or will not assign the molecule at all if reference compounds with similar phenomic profiles are not present in the experiment. For poly-pharmacological compounds, morphological and transcriptional readouts reflect the actions on multiple, functionally unrelated targets that are convolved into a complex phenotype, and their activity on specific and relevant targets must therefore be deconvolved from these phenomic profiles, which is far from trivial. Deconvolution can be achieved using supervised machine learning models to predict target activity 7. This approach requires that many of the phenomically profiled compounds have also been tested in the targetor MoA-specific assays it aims to cover. These compounds are used as training examples to identify the most informative phenomic features to predict activities in each assay of interest. Consequently, the data scale required for supervised deconvolution can be prohibitive. A continuing challenge in drug discovery is the design of an affordable set of phenomic profiling assays with maximal coverage of therapeutic targets. Ideally, this assay set should be able to document a phenotypic response towards at least the vast majority of the 549 human proteins targeted by 999 FDA-approved small-molecule drugs for human disease 12. Until recently, most studies that use phenomic profiling have been restricted in scope, typically using fewer than 100 reference compounds annotated to a limited number of targets 1,2,4,13-15 and are not sufficiently broadly representative to inform design principles for generic sets of phenomic profiling assays. In the current study, we used a nearest-reference approach to explore how various screening parameters affect the ability to distinguish MoAs from each other. We used gene-editing to create a panel of 15 reporter cell lines by introducing different combinations of 12 blue fluorescent protein (BFP), green fluorescent protein (GFP), or red fluorescent protein (RFP)/FusionRed signalling pathway and organelle markers into the A549, HepG2, and WPMY1 cell backgrounds, and profiled these reporter cell lines in live-cell, high-content imaging screens against a library of 1,008 small molecules, manually annotated with 218 unique MoA descriptors, at four concentrations. Results Library of 1,008 reference compounds and 169 natural products. We assembled a set of 1,008 well-characterized reference compounds, composed of FDA-approved drugs and commercially available tool compounds, and manually annotated each compound with one or more MoA descriptors using publicly available information from vendor compound catalogues, chemical databases, and large-scale target annotation projects 12,16-18 (Supplementary Table S1). In total, 218 unique MoA descriptors were assigned to the reference compound set. Of the 1,008 reference compounds, 829 (82%) were labelled with only a single MoA descriptor. Of the 218 MoAs, 132 (61%) were assigned to ≥ 3 co-annotated compounds and 92 (42%) were assigned to ≥ 5 co-annotated compounds (Supplementary Fig. S1). To increase...

Histone deacetylase (HDAC) inhibitors possess therapeutic potential to reverse aberrant epigenetic changes associated with cancers, neurological diseases, and immune disorders. Unfortunately, clinical studies with some HDAC inhibitors displayed delayed cardiac adverse effects, such as atrial fibrillation and ventricular tachycardia. However, the underlying molecular mechanism(s) of HDAC inhibitormediated cardiotoxicity remains poorly understood and is difficult to detect in the early stages of preclinical drug development because of a delayed onset of effects. In the present study, we show for the first time in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs) that HDAC inhibitors (dacinostat, panobinostat, vorinostat, entinostat, and tubastatin-a) induce delayed dose-related cardiac dysfunction at therapeutic concentrations associated with cardiac adverse effects in humans. HDAC inhibitor-mediated delayed effects on the beating properties of hiPS-CMs developed after 12 hours by decreasing the beat rate, shortening the field potential duration, and inducing arrhythmic behavior under form of sustained contractions and fibrillation-like patterns. Transcriptional changes that are common between the cardiotoxic HDAC inhibitors but different from noncardiotoxic treatments identified cardiac-specific genes and pathways related to structural and functional changes in cardiomyocytes. Combining the functional data with epigenetic changes in hiPS-CMs allowed us to identify molecular targets that might explain HDAC inhibitor-mediated cardiac adverse effects in humans. Therefore, hiPS-CMs represent a valuable translational model to assess HDAC inhibitor-mediated cardiotoxicity and support identification of better HDAC inhibitors with an improved benefit-risk profile. STEM CELLS TRANSLATIONAL MEDICINE 2016;5:602-612

By adding biological information, beyond the chemical properties and desired effect of a compound, uncharted compound areas and connections can be explored. In this study, we add transcriptional information for 31K compounds of Janssen's primary screening deck, using the HT L1000 platform and assess (a) the transcriptional connection score for generating compound similarities, (b) machine learning algorithms for generating target activity predictions, and (c) the scaffold hopping potential of the resulting hits. We demonstrate that the transcriptional connection score is best computed from the significant genes only and should be interpreted within its confidence interval for which we provide the stats. These guidelines help to reduce noise, increase reproducibility, and enable the separation of specific and promiscuous compounds. The added value of machine learning is demonstrated for the NR3C1 and HSP90 targets. Support Vector Machine models yielded balanced accuracy values ≥80% when the expression values from DDIT4 & SERPINE1 and TMEM97 & SPR were used to predict the NR3C1 and HSP90 activity, respectively. Combining both models resulted in 22 new and confirmed HSP90-independent NR3C1 inhibitors, providing two scaffolds (i.e., pyrimidine and pyrazolo-pyrimidine), which could potentially be of interest in the treatment of depression (i.e., inhibiting the glucocorticoid receptor (i.e., NR3C1), while leaving its chaperone, HSP90, unaffected). As such, the initial hit rate increased by a factor 300, as less, but more specific chemistry could be screened, based on the upfront computed activity predictions.

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Phenomic profiles are high-dimensional sets of readouts that can comprehensively capture the biological impact of chemical and genetic perturbations in cellular assay systems. Phenomic profiling of compound libraries can be used for compound target identification or mechanism of action (MoA) prediction and other applications in drug discovery. To devise an economical set of phenomic profiling assays, we assembled a library of 1,008 approved drugs and well-characterized tool compounds manually annotated to 218 unique MoAs, and we profiled each compound at four concentrations in live-cell, high-content imaging screens against a panel of 15 reporter cell lines, which expressed a diverse set of fluorescent organelle and pathway markers in three distinct cell lineages. For 41 of 83 testable MoAs, phenomic profiles accurately ranked the reference compounds (AUC-ROC ≥0.9). MoAs could be better resolved by screening compounds at multiple concentrations than by including replicates at a single concentration. Screening additional cell lineages and fluorescent markers increased the number of distinguishable MoAs but this effect quickly plateaued. There remains a substantial number of MoAs that were hard to distinguish from others under the current study's conditions. We discuss ways to close this gap, which will inform the design of future phenomic profiling efforts.