American Association for Cancer Research
Browse
bcd-22-0189_supplementary_methods_suppsm1.docx (623.52 kB)

Supplementary Methods from Next-Generation JAK2 Inhibitors for the Treatment of Myeloproliferative Neoplasms: Lessons from Structure-Based Drug Discovery Approaches

Download (623.52 kB)
journal contribution
posted on 2023-07-27, 15:00 authored by Pramod C. Nair, Jacob Piehler, Denis Tvorogov, David M. Ross, Angel F. Lopez, Jason Gotlib, Daniel Thomas

Supplementary Information: Figure S1 X-ray crystal structure of JNJ-77006621(ball and stick, C-atoms in green) bound to JAK2-PK domain (cartoon, yellow). Key residues are displayed in sticks [C-atoms in green near the optimisation domain (Peuleo et al. 2017, Liosi et al 2020) and C-atoms in yellow at the dimerization domain]. Supplementary Methods.

History

ARTICLE ABSTRACT

Selective inhibitors of Janus kinase (JAK) 2 have been in demand since the discovery of the JAK2 V617F mutation present in patients with myeloproliferative neoplasms (MPN); however, the structural basis of V617F oncogenicity has only recently been elucidated. New structural studies reveal a role for other JAK2 domains, beyond the kinase domain, that contribute to pathogenic signaling. Here we evaluate the structure-based approaches that led to recently-approved type I JAK2 inhibitors (fedratinib and pacritinib), as well as type II (BBT594 and CHZ868) and pseudokinase inhibitors under development (JNJ7706621). With full-length JAK homodimeric structures now available, superior selective and mutation-specific JAK2 inhibitors are foreseeable. The JAK inhibitors currently used for the treatment of MPNs are effective for symptom management but not for disease eradication, primarily because they are not strongly selective for the mutant clone. The rise of computational and structure-based drug discovery approaches together with the knowledge of full-length JAK dimer complexes provides a unique opportunity to develop better targeted therapies for a range of conditions driven by pathologic JAK2 signaling.

Usage metrics

    Blood Cancer Discovery

    Categories

    Keywords

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC