Structure-Based Prediction of Anti-infective Drug Concentrations in the Human Lung Epithelial Lining Fluid

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 antibiot...

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Bibliographic Details
Published in:	Pharmaceutical research Vol. 33; no. 4; pp. 856 - 867
Main Authors:	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
Format:	Journal Article
Language:	English
Published:	New York Springer US 01-04-2016 Springer Springer Nature B.V
Subjects:	Analysis Anti-Bacterial Agents - blood Anti-Bacterial Agents - chemistry Anti-Bacterial Agents - pharmacokinetics Antibiotics Artificial intelligence Biochemistry Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Bronchoalveolar Lavage Fluid - chemistry Computer Simulation Drug therapy Health aspects Humans Infection Lung - metabolism Lung diseases Machine Learning Medical Law Medical research Medicine, Experimental Models, Biological Pharmacology Pharmacology/Toxicology Pharmacy Pneumonia Pneumonia - drug therapy Research Paper Respiratory agents Respiratory Mucosa - metabolism modeling pneumonia pharmacokinetics epithelial lining fluid elastic net machine learning antibiotics lung infection
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0724-8741 1573-904X
DOI:	10.1007/s11095-015-1832-x