Combining Data-Driven and Structure-Based Approaches in Designing Dual PARP1-BRD4 Inhibitors for Breast Cancer Treatment.

Poly(ADP-ribose) polymerase 1 (PARP1) inhibitors have revolutionized the treatment of many cancers with DNA-repairing deficiencies via synthetic lethality. Advocated by the polypharmacology concept, recent evidence discovered that a significantly synergistic effect in increasing the death of cancer cells was observed by simultaneously perturbating the enzymatic activities of bromodomain-containing protein 4 (BRD4) and PARP1. Here, we developed a novel cheminformatics approach combined with a structure-based method aiming to facilitate the design of dual PARP1-BRD4 inhibitors. Instead of linking pharmacophores, the developed approach first identified merged pharmacophores (a pool of amide-containing ring systems), from which phenanthridin-6(5H)-one was further prioritized. Based on this starting point, several small molecules were rationally designed, among which HF4 exhibited low micromolar inhibitory activity against BRD4 and PARP1, particularly exhibiting strong inhibition of BRD4 BD1 with an IC50 value of 204 nM. Furthermore, it demonstrated potent antiproliferative effects against breast cancer gene-deficient and proficient breast cancer cell lines by arresting cell cycle progression and impeding DNA damage repair. Collectively, our systematic efforts to design lead-like molecules have the potential to open doors for the exploration of dual PARP1-BRD4 inhibitors as a promising avenue for breast cancer treatment. Furthermore, the developed approach can be extended to systematically design inhibitors targeting PARP1 and other related targets.

AlphaFold accelerated discovery of psychotropic agonists targeting the trace amine-associated receptor 1.

Artificial intelligence is revolutionizing protein structure prediction, providing unprecedented opportunities for drug design. To assess the potential impact on ligand discovery, we compared virtual screens using protein structures generated by the AlphaFold machine learning method and traditional homology modeling. More than 16 million compounds were docked to models of the trace amine-associated receptor 1 (TAAR1), a G protein-coupled receptor of unknown structure and target for treating neuropsychiatric disorders. Sets of 30 and 32 highly ranked compounds from the AlphaFold and homology model screens, respectively, were experimentally evaluated. Of these, 25 were TAAR1 agonists with potencies ranging from 12 to 0.03 µM. The AlphaFold screen yielded a more than twofold higher hit rate (60%) than the homology model and discovered the most potent agonists. A TAAR1 agonist with a promising selectivity profile and drug-like properties showed physiological and antipsychotic-like effects in wild-type but not in TAAR1 knockout mice. These results demonstrate that AlphaFold structures can accelerate drug discovery.

When Two Become One: Conformational Changes in FXR/RXR Heterodimers Bound to Steroidal Antagonists.

Farnesoid X receptor (FXR) is a nuclear receptor with an essential role in regulating bile acid synthesis and cholesterol homeostasis. FXR activation by agonists is explained by an αAF-2-trapping mechanism; however, antagonism mechanisms are diverse. We discuss microsecond molecular dynamics (MD) simulations investigating our recently reported FXR antagonists 2a and 2 h. We study the antagonist-induced conformational changes in the FXR ligand-binding domain, when compared to the synthetic (GW4064) or steroidal (chenodeoxycholic acid, CDCA) FXR agonists in the FXR monomer or FXR/RXR heterodimer r, and in the presence and absence of the coactivator. Our MD data suggest ligand-specific influence on conformations of different FXR-LBD regions, including the α5/α6 region, αAF-2, and α9-11. Changes in the heterodimerization interface induced by antagonists seem to be associated with αAF-2 destabilization, which prevents both co-activator and co-repressor recruitment. Our results provide new insights into the conformational behaviour of FXR, suggesting that FXR antagonism/agonism shift requires a deeper assessment than originally proposed by crystal structures.

Exploring the Effect of Halogenation in a Series of Potent and Selective A2B Adenosine Receptor Antagonists.

The modulation of the A2B adenosine receptor is a promising strategy in cancer (immuno) therapy, with A2BAR antagonists emerging as immune checkpoint inhibitors. Herein, we report a systematic assessment of the impact of (di- and mono-)halogenation at positions 7 and/or 8 on both A2BAR affinity and pharmacokinetic properties of a collection of A2BAR antagonists and its study with structure-based free energy perturbation simulations. Monohalogenation at position 8 produced potent A2BAR ligands irrespective of the nature of the halogen. In contrast, halogenation at position 7 and dihalogenation produced a halogen-size-dependent decay in affinity. Eight novel A2BAR ligands exhibited remarkable affinity (Ki < 10 nM), exquisite subtype selectivity, and enantioselective recognition, with some eutomers eliciting sub-nanomolar affinity. The pharmacokinetic profile of representative derivatives showed enhanced solubility and microsomal stability. Finally, two compounds showed the capacity of reversing the antiproliferative effect of adenosine in activated primary human peripheral blood mononuclear cells.

Optimization of 2-Amino-4,6-diarylpyrimidine-5-carbonitriles as Potent and Selective A1 Antagonists.

We herein document a large collection of 108 2-amino-4,6-disubstituted-pyrimidine derivatives as potent, structurally simple, and highly selective A1AR ligands. The most attractive ligands were confirmed as antagonists of the canonical cyclic adenosine monophosphate pathway, and some pharmacokinetic parameters were preliminarilly evaluated. The library, built through a reliable and efficient three-component reaction, comprehensively explored the chemical space allowing the identification of the most prominent features of the structure-activity and structure-selectivity relationships around this scaffold. These included the influence on the selectivity profile of the aromatic residues at positions R4 and R6 of the pyrimidine core but most importantly the prominent role to the unprecedented A1AR selectivity profile exerted by the methyl group introduced at the exocyclic amino group. The structure-activity relationship trends on both A1 and A2AARs were conveniently interpreted with rigorous free energy perturbation simulations, which started from the receptor-driven docking model that guided the design of these series.