Venadaparib

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.

Mutations in the tumor-suppressor genes BRCA1 and BRCA2 resulting in BRCA1/2 deficiency are frequently identified in breast, ovarian, prostate, pancreatic, and other cancers. Poly(ADP-ribose) polymerase (PARP) inhibitors (PARPis) selectively kill BRCA1/2-deficient cancer cells by inducing synthetic lethality, providing an effective biomarker-guided strategy for targeted cancer therapy. However, a substantial fraction of cancer patients carrying BRCA1/2 mutations do not respond to PARPis, and most patients develop resistance to PARPis over time, highlighting a major obstacle to PARPi therapy in the clinic. Recent studies have revealed that changes of specific functional defects of BRCA1/2-deficient cells, particularly their defects in suppressing and protecting single-stranded DNA gaps, contribute to the gain or loss of PARPi-induced synthetic lethality. These findings not only shed light on the mechanism of action of PARPis, but also lead to revised models that explain how PARPis selectively kill BRCA-deficient cancer cells. Furthermore, new mechanistic principles of PARPi sensitivity and resistance have emerged from these studies, generating potentially useful guidelines for predicting the PARPi response and design therapies for overcoming PARPi resistance. In this Review, we will discuss these recent studies and put them in context with the classic views of PARPi-induced synthetic lethality, aiming to stimulate the development of new therapeutic strategies to overcome PARPi resistance and improve PARPi therapy.