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Journal of the American Chemical Society
Article . 2019 . Peer-reviewed
License: STM Policy #29
Data sources: Crossref
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Data-Driven Construction of Antitumor Agents with Controlled Polypharmacology

Authors: Chenxiao Da; Dehui Zhang; Michael Stashko; Eleana Vasileiadi; Rebecca E. Parker; Katherine A. Minson; Madeline G. Huey; +13 Authors

Data-Driven Construction of Antitumor Agents with Controlled Polypharmacology

Abstract

Controlling which particular members of a large protein family are targeted by a drug is key to achieving a desired therapeutic response. In this study, we report a rational data-driven strategy for achieving restricted polypharmacology in the design of antitumor agents selectively targeting the TYRO3, AXL, and MERTK (TAM) family tyrosine kinases. Our computational approach, based on the concept of fragments in structural environments (FRASE), distills relevant chemical information from structural and chemogenomic databases to assemble a three-dimensional inhibitor structure directly in the protein pocket. Target engagement by the inhibitors designed led to disruption of oncogenic phenotypes as demonstrated in enzymatic assays and in a panel of cancer cell lines, including acute lymphoblastic and myeloid leukemia (ALL/AML) and nonsmall cell lung cancer (NSCLC). Structural rationale underlying the approach was corroborated by X-ray crystallography. The lead compound demonstrated potent target inhibition in a pharmacodynamic study in leukemic mice.

Keywords

Molecular Structure, Cell Survival, Receptor Protein-Tyrosine Kinases, Antineoplastic Agents, Neoplasms, Experimental, Gene Expression Regulation, Neoplastic, Mice, Cell Line, Tumor, Animals, Humans, Amino Acid Sequence

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    18
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
18
Top 10%
Average
Top 10%
bronze
Related to Research communities
Cancer Research