Canadian real-world experience of asciminib treatment in heavily pre-treated chronic myeloid leukemia (CML) patients who failed multiple lines of tyrosine kinase inhibitor (TKI) therapy.

BACKGROUND: Asciminib is a novel drug specifically targeting ABL myristoyl pocket in the ABL1 protein. METHODS: Forty one patients with chronic myeloid leukemia treated with asciminib from 2018 to 2022 were reviewed and analyzed for the efficacy and tolerability of asciminib using real-world experience data. RESULTS: The median age was 60 years (range 17-90) with a past history of a cardiovascular event in 21 patients (51%). Patients were pretreated with a median of 3 previous tyrosine kinase inhibitors (range 1-5). After a median of 12 months of asciminib (range 3-41), major molecular response (MMR) rate was 39% (n = 11/28) and 42% (n = 5/12) at 6 and 12 months, respectively. Molecular response with 2 log reduction (MR2) was noted in 54% (n = 15/28) and 50% (n = 6/12) at 6 and 12 months. The cumulative incidence of MMR and MR2 was 46.3% and 66% at 12 months. Five patients discontinued asciminib due to treatment failure (n = 3) or thrombocytopenia (n = 2). There were no cardiovascular events. Out of 7 patients treated with high dose asciminib for T315I mutation, 5 patients achieved MMR or deeper response. The event-free survival was 63% at 12 months. CONCLUSION: This study confirmed clinical efficacy and tolerability of asciminib with real-world experience.

Statistical practice in high-throughput screening data analysis.

High-throughput screening is an early critical step in drug discovery. Its aim is to screen a large number of diverse chemical compounds to identify candidate 'hits' rapidly and accurately. Few statistical tools are currently available, however, to detect quality hits with a high degree of confidence. We examine statistical aspects of data preprocessing and hit identification for primary screens. We focus on concerns related to positional effects of wells within plates, choice of hit threshold and the importance of minimizing false-positive and false-negative rates. We argue that replicate measurements are needed to verify assumptions of current methods and to suggest data analysis strategies when assumptions are not met. The integration of replicates with robust statistical methods in primary screens will facilitate the discovery of reliable hits, ultimately improving the sensitivity and specificity of the screening process.