Pyrazole-based cathepsin S inhibitors with improved cellular potency

Highly Selective Sub-Nanomolar Cathepsin S Inhibitors by Merging Fragment Binders with Nitrile Inhibitors

Pharmacological inhibition of cathepsin S (CatS) allows for a specific modulation of the adaptive immune system and many major diseases. Here, we used NMR fragment screening and crystal structure-aided merging to synthesize novel, highly selective CatS inhibitors with picomolar enzymatic Ki values and nanomolar functional activity in human Raji cells. Noncovalent fragment hits revealed binding hotspots, while the covalent inhibitor structure-activity relationship enabled efficient potency optimization.

Calculate protein-ligand binding affinities with the extended linear interaction energy method: application on the Cathepsin S set in the D3R Grand Challenge 3

We participated in the Cathepsin S (CatS) sub-challenge of the Drug Design Data Resource (D3R) Grand Challenge 3 (GC3) in 2017 to blindly predict the binding poses of 24 CatS-bound ligands, the binding affinity ranking of 136 ligands, and the binding free energies of a subset of 33 ligands in Stage 1A and Stage 2. Our submitted predictions ranked relatively well compared to the submissions from other participants. Here we present our methodologies used in the challenge. For the binding pose prediction, we employed the Glide module in the Schrodinger Suite 2017 and AutoDock Vina. For the binding affinity/free energy prediction, we carried out molecular dynamics simulations of the complexes in explicit water solvent with counter ions, and then estimated the binding free energies with our newly developed model of extended linear interaction energy (ELIE), which is inspired by two other popular end-point approaches: the linear interaction energy (LIE) method, and the molecular mechanics with Poisson-Boltzmann surface area solvation method (MM/PBSA). Our studies suggest that ELIE is a good trade-off between efficiency and accuracy, and it is appropriate for filling the gap between the high-throughput docking and scoring methods and the rigorous but much more computationally demanding methods like free energy perturbation (FEP) or thermodynamics integration (TI) in computer-aided drug design (CADD) projects.

Self Organizing Map-Based Classification of Cathepsin k and S Inhibitors with Different Selectivity Profiles Using Different Structural Molecular Fingerprints: Design and Application for Discovery of Novel Hits

The main step in a successful drug discovery pipeline is the identification of small potent compounds that selectively bind to the target of interest with high affinity. However, there is still a shortage of efficient and accurate computational methods with powerful capability to study and hence predict compound selectivity properties. In this work, we propose an affordable machine learning method to perform compound selectivity classification and prediction. For this purpose, we have collected compounds with reported activity and built a selectivity database formed of 153 cathepsin K and S inhibitors that are considered of medicinal interest. This database has three compound sets, two K/S and S/K selective ones and one non-selective KS one. We have subjected this database to the selectivity classification tool ‘Emergent Self-Organizing Maps’ for exploring its capability to differentiate selective cathepsin inhibitors for one target over the other. The method exhibited good clustering performance for selective ligands with high accuracy (up to 100 %). Among the possibilites, BAPs and MACCS molecular structural fingerprints were used for such a classification. The results exhibited the ability of the method for structure-selectivity relationship interpretation and selectivity markers were identified for the design of further novel inhibitors with high activity and target selectivity.

Discovery of novel tetrahydro-pyrazolo [4,3-c] pyridines for the treatment of neuropathic pain: synthesis and neuropharmacology

We disclose the discovery of a novel series of tetrahydropyrido-pyrazoles that are potent inhibitors of tumour necrosis factor-alpha (TNF-α), nitric oxide and cannabinoid receptor subtype 1 (CB₁). We report herein the synthesis and neuropharmacological screening results of the titled compounds in two acute pain and two neuropathic pain models in rodents. Particularly the analogue N-(4-bromophenyl)-3-tert-butyl-5-ethyl-4,5,6,7-tetrahydro-1H-pyrazolo[4,3-c]pyridine-1-carboxamide (8a) exhibited pronounced acute antinociceptive efficacy, also being effective in chronic constriction injury (ED₅₀ = 23.8 mg/kg) and partial sciatic nerve injury (ED₅₀ = 29.0 mg/kg) models with CB₁ receptor activity (IC₅₀ = 49.6 nM) and inhibitory effect on TNF-α (86.4% inhibition at 100 mg/kg). These results suggest the importance of the development of this lead as multi-targeted treatment strategy for neuropathic pain.

Synthesis and Preliminary Biological Study of Bisindolylmethanes Accessed by an Acid-Catalyzed Hydroarylation of Vinylindoles

An acid-catalyzed hydroarylation reaction of vinyl indoles is reported, which tolerates a wide range of heterocycles as the exogenous nucleophile such as indoles, pyrroles, and indolizines. The method rapidly accesses the biologically relevant bisindolylmethane scaffold in good to excellent yields. Evaluation of the biological activity of several synthesized analogues reveals cytotoxic activity against and selectivity for the MCF-7 breast cancer cell line.