Vodobatinib

Vodobatinib is a third‐generation Bcr‐Abl 1 inhibitor, being used in chronic myeloid leukemia treatment. Herein, we report a validated LC–MS/MS method for quantifying vodobatinib from rat dried blood spot (DBS) as per FDA guidelines. Methanol was used as a solvent to extract vodobatinib from the DBS discs. We used an isocratic method with a flow rate of 0.80 mL/min in an Atlantis dC18 (50 × 4.6 mm, 3.0 μm) column with a 2.00 min run time. The retention of vodobatinib and the I.S. was ~1.15 and 1.05 min. The MS/MS ion transitions monitored for vodobatinib and I.S. were m/z 454.20 → 270.05 and 309.15 → 251.15, respectively. The linearity range was 1.04–1039 ng/mL. All the evaluated validation parameters met the acceptance criteria. Hematocrit levels of 35% and 50% yielded consistent concentrations at both low and high QC levels, with accuracy of 1.00%–1.03% and precision below 10%, indicating no hematocrit effect. For intravenous dosing, AUC0–∞ was 9475 ng·h/mL (DBS) versus 10,109 ng·h/mL (plasma), and for oral dosing, Cmax values were 568 ng/mL (DBS) versus 544 ng/mL (plasma). Oral bioavailability was nearly identical between matrices (98.7% for DBS vs. 97.9% for plasma), confirming the reliability of DBS sampling for PK studies of vodobatinib.

Acute myeloid leukemia (AML) is a heterogeneous hematopoietic malignancy with limited therapeutic options for many patients. Discoidin domain receptor 1 (DDR1), a transmembrane tyrosine kinase receptor, has been implicated in AML progression and represents a promising therapeutic target. In this study, we employed a hybrid virtual screening workflow that integrates deep learning-based binding affinity predictions with molecular docking techniques to identify potential DDR1 inhibitors. A multistage screening process involving PSICHIC, KarmaDock, Vina-GPU, and similarity-based scoring was conducted, leading to the selection of seven candidate compounds. The biological evaluation identified Compound 4 as a novel DDR1 inhibitor, demonstrating significant DDR1 inhibitory activity with an IC₅₀ of 46.16 nM and a 99.86% inhibition rate against Z-138 cells at 10 μM. Molecular dynamics simulations and binding free energy calculations further validated the stability and strong binding interactions of Compound 4 with DDR1. This study highlights the utility of combining deep learning models with traditional molecular docking techniques to accelerate the discovery of potent and selective DDR1 inhibitors. The identified compounds hold promise for further development as targeted therapies for AML.