Mivebresib

The P2Y6 receptor, activated by uridine diphosphate (UDP), is a target for antagonists in inflammatory, neurodegenerative, and metabolic disorders, yet few potent and selective antagonists are known to date. This prompted us to use machine learning as a novel approach to aid ligand discovery, with pharmacological evaluation at three P2YR subtypes: initially P2Y6 and subsequently P2Y1 and P2Y14. Relying on extensive published data for P2Y6R agonists, we generated and validated an array of classification machine learning model using the algorithms deep learning (DL), adaboost classifier (ada), Bernoulli NB (bnb), k-nearest neighbors (kNN) classifier, logistic regression (lreg), random forest classifier (rf), support vector classification (SVC), and XGBoost (XGB) classifier models, and the common consensus was applied to molecular selection of 21 diverse structures. Compounds were screened using human P2Y6R-induced functional calcium transients in transfected 1321N1 astrocytoma cells and fluorescent binding inhibition at closely related hP2Y14R expressed in CHO cells. The hit compound ABBV-744, an experimental anticancer drug with a 6-methyl-7-oxo-6,7-dihydro-1H-pyrrolo[2,3-c]pyridine scaffold, had multifaceted interactions with the P2YR family: hP2Y6R inhibition in a non-surmountable fashion, suggesting that noncompetitive antagonism, and hP2Y1R enhancement, but not hP2Y14R binding inhibition. Other machine learning-selected compounds were either weak (experimental anti-asthmatic drug AZD5423 with a phenyl-1H-indazole scaffold) or inactive in inhibiting the hP2Y6R. Experimental drugs TAK-593 and GSK1070916 (100 µM) inhibited P2Y14R fluorescent binding by 50% and 38%, respectively, and all other compounds by < 20%. Thus, machine learning has led the way toward revealing previously unknown modulators of several P2YR subtypes that have varied effects.

High-risk neuroblastoma has a poor prognosis despite intensive treatment, highlighting the need for new therapeutic strategies. Genetic alterations in activators and inactivators of Rho GTPase have been identified in neuroblastoma suggested to activate Rho/Rho-kinase (ROCK) signaling. ROCK has also been implicated in therapy resistance. Therefore, we have explored the efficacy of the dual ROCK inhibitor RKI-1447 in neuroblastoma, emphasizing combination strategies. Treatment with RKI-1447 resulted in decreased growth, increased cell death, and inhibition of N-MYC in vitro and in vivo. A combination screen revealed enhanced effects between RKI-1447 and BET inhibitors. Synergistic effects from RKI-1447 and the BET inhibitor, ABBV-075, were confirmed in various neuroblastoma models, including zebrafish. Interestingly, ABBV-075 increased phosphorylation of both myosin light chain 2 and cofilin, downstream effectors of ROCK, increases that were blocked by adding RKI-1447. The combination treatment also augmented an inhibitory effect on C-MYC and, less pronounced, N-MYC protein expression. BET inhibitors have shown preclinical efficacy against neuroblastoma, but acquired resistance has limited their therapeutic benefit. We reveal that the combination of ROCK and BET inhibitors offers a promising treatment approach that can potentially mitigate resistance to BET inhibitors and reduce toxicity.