ABSTRACT
Cancer patients could combine herbal treatments with their chemotherapy. We consulted VigiBase, a WHO database of individual case safety reports (ICSRs) which archives reports of suspected Adverse Drug Reactions (ADRs) when herbal products are used in conjunction with anti-cancer treatment. We focused on the possible interactions between antineoplastic (L01 ATC class) or hormone antagonists (L02B ATC class) with 10 commonly used herbs (pineapple, green tea, cannabis, black cohosh, turmeric, echinacea, St John's wort, milk thistle and ginger) to compare ADRs described in ICSRs with the literature. A total of 1057 ICSRs were extracted from the database but only 134 were complete enough (or did not concern too many therapeutic lines) to keep them for analysis. Finally, 51 rationalizable ICSRs could be explained, which led us to propose a pharmacokinetic or pharmacodynamic interaction mechanism. Reports concerned more frequently women and half of the rationalizable ICSRs involved Viscum album and Silybum marianum. 5% of the ADRs described could have been avoided if clinicians had had access to the published information. It is also important to note that in 8% of the cases, the ADRs observed were life threatening. Phytovigilance should thus be considered more by health care professionals to best treat cancer patients and for better integrative care.
Subject(s)
Cimicifuga , Drug-Related Side Effects and Adverse Reactions , Echinacea , Hypericum , Drug Interactions , Female , Herb-Drug Interactions , Humans , Silybum marianum , World Health OrganizationABSTRACT
AIMS: In the absence of a commonly agreed dosing protocol based on pharmacokinetic (PK) considerations, the dose and treatment duration for hydroxychloroquine (HCQ) in 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 800 mg day on day 1 followed by 600 mg 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 benefit/risk balance.
Subject(s)
Antiviral Agents/administration & dosage , COVID-19 Drug Treatment , Drug Dosage Calculations , Hydroxychloroquine/administration & dosage , Models, Biological , Adult , Aged , Aged, 80 and over , Antiviral Agents/adverse effects , Antiviral Agents/pharmacokinetics , Arthritis, Rheumatoid/drug therapy , Computer Simulation , Drug Administration Schedule , Female , Humans , Hydroxychloroquine/adverse effects , Hydroxychloroquine/pharmacokinetics , Lupus Erythematosus, Systemic/drug therapy , Male , Middle Aged , Monte Carlo MethodABSTRACT
BACKGROUND AND OBJECTIVE: In the absence of characterization on pharmacokinetics and reference concentrations for hydroxychloroquine in COVID-19 patients, the dose and treatment duration for hydrochloroquine are currently empirical, mainly based on in vitro data, and may vary across national guidelines and clinical study protocols. The aim of this paper is to describe the pharmacokinetics of hydroxychloroquine in COVID-19 patients, considered to be a key step toward its dosing optimization. METHODS: We have developed a population pharmacokinetic model for hydroxychloroquine in COVID-19 patients using prospectively collected pharmacokinetic data from patients either enrolled in a clinical trial or treated with hydroxychloroquine as part of standard of care in two tertiary Belgian hospitals. RESULTS: The final population pharmacokinetic model was a one-compartment model with first-order absorption and elimination. The estimated parameter values were 9.3/h, 860.8 L, and 15.7 L/h for the absorption rate constant, the central compartment volume, and the clearance, respectively. The bioavailability factor was fixed to 0.74 based on previously published models. Model validations by bootstraps, prediction corrected visual predictive checks, and normalized prediction distribution errors gave satisfactory results. Simulations were performed to compare the exposure obtained with alternative dosing regimens. CONCLUSION: The developed models provide useful insight for the dosing optimization of hydroxychloroquine in COVID-19 patients. The present results should be used in conjunction with exposure-efficacy and exposure-safety data to inform optimal dosing of hydroxychloroquine in COVID-19.
