, allowing the in silico estimation of the human in vivo hepatic clearance. (C) 2009 Elsevier B.V. All rights reserved. |
| dirty | 0 |
| eu_rights_str_mv | restrictedAccess |
| format | article |
| fulltext.url.fl_str_mv | https://repositorio.ulisboa.pt/bitstreams/4f9440e7-e537-4833-8f7f-559cb5dfab89/download |
| id | ul_2ea379d7d07d98c8f8f5c4e3b99be49a |
| identifier.doi.fl_str_mv | http://dx.doi.org/10.1016/j.ejps.2009.12.007 |
| instacron_str | ul |
| institution | Universidade de Lisboa |
| instname_str | Universidade de Lisboa |
| language | eng |
| network_acronym_str | ul |
| network_name_str | Repositório da Universidade de Lisboa |
| oai_identifier_str | oai:repositorio.ulisboa.pt:10451/8513 |
| organization_str_mv | urn:organizationAcronym:ul |
| person_str_mv | Paixão, Paulo Gouveia, Luís F. Morais, José A. G. |
| publishDate | 2010 |
| reponame_str | Repositório da Universidade de Lisboa |
| repository_id_str | urn:repositoryAcronym:ul |
| service_str_mv | urn:repositoryAcronym:ul |
| spelling | engporUse of in vitro suspensions of human hepatocytes is currently accepted as one of the most promising tools for prediction of metabolic clearance in new drugs. The possibility of creating computational models based on this data may potentiate the early selection process of new drugs. We present an artificial neural network for modelling human hepatocyte intrinsic clearances (CL(int)) based only on calculated molecular descriptors. In vitro CL(int) data obtained in human hepatocytes suspensions was divided into a train group of 71 drugs for network optimization and a test group of another 18 drugs for early-stop and internal validation resulting in correlations of 0.953 and 0.804 for the train and test group respectively. The model applicability was tested with 112 drugs by comparing the in silica predicted CL(int) with the in vivo CL(int) estimated by the "well-stirred" model based on the in vivo hepatic clearance (CL(H)). Acceptable correlations were observed with r values of 0.508 and 63% of drugs within a 10-fold difference when considering blood binding in acidic drugs only. This model may be a valuable tool for prediction and simulation in the drug development process, allowing the in silico estimation of the human in vivo hepatic clearance. (C) 2009 Elsevier B.V. All rights reserved.application/pdfporPrediction of the in vitro intrinsic clearance determined in suspensions of human hepatocytes by using artificial neural networksPaixão, PauloGouveia, Luís F.Morais, José A. G.HostingInstitutionOrganizationalRepositório Científico de Acesso Aberto da ULisboae-mailmailto:repositorio@reitoria.ulisboa.ptrepositorio@reitoria.ulisboa.ptHandlehttp://hdl.handle.net/10451/8513ISSNIsPartOf0928-09872013-05-28T11:12:15Z2010-03-182010-03-18T00:00:00ZDOIhttp://dx.doi.org/10.1016/j.ejps.2009.12.007http://purl.org/coar/access_right/c_16ecrestricted accessHuman hepatic clearanceIn vitro intrinsic clearanceHuman hepatocytes suspensionIn silico predictionArtificial neural network508634 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_16ecapplication/pdffulltexthttps://repositorio.ulisboa.pt/bitstreams/4f9440e7-e537-4833-8f7f-559cb5dfab89/downloadEuropean Journal of Pharmaceutical Sciences310321 |
| spellingShingle | Prediction of the in vitro intrinsic clearance determined in suspensions of human hepatocytes by using artificial neural networks Paixão, Paulo Human hepatic clearance In vitro intrinsic clearance Human hepatocytes suspension In silico prediction Artificial neural network |
| status | SINGLETON |
| subject.fl_str_mv | Human hepatic clearance In vitro intrinsic clearance Human hepatocytes suspension In silico prediction Artificial neural network |
| title | Prediction of the in vitro intrinsic clearance determined in suspensions of human hepatocytes by using artificial neural networks |
| title_full | Prediction of the in vitro intrinsic clearance determined in suspensions of human hepatocytes by using artificial neural networks |
| title_fullStr | Prediction of the in vitro intrinsic clearance determined in suspensions of human hepatocytes by using artificial neural networks |
| title_full_unstemmed | Prediction of the in vitro intrinsic clearance determined in suspensions of human hepatocytes by using artificial neural networks |
| title_short | Prediction of the in vitro intrinsic clearance determined in suspensions of human hepatocytes by using artificial neural networks |
| title_sort | Prediction of the in vitro intrinsic clearance determined in suspensions of human hepatocytes by using artificial neural networks |
| topic | Human hepatic clearance In vitro intrinsic clearance Human hepatocytes suspension In silico prediction Artificial neural network |
| topic_facet | Human hepatic clearance In vitro intrinsic clearance Human hepatocytes suspension In silico prediction Artificial neural network |
| url | http://dx.doi.org/10.1016/j.ejps.2009.12.007 |
| visible | 1 |