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Recurrence and drug resistance are major challenges in the treatment of acute myeloid leukemia (AML) that spur efforts to identify new clinical targets and active agents. STAT3 has emerged as a potential target in resistant AML, but inhibiting STAT3 function has proven challenging. This paper describes synthetic studies and biological assays for a naphthalene sulfonamide inhibitor class of molecules that inhibit G-CSF-induced STAT3 phosphorylation in cellulo and induce apoptosis in AML cells. We describe two different approaches to inhibitor design: first, variation of substituents on the naphthalene sulfonamide core allows improvements in anti-STAT activity and creates a more thorough understanding of anti-STAT SAR. Second, a novel approach involving hybrid sulfonamide–rhodium( ii ) conjugates tests our ability to use cooperative organic–inorganic binding for drug development, and to use SAR studies to inform metal conjugate design. Both approaches have produced compounds with improved binding potency. In vivo and in cellulo experiments further demonstrate that these approaches can also lead to improved activity in living cells, and that compound 3aa slows disease progression in a xenograft model of AML.
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Data-Driven Math Model of FLT3-ITD Acute Myeloid Leukemia Reveals Potential Therapeutic Targets
FLT3-mutant acute myeloid leukemia (AML) is an aggressive form of leukemia with poor prognosis. Treatment with FLT3 inhibitors frequently produces a clinical response, but the disease nevertheless often recurs. Recent studies have revealed system-wide gene expression changes in FLT3-mutant AML cell lines in response to drug treatment. Here we sought a systems-level understanding of how these cells mediate these drug-induced changes. Using RNAseq data from AML cells with an internal tandem duplication FLT3 mutation (FLT3-ITD) under six drug treatment conditions including quizartinib and dexamethasone, we identified seven distinct gene programs representing diverse biological processes involved in AML drug-induced changes. Based on the literature knowledge about genes from these modules, along with public gene regulatory network databases, we constructed a network of FLT3-ITD AML. Applying the BooleaBayes algorithm to this network and the RNAseq data, we created a probabilistic, data-driven dynamical model of acquired resistance to these drugs. Analysis of this model reveals several interventions that may disrupt targeted parts of the system-wide drug response. We anticipate co-targeting these points may result in synergistic treatments that can overcome resistance and prevent eventual recurrence.
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- Award ID(s):
- 1715826
- PAR ID:
- 10273283
- Date Published:
- Journal Name:
- Journal of Personalized Medicine
- Volume:
- 11
- Issue:
- 3
- ISSN:
- 2075-4426
- Page Range / eLocation ID:
- 193
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/jpm11030193
