Development of a drug–disease simulation model for rituximab in follicular non‐Hodgkin's lymphoma
Bibliographic record
Abstract
WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • Serum concentrations of rituximab influence its clinical efficacy in follicular lymphoma (FL), but its concentration–effect relationship has not been described by pharmacokinetic–pharmacodynamic (PK–PD) modelling. • The genetic polymorphism of FCGR3A influences rituximab efficacy and its in vitro concentration–effect relationship. • Increasing rituximab dose and/or number of infusions may lead to a better clinical response in FL. WHAT THIS PAPER ADDS • This study is the first to describe the concentration–effect relationship of rituximab in populations of FL patients. • This PK–PD model relates progression‐free survival with rituximab concentrations and takes into account the influence of FCGR3A polymorphism. • Clinical trials testing new dosing regimens of rituximab can be designed using this PK–PD model. AIM Rituximab has dramatically improved the survival of patients with non‐Hodgkin's lymphomas (NHL), but the dosing regimen currently used should be optimized. However, the concentration–effect relationship of rituximab has never been described by pharmacokinetic–pharmacodynamic (PK–PD) modelling, precluding the simulation of new dosing regimens. The aim of this study was to develop a PK–PD model of rituximab in relapsed/resistant follicular NHL (FL). METHODS A model describing the relationship between rituximab concentrations and progression‐free survival (PFS) was developed using data extracted from the pivotal study, which evaluated 151 relapsed/resistant FL patients. The influence of FCGR3A genetic polymorphism on the efficacy of rituximab was quantified using data from 87 relapsed/resistant FL patients. The predictive performance of the model was analysed using two independent datasets: a study that evaluated rituximab combined with chemotherapy [rituximab, cyclophosphamide, vincristine, adriamycin and prednisone (R‐CHOP)] in 334 relapsed/resistant FL patients and a study that evaluated rituximab monotherapy in 47 asymptomatic FL patients with known FCGR3A genotype. RESULTS For R‐CHOP, observed and model‐predicted PFS (90% confidence interval) at 24 months were 0.50 and 0.48 (0.40, 0.56), respectively, for the observation arm, and 0.62 and 0.59 (0.50, 0.65), respectively, for the rituximab maintenance arm. For rituximab monotherapy, observed and predicted PFS at 24 months were 0.67 and 0.63, respectively, for FCGR3A‐V/V patients, and 0.41 and 0.36 (0.25, 0.49), respectively, for FCGR3A‐F carriers. CONCLUSIONS Our model provides a satisfactory prediction of PFS at 24 months. It can be used to simulate new dosing regimens of rituximab in populations of FL patients and should improve the design of future clinical trials.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".