Fludarabine during Lymphodepletion Chemotherapy Prior to CAR-T Cell Therapy in Adult Patients with Non-Hodgkin Lymphoma: Evaluation of a Pharmacokinetic Model and Exploration of Optimal Exposure
Bibliographic record
Abstract
Background Optimal fludarabine exposure (cumulative area under the curve - AUC) during lymphodepletion chemotherapy (LD) has been associated with improved clinical outcomes for adult patients (pts) receiving CAR-T cell therapy for non-Hodgkin lymphoma (NHL), using a published population pharmacokinetic (popPK) model. This model has not yet been validated in NHL pts undergoing CAR-T cell therapy. The objectives of this study are to first to perform an external evaluation of the popPK model; second, to explore the relationship between fludarabine exposure and clinical outcomes after CAR-T cell therapy. Methods We conducted a prospective observational study. Pts provided 7 to 9 blood samples over 3 days during LD prior to CAR-T therapy. Fludarabine plasma concentration was determined by liquid chromatography-mass spectrometry. The external evaluation comprised a visual inspection of the predicted/measured concentrations, accompanied by a statistical evaluation (bias ± 20%; imprecision < 30%). A comparison was made between fludarabine exposure obtained using a priori (population predicted AUC – only covariates) or a posteriori (measured AUC - pts PK profiles and individual covariates) prediction. In addition, the clinical outcomes (progression-free survival, CRS, ICANS) after CAR-T were obtained from pts charts and stratified for measured fludarabine exposure. NONMEM and SPSS were used to perform pharmacokinetic and statistical analyses. Results A total of 100 samples were obtained from 13 pts (Figure 1) for determination of fludarabine concentrations. Population prediction performance of the published popPK model was unsatisfactory (bias -20.7%; inaccuracy 21.2%). However, when patient PK profiles and individual covariates were included, the predictive performance showed improvement (bias -0.85%; inaccuracy 3.04%). Measured fludarabine exposure was significantly different from the population predicted exposure (median cumulative AUC of 20.6 mg*h/L vs 18.1 mg*h/L, p < 0.01, paired t-test) (Figure 2). Median follow-up was 95 days after CAR-T infusion for all pts (range 31-210 days), with 4 out of 6 pts with cumulative AUC < 20 mg*h/L experiencing disease progression . No progression event occured among 7 pts with cumulative AUC ≥ 20 mg*h/L (Figure 3). No relationship was observed between fludarabine exposure and toxicity (CRS grade 1-2 in 92 % of pts, ICANS grade 1-4 in 53 % of pts). Conclusion In a real-world population of adult NHL pts, the measured fludarabine exposure is significantly higher than the predicted exposure from a published popPK model. Updating this model could enhance its predictive capabilities. The use of popPK-guided dosing to achieve the desired fludarabine cumulative AUC during LD remains a topic of interest and may lead to improved clinical outcomes in CAR-T cell therapy.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".