The validation of new CHD1L inhibitors as a therapeutic strategy for cancer

Chromodomain helicase DNA-binding protein 1 like (CHD1L) is an oncogene that promotes tumor progression, metastasis, and multidrug resistance. CHD1L expression is indicative of poor outcomes and low survival in cancer patients with various cancer types. Herein, we report a set of CHD1L inhibitors (CHD1Li) discovered from high-throughput screening and evaluated using enzyme inhibition, 3D tumor organoid cytotoxicity and mechanistic assays. The structurally distinct compounds 8-11 emerged as hits with promising bioactivity by targeting CHD1L. CHD1Li were further examined for their stability in human and mouse liver microsomes, which showed compounds 9 and 11 to be the most metabolically stable. Additionally, molecular modeling studies of CHD1Li with the target protein shed light on key pharmacophore features driving CHD1L binding. Taken together, these results expand the chemical space of CHD1Li as a potential targeted therapy for colorectal cancer and other cancers.

Identification of new hit to lead magmas inhibitors as potential therapeutics for glioblastoma

In continuation of our previous efforts for the development of potent small molecules against brain cancer, herein we synthesized seventeen new compounds and tested their anti-gliomapotential against established glioblastoma cell lines, namely, D54MG, U251, and LN-229 as well as patient derived cell lines (DB70 and DB93). Among them, the carboxamide derivatives, BT-851 and BT-892 were found to be the most active leads in comparison to our established hit compound BT#9.The SAR studies of our hit BT#9 compound resulted in the development of two new lead compounds by hit to lead strategy. The detailed biological studies are currently underway. The active compounds could possibly act as template for the future development of newer anti-glioma agents.

User-Friendly Quantum Mechanics: Applications for Drug Discovery

Quantum mechanics (QM) methods provide a fine description of receptor-ligand interactions and of chemical reactions. Their use in drug design and drug discovery is increasing, especially for complex systems including metal ions in the binding sites, for the design of highly selective inhibitors, for the optimization of bi-specific compounds, to understand enzymatic reactions, and for the study of covalent ligands and prodrugs. They are also used for generating molecular descriptors for predictive QSAR/QSPR models and for the parameterization of force fields. Thanks to the continuous increase of computational power offered by GPUs and to the development of sophisticated algorithms, QM methods are becoming part of the standard tools used in computer-aided drug design (CADD). We present the most used QM methods and software packages, and we discuss recent representative applications in drug design and drug discovery.

Genetically Encoded Cyclic Peptide Libraries: From Hit to Lead and Beyond

With the increasing utilization of high-throughput screening for lead identification in drug discovery, the need for easily constructed and diverse libraries which cover significant chemical space is greater than ever. Cyclic peptides address this need; they combine the advantageous properties of peptides (ease of production, high diversity, high potential specificity) with increased resistance to proteolysis and often increased biological activity (due to conformational locking). There are a number of techniques for the generation and screening of cyclic peptide libraries. As drug discovery moves toward tackling challenging targets, such as protein-protein interactions, cyclic peptide libraries are expected to continue producing hits where small molecule libraries may be stymied. However, it is important to design robust systems for the generation and screening of these large libraries, and to be able to make sense of structure-activity relationships in these highly variable scaffolds. There are a plethora of possible modifications that can be made to cyclic peptides, which is both a weakness and a strength of these scaffolds; high variability will allow more precise tuning of leads to targets, but exploring the whole range of modifications may become an overwhelming challenge.

A cell-based approach to characterize antimicrobial compounds through kinetic dose response

The rapid spread of antibiotic resistance has created a pressing need for the development of novel drug screening platforms. Herein, we report on the use of cell-based kinetic dose response curves for small molecule characterization in antibiotic discovery efforts. Kinetically monitoring bacterial growth at sub-inhibitory concentrations of antimicrobial small molecules generates unique dose response profiles. We show that clustering of profiles by growth characteristics can classify antibiotics by mechanism of action. Furthermore, changes in growth kinetics have the potential to offer insight into the mechanistic action of novel molecules and can be used to predict off-target effects generated through structure-activity relationship studies. Kinetic dose response also allows for detection of unstable compounds early in the lead development process. We propose that this kinetic approach is a rapid and cost-effective means to gather critical information on antimicrobial small molecules during the hit selection and lead development pipeline.

Lead identification of novel and selective TYK2 inhibitors

A therapeutic rationale is proposed for the treatment of inflammatory diseases, such as psoriasis and inflammatory bowel diseases (IBD), by selective targeting of TYK2. Hit triage, following a high-throughput screen for TYK2 inhibitors, revealed pyridine 1 as a promising starting point for lead identification. Initial expansion of 3 separate regions of the molecule led to eventual identification of cyclopropyl amide 46, a potent lead analog with good kinase selectivity, physicochemical properties, and pharmacokinetic profile. Analysis of the binding modes of the series in TYK2 and JAK2 crystal structures revealed key interactions leading to good TYK2 potency and design options for future optimization of selectivity.