ArticleClinical pharmacology and therapeutics2020
Impact of Disease on Plasma and Lung Exposure of Chloroquine, Hydroxychloroquine and Azithromycin: Application of PBPK Modeling.
Article in Clinical pharmacology and therapeutics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- The effectiveness of non-invasive brain stimulation in treatment of major depressive disorder (MDD): a systematic review and transfer analysis.Journal of neural transmission (Vienna, Austria : 1996) · 2025Pooled it
- Hydroxychloroquine use and progression or prognosis of COVID-19: a systematic review and meta-analysis.Naunyn-Schmiedeberg's archives of pharmacology · 2021Pooled it
- Trial
- Mechanistic Insights into Cytokine Antagonist-Drug Interactions: A Physiologically Based Pharmacokinetic Modelling Approach with Tocilizumab as a Case Study.Pharmaceutics · 2025Article
- Evaluating tigecycline dosing for hospital-acquired pneumonia patients: insights from physiologically-based pharmacokinetic modeling of lung exposure.Antimicrobial agents and chemotherapy · 2025Article
- PK/PD-Guided Strategies for Appropriate Antibiotic Use in the Era of Antimicrobial Resistance.Antibiotics (Basel, Switzerland) · 2025Review
- PBPK-led assessment of antimalarial drugs as candidates for Covid-19: Simulating concentrations at the site of action to inform repurposing strategies.Clinical and translational science · 2024Article
- Quantitative pulmonary pharmacokinetics of tetrandrine for SARS-CoV-2 repurposing: a physiologically based pharmacokinetic modeling approach.Frontiers in pharmacology · 2024Article
- Development and application of a PBPK modeling strategy to support antimalarial drug development.CPT: pharmacometrics & systems pharmacology · 2023Article
- Simulation of optimal dose regimens of photoactivated curcumin for antimicrobial resistance pneumonia in COVID-19 patients: A modeling approach.Infectious Disease Modelling · 2023Article
- Pharmacokinetics under the COVID-19 storm.British journal of clinical pharmacology · 2023Article
- Physiologically Based Pharmacokinetic Modeling Approaches for Patients With SARS-CoV-2 Infection: A Case Study With Imatinib.Journal of clinical pharmacology · 2022Article
- Developing New Treatments for COVID-19 through Dual-Action Antiviral/Anti-Inflammatory Small Molecules and Physiologically Based Pharmacokinetic Modeling.International journal of molecular sciences · 2022Review
- Quantitative Proteomics in Translational Absorption, Distribution, Metabolism, and Excretion and Precision Medicine.Pharmacological reviews · 2022Review
- Physiologically-Based Pharmacokinetics Modeling for Hydroxychloroquine as a Treatment for Malaria and Optimized Dosing Regimens for Different Populations.Journal of personalized medicine · 2022Article
- Pulmonary Delivery of Aerosolized Chloroquine and Hydroxychloroquine to Treat COVID-19: In Vitro Experimentation to Human Dosing Predictions.The AAPS journal · 2022Article
- Comment on: "Penetration of Antibacterial Agents into Pulmonary Epithelial Lining Fluid: An Update".Clinical pharmacokinetics · 2022Article
- Authors' Reply to De Sutter, De Waele, and Vermeulen: "Penetration of Antibacterial Agents into Pulmonary Epithelial Lining Fluid: An Update".Clinical pharmacokinetics · 2022Article
- Translational Modeling of Chloroquine and Hydroxychloroquine Dosimetry in Human Airways for Treating Viral Respiratory Infections.Pharmaceutical research · 2022Article
- Hydroxychloroquine and azithromycin used alone or combined are not effective against SARS-CoV-2 ex vivo and in a hamster model.Antiviral research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We use a mechanistic lung model to demonstrate that accumulation of chloroquine (CQ), hydroxychloroquine (HCQ), and azithromycin (AZ) in the lungs is sensitive to changes in lung pH, a parameter that can be affected in patients with coronavirus disease 2019 (COVID-19). A reduction in pH from 6.7 to 6 in the lungs, as observed in respiratory disease, led to 20-fold, 4.0-fold, and 2.7-fold increases in lung exposure of CQ, HCQ, and AZ, respectively. Simulations indicated that the relatively high concentrations of CQ and HCQ in lung tissue were sustained long after administration of the drugs had stopped. Patients with COVID-19 often present with kidney failure. Our simulations indicate that renal impairment (plus lung pH reduction) caused 30-fold, 8.0-fold, and 3.4-fold increases in lung exposures for CQ, HCQ, and AZ, respectively, with relatively small accompanying increases (20 to 30%) in systemic exposure. Although a number of different dosage regimens were assessed, the purpose of our study was not to provide recommendations for a dosing strategy, but to demonstrate the utility of a physiologically-based pharmacokinetic modeling approach to estimate lung concentrations. This, used in conjunction with robust in vitro and clinical data, can help in the assessment of COVID-19 therapeutics going forward.
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Registered trials
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.