Population Pharmacokinetics of Orvacabtagene Autoleucel, an Autologous BCMA-Directed Chimeric Antigen Receptor T-cell Product, in Patients with Relapsed/Refractory Multiple Myeloma
Bibliographic record
Abstract
PURPOSE: Orvacabtagene autoleucel (orva-cel; JCARH125), a chimeric antigen receptor T-cell therapy targeting B-cell maturation antigen, was evaluated in patients with relapsed/refractory multiple myeloma in the EVOLVE phase I/II study (NCT03430011). We applied a modified piecewise model to characterize orva-cel transgene kinetics and assessed the impact of various covariates on its pharmacokinetics (PK). EXPERIMENTAL DESIGN: The population PK analysis included 159 patients from the EVOLVE study. Traditional piecewise models, employing a first-order expansion rate with or without lag time followed by a biexponential contraction phase, were compared with a modified model incorporating a cell number-dependent expansion phase aligned with cellular physiology. Covariates assessed encompassed baseline demographics, dose levels (50-600 × 106 CD3+ chimeric antigen receptor+ T cells), prior/concomitant medications, baseline disease burden, and antitherapeutic antibody status. RESULTS: Traditional piecewise models failed to accurately describe maximum orva-cel transgene level (Cmax) and underestimated the time to Cmax (Tmax). Our modified model incorporating a cell number-dependent expansion rate outperformed traditional models by (i) more accurately capturing the cellular expansion phase and (ii) yielding a Tmax that closely matches observed values. Additionally, dose level, percentage of plasma cells in bone marrow, and treatment-induced antitherapeutic antibody were identified as statistically significant covariates and associated with orva-cel expansion and/or persistence. CONCLUSIONS: Orva-cel PK was adequately described by the modified piecewise model incorporating a cell number-dependent expansion phase, which aligns closely with T-cell biology.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".