Pharmacokinetic /Pharmacodynamic Considerations of Alternate Dosing Strategies of Tocilizumab in COVID-19
Bibliographic record
Abstract
Abstract Tocilizumab is one of few treatments that have been shown to improve mortality in patients with COVID-19, but increased demand has led to relative global shortages. Recently, it has been suggested that lower doses, or fixed doses, of tocilizumab could be a potential solution to conserve the limited global supply while conferring equivalent therapeutic benefit to the dosing regimens studied in major trials. The relationship between tocilizumab dose, exposure, and response in COVID-19 has not been adequately characterized. There are a number of pharmacokinetic (PK) parameters which likely differ between patients with severe COVID-19 and patients in whom tocilizumab was studied during the FDA approval process. Likewise, it is unclear whether a threshold exposure is necessary for tocilizumab efficacy. The safety and efficacy of fixed versus weight-based dosing of tocilizumab has been evaluated outside of COVID-19, but it is uncertain if these observations are generalizable to severe or critical COVID-19. In the current review, we consider the potential advantages and limitations of alternative tocilizumab dosing strategies. Leveraging PK models and simulation analyses, we demonstrate that a fixed single dose of tocilizumab 400 mg is unlikely to produce PK exposures equivalent to those achieved in the REMAP-CAP trial, though weight-stratified dosing appears to produce more uniform exposure distribution. Data from current and future trials could provide PK/PD insight to better inform dosing strategies at the bedside. Ultimately, rational dosing strategies that balance available limited supply with patient needs are required.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".