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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.

Agonist shift assays feature cross-titrations of allosteric modulators and orthosteric ligands. Information generated in agonist shift assays can include a modulator's effect on the orthosteric agonist's potency (alpha) and efficacy (beta), as well as direct agonist activity of the allosteric ligand (tauB) and the intrinsic binding affinity of the modulator to the unoccupied receptor (KB). Because of the heavy resource demand and complex data handling, these allosteric parameters are determined infrequently during the course of a drug discovery program and on a relatively small subset of compounds. Automation of agonist shift assays enables this data-rich analysis to evaluate a larger number of compounds, offering the potential to differentiate compound classes earlier and prospectively prioritize based on desired molecular pharmacology. A high-throughput calcium-imaging agonist shift assay was pursued to determine the allosteric parameters of over 1000 positive allosteric modulator (PAM) molecules for the human muscarinic acetylcholine receptor 1 (M 1 ). Control compounds were run repeatedly to demonstrate internal consistency. Comparisons between potency measurements and the allosteric parameter results demonstrate that these different types of measurements do not necessarily correlate, highlighting the importance of fully characterizing and understanding the allosteric properties of leads.

GluK1, a kainate subtype of ionotropic glutamate receptors, exhibits an expression pattern in the CNS consistent with involvement in pain processing and migraine. Antagonists of GluK1 have been shown to reduce pain signaling in the spinal cord and trigeminal nerve, and are predicted to provide pain and migraine relief. We developed an ultra-high-throughput small-molecule screen to identify antagonists of GluK1. Using the calcium indicator dye fluo-4, a multimillion-member small-molecule library was screened in 1536-well plate format on the FLIPR (Fluorescent Imaging Plate Reader) Tetra against cells expressing a calcium-permeable GluK1. Following confirmation in the primary assay and subsequent counter-screen against the endogenous Par-1 receptor, 6100 compounds were selected for dose titration to assess potency and selectivity. Final triage of 1000 compounds demonstrating dose-dependent inhibition with IC50 values of less than 12 µM was performed in an automated whole-cell patch clamp electrophysiology assay. Although a weak correlation between electrophysiologically active and calcium-imaging active compounds was observed, the identification of electrophysiologically active compounds with a range of kinetic profiles revealed a broad spectrum of mechanisms of action., (© 2015 Society for Laboratory Automation and Screening.)