Identification of Small-Molecule Antagonists Targeting the Growth Hormone Releasing Hormone Receptor (GHRHR).

The growth hormone-releasing hormone receptor (GHRHR) belongs to Class B1 of G protein-coupled receptors (GPCRs). Class B1 GPCR peptides such, as growth hormone-releasing hormone (GHRH), have been proposed to bind in a two-step model, where first the C-terminal region of the peptide interacts with the extracellular domain of the receptor and, subsequently, the N-terminus interacts with the seven transmembrane domain of the receptor, resulting in activation. The GHRHR has recently been highlighted as a promising drug target toward several types of cancer and has been shown to be overexpressed in prostate, breast, pancreatic, and ovarian cancer. Indeed, peptide GHRHR antagonists have displayed promising results in many cancer models. However, no nonpeptide GHRHR-targeting compounds have yet been identified. We have utilized several computational tools to target GHRHR and identify potential small-molecule compounds directed at this receptor. These compounds were validated in vitro using a cyclic adenosine monophosphate (cAMP) ELISA to measure activity at the GHRHR. In vitro results suggest that several of the novel small-molecule compounds could inhibit GHRH-induced cAMP accumulation. Preliminary analysis of the specificity/selectivity of one of the most effective hit compounds indicated that the effect seen was via inhibition of the GHRHR. We therefore report the first nonpeptide antagonists of GHRHR and propose a structural basis for inhibition induced by the compounds, which may assist in the future design of lead GHRHR compounds for treating disorders attributed to dysregulated/aberrant GHRHR signaling.

A Robust Machine Learning Framework Built Upon Molecular Representations Predicts CYP450 Inhibition: Toward Precision in Drug Repurposing.

Human cytochrome P450 (CYP450) enzymes play a crucial role in drug metabolism and pharmacokinetics. CYP450 inhibition can lead to toxicity, in particular when drugs are co-administered with other drugs and xenobiotics or in the case of polypharmacy. Predicting CYP450 inhibition is also important for rational drug discovery and development, and precision in drug repurposing. In this overarching context, digital transformation of drug discovery and development, for example, using machine and deep learning approaches, offers prospects for prediction of CYP450 inhibition through computational models. We report here the development of a majority-voting machine learning framework to classify inhibitors and noninhibitors for seven major human liver CYP450 isoforms (CYP1A2, CYP2A6, CYP2B6, CYP2C9, CYP2C19, CYP2D6, and CYP3A4). For the machine learning models reported herein, we employed interaction fingerprints that were derived from molecular docking simulations, thus adding an additional layer of information for protein-ligand interactions. The proposed machine learning framework is based on the structure of the binding site of isoforms to produce predictions beyond previously reported approaches. Also, we carried out a comparative analysis so as to identify which representation of test compounds (molecular descriptors, molecular fingerprints, or protein-ligand interaction fingerprints) affects the predictive performance of the models. This work underlines the ways in which the structure of the enzyme catalytic site influences machine learning predictions and the need for robust frameworks toward better-informed predictions.

Design and synthesis of a novel non peptide CN-NFATc signaling inhibitor for tumor suppression in triple negative breast cancer.

The Ca2+/calmodulin-mediated phosphatase activity of calcineurin (CN) integrates calcium-mediated signaling with gene expression programs involved in the control of essential cellular processes in health and disease, such as the immune response and the pathogenesis of cancer progression and metastasis. In addition, CN is the target of the immunosuppressive drugs cyclosporine A (CsA) and FK-506 which are the cornerstone of immunosuppressant therapy. Unfortunately, long-term administration of these drugs results in severe side effects. Herein, we describe the design, synthesis and evaluation of new synthetic compounds that are capable of inhibiting NFATc activity in a dose-dependent manner, without interfering on CN phosphatase activity. These compounds were designed using the structure-based pharmacophore model of a peptide-derived PxIxIT sequence binding to calcineurin A subunit. Moreover, these compounds inhibit NFATc-dependent cytokine gene expression, secretion and proliferation of human T CD4+ cells. More importantly, compound 5a reduces tumor weight and shows a tendency to reduce tumor angiogenesis in an orthotopic immunocompetent mouse model of triple negative breast cancer, suggesting that 5a has tumor suppressor activity. These findings validate compound 5a as an agent with therapeutic activity against CN-NFATc and highlight its potential as a tool for drug development with therapeutic purposes.