Advancing Biological Understanding and Therapeutics Discovery with Small-Molecule Probes

Small-molecule probes can illuminate biological processes and aid in the assessment of emerging therapeutic targets by perturbing biological systems in a manner distinct from other experimental approaches. Despite the tremendous promise of chemical tools for investigating biology and disease, small-molecule probes were unavailable for most targets and pathways as recently as a decade ago. In 2005, the U.S. National Institutes of Health launched the decade-long Molecular Libraries Program with the intent of innovating in and broadening access to small-molecule science. This Perspective describes how novel small-molecule probes identified through the program are enabling the exploration of biological pathways and therapeutic hypotheses not otherwise testable. These experiences illustrate how small-molecule probes can help bridge the chasm between biological research and the development of medicines, but also highlight the need to innovate the science of therapeutic discovery.Chemistry and Chemical Biolog

Global analysis reveals families of chemical motifs enriched for HERG inhibitors.

Promiscuous inhibition of the human ether-à-go-go-related gene (hERG) potassium channel by drugs poses a major risk for life threatening arrhythmia and costly drug withdrawals. Current knowledge of this phenomenon is derived from a limited number of known drugs and tool compounds. However, in a diverse, naïve chemical library, it remains unclear which and to what degree chemical motifs or scaffolds might be enriched for hERG inhibition. Here we report electrophysiology measurements of hERG inhibition and computational analyses of >300,000 diverse small molecules. We identify chemical 'communities' with high hERG liability, containing both canonical scaffolds and structurally distinctive molecules. These data enable the development of more effective classifiers to computationally assess hERG risk. The resultant predictive models now accurately classify naïve compound libraries for tendency of hERG inhibition. Together these results provide a more complete reference map of characteristic chemical motifs for hERG liability and advance a systematic approach to rank chemical collections for cardiotoxicity risk

Experimental evaluation of MLSMR-derived ensemble hERG modeling.

<p>(<b>a</b>) Scatterplot and histogram distribution of predicted blocker numbers for ChemBridge DIVERSet 384-well plates. Experimentally evaluated plates for high (red) and low (light blue) predicted hERG inhibition are highlighted. (<b>b</b>) Correlation of predicted and experimentally observed blockers for eight test plates. (<b>c</b>) Pie graphs of true positive rate (recall) for high and low-risk plates at 10 μM concentration. Area is proportional to the number of experimentally observed blockers. Light color indicates false negatives, dark color true positives.</p

Structural similarity within and between six populations of compounds in the MLSMR assigned by activity-predictability classes.

<p>(<b>a</b>) Summary network visualizing the relationships of six possible combinations of activity (blocker and nonblocker) and predictability (predictable, inconsistent and unpredictable) classes. Each node represents the population of compounds of the same activity-predictability assignment, with edge width representing relative structural similarity quantified by relative connection density (see <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118324#sec010" target="_blank">Methods</a></b>), within and between each population using the structure network defined in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118324#pone.0118324.g001" target="_blank">Fig 1</a></b>. Node sizes for P-B, I-B and U-B represent enrichment of blockers among compounds with high, intermediate, and low hBS compared to the distribution of the entire dataset. Similarly, P-NB, I-NB and U-NB sizes represent enrichment of nonblockers among compounds with low, intermediate, and high hBS. (<b>b</b>) An example cluster that highlights connection patterns within and between P-B, U-NB and I-NB compounds. (<b>c</b>) An example cluster that highlights connection patterns within and between P-NB, U-B and I-NB compounds. (<b>d</b>) An example cluster in which inconsistent and unpredictable compounds are more pronounced. Networks are generated using Cytoscape 2.8.2.</p

Novel structural determinants of hERG inhibitions.

<p>(<b>a</b>) (Left) Classical charged hERG pharmacophore consisting of positively charged basic nitrogen (blue) and hydrophobic groups (red), demonstrated by cisapride (I), thioridazine (II) and astemizole (III). (Right) Distribution and density of LogP values for neutral and charged hERG blockers in D2644 collection (right) and MLSMR (left). (<b>b</b>) Density of chemical space mapped using largest and smallest BCUT^TM charge descriptors for uncharged hERG blockers in D2644 (Left) and MLSMR (Center), and MLSMR library (Right). Red outlines denote enriched regions for neutral hERG blocker patterns in (<b>c</b>), (<b>d</b>). (<b>c</b>) Distribution of hERG inhibition for compounds containing prazosin fragment (red) compared to MLSMR library (blue). (<b>d</b>) As (<b>c</b>), for compounds containing illustrated triazatricyclo scaffold.</p

Continuity of structure variants between blockers and nonblockers in the MLSMR and previous hERG datasets.

<p>Each dataset is represented by a structure network as described in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118324#pone.0118324.g001" target="_blank">Fig. 1</a></b>. Compound neighbors are classified as blocker (>50% hERG Inhibition at 10 μM) and nonblocker (<50%). The frequency of compounds with a given number of blocker and nonblocker neighbors in each dataset is plotted, with white cells representing empty data, and the origin representing singleton compounds with no neighbors. Grid points along the vertical axis (horizontal axis) represent compounds with a majority blocker neighbors (nonblocker neighbors). The region along the diagonal represents the transition zone where compounds possess mixed blocker and nonblocker neighbors. This transition zone is illustrated by three example neighborhoods containing blockers (red nodes) and nonblockers (light blue nodes).</p

Potent hERG blockers exhibit preferential structural similarity.

<p>(<b>a</b>) Schematic diagram of chemical structure network analysis. The chemical-club coefficient (ChC) measures the density of connections (structurally similar pairs) among query compounds (red nodes) above a given threshold of hERG inhibition (top, red bars). (<b>b</b>) ChC calculation plotted for the MLSMR library for randomized (blue, mean +/- 3 standard deviations for randomized datasets) and observed (red) activity for 10 μM data, where compound adjacency is judged by a Tanimoto Coefficient > 0.7 for FCFP_6 circular fingerprints.</p

Profiling Diverse Compounds by Flux- and Electrophysiology-Based Primary Screens for Inhibition of Human Ether-à-go-go Related Gene Potassium Channels

Compound effects on cloned human Ether-à-go-go related gene (hERG) potassium channels have been used to assess the potential cardiac safety liabilities of drug development candidate compounds. In addition to radioactive ligand displacement tests, two other common approaches are surrogate ion-based flux assays and electrophysiological recordings. The former has much higher throughput, whereas the latter measures directly the effects on ionic currents. Careful characterization in earlier reports has been performed to compare the relative effectiveness of these approaches for known hERG blockers, which often yielded good overall correlation. However, cases were reported showing significant and reproducible differences in potency and/or sensitivity by the two methods. This raises a question concerning the rationale and criteria on which an assay should be selected for evaluating unknown compounds. To provide a general basis for considering assays to profile large compound libraries for hERG activity, we have conducted parallel flux and electrophysiological analyses of 2,000 diverse compounds, representative of the 300,000 compound collection of NIH Molecular Library Small Molecular Repository (MLSMR). Our results indicate that at the conventional testing concentration 1.0 μM, the overlap between the two assays ranges from 32% to 50% depending on the hit selection criteria. There was a noticeable rate of false negatives by the thallium-based assay relative to electrophysiological recording, which may be greatly reduced under modified comparative conditions. As these statistical results identify a preferred method for cardiac safety profiling of unknown compounds, they suggest an efficient method combining flux and electrophysiological assays to rapidly profile hERG liabilities of large collection of naive compounds