Hydroxychloroquine in Patients with Rheumatic Disease Complicated by COVID-19: Clarifying Target Exposures and the Need for Clinical Trials
Bibliographic record
Abstract
Objective. To characterize hydroxychloroquine (HCQ) exposure in patients with rheumatic disease receiving longterm HCQ compared to target concentrations with reported antiviral activity against the coronavirus disease 2019 caused by SARS-CoV-2 (COVID-19). Method. We evaluated total HCQ concentrations in serum and plasma from published literature values, frozen serum samples from a pediatric systemic lupus erythematosus trial, and simulated concentrations using a published pharmacokinetic model during pregnancy. For each source, we compared observed or predicted HCQ concentrations to target concentrations with reported antiviral activity against SARS-CoV-2. Results. The average total serum/plasma HCQ concentrations were below the lowest SARS-CoV-2 target of 0.48 mg/l in all studies. Assuming the highest antiviral target exposure (total plasma concentration of 4.1 mg/l), all studies had about one-tenth the necessary concentration for in vitro viral inhibition. Pharmacokinetic model simulations confirmed that pregnant adults receiving common dosing for rheumatic diseases did not achieve target exposures; however, the models predict that a dosage of 600 mg once a day during pregnancy would obtain the lowest median target exposure for most patients after the first dose. Conclusion. We found that the average patient receiving treatment with HCQ for rheumatic diseases, including children and non-pregnant/pregnant adults, are unlikely to achieve total serum or plasma concentrations shown to inhibit SARS-CoV-2 in vitro . Nevertheless, patients receiving HCQ long term may have tissue concentrations far exceeding that of serum/plasma. Because the therapeutic window for HCQ in the setting of SARS-CoV-2 is unknown, well-designed clinical trials that include patients with rheumatic disease are urgently needed to characterize the efficacy, safety, and target exposures for HCQ.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".