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. 2023 Jul;16(7):1243-1257.
doi: 10.1111/cts.13527. Epub 2023 Apr 29.

A linked physiologically based pharmacokinetic model for hydroxychloroquine and metabolite desethylhydroxychloroquine in SARS-CoV-2(-)/(+) populations

Affiliations

A linked physiologically based pharmacokinetic model for hydroxychloroquine and metabolite desethylhydroxychloroquine in SARS-CoV-2(-)/(+) populations

Claire Steinbronn et al. Clin Transl Sci. 2023 Jul.

Abstract

Hydroxychloroquine (HCQ) is Food and Drug Administration (FDA)-approved for malaria, systemic and chronic discoid lupus erythematosus, and rheumatoid arthritis. Because HCQ has a proposed multimodal mechanism of action and a well-established safety profile, it is often investigated as a repurposed therapeutic for a range of indications. There is a large degree of uncertainty in HCQ pharmacokinetic (PK) parameters which complicates dose selection when investigating its use in new disease states. Complications with HCQ dose selection emerged as multiple clinical trials investigated HCQ as a potential therapeutic in the early stages of the COVID-19 pandemic. In addition to uncertainty in baseline HCQ PK parameters, it was not clear if disease-related consequences of SARS-CoV-2 infection/COVID-19 would be expected to impact the PK of HCQ and its primary metabolite desethylhydroxychloroquine (DHCQ). To address the question whether SARS-CoV-2 infection/COVID-19 impacted HCQ and DHCQ PK, dried blood spot samples were collected from SARS-CoV-2(-)/(+) participants administered HCQ. When a previously published physiologically based pharmacokinetic (PBPK) model was used to fit the data, the variability in exposure of HCQ and DHCQ was not adequately captured and DHCQ concentrations were overestimated. Improvements to the previous PBPK model were made by incorporating the known range of blood to plasma concentration ratios (B/P) for each compound, adjusting HCQ and DHCQ distribution settings, and optimizing DHCQ clearance. The final PBPK model adequately captured the HCQ and DHCQ concentrations observed in SARS-CoV-2(-)/(+)participants, and incorporating COVID-19-associated changes in cytochrome P450 activity did not further improve model performance for the SARS-CoV-2(+) population.

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Conflict of interest statement

A.B. reports research support from the US National Institutes of Health, the Bill and Melinda Gates Foundation, the Foundation for Innovative New Diagnostics, and the New York City Department of Health and Mental Hygiene, and compensation for consulting services from Gates Ventures. C.J. reports consultancies to AbbVie, GSK, and Gilead, and receives research support from Gilead and royalties from UpToDate. R.V.B. reports Regeneron Pharmaceuticals provided conference abstract and manuscript writing support outside the submitted work. R.L. serves on the Scientific Advisory Board for Gilead and Merck in addition to reporting consultancy agreements for Cepheid and Janssen. All the other authors declared no competing interests for this work.

Figures

FIGURE 1
FIGURE 1
Pharmacokinetic data for hydroxychloroquine (HCQ) and desethylhydroxychloroquine (DHCQ) in SARS‐CoV‐2 (−)/(+) participants. HCQ and DHCQ concentrations were determined for 94 SARS‐CoV‐2(−) participants who received HCQ 200 mg twice daily for 3 days followed by 200 mg daily for 11 days (a). HCQ and DHCQ concentrations were determined for 34 SARS‐CoV‐2(+) participants who received HCQ 400 mg twice daily for 1 day followed by 200 mg twice daily for 9 days (b). Significant variability in HCQ and DHCQ concentrations was observed within both study groups.
FIGURE 2
FIGURE 2
Base physiologically based pharmacokinetic (PBPK) model performance with hydroxychloroquine (HCQ) and desethylhydroxychloroquine (DHCQ) pharmacokinetic (PK) data observed in SARS‐CoV‐2(−) participants. The base model does not account for interindividual variability in HCQ and DHCQ concentrations (a). The PBPK model performance was improved by incorporating the known range of blood to plasma concentration ratios for each compound (b). AAFE, absolute average fold error; AFE, average fold error; B/P, blood to plasma concentration ratio; DHCQ, desethylhydroxychloroquine; HCQ, hydroxychloroquine; PEP, post‐exposure prophylaxis; WPI, proportion within 95% prediction intervals.
FIGURE 3
FIGURE 3
Hydroxychloroquine (HCQ) and desethylhydroxychloroquine (DHCQ) distribution parameters within the physiologically based pharmacokinetic (PBPK) model were optimized using pharmacokinetic (PK) data collected over 4000 h after a single intravenous dose of HCQ to two volunteers. The observed data were extracted from the two studies., The base model does not capture the terminal phase of HCQ and DHCQ concentrations (a). After incorporating the blood to plasma concentration ratio variability and optimizing tissue–plasma partition coefficients (K p) for HCQ and DHCQ, the model fit improves for both HCQ and DHCQ (b). AAFE, absolute average fold error; AFE, average fold error; B/P, blood to plasma concentration ratio; DHCQ, desethylhydroxychloroquine; HCQ, hydroxychloroquine; Vol 1, Volunteer 1; Vol 4, Volunteer 4; WPI, proportion within 95% prediction intervals.
FIGURE 4
FIGURE 4
Desethylhydroxychloroquine (DHCQ) clearance was optimized using the DHCQ concentrations observed in SARS‐CoV‐2(−) participants. Using the updated distribution model, the model overestimates DHCQ exposure (a). As expected, the optimized clearance value for DHCQ improves the model fit (b). AAFE, absolute average fold error; AFE, average fold error; DHCQ, desethylhydroxychloroquine; HCQ, hydroxychloroquine; PEP, post‐exposure prophylaxis; WPI, proportion within 95% prediction intervals.
FIGURE 5
FIGURE 5
Model performance in SARS‐CoV‐2(+) participants. The optimized physiologically based pharmacokinetic (PBPK) model incorporated the dosing regimen for SARS‐CoV‐2(+) participants (400 mg twice daily x 1 day followed by 200 mg twice daily x 9 days) in a virtual population of healthy volunteers (a) and mild SARS‐CoV‐2(+) participants (b). Overall, the model captures the observed dataset prior to incorporating changes in cytochrome P450 activity that may be observed with mild COVID‐19 disease. AAFE, absolute average fold error; AFE, average fold error; DHCQ, desethylhydroxychloroquine; HCQ, hydroxychloroquine; WPI, proportion within 95% prediction intervals.

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