title: Structure-Based Prediction of Anti-infective Drug Concentrations in the Human Lung Epithelial Lining Fluid.
abstract: OBJECTIVE
  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.
authors.affiliation: Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC, Leiden, Netherlands.
authors.forename: Pyry A J
authors.initials: PA
authors.lastname: Välitalo
authors.affiliation: Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC, Leiden, Netherlands.
authors.forename: Koen
authors.initials: K
authors.lastname: Griffioen
authors.affiliation: Merck & Co. Inc., Kenilworth, New Jersey, USA.
authors.forename: Matthew L
authors.initials: ML
authors.lastname: Rizk
authors.affiliation: Merck & Co. Inc., Kenilworth, New Jersey, USA.
authors.forename: Sandra A G
authors.initials: SA
authors.lastname: Visser
authors.affiliation: Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC, Leiden, Netherlands.
authors.forename: Meindert
authors.initials: M
authors.lastname: Danhof
authors.affiliation: University at Buffalo, Buffalo, New York, USA.
authors.forename: Gaori
authors.initials: G
authors.lastname: Rao
authors.affiliation: Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC, Leiden, Netherlands.
authors.forename: Piet H
authors.initials: PH
authors.lastname: van der Graaf
authors.affiliation: 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.
authors.forename: J G Coen
authors.initials: JG
authors.lastname: van Hasselt
mesh_terms: D000900:Anti-Bacterial Agents / Q000097:blood / Q000737:chemistry* / Q000493:pharmacokinetics*; D001992:Bronchoalveolar Lavage Fluid / Q000737:chemistry; D003198:Computer Simulation; D006801:Humans; D008168:Lung / Q000378:metabolism*; D000069550:Machine Learning; D008954:Models, Biological; D011014:Pneumonia / Q000188:drug therapy; D020545:Respiratory Mucosa / Q000378:metabolism*
chemical_list: D000900:Anti-Bacterial Agents
keywords: antibiotics; elastic net; epithelial lining fluid; lung infection; machine learning; modeling; pharmacokinetics; pneumonia