Implementing Reverse Phase Protein Array Profiling as a Sensitive Method for the Early Pre-Clinical Detection of Off-Target Toxicities Associated with Sunitinib Malate

Crizotinib and Sunitinib Induce Hepatotoxicity and Mitochondrial Apoptosis in L02 Cells via ROS and Nrf2 Signaling Pathway

Considerable attention has been raised on crizotinib- and sunitinib-induced hepatotoxicity, but the underlying mechanisms need further examination. In addition, limited therapeutic strategies exist to reduce the liver damage caused by crizotinib and sunitinib. This study investigated the mechanisms of crizotinib- and sunitinib-induced hepatotoxicity and the potential mitigation through ROS and Nrf2 signaling. Firstly, crizotinib and sunitinib reduced cell viability in human liver cells (L02 cells) and triggered dramatic liver injury in mice. Subsequently, we found that crizotinib and sunitinib activated the oxidative stress response (decreased level of GPx and SOD, and increased MDA content) in vivo. Crizotinib and sunitinib also stimulated hepatocyte mitochondrial apoptosis and necrosis in L02 cells in a dose-dependent manner. In vivo studies further confirmed that crizotinib and sunitinib decreased mitochondrial membrane potential and activated apoptosis-associated proteins (cleaved-PARP, cleaved caspase3, cytochrome c, Bcl2 and Bax). Furthermore, mechanistic investigations demonstrated that crizotinib and sunitinib accumulated ROS and inhibited Nrf2 signaling, and that ROS scavenger NAC and Nrf2 agonist tBHQ alleviated the extent of cell damage and the mitochondrial apoptosis during crizotinib- and sunitinib-induced hepatotoxicity in L02 cells. Collectively, these findings indicated that NAC and tBHQ play the crucial roles in crizotinib- and sunitinib-induced mitochondrial apoptosis via the regulation of oxidative stress.

Proteomics for cancer drug design

Introduction: Signal transduction cascades drive cellular proliferation, apoptosis, immune, and survival pathways. Proteins have emerged as actionable drug targets because they are often dysregulated in cancer, due to underlying genetic mutations, or dysregulated signaling pathways. Cancer drug development relies on proteomic technologies to identify potential biomarkers, mechanisms-of-action, and to identify protein binding hot spots. Areas covered: Brief summaries of proteomic technologies for drug discovery include mass spectrometry, reverse phase protein arrays, chemoproteomics, and fragment based screening. Protein-protein interface mapping is presented as a promising method for peptide therapeutic development. The topic of biosimilar therapeutics is presented as an opportunity to apply proteomic technologies to this new class of cancer drug. Expert opinion: Proteomic technologies are indispensable for drug discovery. A suite of technologies including mass spectrometry, reverse phase protein arrays, and protein-protein interaction mapping provide complimentary information for drug development. These assays have matured into well controlled, robust technologies. Recent regulatory approval of biosimilar therapeutics provides another opportunity to decipher the molecular nuances of their unique mechanisms of action. The ability to identify previously hidden protein hot spots is expanding the gamut of potential drug targets. Proteomic profiling permits lead compound evaluation beyond the one drug, one target paradigm.

Reproducibility and Crossplatform Validation of Reverse-Phase Protein Array Data

Reverse-phase protein array (RPPA) technology is a high-throughput antibody- and microarray-based approach for the rapid profiling of levels of proteins and protein posttranslational modifications in biological specimens. The technology consumes small amounts of samples, can sensitively detect low-abundance proteins and posttranslational modifications, enables measurements of multiple signaling pathways in parallel, has the capacity to analyze large sample numbers, and offers robust interexperimental reproducibility. These features of RPPA experiments have motivated and enabled the use of RPPA technology in various biomedical, translational, and clinical applications, including the delineation of molecular mechanisms of disease, profiling of druggable signaling pathway activation, and search for new prognostic markers. Owing to the complexity of many of these applications, such as developing multiplex protein assays for diagnostic laboratories or integrating posttranslational modification-level data using large-scale proteogenomic approaches, robust and well-validated data are essential. There are many distinct components of an RPPA workflow, and numerous possible technical setups and analysis parameter options exist. The differences between RPPA platform setups around the world offer opportunities to assess and minimize interplatform variation. Crossplatform validation may also aid in the evaluation of robust, platform-independent protein markers of disease and response to therapy.