Evaluating Angiogenic Potential of Small Molecules Using Genetic Network Approaches.

Control of microvascular network growth is critical to treatment of ischemic tissue diseases and enhancing regenerative capacity of tissue engineering implants. Conventional therapeutic strategies for inducing angiogenesis aim to deliver one or more proangiogenic cytokines or to over-express known pro-angiogenic genes, but seldom address potential compensatory or cooperative effects between signals and the overarching signaling pathways that determine successful outcomes. An emerging grand challenge is harnessing the expanding knowledge base of angiogenic signaling pathways toward development of successful new therapies. We previously performed drug optimization studies by various substitutions of a 2-(2,6-dioxo-3-piperidyl)isoindole-1,3-dione scaffold to discover novel bioactive small molecules capable of inducing growth of microvascular networks, the most potent of which we termed phthalimide neovascularization factor 1 (PNF1, formerly known as SC-3-149). We then showed that PNF-1 regulates the transcription of signaling molecules that are associated with vascular initiation and maturation in a time-dependent manner through a novel pathway compendium analysis in which transcriptional regulatory networks of PNF-1-stimulated microvascular endothelial cells are overlaid with literature-derived angiogenic pathways. In this study, we generated three analogues (SC-3-143, SC-3-263, SC-3-13) through systematic transformations to PNF1 to evaluate the effects of electronic, steric, chiral, and hydrogen bonding changes on angiogenic signaling. We then expanded our compendium analysis toward these new compounds. Variables obtained from the compendium analysis were then used to construct a PLSR model to predict endothelial cell proliferation. Our combined approach suggests mechanisms of action involving suppression of VEGF pathways through TGF-β andNR3C1 network activation

Evaluating Angiogenic Potential of Small Molecules Using Genetic Network Approaches

Control of microvascular network growth is critical to treatment of ischemic tissue diseases and enhancing regenerative capacity of tissue engineering implants. Conventional therapeutic strategies for inducing angiogenesis aim to deliver one or more pro-angiogenic cytokines or to over-express known pro-angiogenic genes, but seldom address potential compensatory or cooperative effects between signals and the overarching signaling pathways that determine successful outcomes. An emerging grand challenge is harnessing the expanding knowledge base of angiogenic signaling pathways toward development of successful new therapies. We previously performed drug optimization studies by various substitutions of a 2-(2,6-dioxo-3-piperidyl)isoindole-1,3-dione scaffold to discover novel bioactive small molecules capable of inducing growth of microvascular networks, the most potent of which we termed phthalimide neovascularization factor 1 (PNF1, formerly known as SC-3-149). We then showed that PNF-1 regulates the transcription of signaling molecules that are associated with vascular initiation and maturation in a time-dependent manner through a novel pathway compendium analysis in which transcriptional regulatory networks of PNF-1-stimulated microvascular endothelial cells are overlaid with literature-derived angiogenic pathways. In this study, we generated three analogues (SC-3-143, SC-3-263, SC-3-13) through systematic transformations to PNF1 to evaluate the effects of electronic, steric, chiral, and hydrogen bonding changes on angiogenic signaling. We then expanded our compendium analysis toward these new compounds. Variables obtained from the compendium analysis were then used to construct a PLSR model to predict endothelial cell proliferation. Our combined approach suggests mechanisms of action involving suppression of VEGF pathways through TGF-β and NR3C1 network activation. Keywords: PNF-1; Pathway compendium analysis; Phthalimide compoundsNational Institutes of Health (U.S.) (Grant R01-DE019935)National Institutes of Health (U.S.) (Grant R01-AR056445

Identification of a thalidomide derivative that selectively targets tumorigenic liver progenitor cells and comparing its effects with lenalidomide and sorafenib

© 2016 Elsevier Masson SASBackground & aims The availability of non-tumorigenic and tumorigenic liver progenitor cell (LPC) lines affords a method to screen putative anti-liver cancer agents to identify those that are selectively effective. To prove this principle we tested thalidomide and a range of its derivatives and compared them to lenalidomide and sorafenib, to assess their growth-inhibitory effects. Methods Cell growth, the mitotic and apoptotic index of cell cultures were measured using the Cellavista instrument (SynenTec) using commercially available reagents. Results Neither lenalidomide nor thalidomide (100 µM) affected tumorigenic LPCs but killed their non-tumorigenic counterparts. Sorafenib arrested growth in both cell types. All but two derivatives of thalidomide were ineffective; of the two effective derivatives, one (thalidomide C1) specifically affected the tumorigenic cell line (10 µM). Mitotic and apoptotic analyses revealed that thalidomide C1 induced apoptotic cell death and not mitotic arrest. Conclusions This study shows that screens incorporating non-tumorigenic and tumorigenic liver cell lines are a sound approach to identify agents that are effective and selective. A high throughput instrument such as the Cellavista affords robust and reproducible objective measurements with a large number of replicates that are reliable. These experiments show that neither lenalidomide nor thalidomide are potentially useful for anti-liver cancer therapy as they kill non-tumorigenic liver cells and not their tumorigenic counterparts. Sorafenib in contrast, is highly effective, but not selective. One tested thalidomide derivative has potential as an anti-tumor drug since it induced growth arrest; and importantly, it selectively induced apoptotic cell death only in tumorigenic liver progenitor cells