Hit Identification of a Novel Quinazoline Sulfonamide as a Promising EphB3 Inhibitor: Design, Virtual Combinatorial Library, Synthesis, Biological Evaluation, and Docking Simulation Studies

EphB3 is a major key player in a variety of cellular activities, including cell migration, proliferation, and apoptosis. However, the exact role of EphB3 in cancer remains ambiguous. Accordingly, new EphB3 inhibitors can increase the understanding of the exact roles of the receptor and may act as promising therapeutic candidates. Herein, a hybrid approach of structure-based design and virtual combinatorial library generated 34 quinazoline sulfonamides as potential selective EphB3 inhibitors. A molecular docking study over EphB3 predicted the binding affinities of the generated library, and the top seven hit compounds (3a and 4a–f), with GlideScore ≥ −6.20 Kcal/mol, were chosen for further MM-GBSA calculations. Out of the seven top hits, compound 4c showed the highest MM-GBSA binding free energy (−74.13 Kcal/mol). To validate these predicted results, compounds 3a and 4a–f were synthesized and characterized using NMR, HRMS, and HPLC. The biological evaluation revealed compound 4c as a potent EphB3 inhibitory lead (IC50 = 1.04 µM). The screening of 4c over a mini-panel of kinases consisting of EGFR, Aurora A, Aurora B, CDK2/cyclin A, EphB1, EphB2, EphB4, ERBB2/HER2, and KDR/VEGFR2, showed a promising selective profile against EphB3 isoform. A dose-dependent assay of compound 4c and a molecular docking study over the different forms of EphB provided insights into the elicited biological activities and highlighted reasonable explanations of the selectivity.

Analysis of the uncharted, druglike property space by self-organizing maps

Physicochemical properties are fundamental to predict the pharmacokinetic and pharmacodynamic behavior of drug candidates. Easily calculated descriptors such as molecular weight and logP have been found to correlate with the success rate of clinical trials. These properties have been previously shown to highlight a sweet-spot in the chemical space associated with favorable pharmacokinetics, which is superior against other regions during hit identification and optimization. In this study, we applied self-organizing maps (SOMs) trained on sixteen calculated properties of a subset of known drugs for the analysis of commercially available compound databases, as well as public biological and chemical databases frequently used for drug discovery. Interestingly, several regions of the property space have been identified that are highly overrepresented by commercially available chemical libraries, while we found almost completely unoccupied regions of the maps (commercially neglected chemical space resembling the properties of known drugs). Moreover, these underrepresented portions of the chemical space are compatible with most rigorous property filters applied by the pharma industry in medicinal chemistry optimization programs. Our results suggest that SOMs may be directly utilized in the strategy of library design for drug discovery to sample previously unexplored parts of the chemical space to aim at yet-undruggable targets. Graphic abstract

Surfaceome CRISPR screen identifies OLFML3 as a rhinovirus-inducible IFN antagonist

Background Rhinoviruses (RVs) cause more than half of common colds and, in some cases, more severe diseases. Functional genomics analyses of RVs using siRNA or genome-wide CRISPR screen uncovered a limited set of host factors, few of which have proven clinical relevance. Results Herein, we systematically compare genome-wide CRISPR screen and surface protein-focused CRISPR screen, referred to as surfaceome CRISPR screen, for their efficiencies in identifying RV host factors. We find that surfaceome screen outperforms the genome-wide screen in the success rate of hit identification. Importantly, using the surfaceome screen, we identify olfactomedin-like 3 (OLFML3) as a novel host factor of RV serotypes A and B, including a clinical isolate. We find that OLFML3 is a RV-inducible suppressor of the innate immune response and that OLFML3 antagonizes type I interferon (IFN) signaling in a SOCS3-dependent manner. Conclusion Our study suggests that RV-induced OLFML3 expression is an important mechanism for RV to hijack the immune system and underscores surfaceome CRISPR screen in identifying viral host factors.

Digging for the Discovery of SARS-CoV-2 nsp12 Inhibitors: A Pharmacophore-Based and Molecular Dynamics Simulation Study

Background: The severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) is a grave global threat causing Coronavirus Disease 2019 (COVID-19). The therapeutics are urgently needed to cure patients severely infected with COVID-19. The aim of the study was to investigate for potential candidates of nsp12 inhibitors by searching for druggable cavity pockets within the viral protein and drug discovery.Methods: The crystallographic structure of SARS-CoV-2 nsp12 was searched for strong druggable cavity pockets and pharmacophore features by the CavityPlus server. The features were selected for high-throughput screening (HTS) of a chemical library of ZINC natural products and hit identification database by ZINCPharmer. Autodock Vina was furthered utilized for estimation of hits' affinities to nsp12. A lead compound with the highest affinity to nsp12 was simulated dynamically by GROMACS for 10 nanoseconds (ns) to measure the hit stability in complex with nsp12 and conformational changes.Results: 1 of 6 cavities with the highest score was selected for extraction of pharmacophore features and hit-identification. 9 pharmacophores were screened, and a total of unique 1274 hits were identified. One compound, ZINV03977803, with an -11.0 Kcal.mol-1 affinity was selected as the lead compound for molecular dynamic simulation (MDS). The results showed stable interaction between ZINV03977803 and nsp12 during 10 ns of simulation. The room-mean-square of deviation (RMSD) measure showed dramatically high conformational changes in the complex of ZINV03977803 and nsp12 compare two the viral proteins alone.Conclusions: The lead compound ZINV03977803 showed stable interaction with higher potential and hydrogen bonding with the catalytic subunit of SARS-CoV-2, nsp12. It could also inhibit the SARS-CoV-2 life cycle by direct interaction with nsp12 and inhibits RdRp complex formation.

Druggability of cavity pockets within SARS-CoV-2 spike glycoprotein and pharmacophore-based drug discovery

Aim: Virus spike glycoprotein of SARS-CoV-2 is a good target for drug discovery. Objective: To examine the potential for druggability of spike protein for pharmacophore-based drug discovery and to investigate the binding affinity of natural products with SARS-CoV-2 spike protein. Methods: Druggable cavities were searched though CavityPlus. A pharmacophore was built and used for hit identification. Autodock Vina was used to evaluate the hits' affinities. 10 chemical derivatives were also made from the chemical backbone to optimize the lead compound. Results: 10 druggable cavities were found within the glycoprotein spike. Only one cavity with the highest score at the binding site was selected for pharmacophore extraction. Hit identification resulted in the identification of 410 hits. Discussion: This study provides a druggable region within viral glycoprotein and a candidate compound to block viral entry.

Development and Application of Activity-based Fluorescent Probes for High-Throughput Screening

: High-throughput screening facilitates the rapid identification of novel hit compounds; however, it remains challenging to design effective high-throughput assays, partially due to the difficulty of achieving sensitivity in the assay techniques. Among the various analytical methods that are used, fluorescence-based assays dominate owing to their high sensitivity and ease of operation. Recent advances in activity-based sensing/imaging have further expanded the availability of fluorescent probes as monitors for high-throughput screening of result outputs. In this study, we have reviewed various activity-based fluorescent probes used in high-throughput screening assays, emphasizing their structure-related working mechanisms. Moreover, we have explored the possibility of the development of additional and better probes to boost hit identification and drug development against various targets.

CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities

<p></p><p>The accurate description of protein binding sites is essential to the determination of similarity and the application of machine learning methods to relate the binding sites to observed functions. This work describes CAVIAR, a new open source tool for generating descriptors for binding sites, using protein structures in PDB and mmCIF format as well as trajectory frames from molecular dynamics simulations as input. The applicability of CAVIAR descriptors is showcased by computing machine learning predictions of binding site ligandability. The method can also automatically assign subcavities, even in the absence of a bound ligand. The defined subpockets mimic the empirical definitions used in medicinal chemistry projects. It is shown that the experimental binding affinity scales relatively well with the number of subcavities filled by the ligand, with compounds binding to more than three subcavities having nanomolar or better affinities to the target. The CAVIAR descriptors and methods can be used in any machine learning-based investigations of problems involving binding sites, from protein engineering to hit identification. The full software code is available on GitHub and a conda package is hosted on Anaconda cloud.</p><p></p>

Actives from MMV Open Access Boxes? A suggested way forward

It is estimated that more than 1 billion people across the world are affected by a neglected tropical disease (NTD) that requires medical intervention. These diseases tend to afflict people in areas with high rates of poverty and cost economies billions of dollars every year. Collaborative drug discovery efforts are required to reduce the burden of these diseases in endemic regions. The release of “Open Access Boxes” is an initiative launched by Medicines for Malaria Venture (MMV) in collaboration with its partners to catalyze new drug discovery in neglected diseases. These boxes are mainly requested by biology researchers across the globe who may not otherwise have access to compounds to screen nor knowledge of the workflow that needs to be followed after identification of actives from their screening campaigns. Here, we present guidelines on how to move such actives beyond the hit identification stage, to help in capacity strengthening and enable a greater impact of the initiative.

Identification of the FDA-Approved Drug Pyrvinium as a Novel Inhibitor of PD-1/PD-L1 Interaction

<p>Immune checkpoint blockade involving inhibition of the PD-1/PD-L1 interaction has provided unprecedented clinical benefits in treating a variety of tumors. To date, a total of six antibodies that bind to either PD-1 or PD-L1 protein and in turn inhibit the PD-1/PD-L1 interaction have received clinical approvals. Despite being highly effective, these expensive large biotherapeutics possess several inherent pharmacokinetic limitations that can be successfully overcome through the use of low-molecular-weight inhibitors. One such promising approach involves small-molecule induced dimerization and sequestration of PD-L1, leading to effective PD-1/PD-L1 inhibition. Herein, we present discovery of such potential bioactive PD-L1 dimerizers through a structure- and ligand-based screening of a focused library of approved and investigational drugs worldwide. The most promising compound Pyrvinium, an FDA-approved anthelmintic drug, showed IC₅₀ value of ~29.66 µM. It is noteworthy that Pyrvinium, being an approved drug, may prove especially suitable as a good starting point for further medicinal chemistry efforts, leading to design and development of even more potent structural analogs as selective PD-1/PD-L1 inhibitors. Furthermore, the adopted integrated virtual screening protocol may prove useful in screening other larger databases of lead- and drug-like molecules for hit identification in the domain of small-molecule PD-1/PD-L1 inhibitors.<br></p>