Single, very low rituximab doses in healthy volunteers - a pilot and a randomized trial: implications for dosing and biosimilarity testing
Bibliographic record
Abstract
Abstract There are no dose-finding trials available for rituximab that could guide dosing in non-malignant diseases. We hypothesized that currently used doses (≥375 mg/m 2 ) exceed several hundred-fold the half-maximal effective dose, which is most sensitive for detecting putative differences between biosimilars and important for dose finding. In an open label, exploratory trial healthy volunteers received single infusions of rituximab at doses of 0.1, 0.3 or 1.0 mg/m 2 . Subsequently, in a double-blind, randomized trial healthy volunteers received single infusions of two rituximab products at doses of 0.1 and 0.3 mg/m 2 . In the exploratory trial rituximab transiently depleted CD20+ cells by a mean 68% (range: 57–95%), 74% (55–82%) and 97% (94–100%) immediately after the infusion of 0.1 (n = 4), 0.3 (n = 4) and 1 mg/m 2 (n = 8), respectively. In the randomized trial CD20+ cells decreased by a mean 48% (25–84%) − 55% (26–85%) and 81 (67–89%) – 87% (77–96%) after infusion of 0.1 mg/m 2 (n = 12) or 0.3 mg/m 2 (n = 8 proposed biosimilar, n = 4 reference product) of the proposed biosimilar or the reference product, respectively. It is important to understand that in healthy volunteers <1% of the authorized rituximab doses depletes almost all circulating B lymphocytes. Thus, for non-malignant diseases alternative, more cost-effective dosing regimens seem plausible, but require clinical testing. (EudraCT-No. 2010–023781–45; EudraCT-No. 2013–001077–24).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".