Use of subcutaneous tocilizumab to prepare intravenous solutions for COVID-19 emergency shortage: Comparative analytical study of physicochemical quality attributes
Bibliographic record
Abstract
COVID-19, a disease caused by the novel coronavirus SARS-CoV-2, has produced a serious emergency for global public health, placing enormous stress on national health systems in many countries. Several studies suggest that cytokine storms (interleukins) may play an important role in severe cases of COVID-19. Neutralizing key inflammatory factors in cytokine release syndrome (CRS) could therefore be of great value in reducing the mortality rate. Tocilizumab (TCZ) in its intravenous (IV) form of administration -RoActemra® 20 mg/mL (Roche)-is indicated for treatment of severe CRS patients. Preliminary investigations have concluded that inhibition of IL-6 with TCZ appears to be efficacious and safe, with several ongoing clinical trials. This has led to a huge increase in demand for IV TCZ for treating severe COVID-19 patients in hospitals, which has resulted in drug shortages. Here, we present a comparability study assessing the main critical physicochemical attributes of TCZ solutions used for infusion, at 6 mg/mL and 4 mg/mL, prepared from RoActemra® 20 mg/mL (IV form) and from RoActemra® 162 mg (0.9 mL solution pre-filled syringe, subcutaneous(SC) form), to evaluate the use of the latter for preparing clinical solutions required for IV administration, so that in a situation of shortage of the IV medicine, the SC form could be used to prepare the solutions for IV delivery of TCZ. It is important to remember that during the current pandemic all the medicines are used off-label, since none of them has yet been approved for the treatment of COVID-19.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".