Discovery of Clinical Candidate AZD5462, a Selective Oral Allosteric RXFP1 Agonist for Treatment of Heart Failure.

Optimization of the highly potent and selective, yet metabolically unstable and poorly soluble hRXFP1 agonist AZ7976 led to the identification of the clinical candidate, AZD5462. Assessment of RXFP1-dependent cell signaling demonstrated that AZD5462 activates a highly similar panel of downstream pathways as relaxin H2 but does not modulate relaxin H2-mediated cAMP second messenger responsiveness. The therapeutic potential of AZD5462 was assessed in a translatable cynomolgus monkey heart failure model. Following 8 weeks of treatment with AZD5462, robust improvements in functional cardiac parameters including LVEF were observed at weeks 9, 13, and 17 without changes in heart rate or mean arterial blood pressure. AZD5462 was well tolerated in both rat and cynomolgus monkey and has successfully completed phase I studies in healthy volunteers. In summary, AZD5462 is a small molecule pharmacological mimetic of relaxin H2 signaling at RXFP1 and holds promise as a potential therapeutic approach to treat heart failure patients.

Identification of Novel Series of Potent and Selective Relaxin Family Peptide Receptor 1 (RXFP1) Agonists.

Relaxin H2 is a clinically relevant peptide agonist for relaxin family peptide receptor 1 (RXFP1), but a combination of this hormone's short plasma half-life and the need for injectable delivery limits its therapeutic potential. We sought to overcome these limitations through the development of a potent small molecule (SM) RXFP1 agonist. Although two large SM HTS campaigns failed in identifying suitable hit series, we uncovered novel chemical space starting from the only known SM RXFP1 agonist series, represented by ML290. Following a design-make-test-analyze strategy based on improving early dose to man ranking, we discovered compound 42 (AZ7976), a highly selective RXFP1 agonist with sub-nanomolar potency. We used AZ7976, its 10 000-fold less potent enantiomer 43 and recombinant relaxin H2 to evaluate in vivo pharmacology and demonstrate that AZ7976-mediated heart rate increase in rats was a result of RXFP1 agonism. As a result, AZ7976 was selected as lead for continued optimization.

Discovery of Apararenone (MT-3995) as a Highly Selective, Potent, and Novel Nonsteroidal Mineralocorticoid Receptor Antagonist.

Overactivation of the mineralocorticoid receptor (MR) is involved in many diseases, such as hypertension, kidney disease, and heart failure. Thus, MR antagonists (MRAs) are expected to be beneficial to patients with these diseases. In order to identify novel nonsteroidal MRAs that overcome the issues of already marketed steroidal MRAs, we searched for new compounds guided by our hypothesis that T-shaped compounds with a hydrophobic core structure, two polar functional groups at both extremities able to interact with MR, and a bulky substituent that can interfere with the folding of the C-terminal helix 12 may exhibit antagonist activity toward MR. We discovered that the novel 1,4-benzoxazin-3-one derivative 19 (apararenone: MT-3995) acted as a highly selective and potent nonsteroidal MRA. Apararenone exhibited a more potent antihypertensive and organ-protective activity than steroidal MRA eplerenone in a primary aldosteronism rat model obtained by infusing aldosterone in uninephrectomized rats.

Predicted Contributions of Flavin-containing Monooxygenases to the N-oxygenation of Drug Candidates Based on their Estimated Base Dissociation Constants.

AIMS: Base dissociation constants of 30 model chemicals were investigated to constitute potential determinant factors predicting the contributions of flavin-containing monooxygenases (FMOs). BACKGROUND: The contributions of FMOs to the metabolic elimination of new drug candidates could be underestimated under certain experimental conditions during drug development. OBJECTIVE: A method for predicting metabolic sites and the contributions of FMOs to N-oxygenations is proposed using a molecular descriptor, the base dissociation constant (pKa base), which can be estimated in silico using commonly available chemoinformatic prediction systems. METHODS: Model drugs and their oxidative pathways were surveyed in the literature to investigate the roles of FMOs in their N-oxygenations. The acid and base dissociation constants of the nitrogen moieties of 30 model substrates were estimated using well-established chemoinformatic software. RESULTS: The base dissociation constants of 30 model chemicals were classified into two groups based on the reported optimal in vitro pH of 8.4 for FMO enzymes as a key determinant factor. Among 18 substrates (e.g., trimethylamine, benzydamine, and itopride) with pKa (base) values in the range of 8.4-9.8, all N-oxygenated metabolites were reported to be predominantly catalyzed by FMOs. Except for three cases (xanomeline; L-775,606; and tozasertib), the nine substrates with pKa (base) values in the range 2.7-7.9 were only moderately or minorly N-oxygenated by FMOs in addition to their major metabolic pathway of oxidation mediated by cytochrome P450s. N-Oxygenation of T-1032 (with a pKa of 4.8) is mediated predominantly by P450 3A5, but not by FMO1/3. CONCLUSION: The predicted contributions of FMOs to the N-oxygenation of drug candidates can be simply estimated using classic base dissociation constants.

Computational prediction of cytochrome P450 inhibition and induction.

Cytochrome P450 (CYP) enzymes play an important role in the phase I metabolism of many xenobiotics. Most drug-drug interactions (DDIs) associated with CYP are caused by either CYP inhibition or induction. The early detection of potential DDIs is highly desirable in the pharmaceutical industry because DDIs can cause serious adverse events, which can lead to poor patient health and drug development failures. Recently, many computational studies predicting CYP inhibition and induction have been reported. The current computational modeling approaches for CYP metabolism are classified as ligand- and structure-based; various techniques, such as quantitative structure-activity relationships, machine learning, docking, and molecular dynamic simulation, are involved in both the approaches. Recently, combining these two approaches have resulted in improvements in the prediction accuracy of DDIs. In this review, we present important, recent developments in the computational prediction of the inhibition of four clinically crucial CYP isoforms (CYP1A2, 2C9, 2D6, and 3A4) and three nuclear receptors (aryl hydrocarbon receptor, constitutive androstane receptor, and pregnane X receptor) involved in the induction of CYP1A2, 2B6, and 3A4, respectively.

Precise prediction of activators for the human constitutive androstane receptor using structure-based three-dimensional quantitative structure-activity relationship methods.

The constitutive androstane receptor (CAR, NR1I3) regulates the expression of numerous drug-metabolizing enzymes and transporters. The upregulation of various enzymes, including CYP2B6, by CAR activators is a critical problem leading to clinically severe drug-drug interactions (DDIs). To date, however, few effective computational approaches for identifying CAR activators exist. In this study, we aimed to develop three-dimensional quantitative structure-activity relationship (3D-QSAR) models to predict the CAR activating potency of compounds emerging in the drug-discovery process. Models were constructed using comparative molecular field analysis (CoMFA) based on the molecular alignments of ligands binding to CAR, which were obtained from ensemble ligand-docking using 28 compounds as a training set. The CoMFA model, modified by adding a lipophilic parameter with calculated logD7.4 (S+logD7.4), demonstrated statistically good predictive ability (r2 = 0.99, q2 = 0.74). We also confirmed the excellent predictability of the 3D-QSAR model for CAR activation (r2pred = 0.71) using seven compounds as a test set for external validation. Collectively, our results indicate that the 3D-QSAR model developed in this study provides precise prediction of CAR activating potency and, thus, should be useful for selecting drug candidates with minimized DDI risk related to enzyme-induction in the early drug-discovery stage.

In Silico Prediction of hPXR Activators Using Structure-Based Pharmacophore Modeling.

The activation of pregnane X receptor (PXR), a member of the nuclear receptor superfamily, can mediate potential drug-drug interactions by regulating the expression of several drug-mediated enzymes and transporters, resulting in reduced therapeutic efficacy or increased toxicity by producing reactive metabolites. Therefore, in the early stage of drug development, it is important to predict these risks using an in silico approach. We constructed a human PXR (hPXR) pharmacophore model based on known structural information of compounds that activate PXR. We evaluated the prediction accuracy of the model using data sets generated on 68 original synthetic compounds from the Mitsubishi Tanabe Pharma Corporation and over 2500 drugs from the National Institutes of Health Chemical Genomics Center Pharmaceutical Collection for their ability to activate hPXR. The prediction accuracies of the PXR pharmacophore model were 0.78 and 0.86 for the Mitsubishi Tanabe Pharma Corporation and National Institutes of Health Chemical Genomics Center Pharmaceutical Collection, respectively. The compounds resulting in the smallest root-mean square deviation hit by pharmacophore search were the well-known PXR inducers such as Bosentan. These results suggest that using the in silico approach developed in this study is useful to identify potential hPXR activators and modify the drug design during the early stage of drug development.

3-Cyano-6-(5-methyl-3-pyrazoloamino)pyridines: selective Aurora A kinase inhibitors.

A new class of Aurora A kinase inhibitor was created by transforming 4-(5-methyl-3-pyrazoloamino)pyrimidine moiety of VX-680 to 3-cyano-6-(5-methyl-3pyrazoloamino)pyridine. Compound 6 exhibited a potent Aurora A kinase inhibitory activity, excellent selectivity to Aurora B kinase and other 60 kinases, good cell permeability and good PK profile. Therefore compound 6 was effective in antitumor mice model at a dose of 30 mg/kg po qd without decrease of body weight.