ArticlePharmaceutical research2016
Structure-Based Prediction of Anti-infective Drug Concentrations in the Human Lung Epithelial Lining Fluid.
Article in Pharmaceutical research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 15 citations in OpenAlex.
- Intrapulmonary Pharmacokinetics of Relebactam, a Novel β-Lactamase Inhibitor, Dosed in Combination with Imipenem-Cilastatin in Healthy Subjects.Antimicrobial agents and chemotherapy · 2018Trial
- Evaluating renal function and age as predictors of amikacin clearance in neonates: model-based analysis and optimal dosing strategies.British journal of clinical pharmacology · 2016Trial
- Pooled population pharmacokinetic model of imipenem in plasma and the lung epithelial lining fluid.British journal of clinical pharmacology · 2016Trial
- Article
- Evidence of intrapulmonary penetration of nacubactam, a novel β-lactamase inhibitor, following coadministration of nacubactam with meropenem in healthy adults.Antimicrobial agents and chemotherapy · 2026Article
- QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool.Journal of cheminformatics · 2024Article
- Application of machine learning in combination with mechanistic modeling to predict plasma exposure of small molecules.Frontiers in systems biology · 2023Article
- Physiologically Based Modelling Framework for Prediction of Pulmonary Pharmacokinetics of Antimicrobial Target Site Concentrations.Clinical pharmacokinetics · 2022Article
- Intracellular Accumulation of Novel and Clinically Used TB Drugs Potentiates Intracellular Synergy.Microbiology spectrum · 2021Article
- Design principles of collateral sensitivity-based dosing strategies.Nature communications · 2021Article
- Observational
- Validation of a Model Predicting Anti-infective Lung Penetration in the Epithelial Lining Fluid of Humans.Pharmaceutical research · 2018Article
- Importance of Drug Pharmacokinetics at the Site of Action.Clinical and translational science · 2017Review
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeObtaining 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).
methodsEPR 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.
resultsEPR 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%.
conclusionsThis model is of relevance to inform dose selection and optimization during antibiotic drug development of agents targeting lung infections.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.