Systems-pharmacology dissection of a drug synergy in imatinib-resistant CML

Occurrence of the BCR-ABL[superscript T315I] gatekeeper mutation is among the most pressing challenges in the therapy of chronic myeloid leukemia (CML). Several BCR-ABL inhibitors have multiple targets and pleiotropic effects that could be exploited for their synergistic potential. Testing combinations of such kinase inhibitors identified a strong synergy between danusertib and bosutinib that exclusively affected CML cells harboring BCR-ABL[superscript T315I]. To elucidate the underlying mechanisms, we applied a systems-level approach comprising phosphoproteomics, transcriptomics and chemical proteomics. Data integration revealed that both compounds targeted Mapk pathways downstream of BCR-ABL, resulting in impaired activity of c-Myc. Using pharmacological validation, we assessed that the relative contributions of danusertib and bosutinib could be mimicked individually by Mapk inhibitors and collectively by downregulation of c-Myc through Brd4 inhibition. Thus, integration of genome- and proteome-wide technologies enabled the elucidation of the mechanism by which a new drug synergy targets the dependency of BCR-ABL[superscript T315I] CML cells on c-Myc through nonobvious off targets

Diffusion and scoring methods.

<p><b>A</b>. Schematic representation of a diffusion process in a “toy” network. The proteins (nodes) labeled 1 and 12 are drug targets (in practice they could have distinct weights based on the abundance score but here they have the same for simplicity). After completion of the diffusion process, the entire network is assigned probabilities (black=maximum, white=minimum). Nodes close to the targets typically receive higher probabilities and, due to the network topology, synergistic effects are obtained for nodes close to several targets (marked as “S”) or linked in multiple ways to a single target (marked as “T”). <b>B</b>. Principle of scoring illustrated in part of the network: two diffusion processes are performed separately on the PPI network (partially featured): one yields a model of the drug treatment effect (red) and the second one yields a model of the influence of the disease (green). Combining the two set of scores allows for the computation of a correlation score (blue) that measures the adequacy of a drug treatment for a disease.</p

Hybrid drug-protein/protein-protein interaction networks of specific drug binding proteins.

<p>Individual cellular target profiles of nilotinib (green), dasatinib (red), bosutinib (yellow) and bafetinib (blue) were intersected with each other and overlaid with PPI data from public databases. Protein kinases and the oxidoreductase NQO2, as a validated target of nilotinib and to lesser extent of bafetinib, were considered to be direct drug binders (solid lines) and color-coded according to the drug they were interacting with. Shared kinase targets display a split color code. All other non-kinase proteins were assumed to be indirect binders (dashed lines) and displayed in grey. The analysis reveals distinct protein complexes, which are enriched by particular drugs and which are highlighted with the respectively colored background. <b>A</b>. Z-119 drug-protein interaction network. <b>B</b>. BV-173 drug-protein interaction network.</p

Graphical representation of binding specificity assessment.

<p>Using the example of dasatinib and BV-173 cells, the average spectral counts obtained from chemical proteomics were compared with the respective competition experiments in the presence of 20 µM free drug in a double-logarithmic plot. Specific ( ♦) and non-specific (◊) binders were identified by definition of a specificity gate (grey area) with a ratio threshold of 2 and a minimum average spectral count of 10. For proteins that were not identified in the competition experiment, the minimum average spectral count was lowered to 1. </p

Differential drug effects on cellular tyrosine phosphorylation.

<p>Cells were treated for 30 min with bafetinib (800 nM), bosutinib (400 nM), dasatinib (100 nM) and nilotinib (4 µM), which are concentrations equivalent to reported maximum patient plasma concentrations, and DMSO control. Effects of individual drugs were determined by immunoblot analysis for BCR-ABL (α-ABL) and total phosphotyrosine (α-pY). Actin served as loading control. <b>A</b>. Dasatinib had the strongest impact on cellular tyrosine phosphorylation in BV-173 cells while the effects of bafetinib, nilotinib and particularly bosutinib were less pronounced. <b>B</b>. Dasatinib completely abolished cellular tyrosine phosphorylation in Z-119 cells. BCR-ABL levels were not appreciably affected, but it’s phosphorylation (marked by arrow) was inhibited by the drugs in either cell line.</p

Schematic outline of the integrated chemical proteomics and computational biology strategy.

<p><b>A</b>. Drug-protein interaction networks are generated by chemical proteomics while the protein-protein interaction (PPI) network is derived from public databases and modified to represent the specific disease. The interaction networks are correlated through a random walk approach across the PPI network using proteins from the drug-protein network as entry points. The resulting correlation scores are subsequently validated by cell proliferation assays. <b>B</b>. Chemical structures of the four second-generation BCR-ABL tyrosine kinase inhibitors dasatinib (Sprycel, BMS-354825), nilotinib (<i>Tasigna</i>, AMN107), bosutinib (SKI-606) and bafetinib (INNO-406, NS-187) as well as their coupleable analogues c-dasatinib, c-nilotinib, c-bosutinib and c-bafetinib that were used for immobilization and subsequent chemical proteomics experiments.</p

Artemisinins Target GABAA Receptor Signaling and Impair α Cell Identity

Type 1 diabetes is characterized by the destruction of pancreatic β cells, and generating new insulin-producing&nbsp;cells from other cell types is a major aim of regenerative medicine. One promising approach is transdifferentiation of developmentally related pancreatic cell types, including glucagon-producing α cells. In a genetic model, loss of the master regulatory transcription factor Arx is sufficient to induce the conversion of α cells to functional β-like cells. Here, we identify artemisinins as small molecules that functionally repress Arx by causing its translocation to the cytoplasm. We show that the protein gephyrin is the mammalian target of these antimalarial drugs and that the mechanism of action of these molecules depends on the enhancement of GABAA receptor signaling. Our results in zebrafish, rodents, and primary human pancreatic islets identify gephyrin as a druggable target for the regeneration of pancreatic β cell mass from α cells