Initial performance of pirtobrutinib: Real-world outcomes among CLL patients after prior covalent-BTKi and BCL2i use
Bibliographic record
Abstract
Abstract Background: Non-covalent Bruton's tyrosine kinase inhibitors (BTKi) are third-generation BTKis that utilize non-covalent bonding to improve efficacy and safety by reducing unintended binding and off-target effects. A medication from this drug class, pirtobrutinib was approved by the FDA for chronic lymphocytic leukemia/small lymphocytic leukemia (CLL/SLL) in December 2023. Since pirtobrutinib was only recently approved, knowledge of its long-term performance in the real-world is limited. This study aims to evaluate the initial real-world performance of pirtobrutinib compared to non-pirtobrutinib utilizers. Methods: The IntegraConnect PrecisionQ Database, which contains electronic health records from 3 million deidentified patients in the United States, was used to create a retrospective cohort of CLL/SLL patients who were eligible for pirtobrutinib. Eligible patients were 18 years or older, had a history of using at least one covalent BTKi and one B-cell lymphoma 2 inhibitor (BCL2i) and had a minimum of two clinic visits. Utilizing data from 12/1/2023 through 2/28/2025, patients were classified into pirtobrutinib utilizers and non-pirtobrutinib utilizers. Time to next treatment (TTNT; a surrogate for progression-free survival) was calculated for each patient group, a Kaplan Meier curve was generated, and a Cox proportional hazards model was fit to describe and compare TTNT between pirtobrutinib and non-pirtobrutinib utilizers. Results: Among the 135 patients who met eligibility criteria, 42 utilized pirtobrutinib (31%) and 93 did not utilize pirtobrutinib (69%). The median follow-up time for pirtobrutinib utilizers was 7.1 months, and the median follow-up time for non-pirtobrutinib utilizers was 11.1 months. The mean age at treatment was similar for the pirtobrutinib and non-pirtobrutinib groups: 71.9 (standard deviation [SD] 8.9) vs. 72.4 (SD 9.6) years. Gender (74% vs. 71% males, respectively) and racial composition (67% vs. 74% White, respectively) were also similar across the two groups. All patients received at least one covalent BTKi and a BCL2i. After identification, the non-pirtobrutinib patients (n=93) utilized a variety of different medications in their next line of therapy including 49 patients (52.7%) who used a covalent BTKi (BTKi-c), 21 patients (22.6%) who used a BCL2i, 12 patients (12.9%) who used chemotherapy, 9 patients (9.7%) who used a monoclonal antibody, and 2 patients (2.2%) who used a PI3K inhibitor. At 3 months, the estimated probability of continuing pirtobrutinib therapy was 100%, and the estimated probability of continuing non-pirtobrutinib therapy was 96%. At 6 months, the probability of continuing pirtobrutinib therapy was 96%, and the probability of continuing non-pirtobrutinib therapy was 77%. Using an adjusted Cox proportional hazards model with a 12-month follow-up period, patients receiving pirtobrutinib were less likely to change treatment compared to non-pirtobrutinib users adjusting for age at treatment, race, and gender [hazard ratio (HR) 0.19 (95% confidence interval (CI) 0.04, 0.79), p=0.02]. Conclusions: In our real-world analysis, we found that pirtobrutinib utilizers were less likely to change therapy compared to non-pirtobrutinib utilizers. Since pirtobrutinib is a relatively new treatment, we were only able to examine a small number of utilizers with limited follow-up. As time progresses, we will re-analyze our data to extend the follow-up period and increase the number of pirtobrutinib utilizers included in our analysis. Although further study is warranted, our initial findings suggest that for patients who have progressed through a covalent BTKi and a BCL2i, pirtobrutinib provided a helpful alternative to potentially delay further progression of CLL/SLL.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".