PropertyValue
?:abstract
  • AIMS In the absence of a commonly agreed dosing protocol based on pharmacokinetic considerations, the dose and treatment duration for hydroxychloroquine (HCQ) COVID-19 disease currently vary across national guidelines and clinical study protocols. We have used a model-based approach to explore the relative impact of alternative dosing regimens proposed in different dosing protocols for hydroxychloroquine in COVID-19. METHODS We compared different PK exposures using Monte Carlo simulations based on a previously published population pharmacokinetic model in patients with rheumatoid arthritis, externally validated using both independent data in lupus erythematous patients and recent data in French COVID-19 patients. Clinical efficacy and safety information from COVID-19 patients treated with HCQ were used to contextualize and assess the actual clinical value of the model predictions. RESULTS Literature and observed clinical data confirm the variability in clinical responses in COVID-19 when treated with the same fixed doses. Confounding factors were identified that should be taken into account for dose recommendation. For 80% of patients, doses higher than 800mg day on D1 followed by 600mg daily on following days might not be needed for being cured. Limited adverse drug reactions have been reported so far for this dosing regimen, most often confounded by co-medications, comorbidities or underlying COVID-19 disease effects. CONCLUSION Our results were clear indicating the unmet need for characterization of target PK exposures to inform HCQ dosing optimization in COVID-19. Dosing optimization for HCQ in COVID-19 is still an unmet need. Efforts in this sense are a prerequisite for best the benefit/risk balance.
?:creator
?:doi
  • 10.1111/bcp.14436
?:doi
?:journal
  • British_journal_of_clinical_pharmacology
?:license
  • unk
?:pmid
?:pmid
  • 32559820
?:publication_isRelatedTo_Disease
?:source
  • Medline
?:title
  • Model informed dosing of Hydroxycholoroquine in COVID-19 patients: Learnings from the recent experience, remaining uncertainties and Gaps.
?:type
?:year
  • 2020-06-19

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