Tozasertib

Drug metabolites are critical for assessing drug efficacy, pharmacokinetics, and safety. Biocatalysis offers a selective and sustainable route to their synthesis. In this study, a Baeyer-Villiger monooxygenase from Acinetobacter radioresistens (Ar-BVMO) was immobilized onto bare iron oxide nanoparticles (BIONs), producing a reusable biocatalytic system (BVMO@BION) for drug metabolite production. The magnetic properties of BIONs facilitated easy recovery and reuse of the biocatalyst, making the system practical for repeated use. High loading efficiency (0.14 mg enzyme per mg of BIONs) was achieved through histidine-tag-mediated binding. The immobilized enzyme exhibited enhanced thermostability, increasing its melting temperature from 46.3 °C to 54.9 °C, and reduced nanoparticle aggregation. The system demonstrated robust activity for Baeyer-Villiger and S/N-oxidation reactions. Notably, BVMO@BION achieved over 95 % conversion efficiency for the N-oxidation of tozasertib (an anti-cancer drug) across nine reaction cycles (2 h each) over 3 days, while activity recovery values ranged from 81 % to 127 %. For S-oxidation of ethionamide (an antibiotic used in multidrug-resistant tuberculosis) approximately 26 % conversion was consistently achieved across eight 1-hour cycles. This work demonstrates that BVMO@BION is a robust, magnetically recoverable platform for repeated and selective drug metabolite synthesis, supporting greener and more efficient pharmaceutical development.

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.