Optimization of TEAD P-Site Binding Fragment Hit into In Vivo Active Lead MSC-4106.

The dysregulated Hippo pathway and, consequently, hyperactivity of the transcriptional YAP/TAZ-TEAD complexes is associated with diseases such as cancer. Prevention of YAP/TAZ-TEAD triggered gene transcription is an attractive strategy for therapeutic intervention. The deeply buried and conserved lipidation pocket (P-site) of the TEAD transcription factors is druggable. The discovery and optimization of a P-site binding fragment (1) are described. Utilizing structure-based design, enhancement in target potency was engineered into the hit, capitalizing on the established X-ray structure of TEAD1. The efforts culminated in the optimized in vivo tool MSC-4106, which exhibited desirable potency, mouse pharmacokinetic properties, and in vivo efficacy. In close correlation to compound exposure, the time- and dose-dependent downregulation of a proximal biomarker could be shown.

Structure-Based Discovery of Novel Ligands for the Orexin 2 Receptor.

The orexin receptors are peptide-sensing G protein-coupled receptors that are intimately linked with regulation of the sleep/wake cycle. We used a recently solved X-ray structure of the orexin receptor subtype 2 in computational docking calculations with the aim to identify additional ligands with unprecedented chemotypes. We found validated ligands with a high hit rate of 29% out of those tested, none of them showing selectivity with respect to the orexin receptor subtype 1. Furthermore, of the higher-affinity compounds examined, none showed any agonist activity. While novel chemical structures can thus be found, selectivity is a challenge owing to the largely identical binding pockets.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects.

Accurate ranking of compounds with regards to their binding affinity to a protein using computational methods is of great interest to pharmaceutical research. Physics-based free energy calculations are regarded as the most rigorous way to estimate binding affinity. In recent years, many retrospective studies carried out both in academia and industry have demonstrated its potential. Here, we present the results of large-scale prospective application of the FEP+ method in active drug discovery projects in an industry setting at Merck KGaA, Darmstadt, Germany. We compare these prospective data to results obtained on a new diverse, public benchmark of eight pharmaceutically relevant targets. Our results offer insights into the challenges faced when using free energy calculations in real-life drug discovery projects and identify limitations that could be tackled by future method development. The new public data set we provide to the community can support further method development and comparative benchmarking of free energy calculations.

Structure-based development of a subtype-selective orexin 1 receptor antagonist.

Orexins are neuropeptides that activate the rhodopsin-like G protein-coupled receptors OX1R and OX2R. The orexin system plays an important role in the regulation of the sleep-wake cycle and the regulation of feeding and emotions. The nonselective orexin receptor antagonist suvorexant has been the first drug on the market targeting the orexin system and is prescribed for the treatment of insomnia. Subtype-selective OX1R antagonists are valuable tools to further investigate the functions and physiological role of the OX1R in vivo and promising lead compounds for the treatment of drug addiction, anxiety, pain or obesity. Starting from the OX1R and OX2R crystal structures bound to suvorexant, we exploited a single amino acid difference in the orthosteric binding site by using molecular docking and structure-based drug design to optimize ligand interactions with the OX1R while introducing repulsive interactions with the OX2R. A newly established enantiospecific synthesis provided ligands showing up to 75-fold selectivity for the OX1R over the OX2R subtype. The structure of a new OX1R antagonist with subnanomolar affinity (JH112) was determined by crystallography in complex with the OX1R and corresponded closely to the docking-predicted geometry. JH112 exhibits high selectivity over a panel of different GPCRs, is able to cross the blood-brain barrier and acts as slowly diffusing and insurmountable antagonist for Gq protein activation and in particular ß-arrestin-2 recruitment at OX1R. This study demonstrates the potential of structure-based drug design to develop more subtype-selective GPCR ligands with potentially reduced side effects and provides an attractive probe molecule and lead compound.

Identification of Methionine Aminopeptidase-2 (MetAP-2) Inhibitor M8891: A Clinical Compound for the Treatment of Cancer.

The recently disclosed next generation of reversible, selective, and potent MetAP-2 inhibitors introduced a cyclic tartronic diamide scaffold. However, the lead compound 1a suffered from enterohepatic circulation, preventing further development. Nevertheless, 1a served as a starting point for further optimization. Maintaining potent antiproliferation activity, while improving other compound properties, enabled the generation of an attractive array of new MetAP-2 inhibitors. The most promising derivatives were identified by a multiparameter analysis of the compound properties. Essential for the efficient selection of candidates with in vivo activity was the identification of molecules with a long residence time on the target protein, high permeability, and low efflux ratio not only in Caco-2 but also in the MDR-MDCK cell line. Orally bioavailable, potent, and reversible MetAP-2 inhibitors impede the growth of primary endothelial cells and demonstrated antitumoral activity in mouse models. This assessment led to the nomination of the clinical development compound M8891, which is currently in phase I clinical testing in oncology patients.

Similarity- and Substructure-Based Development of ß2-Adrenergic Receptor Ligands Based on Unusual Scaffolds.

The ß2-adrenergic receptor (ß2AR) is a G protein-coupled receptor (GPCR) and a well-explored target. Here, we report the discovery of 13 ligands, ten of which are novel, of this particular GPCR. They have been identified by similarity- and substructure-based searches using multiple ligands, which were described in an earlier study, as starting points. Of note, two of the molecules used as queries here distinguish themselves from other ß2AR antagonists by their unique scaffold. The molecules described in this work allow us to explore the ligand space around the previously reported molecules in greater detail, leading to insights into their structure-activity relationship. We also report experimental binding and selectivity data and putative binding modes for the novel molecules.

PrenDB, a Substrate Prediction Database to Enable Biocatalytic Use of Prenyltransferases.

Prenyltransferases of the dimethylallyltryptophan synthase (DMATS) superfamily catalyze the attachment of prenyl or prenyl-like moieties to diverse acceptor compounds. These acceptor molecules are generally aromatic in nature and mostly indole or indole-like. Their catalytic transformation represents a major skeletal diversification step in the biosynthesis of secondary metabolites, including the indole alkaloids. DMATS enzymes thus contribute significantly to the biological and pharmacological diversity of small molecule metabolites. Understanding the substrate specificity of these enzymes could create opportunities for their biocatalytic use in preparing complex synthetic scaffolds. However, there has been no framework to achieve this in a rational way. Here, we report a chemoinformatic pipeline to enable prenyltransferase substrate prediction. We systematically catalogued 32 unique prenyltransferases and 167 unique substrates to create possible reaction matrices and compiled these data into a browsable database named PrenDB. We then used a newly developed algorithm based on molecular fragmentation to automatically extract reactive chemical epitopes. The analysis of the collected data sheds light on the thus far explored substrate space of DMATS enzymes. To assess the predictive performance of our virtual reaction extraction tool, 38 potential substrates were tested as prenyl acceptors in assays with three prenyltransferases, and we were able to detect turnover in >55% of the cases. The database, PrenDB (www.kolblab.org/prendb.php), enables the prediction of potential substrates for chemoenzymatic synthesis through substructure similarity and virtual chemical transformation techniques. It aims at making prenyltransferases and their highly regio- and stereoselective reactions accessible to the research community for integration in synthetic work flows.

Fragment-based similarity searching with infinite color space.

Fragment-based searching and abstract representation of molecular features through reduced graphs have separately been used for virtual screening. Here, we combine these two approaches and apply the algorithm RedFrag to virtual screens retrospectively and prospectively. It uses a new type of reduced graph that does not suffer from information loss during its construction and bypasses the necessity of feature definitions. Built upon chemical epitopes resulting from molecule fragmentation, the reduced graph embodies physico-chemical and 2D-structural properties of a molecule. Reduced graphs are compared with a continuous-similarity-distance-driven maximal common subgraph algorithm, which calculates similarity at the fragmental and topological levels. The performance of the algorithm is evaluated by retrieval experiments utilizing precompiled validation sets. By predicting and experimentally testing ligands for endothiapepsin, a challenging model protease, the method is assessed in a prospective setting. Here, we identified five novel ligands with affinities as low as 2.08 µM.