Inhibition of ADAM17 impairs endothelial cell necroptosis and blocks metastasis

Metastasis is the major cause of death in cancer patients. Circulating tumor cells need to migrate through the endothelial layer of blood vessels to escape the hostile circulation and establish metastases at distant organ sites. Here, we identified the membrane-bound metalloprotease ADAM17 on endothelial cells as a key driver of metastasis. We show that TNFR1-dependent tumor cell–induced endothelial cell death, tumor cell extravasation, and subsequent metastatic seeding is dependent on the activity of endothelial ADAM17. Moreover, we reveal that ADAM17-mediated TNFR1 ectodomain shedding and subsequent processing by the γ-secretase complex is required for the induction of TNF-induced necroptosis. Consequently, genetic ablation of ADAM17 in endothelial cells as well as short-term pharmacological inhibition of ADAM17 prevents long-term metastases formation in the lung. Thus, our data identified ADAM17 as a novel essential regulator of necroptosis and as a new promising target for antimetastatic and advanced-stage cancer therapie

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery