Findings from a retrospective, real-world study demonstrated that zanubrutinib (Brukinsa) improved time to next treatment (TTNT) and overall survival (OS) compared with acalabrutinib (Calquence) in patients with treatment-naive chronic lymphocytic leukemia (CLL).1 These data were presented in a poster at the
Although the median TTNT was not reached (NR) for both zanubrutinib (n = 5819) and acalabrutinib (n = 10,969), an unadjusted Cox model showed a statistically significant improvement in TTNT with zanubrutinib (HR, 0.88; 95% CI, 0.79-0.97; P = .009). This benefit was also reflected in an inverse probability of treatment weighting (IPTW) adjusted model (HR, 0.89; 95% CI, 0.80-0.98; P = .020). At 18 months, 24 months, and 30 months, 88% (95% CI, 87%-89%), 85% (95% CI, 84%-87%), and 84% (95% CI, 82%-85%) of zanubrutinib-treated patients, respectively, had not progressed to a next treatment. These respective rates were 86% (95% CI, 86%-87%), 83% (95% CI, 82%-84%), and 80% (95% CI, 79%-81%) for patients treated with acalabrutinib.
Regarding OS, the median was NR for all cohorts, but a statistically significant improvement with zanubrutinib was observed in both the unadjusted (HR, 0.72; 95% CI, 0.62-082; P < .001) and IPTW-adjusted (HR, 0.75; 95% CI, 0.65-0.86; P < .001). models. The 18-. 24-, and 30-month OS rates for zanubrutinib were 94%, 92%, and 91%, respectively. These respective rates were 93%, 91%, and 89% for acalabrutinib.
“These findings were consistent across unadjusted and IPTW-adjusted analyses, providing robust real-world comparative evidence among a large sample of insured patients in the United States on the clinical effectiveness of zanubrutinib in clinical practice,” lead study author Ryan Jacobs, MD, and colleagues wrote in the poster. “Collectively, these results help inform treatment selection in treatment-naive [patients with] CLL, where optimizing disease control and tolerability remain important unmet [needs].”
Jacobs is the clinical director of the Lymphoma Division and an associate professor of medicine at Atrium Health Levine Cancer of the Wake Forest University School of Medicine in Charlotte, North Carolina.
Investigators conducted a retrospective, observational cohort study using the Komodo database, a large US administrative claims database capturing longitudinal real-world data from January 2015 to August 2025.
The study included patients at least 18 years of age with confirmed CLL/small lymphocytic lymphoma (SLL) who initiated acalabrutinib between November 2019 and May 2025, or zanubrutinib between January 2023 and May 2025 as first-line therapy. Prior CLL/SLL treatment or clinical trial participation was not allowed.
Among the 511,034 total patients with CLL/SLL identified, 5819 and 10,969 comprised the zanubrutinib and acalabrutinib cohorts, respectively. The primary goals of the study were to compare baseline characteristics between the cohorts, along with TTNT and OS outcomes.
Patients had a median age of 74.0 years in the zanubrutinib arm and 73.0 years in the acalabrutinib arm. The population was predominantly male (anubruitnib, 59%; acalabrutinib, 62%). Patients in the zanubrutinib arm had a mean Charlson Comorbidity Index score of 2.52 (SD, 2.6) compared with 2.48 (SD, 2.6) for the acalabrutinib arm.
The median follow-up was 12.8 months (IQR, 7.5-19.1) for zanubrutinib and 16.4 months (IQR, 4.9-35.4) for acalabrutinib.
Jacobs and colleagues acknowledged the potential for miscoding or delayed coding that could lead to under-identification or misclassification of clinical events and procedures. Since mortality data were derived from multiple linked sources, delays in capture or misreporting may have affected OS estimates.
Differences in launch timelines between the two BTK inhibitors resulted in variable follow-up durations across treatment cohorts since
Additionally, the study included patients with CLL who initiated acalabrutinib between 2020 and 2022, coinciding with the COVID-19 pandemic, while zanubrutinib had not yet been approved for CLL/SLL at the time.
Authors also outlined concerns related to residual confounding and selection bias due to lack of information on key clinical variables for CLL disease aggressiveness, including the status of TP53 mutations, IGHV mutations, and fluorescence in situ hybridization test results.
