Utilize este identificador para referenciar este registo: http://hdl.handle.net/10451/21283
Título: Prediction of drug distribution within blood
Autor: Paixao, Paulo
Gouveia, Luis F.
Morais, Jose A. G.
Palavras-chave: Pharmacology & Pharmacy
Data: 2009
Editora: ELSEVIER SCIENCE BV
Citação: EUROPEAN JOURNAL OF PHARMACEUTICAL SCIENCES. - Vol. 36, n. 40302 (2009), p. 544-554
Resumo: Drug distribution in blood, defined as drug blood-to-plasma concentration ratio (R-b), is a fundamental pharmacokinetic parameter. it relates the plasma clearance to the blood clearance, enabling the physiological interpretation of this parameter. Although easily experimentally deter-mined, Rb values are lacking for the vast majority of drugs. We present a systematic approach using mechanistic, partial least squares (PLS) regression and artificial neural network (ANN) models to relate various in vitro and in silico molecular descriptors to a dataset of 93 drug Rb values collected in the literature. The ANN model resulted in the best overall approach, with r(2) = 0.927 and r(2) = 0.871 for the train and the test sets, respectively. PLS regression presented r(2) = 0.557 for the train and r(2) = 0.656 for the test set. The mechanistic model provided the worst results, with r(2) = 0.342 and, additionally, is limited to drugs with a basic ionised group with pKa 7. The ANN model for drug distribution in blood can be a valuable tool in clinical pharmacokinetics as well as in new drug design, providing predictions of Rb with a percentage of correct values within a 1.25-fold error of 86%, 84% and 87% in the train, test and validation set of data. (c) 2008 Elsevier B.V. All rights reserved.. - Fundacao para a Ciencia e a Tecnologia [SFRH/BD/28545/2006]. - This work was partially supported by project number SFRH/BD/28545/2006 from Fundacao para a Ciencia e a Tecnologia.
URI: http://hdl.handle.net/10451/21283
DOI: http://dx.doi.org/10.1016/j.ejps.2008.12.011
ISSN: 0928-0987
Aparece nas colecções:FF - Produção Científica 2000-2009

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