Linking High-Throughput Screens to Identify MoAs and Novel Inhibitors of Mycobacterium tuberculosis Dihydrofolate Reductase.

Though phenotypic and target-based high-throughput screening approaches have been employed to discover new antibiotics, the identification of promising therapeutic candidates remains challenging. Each approach provides different information, and understanding their results can provide hypotheses for a mechanism of action (MoA) and reveal actionable chemical matter. Here, we describe a framework for identifying efficacy targets of bioactive compounds. High throughput biophysical profiling against a broad range of targets coupled with machine learning was employed to identify chemical features with predicted efficacy targets for a given phenotypic screen. We validate the approach on data from a set of 55 000 compounds in 24 historical internal antibacterial phenotypic screens and 636 bacterial targets screened in high-throughput biophysical binding assays. Models were built to reveal the relationships between phenotype, target, and chemotype, which recapitulated mechanisms for known antibacterials. We also prospectively identified novel inhibitors of dihydrofolate reductase with nanomolar antibacterial efficacy against Mycobacterium tuberculosis. Molecular modeling provided structural insight into target-ligand interactions underlying selective killing activity toward mycobacteria over human cells.

Prospective Assessment of Virtual Screening Heuristics Derived Using a Novel Fusion Score.

High-throughput screening (HTS) is a widespread method in early drug discovery for identifying promising chemical matter that modulates a target or phenotype of interest. Because HTS campaigns involve screening millions of compounds, it is often desirable to initiate screening with a subset of the full collection. Subsequently, virtual screening methods prioritize likely active compounds in the remaining collection in an iterative process. With this approach, orthogonal virtual screening methods are often applied, necessitating the prioritization of hits from different approaches. Here, we introduce a novel method of fusing these prioritizations and benchmark it prospectively on 17 screening campaigns using virtual screening methods in three descriptor spaces. We found that the fusion approach retrieves 15% to 65% more active chemical series than any single machine-learning method and that appropriately weighting contributions of similarity and machine-learning scoring techniques can increase enrichment by 1% to 19%. We also use fusion scoring to evaluate the tradeoff between screening more chemical matter initially in lieu of replicate samples to prevent false-positives and find that the former option leads to the retrieval of more active chemical series. These results represent guidelines that can increase the rate of identification of promising active compounds in future iterative screens.

Development of a Platform to Enable Fully Automated Cross-Titration Experiments.

In the triage of hits from a high-throughput screening campaign or during the optimization of a lead compound, it is relatively routine to test compounds at multiple concentrations to determine potency and maximal effect. Additional follow-up experiments, such as agonist shift, can be quite valuable in ascertaining compound mechanism of action (MOA). However, these experiments require cross-titration of a test compound with the activating ligand of the receptor requiring 100-200 data points, severely limiting the number tested in MOA assays in a screening triage. We describe a process to enhance the throughput of such cross-titration experiments through the integration of Hewlett Packard's D300 digital dispenser onto one of our robotics platforms to enable on-the-fly cross-titration of compounds in a 1536-well plate format. The process handles all the compound management and data tracking, as well as the biological assay. The process relies heavily on in-house-built software and hardware, and uses our proprietary control software for the platform. Using this system, we were able to automate the cross-titration of compounds for both positive and negative allosteric modulators of two different G protein-coupled receptors (GPCRs) using two distinct assay detection formats, IP1 and Ca2+ detection, on nearly 100 compounds for each target.

Maximizing diversity from a kinase screen: identification of novel and selective pan-Trk inhibitors for chronic pain.

We have identified several series of small molecule inhibitors of TrkA with unique binding modes. The starting leads were chosen to maximize the structural and binding mode diversity derived from a high throughput screen of our internal compound collection. These leads were optimized for potency and selectivity employing a structure based drug design approach adhering to the principles of ligand efficiency to maximize binding affinity without overly relying on lipophilic interactions. This endeavor resulted in the identification of several small molecule pan-Trk inhibitor series that exhibit high selectivity for TrkA/B/C versus a diverse panel of kinases. We have also demonstrated efficacy in both inflammatory and neuropathic pain models upon oral dosing. Herein we describe the identification process, hit-to-lead progression, and binding profiles of these selective pan-Trk kinase inhibitors.

Discovery of wall teichoic acid inhibitors as potential anti-MRSA ß-lactam combination agents.

Innovative strategies are needed to combat drug resistance associated with methicillin-resistant Staphylococcus aureus (MRSA). Here, we investigate the potential of wall teichoic acid (WTA) biosynthesis inhibitors as combination agents to restore ß-lactam efficacy against MRSA. Performing a whole-cell pathway-based screen, we identified a series of WTA inhibitors (WTAIs) targeting the WTA transporter protein, TarG. Whole-genome sequencing of WTAI-resistant isolates across two methicillin-resistant Staphylococci spp. revealed TarG as their common target, as well as a broad assortment of drug-resistant bypass mutants mapping to earlier steps of WTA biosynthesis. Extensive in vitro microbiological analysis and animal infection studies provide strong genetic and pharmacological evidence of the potential effectiveness of WTAIs as anti-MRSA ß-lactam combination agents. This work also highlights the emerging role of whole-genome sequencing in antibiotic mode-of-action and resistance studies.

Discovery and X-ray crystallographic analysis of a spiropiperidine iminohydantoin inhibitor of beta-secretase.

A high-throughput screen at 100 microM inhibitor concentration for the BACE-1 enzyme revealed a novel spiropiperidine iminohydantoin aspartyl protease inhibitor template. An X-ray cocrystal structure with BACE-1 revealed a novel mode of binding whereby the inhibitor interacts with the catalytic aspartates via bridging water molecules. Using the crystal structure as a guide, potent compounds with good brain penetration were designed.

Miniaturization and automation of an ubiquitin ligase cascade enzyme-linked immunosorbent assay in 1,536-well format.

Enzyme-linked immunosorbent assays (ELISAs) are a long established and widely used assay format for drug discovery and diagnostics. They offer many advantages over homogeneous assay formats, including high sensitivity and separation (wash) steps that remove detection-interfering compounds. Many high-throughput screening assays are now performed in miniaturized formats (1,536- and 3,456-well plates) for higher throughput and lower reagent consumption. With miniaturization, separation steps in assays such as ELISA can become difficult to implement. Here we report on the implementation of the Kalypsys, Inc. (San Diego, CA) 1,536-well plate washer to enable the successful miniaturization and full automation of an ELISA that monitors ubiquitin ligase activity. The 1,536-well plate ELISA was robust and used for the high-throughput screening of a large screening collection (>1 million compounds).

A cell-based beta-lactamase reporter gene assay for the identification of inhibitors of hepatitis C virus replication.

This report describes the development, optimization, and implementation of a cell-based assay for high-throughput screening (HTS) to identify inhibitors to hepatitis C virus (HCV) replication. The assay is based on a HCV subgenomic RNA replicon that expresses beta-lactamase as a reporter for viral replication in enhanced Huh-7 cells. The drug targets in this assay are viral and cellular enzymes required for HCV replication, which are monitored by fluorescence resonance energy transfer using cell-permeable CCF4-AM as a beta-lactamase substrate. Digital image processing was used to visualize cells that harbor viral RNA and to optimize key assay development parameters such as transfection and culturing conditions to obtain a cell line which produced a robust assay window. Formatting the assay for compound screening was problematic due to small signal-to-background ratio and reduced potency to known HCV inhibitors. These technical difficulties were solved by using clavulanic acid, an irreversible inhibitor of beta-lactamase, to eliminate residual beta-lactamase activity after HCV replication was terminated, thus resulting in an improved assay window. HTS was carried out in 384-well microplate format, and the signal-to-background ratio and Z factor for the assay plates during the screen were approximately 13-fold and 0.5, respectively.