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Structure-Based Prediction of Anti-infective Drug Concentrations in the Human Lung Epithelial Lining Fluid.
Välitalo, Pyry A J; Griffioen, Koen; Rizk, Matthew L; Visser, Sandra A G; Danhof, Meindert; Rao, Gaori; van der Graaf, Piet H; van Hasselt, J G Coen.
Afiliación
  • Välitalo PA; Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC, Leiden, Netherlands.
  • Griffioen K; Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC, Leiden, Netherlands.
  • Rizk ML; Merck & Co. Inc., Kenilworth, New Jersey, USA.
  • Visser SA; Merck & Co. Inc., Kenilworth, New Jersey, USA.
  • Danhof M; Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC, Leiden, Netherlands.
  • Rao G; University at Buffalo, Buffalo, New York, USA.
  • van der Graaf PH; Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC, Leiden, Netherlands.
  • van Hasselt JG; Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC, Leiden, Netherlands. jgc.vanhasselt@gmail.com.
Pharm Res ; 33(4): 856-67, 2016 Apr.
Article en En | MEDLINE | ID: mdl-26626793
PURPOSE: Obtaining pharmacologically relevant exposure levels of antibiotics in the epithelial lining fluid (ELF) is of critical importance to ensure optimal treatment of lung infections. Our objectives were to develop a model for the prediction of the ELF-plasma concentration ratio (EPR) of antibiotics based on their chemical structure descriptors (CSDs). METHODS: EPR data was obtained by aggregating ELF and plasma concentrations from historical clinical studies investigating antibiotics and associated agents. An elastic net regularized regression model was used to predict EPRs based on a large number of CSDs. The model was tuned using leave-one-drug-out cross validation, and the predictions were further evaluated using a test dataset. RESULTS: EPR data of 56 unique compounds was included. A high degree of variability in EPRs both between- and within drugs was apparent. No trends related to study design or pharmacokinetic factors could be identified. The model predicted 80% of the within-drug variability (R(2) WDV) and 78.6% of drugs were within 3-fold difference from the observations. Key CSDs were related to molecular size and lipophilicity. When predicting EPRs for a test dataset the R(2) WDV was 75%. CONCLUSIONS: This model is of relevance to inform dose selection and optimization during antibiotic drug development of agents targeting lung infections.
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Líquido del Lavado Bronquioalveolar / Mucosa Respiratoria / Pulmón / Antibacterianos Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Pharm Res Año: 2016 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Líquido del Lavado Bronquioalveolar / Mucosa Respiratoria / Pulmón / Antibacterianos Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Pharm Res Año: 2016 Tipo del documento: Article