Publicação

Prediction of the in vitro intrinsic clearance determined in suspensions of human hepatocytes by using artificial neural networks

Detalhes bibliográficos
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

Descrição
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.