Identification of Small-Molecule Frequent Hitters from AlphaScreen High-Throughput Screens

ChemFH: an integrated tool for screening frequent false positives in chemical biology and drug discovery

High-throughput screening rapidly tests an extensive array of chemical compounds to identify hit compounds for specific biological targets in drug discovery. However, false-positive results disrupt hit compound screening, leading to wastage of time and resources. To address this, we propose ChemFH, an integrated online platform facilitating rapid virtual evaluation of potential false positives, including colloidal aggregators, spectroscopic interference compounds, firefly luciferase inhibitors, chemical reactive compounds, promiscuous compounds, and other assay interferences. By leveraging a dataset containing 823 391 compounds, we constructed high-quality prediction models using multi-task directed message-passing network (DMPNN) architectures combining uncertainty estimation, yielding an average AUC value of 0.91. Furthermore, ChemFH incorporated 1441 representative alert substructures derived from the collected data and ten commonly used frequent hitter screening rules. ChemFH was validated with an external set of 75 compounds. Subsequently, the virtual screening capability of ChemFH was successfully confirmed through its application to five virtual screening libraries. Furthermore, ChemFH underwent additional validation on two natural products and FDA-approved drugs, yielding reliable and accurate results. ChemFH is a comprehensive, reliable, and computationally efficient screening pipeline that facilitates the identification of true positive results in assays, contributing to enhanced efficiency and success rates in drug discovery. ChemFH is freely available via https://chemfh.scbdd.com/.

Tackling assay interference associated with small molecules

Biochemical and cell-based assays are essential to discovering and optimizing efficacious and safe drugs, agrochemicals and cosmetics. However, false assay readouts stemming from colloidal aggregation, chemical reactivity, chelation, light signal attenuation and emission, membrane disruption, and other interference mechanisms remain a considerable challenge in screening synthetic compounds and natural products. To address assay interference, a range of powerful experimental approaches are available and in silico methods are now gaining traction. This Review begins with an overview of the scope and limitations of experimental approaches for tackling assay interference. It then focuses on theoretical methods, discusses strategies for their integration with experimental approaches, and provides recommendations for best practices. The Review closes with a summary of the critical facts and an outlook on potential future developments.

Lead Optimization of Small Molecule ENL YEATS Inhibitors to Enable In Vivo Studies: Discovery of TDI-11055

Eleven-nineteen leukemia (ENL) is an epigenetic reader protein that drives oncogenic transcriptional programs in acute myeloid leukemia (AML). AML is one of the deadliest hematopoietic malignancies, with an overall 5-year survival rate of 27%. The epigenetic reader activity of ENL is mediated by its YEATS domain that binds to acetyl and crotonyl marks on histone tails and colocalizes with promoters of actively transcribed genes that are essential for leukemia. Prior to the discovery of TDI-11055, existing inhibitors of ENL YEATS showed in vitro potency, but had not shown efficacy in in vivo animal models. During the course of the medicinal chemistry campaign described here, we identified ENL YEATS inhibitor TDI-11055 that has an improved pharmacokinetic profile and is appropriate for in vivo evaluation of the ENL YEATS inhibition mechanism in AML.

Evaluating Scalable Supervised Learning for Synthesize-on-Demand Chemical Libraries

Traditional small-molecule drug discovery is a time-consuming and costly endeavor. High-throughput chemical screening can only assess a tiny fraction of drug-like chemical space. The strong predictive power of modern machine-learning methods for virtual chemical screening enables training models on known active and inactive compounds and extrapolating to much larger chemical libraries. However, there has been limited experimental validation of these methods in practical applications on large commercially available or synthesize-on-demand chemical libraries. Through a prospective evaluation with the bacterial protein-protein interaction PriA-SSB, we demonstrate that ligand-based virtual screening can identify many active compounds in large commercial libraries. We use cross-validation to compare different types of supervised learning models and select a random forest (RF) classifier as the best model for this target. When predicting the activity of more than 8 million compounds from Aldrich Market Select, the RF substantially outperforms a naïve baseline based on chemical structure similarity. 48% of the RF's 701 selected compounds are active. The RF model easily scales to score one billion compounds from the synthesize-on-demand Enamine REAL database. We tested 68 chemically diverse top predictions from Enamine REAL and observed 31 hits (46%), including one with an IC50 value of 1.3 μM.

A rapid cell-free expression and screening platform for antibody discovery

Antibody discovery is bottlenecked by the individual expression and evaluation of antigen-specific hits. Here, we address this bottleneck by developing a workflow combining cell-free DNA template generation, cell-free protein synthesis, and binding measurements of antibody fragments in a process that takes hours rather than weeks. We apply this workflow to evaluate 135 previously published antibodies targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), including all 8 antibodies previously granted emergency use authorization for coronavirus disease 2019 (COVID-19), and demonstrate identification of the most potent antibodies. We also evaluate 119 anti-SARS-CoV-2 antibodies from a mouse immunized with the SARS-CoV-2 spike protein and identify neutralizing antibody candidates, including the antibody SC2-3, which binds the SARS-CoV-2 spike protein of all tested variants of concern. We expect that our cell-free workflow will accelerate the discovery and characterization of antibodies for future pandemics and for research, diagnostic, and therapeutic applications more broadly.

Rapid discovery and classification of inhibitors of coronavirus infection by pseudovirus screen and amplified luminescence proximity homogeneous assay

To identify potent antiviral compounds, we introduced a high-throughput screen platform that can rapidly classify hit compounds according to their target. In our platform, we performed a compound screen using a lentivirus-based pseudovirus presenting a spike protein of coronavirus, and we evaluated the hit compounds using an amplified luminescence proximity homogeneous assay (alpha) test with purified host receptor protein and the receptor binding domain of the viral spike. With our screen platform, we were able to identify both spike-specific compounds (class I) and broad-spectrum antiviral compounds (class II). Among the hit compounds, thiosemicarbazide was identified to be selective to the interaction between the viral spike and its host cell receptor, and we further optimized the binding potency of thiosemicarbazide through modification of the pyridine group. Among the class II compounds, we found raloxifene and amiodarone to be highly potent against human coronaviruses including Middle East respiratory syndrome coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus (SARS-CoV), and SARS-CoV-2. In particular, using analogs of the benzothiophene moiety, which is also present in raloxifene, we have identified benzothiophene as a novel structural scaffold for broad-spectrum antivirals. This work highlights the strong utility of our screen platform using a pseudovirus assay and an alpha test for rapid identification of potential antiviral compounds and their mechanism of action, which can lead to the accelerated development of therapeutics against newly emerging viral infections.

Optimization of an in Silico Protocol Using Probe Permeabilities to Identify Membrane Pan-Assay Interference Compounds

Membrane pan-assay interference compounds (PAINS) are a class of molecules that interact nonspecifically with lipid bilayers and alter their physicochemical properties. An early identification of these compounds avoids chasing false leads and the needless waste of time and resources in drug discovery campaigns. In this work, we optimized an in silico protocol on the basis of umbrella sampling (US)/molecular dynamics (MD) simulations to discriminate between compounds with different membrane PAINS behavior. We showed that the method is quite sensitive to membrane thickness fluctuations, which was mitigated by changing the US reference position to the phosphate atoms of the closest interacting monolayer. The computational efficiency was improved further by decreasing the number of umbrellas and adjusting their strength and position in our US scheme. The inhomogeneous solubility-diffusion model (ISDM) used to calculate the membrane permeability coefficients confirmed that resveratrol and curcumin have distinct membrane PAINS characteristics and indicated a misclassification of nothofagin in a previous work. Overall, we have presented here a promising in silico protocol that can be adopted as a future reference method to identify membrane PAINS.

Methods to Detect Small Molecule Inhibition of RING E3 Ligase Activity

Protein ubiquitination is an essential post-translational modification that regulates a large number of cellular processes. This reaction is facilitated by the consecutive action of three central enzymes, i.e., E1 activating enzyme, E2 conjugating enzyme, and the E3 ligase. More than 600 E3 enzymes guarantee the specificity and selectivity of these reactions and thus represent an exciting, while highly underrepresented, class of drug targets. Specific methods can be employed to monitor their activity and thus query compound libraries for inhibitory small molecules. Here, we describe two protocols-one high-throughput and one low-throughput method-to detect E3 ligase activity and test small molecule modulation. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: AlphaScreen assay to measure TRAF6-Ubc13 interaction Basic Protocol 2: Gel-based in vitro ubiquitination assay (K63-linked chains).

Novel Trypanocidal Inhibitors that Block Glycosome Biogenesis by Targeting PEX3-PEX19 Interaction

Human pathogenic trypanosomatid parasites harbor a unique form of peroxisomes termed glycosomes that are essential for parasite viability. We and others previously identified and characterized the essential Trypanosoma brucei ortholog TbPEX3, which is the membrane-docking factor for the cytosolic receptor PEX19 bound to the glycosomal membrane proteins. Knockdown of TbPEX3 expression leads to mislocalization of glycosomal membrane and matrix proteins, and subsequent cell death. As an early step in glycosome biogenesis, the PEX3–PEX19 interaction is an attractive drug target. We established a high-throughput assay for TbPEX3–TbPEX19 interaction and screened a compound library for small-molecule inhibitors. Hits from the screen were further validated using an in vitro ELISA assay. We identified three compounds, which exhibit significant trypanocidal activity but show no apparent toxicity to human cells. Furthermore, we show that these compounds lead to mislocalization of glycosomal proteins, which is toxic to the trypanosomes. Moreover, NMR-based experiments indicate that the inhibitors bind to PEX3. The inhibitors interfering with glycosomal biogenesis by targeting the TbPEX3–TbPEX19 interaction serve as starting points for further optimization and anti-trypanosomal drug development.

Accounting for Artifacts in High-Throughput Toxicity Assays

Compound activity identification is the primary goal in high throughput screening (HTS) assays. However, assay artifacts including both systematic (e.g., compound autofluorescence) and nonsystematic (e.g., noise) complicate activity interpretation. In addition, other than the traditional potency parameter, half-maximal effect concentration [EC₅₀], additional activity parameters (e.g., point-of-departure [POD] and weighted area-under-the-curve [wAUC]) could be derived from HTS data for activity profiling. A data analysis pipeline has been developed to handle the artifacts, and to provide compound activity characterization with either binary or continuous metrics. This chapter outlines the steps in the pipeline using Tox21 estrogen receptor (ER) β-lactamase assays, including the formats to identify either agonists or antagonists, as well as the counterscreen assays for identifying artifacts as examples. The steps can be applied to other lower throughput assays with concentration-response data.

Gains from no real PAINS: Where 'Fair Trial Strategy' stands in the development of multi-target ligands

Compounds that selectively modulate multiple targets can provide clinical benefits and are an alternative to traditional highly selective agents for unique targets. High-throughput screening (HTS) for multitarget-directed ligands (MTDLs) using approved drugs, and fragment-based drug design has become a regular strategy to achieve an ideal multitarget combination. However, the unexpected presence of pan-assay interference compounds (PAINS) suspects in the development of MTDLs frequently results in nonspecific interactions or other undesirable effects leading to artefacts or false-positive data of biological assays. Publicly available filters can help to identify PAINS suspects; however, these filters cannot comprehensively conclude whether these suspects are "bad" or innocent. Additionally, these in silico approaches may inappropriately label a ligand as PAINS. More than 80% of the initial hits can be identified as PAINS by the filters if appropriate biochemical tests are not used resulting in false positive data that are unacceptable for medicinal chemists in manuscript peer review and future studies. Therefore, extensive offline experiments should be used after online filtering to discriminate "bad" PAINS and avoid incorrect evaluation of good scaffolds. We suggest that the use of "Fair Trial Strategy" to identify interesting molecules in PAINS suspects to provide certain structure‒function insight in MTDL development.

Highly Accurate Filters to Flag Frequent Hitters in AlphaScreen Assays by Suggesting their Mechanism

AlphaScreen is one of the most widely used assay technologies in drug discovery due to its versatility, dynamic range and sensitivity. However, a presence of false positives and frequent hitters contributes to difficulties with an interpretation of measured HTS data. Although filters do exist to identify frequent hitters for AlphaScreen, they are frequently based on privileged scaffolds. The development of such filters is time consuming and requires deep domain knowledge. Recently, machine learning and artificial intelligence methods are emerging as important tools to advance drug discovery and chemoinformatics, including their application to identification of frequent hitters in screening assays. However, the relative performance and complementarity of the Machine Learning and scaffold-based techniques has not yet been comprehensively compared. In this study, we analysed filters based on the privileged scaffolds with filters built using machine learning. Our results demonstrate that machine-learning methods provide more accurate filters for identification of frequent hitters in AlphaScreen assays than scaffold-based methods and can be easily redeveloped once new data are measured. We present highly accurate models to identify frequent hitters in AlphaScreen assays.

Luminescence Energy Transfer-Based Screening and Target Engagement Approaches for Chemical Biology and Drug Discovery

Luminescence is characterized by the spontaneous emission of light resulting from either chemical or biological reactions. Because of their high sensitivity, reduced background interference, and applicability to numerous situations, luminescence-based assay strategies play an essential role in early-stage drug discovery. Newer developments in luminescence-based technologies have dramatically affected the ability of researchers to investigate molecular binding events. At the forefront of these developments are the nano bioluminescence resonance energy transfer (NanoBRET) and amplified luminescent proximity homogeneous assay (Alpha) technologies. These technologies have opened up numerous possibilities for analyzing the molecular biophysical properties of complexes in environments such as cell lysates. Moreover, NanoBRET enables the validation and quantitation of the interactions between therapeutic targets and small molecules in live cells, representing an essential benchmark for preclinical drug discovery. Both techniques involve proximity-based luminescence energy transfer, in which excited-state energy is transferred from a donor to an acceptor, where the efficiency of transfer depends on proximity. Both approaches can be applied to high-throughput compound screening in biological samples, with the NanoBRET assay providing opportunities for live-cell screening. Representative applications of both technologies for assessing physical interactions and associated challenges are discussed.

Brief Guide: Experimental Strategies for High-Quality Hit Selection from Small-Molecule Screening Campaigns

Small-molecule screening is a powerful approach to identify modulators of either specific biological targets or cellular pathways with phenotypic endpoints. A myriad of assay technologies are available to assess the activity of enzymes, monitor protein-protein interactions, measure transcription factor activity in reporter assays, or detect cellular features and activities using high-content imaging. A common challenge during small-molecule screening is, however, the presence of hit compounds generating assay interference, thereby producing false-positive hits. Thus, efforts are needed to uncover such interferences to prioritize high-quality hits for further analysis. This process encompasses (1) computational approaches to flag undesirable compounds, and (2) the use of experimental approaches like counter, orthogonal, and cellular fitness screens to identify and eliminate artifacts. In this brief guide, we provide an overview for first-time users, highlighting experimental screening strategies to prioritize high-quality bioactive hits from high-throughput screening/high-content screening (HTS/HCS) campaigns.

Nuisance compounds in cellular assays

Compounds that exhibit assay interference or undesirable mechanisms of bioactivity ("nuisance compounds") are routinely encountered in cellular assays, including phenotypic and high-content screening assays. Much is known regarding compound-dependent assay interferences in cell-free assays. However, despite the essential role of cellular assays in chemical biology and drug discovery, there is considerably less known about nuisance compounds in more complex cell-based assays. In our view, a major obstacle to realizing the full potential of chemical biology will not just be difficult-to-drug targets or even the sheer number of targets, but rather nuisance compounds, due to their ability to waste significant resources and erode scientific trust. In this review, we summarize our collective academic, government, and industry experiences regarding cellular nuisance compounds. We describe assay design strategies to mitigate the impact of nuisance compounds and suggest best practices to efficiently address these compounds in complex biological settings.

Evolution of Novartis' Small Molecule Screening Deck Design

This article summarizes the evolution of the screening deck at the Novartis Institutes for BioMedical Research (NIBR). Historically, the screening deck was an assembly of all available compounds. In 2015, we designed a first deck to facilitate access to diverse subsets with optimized properties. We allocated the compounds as plated subsets on a 2D grid with property based ranking in one dimension and increasing structural redundancy in the other. The learnings from the 2015 screening deck were applied to the design of a next generation in 2019. We found that using traditional leadlikeness criteria (mainly MW, clogP) reduces the hit rates of attractive chemical starting points in subset screening. Consequently, the 2019 deck relies on solubility and permeability to select preferred compounds. The 2019 design also uses NIBR's experimental assay data and inferred biological activity profiles in addition to structural diversity to define redundancy across the compound sets.

Scopy: an integrated negative design python library for desirable HTS/VS database design

BACKGROUND

High-throughput screening (HTS) and virtual screening (VS) have been widely used to identify potential hits from large chemical libraries. However, the frequent occurrence of 'noisy compounds' in the screened libraries, such as compounds with poor drug-likeness, poor selectivity or potential toxicity, has greatly weakened the enrichment capability of HTS and VS campaigns. Therefore, the development of comprehensive and credible tools to detect noisy compounds from chemical libraries is urgently needed in early stages of drug discovery.

RESULTS

In this study, we developed a freely available integrated python library for negative design, called Scopy, which supports the functions of data preparation, calculation of descriptors, scaffolds and screening filters, and data visualization. The current version of Scopy can calculate 39 basic molecular properties, 3 comprehensive molecular evaluation scores, 2 types of molecular scaffolds, 6 types of substructure descriptors and 2 types of fingerprints. A number of important screening rules are also provided by Scopy, including 15 drug-likeness rules (13 drug-likeness rules and 2 building block rules), 8 frequent hitter rules (four assay interference substructure filters and four promiscuous compound substructure filters), and 11 toxicophore filters (five human-related toxicity substructure filters, three environment-related toxicity substructure filters and three comprehensive toxicity substructure filters). Moreover, this library supports four different visualization functions to help users to gain a better understanding of the screened data, including basic feature radar chart, feature-feature-related scatter diagram, functional group marker gram and cloud gram.

CONCLUSION

Scopy provides a comprehensive Python package to filter out compounds with undesirable properties or substructures, which will benefit the design of high-quality chemical libraries for drug design and discovery. It is freely available at https://github.com/kotori-y/Scopy.

Design of Drug-Like Protein-Protein Interaction Stabilizers Guided By Chelation-Controlled Bioactive Conformation Stabilization

Protein-protein interactions (PPIs) of 14-3-3 proteins are a model system for studying PPI stabilization. The complex natural product Fusicoccin A stabilizes many 14-3-3 PPIs but is not amenable for use in SAR studies, motivating the search for more drug-like chemical matter. However, drug-like 14-3-3 PPI stabilizers enabling such studies have remained elusive. An X-ray crystal structure of a PPI in complex with an extremely low potency stabilizer uncovered an unexpected non-protein interacting, ligand-chelated Mg²⁺ leading to the discovery of metal-ion-dependent 14-3-3 PPI stabilization potency. This originates from a novel chelation-controlled bioactive conformation stabilization effect. Metal chelation has been associated with pan-assay interference compounds (PAINS) and frequent hitter behavior, but chelation can evidently also lead to true potency gains and find use as a medicinal chemistry strategy to guide compound optimization. To demonstrate this, we exploited the effect to design the first potent, selective, and drug-like 14-3-3 PPI stabilizers.

Anticancer drug discovery by targeting cullin neddylation

Protein neddylation is a post-translational modification which transfers the ubiquitin-like protein NEDD8 to a lysine residue of the target substrate through a three-step enzymatic cascade. The best-known substrates of neddylation are cullin family proteins, which are the core component of Cullin–RING E3 ubiquitin ligases (CRLs). Given that cullin neddylation is required for CRL activity, and CRLs control the turn-over of a variety of key signal proteins and are often abnormally activated in cancers, targeting neddylation becomes a promising approach for discovery of novel anti-cancer therapeutics. In the past decade, we have witnessed significant progress in the field of protein neddylation from preclinical target validation, to drug screening, then to the clinical trials of neddylation inhibitors. In this review, we first briefly introduced the nature of protein neddylation and the regulation of neddylation cascade, followed by a summary of all reported chemical inhibitors of neddylation enzymes. We then discussed the structure-based targeting of protein–protein interaction in neddylation cascade, and finally the available approaches for the discovery of new neddylation inhibitors. This review will provide a focused, up-to-date and yet comprehensive overview on the discovery effort of neddylation inhibitors.

Small molecule NSC1892 targets the CUL4A/4B-DDB1 interactions and causes impairment of CRL4DCAF4 E3 ligases to inhibit colorectal cancer cell growth

Cullin 4A and 4B (CUL4A and 4B) function as oncogenes in colorectal cancer (CRC) cells. Both of them conservatively associate with DNA damage-binding protein 1 (DDB1) and DDB1-CUL4-associated factor 4 (DCAF4) to form Cullin-RING E3 ligases known as CRL4^DCAF4, which specifically ubiquitinate and degrade tumor suppressor ST7 (suppression of tumorigenicity 7). Knockdown either CUL4A/4B or DDB1 significantly inhibits tumor cell growth in vitro and in vivo. Thus, targeting these CRL4^DCAF4 components and their interactions may be an effective strategy for the therapy of CRC. In this study, we developed an in vitro AlphaScreen assay to identify small molecules targeting the CUL4A-DDB1 interaction. We obtained a compound NSC1892, which strongly disrupted the CUL4A-DDB1 interaction (IC₅₀ = 1.8 μM). Oncogenic phenotype analyses indicated that NSC1892 showed significant cytotoxicity to decrease cell proliferation, colony formation and invasion in CRC cells. Biochemical analyses demonstrated that NSC1892 treatment did not change CUL4A and CUL4B protein levels, but caused the degradation of DDB1, thereby leading to the impaired assembly of CRL4^DCAF4 E3 ligases and resulting in the accumulation of ST7. The administration of NSC1892 in mice also significantly inhibited tumor growth through degrading DDB1 and accumulating ST7. Interestingly, NSC1892 also showed promising cytotoxicity to decrease the growth of other CUL4A- or CUL4B-overexpressing tumor cells such as SKOV3 ovarian cells and Saos2 osteosarcoma cells. Our results provide a new avenue for the development of a therapeutic compound targeting tumors through disrupting the CUL4-DDB1 interaction.

Using Microscale Thermophoresis to Characterize Hits from High-Throughput Screening: A European Lead Factory Perspective

High-throughput screening (HTS) is a proven method for discovering new lead matter for drug discovery and chemical biology. To maximize the likelihood of identifying genuine binders to a molecular target, and avoid wasting resources following up compounds with unproductive/nonspecific mechanisms of action, it is important to employ a range of assays during an HTS campaign that build confidence of target engagement for hit compounds. Biophysical methods that measure direct target/compound engagement have established themselves as key techniques in generating this confidence, and they are now integral to the latter stages of HTS triage at the European Lead Factory (ELF). One relatively new technique that the ELF is using is microscale thermophoresis (MST), which measures the differences in rate of movement through a temperature gradient that are caused when single molecular species form complexes. Here we provide an overview of the MST assay development workflow that the ELF employs and a perspective of our experience to date of using MST to triage the output of HTS campaigns and how it compares and contrasts with the use of other biophysical techniques.

Detecting Secretory Proteins by Acoustic Droplet Ejection in Multiplexed High-Throughput Applications

Nearly one-third of the encoded proteome is comprised of secretory proteins that enable communication between cells and organ systems, playing a ubiquitous role in human health and disease. High-throughput detection of secreted proteins would enhance efforts to identify therapies for secretion-related diseases. Using the Z mutant of alpha-1 antitrypsin as a human secretory model, we have developed 1536-well high-throughput screening assays that utilize acoustic droplet ejection to transfer nanoliter volumes of sample for protein quantification. Among them, the acoustic reverse phase protein array (acoustic RPPA) is a multiplexable, low-cost immunodetection technology for native, endogenously secreted proteins from physiologically relevant model systems like stem cells that is compatible with plate-based instrumentation. Parallel assay profiling with the LOPAC¹²⁸⁰ chemical library validated performance and orthogonality between a secreted bioluminescent reporter and acoustic RPPA method by consistently identifying secretory modulators with comparable concentration response relationships. Here, we introduce a robust, multiplexed drug discovery platform coupling extracellular protein quantification by acoustic RPPA with intracellular and cytotoxicity analyses from single wells, demonstrating proof-of-principle applications for human induced pluripotent stem cell-derived hepatocytes.

Inhibition of CPAP-tubulin interaction prevents proliferation of centrosome-amplified cancer cells

Centrosome amplification is a hallmark of human cancers that can trigger cancer cell invasion. To survive, cancer cells cluster amplified extra centrosomes and achieve pseudobipolar division. Here, we set out to prevent clustering of extra centrosomes. Tubulin, by interacting with the centrosomal protein CPAP, negatively regulates CPAP‐dependent peri‐centriolar material recruitment, and concurrently microtubule nucleation. Screening for compounds that perturb CPAP–tubulin interaction led to the identification of CCB02, which selectively binds at the CPAP binding site of tubulin. Genetic and chemical perturbation of CPAP–tubulin interaction activates extra centrosomes to nucleate enhanced numbers of microtubules prior to mitosis. This causes cells to undergo centrosome de‐clustering, prolonged multipolar mitosis, and cell death. 3D‐organotypic invasion assays reveal that CCB02 has broad anti‐invasive activity in various cancer models, including tyrosine kinase inhibitor (TKI)‐resistant EGFR‐mutant non‐small‐cell lung cancers. Thus, we have identified a vulnerability of cancer cells to activation of extra centrosomes, which may serve as a global approach to target various tumors, including drug‐resistant cancers exhibiting high incidence of centrosome amplification.

Identification of Inhibitors of the dNTP Triphosphohydrolase SAMHD1 Using a Novel and Direct High-Throughput Assay

The dNTP triphosphohydrolase SAMHD1 is a regulator of cellular dNTP pools. Given its central role in nucleotide metabolism, SAMHD1 performs important functions in cellular homeostasis, cell cycle regulation, and innate immunity. It therefore represents a high-profile target for small molecule drug design. SAMHD1 has a complex mechanism of catalytic activation that makes the design of an activating compound challenging. However, an inhibitor of SAMHD1 could serve multiple therapeutic roles, including the potentiation of antiviral and anticancer drug regimens. The lack of high-throughput screens that directly measure SAMHD1 catalytic activity has impeded efforts to identify inhibitors of SAMHD1. Here we describe a novel high-throughput screen that directly measures SAMHD1 catalytic activity. This assay results in a colorimetric end point that can be read spectrophotometrically and utilizes bis(4-nitrophenyl) phosphate as the substrate and Mn²⁺ as the activating cation that facilitates catalysis. When used to screen a library of Food and Drug Administration-approved drugs, this HTS identified multiple novel compounds that inhibited SAMHD1 dNTPase activity at micromolar concentrations.

Investigating the Behavior of Published PAINS Alerts Using a Pharmaceutical Company Data Set

Biochemical assay interference is becoming increasingly recognized as a significant waste of resource in drug discovery, both in industry and academia. A seminal publication from Baell and Holloway raised the awareness of this issue, and they published a set of alerts to identify what they described as PAINS (pan-assay interference compounds). These alerts have been taken up by drug discovery groups, even though the original paper had a somewhat limited data set. Here, we have taken Lilly’s far larger internal data set to assess the PAINS alerts on four criteria: promiscuity (over six assay formats including AlphaScreen), compound stability, cytotoxicity, and presence of a high Hill slope as a surrogate for non-1:1 protein–ligand binding. It was found that only three of the alerts show pan-assay promiscuity, and the alerts appear to encode primarily AlphaScreen promiscuous molecules. Although not enriching for pan-assay promiscuity, many of the alerts do encode molecules that are unstable, show cytotoxicity, and increase the prevalence of high Hill slopes.

Automated MALDI Target Preparation Concept: Providing Ultra-High-Throughput Mass Spectrometry–Based Screening for Drug Discovery

Label-free, mass spectrometric (MS) deciphering of enzymatic reactions by direct analysis of substrate-to-product conversion provides the next step toward more physiological relevant assays within drug discovery campaigns. Reduced risk of suffering from compound interference combined with diminished necessity for tailored signal mediators emphasizes the valuable role of label-free readouts. However, MS-based detection has not hitherto met high-throughput screening (HTS) requirements because of the lack of HTS-compatible sample introduction. In the present study, we report on a fully automated liquid-handling concept built in-house to concatenate biochemical assays with matrix-assisted laser desorption/ionization time-of-flight closing this technological gap. The integrated reformatting from 384- to 1536-well format enables cycle times of 0.6 s/sample for automated spotting and 0.4 s/sample for MS analysis, matching the requirements of HTS compatibility. In-depth examination of spotting quality, quantification accuracy, and instrument robustness together with the implementation of a protein tyrosine phosphatase 1B (PTP1B) inhibitor screening (4896 compounds) demonstrate the potential of the heavily inquired HTS integration of the label-free MS readout. Overall, the presented data demonstrate that the introduced automation concept makes label-free MS-based readouts accessible for HTS within drug discovery campaigns but also in other research areas requiring ultrafast MS-based detection.

Modeling Small-Molecule Reactivity Identifies Promiscuous Bioactive Compounds

Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that appear active in many high-throughput screening experiments against diverse targets but are often false-positives which may not be easily developed into successful probes. These compounds can exhibit bioactivity due to nonspecific, intractable mechanisms of action and/or by interference with specific assay technology readouts. Such "frequent hitters" are now commonly identified using substructure filters, including pan assay interference compounds (PAINS). Herein, we show that mechanistic modeling of small-molecule reactivity using deep learning can improve upon PAINS filters when modeling promiscuous bioactivity in PubChem assays. Without training on high-throughput screening data, a deep learning model of small-molecule reactivity achieves a sensitivity and specificity of 18.5% and 95.5%, respectively, in identifying promiscuous bioactive compounds. This performance is similar to PAINS filters, which achieve a sensitivity of 20.3% at the same specificity. Importantly, such reactivity modeling is complementary to PAINS filters. When PAINS filters and reactivity models are combined, the resulting model outperforms either method alone, achieving a sensitivity of 24% at the same specificity. However, as a probabilistic model, the sensitivity and specificity of the deep learning model can be tuned by adjusting the threshold. Moreover, for a subset of PAINS filters, this reactivity model can help discriminate between promiscuous and nonpromiscuous bioactive compounds even among compounds matching those filters. Critically, the reactivity model provides mechanistic hypotheses for assay interference by predicting the precise atoms involved in compound reactivity. Overall, our analysis suggests that deep learning approaches to modeling promiscuous compound bioactivity may provide a complementary approach to current methods for identifying promiscuous compounds.

Targeting TRAF6 E3 ligase activity with a small-molecule inhibitor combats autoimmunity

Constitutive NF-κB signaling represents a hallmark of chronic inflammation and autoimmune diseases. The E3 ligase TNF receptor-associated factor 6 (TRAF6) acts as a key regulator bridging innate immunity, pro-inflammatory cytokines, and antigen receptors to the canonical NF-κB pathway. Structural analysis and point mutations have unraveled the essential role of TRAF6 binding to the E2-conjugating enzyme ubiquitin-conjugating enzyme E2 N (Ubc13 or UBE2N) to generate Lys⁶³-linked ubiquitin chains for inflammatory and immune signal propagation. Genetic mutations disrupting TRAF6-Ubc13 binding have been shown to reduce TRAF6 activity and, consequently, NF-κB activation. However, to date, no small-molecule modulator is available to inhibit the TRAF6-Ubc13 interaction and thereby counteract NF-κB signaling and associated diseases. Here, using a high-throughput small-molecule screening approach, we discovered an inhibitor of the TRAF6-Ubc13 interaction that reduces TRAF6-Ubc13 activity both in vitro and in cells. We found that this compound, C25-140, impedes NF-κB activation in various immune and inflammatory signaling pathways also in primary human and murine cells. Importantly, C25-140 ameliorated inflammation and improved disease outcomes of autoimmune psoriasis and rheumatoid arthritis in preclinical in vivo mouse models. Hence, the first-in-class TRAF6-Ubc13 inhibitor C25-140 expands the toolbox for studying the impact of the ubiquitin system on immune signaling and underscores the importance of TRAF6 E3 ligase activity in psoriasis and rheumatoid arthritis. We propose that inhibition of TRAF6 activity by small molecules represents a promising novel strategy for targeting autoimmune and chronic inflammatory diseases.

Accounting Artifacts in High-Throughput Toxicity Assays

Compound activity identification is the primary goal in high-throughput screening (HTS) assays. However, assay artifacts including both systematic (e.g., compound auto-fluorescence) and nonsystematic (e.g., noise) complicate activity interpretation. In addition, other than the traditional potency parameter, half-maximal effect concentration (EC50), additional activity parameters (e.g., point-of-departure, POD) could be derived from HTS data for activity profiling. A data analysis pipeline has been developed to handle the artifacts and to provide compound activity characterization with either binary or continuous metrics. This chapter outlines the steps in the pipeline using Tox21 glucocorticoid receptor (GR) β-lactamase assays, including the formats to identify either agonists or antagonists, as well as the counter-screen assays for identifying artifacts as examples. The steps can be applied to other lower-throughput assays with concentration-response data.

High-Throughput Chemical Probing of Full-Length Protein-Protein Interactions

Human biology is regulated by a complex network of protein-protein interactions (PPIs), and disruption of this network has been implicated in many diseases. However, the targeting of PPIs remains a challenging area for chemical probe and drug discovery. Although many methodologies have been put forth to facilitate these efforts, new technologies are still needed. Current biochemical assays for PPIs are typically limited to motif-domain and domain-domain interactions, and assays that will enable the screening of full-length protein systems, which are more biologically relevant, are sparse. To overcome this barrier, we have developed a new assay technology, "PPI catalytic enzyme-linked click chemistry assay" or PPI cat-ELCCA, which utilizes click chemistry to afford catalytic signal amplification. To validate this approach, we have applied PPI cat-ELCCA to the eIF4E-4E-BP1  and eIF4E-eIF4G PPIs, key regulators of cap-dependent mRNA translation. Using these examples, we have demonstrated that PPI cat-ELCCA is amenable to full-length proteins, large (>200 kDa) and small (∼12 kDa), and is readily adaptable to automated high-throughput screening. Thus, PPI cat-ELCCA represents a powerful new tool in the toolbox of assays available to scientists interested in the targeting of disease-relevant PPIs.

Development of an Inhibitor Screening Assay for Mono-ADP-Ribosyl Hydrolyzing Macrodomains Using AlphaScreen Technology

Protein mono-ADP-ribosylation is a posttranslational modification involved in the regulation of several cellular signaling pathways. Cellular ADP-ribosylation is regulated by ADP-ribose hydrolases via a hydrolysis of the protein-linked ADP-ribose. Most of the ADP-ribose hydrolases share a macrodomain fold. Macrodomains have been linked to several diseases, such as cancer, but their cellular roles are mostly unknown. Currently, there are no inhibitors available targeting the mono-ADP-ribose hydrolyzing macrodomains. We have developed a robust AlphaScreen assay for the screening of inhibitors against macrodomains having mono-ADP-ribose hydrolysis activity. We utilized this assay for validatory screening against human MacroD1 and identified five compounds inhibiting the macrodomain. Dose-response measurements and an orthogonal assay further validated four of these compounds as MacroD1 inhibitors. The developed assay is homogenous, easy to execute, and suitable for the screening of large compound libraries. The assay principle can also be adapted for other ADP-ribose hydrolyzing macrodomains, which can utilize a biotin-mono-ADP-ribosylated protein as a substrate.

Discovery of Small Molecules Targeting the Synergy of Cardiac Transcription Factors GATA4 and NKX2-5

Transcription factors are pivotal regulators of gene transcription, and many diseases are associated with the deregulation of transcriptional networks. In the heart, the transcription factors GATA4 and NKX2-5 are required for cardiogenesis. GATA4 and NKX2-5 interact physically, and the activation of GATA4, in cooperation with NKX2-5, is essential for stretch-induced cardiomyocyte hypertrophy. Here, we report the identification of four small molecule families that either inhibit or enhance the GATA4-NKX2-5 transcriptional synergy. A fragment-based screening, reporter gene assay, and pharmacophore search were utilized for the small molecule screening, identification, and optimization. The compounds modulated the hypertrophic agonist-induced cardiac gene expression. The most potent hit compound, N-[4-(diethylamino)phenyl]-5-methyl-3-phenylisoxazole-4-carboxamide (3, IC₅₀ = 3 μM), exhibited no activity on the protein kinases involved in the regulation of GATA4 phosphorylation. The identified and chemically and biologically characterized active compound, and its derivatives may provide a novel class of small molecules for modulating heart regeneration.

An in vivo high-throughput screening for riboswitch ligands using a reverse reporter gene system

Riboswitches are bacterial RNA elements that regulate gene expression in response to metabolite or ion abundance and are considered as potential drug targets. In recent years a number of methods to find non-natural riboswitch ligands have been described. Here we report a high-throughput in vivo screening system that allows identifying OFF-riboswitch modulators in a 384 well bioluminescence assay format. We use a reverse reporter gene setup in Bacillus subtilis, consisting of a primary screening assay, a secondary assay as well as counter assays to detect compounds in a library of 1,280 molecules that act on the guanine-responsive xpt riboswitch from B. anthracis. With this in vivo high-throughput approach we identified several hit compounds and could validate the impact of one of them on riboswitch-mediated gene regulation, albeit this might not be due to direct binding to the riboswitch. However, our data demonstrate the capability of our screening assay for bigger high-throughput screening campaigns. Furthermore, the screening system described here can not only be generally employed to detect non-natural ligands or compounds influencing riboswitches acting as genetic OFF switches, but it can also be used to investigate natural ligands of orphan OFF-riboswitches.

Epigenetic assays for chemical biology and drug discovery

The implication of epigenetic abnormalities in many diseases and the approval of a number of compounds that modulate specific epigenetic targets in a therapeutically relevant manner in cancer specifically confirms that some of these targets are druggable by small molecules. Furthermore, a number of compounds are currently in clinical trials for other diseases including cardiovascular, neurological and metabolic disorders. Despite these advances, the approved treatments for cancer only extend progression-free survival for a relatively short time and being associated with significant side effects. The current clinical trials involving the next generation of epigenetic drugs may address the disadvantages of the currently approved epigenetic drugs.

The identification of chemical starting points of many drugs often makes use of screening in vitro assays against libraries of synthetic or natural products. These assays can be biochemical (using purified protein) or cell-based (using for example, genetically modified, cancer cell lines or primary cells) and performed in microtiter plates, thus enabling a large number of samples to be tested. A considerable number of such assays are available to monitor epigenetic target activity, and this review provides an overview of drug discovery and chemical biology and describes assays that monitor activities of histone deacetylase, lysine-specific demethylase, histone methyltransferase, histone acetyltransferase and bromodomain. It is of critical importance that an appropriate assay is developed and comprehensively validated for a given drug target prior to screening in order to improve the probability of the compound progressing in the drug discovery value chain.

Phantom PAINS: Problems with the Utility of Alerts for Pan-Assay INterference CompoundS

The use of substructural alerts to identify Pan-Assay INterference compoundS (PAINS) has become a common component of the triage process in biological screening campaigns. These alerts, however, were originally derived from a proprietary library tested in just six assays measuring protein-protein interaction (PPI) inhibition using the AlphaScreen detection technology only; moreover, 68% (328 out of the 480 alerts) were derived from four or fewer compounds. In an effort to assess the reliability of these alerts as indicators of pan-assay interference, we performed a large-scale analysis of the impact of PAINS alerts on compound promiscuity in bioassays using publicly available data in PubChem. We found that the majority (97%) of all compounds containing PAINS alerts were actually infrequent hitters in AlphaScreen assays measuring PPI inhibition. We also found that the presence of PAINS alerts, contrary to expectations, did not reflect any heightened assay activity trends across all assays in PubChem including AlphaScreen, luciferase, beta-lactamase, or fluorescence-based assays. In addition, 109 PAINS alerts were present in 3570 extensively assayed, but consistently inactive compounds called Dark Chemical Matter. Finally, we observed that 87 small molecule FDA-approved drugs contained PAINS alerts and profiled their bioassay activity. Based on this detailed analysis of PAINS alerts in nonproprietary compound libraries, we caution against the blind use of PAINS filters to detect and triage compounds with possible PAINS liabilities and recommend that such conclusions should be drawn only by conducting orthogonal experiments.

A High-Throughput Screening Strategy for Development of RNF8-Ubc13 Protein-Protein Interaction Inhibitors

The ubiquitin-proteasome system plays an essential role in a broad range of cellular signaling pathways. Ubiquitination is a posttranslational protein modification that involves the action of an enzymatic cascade (E1, E2, and E3 enzymes) for the covalent attachment of ubiquitin to target proteins. The emerging knowledge of the molecular mechanisms and correlation of deregulation of the ubiquitin system in human diseases is uncovering new opportunities for therapeutics development. The E3 ligase RNF8 acts in cooperation with the heterodimeric E2 enzyme Ubc13/Uev1a to generate ubiquitin conjugates at the sides of DNA double-strand breaks, and recent findings suggest RNF8 as a potential therapeutic target for the treatment of breast cancer. Here, we present a novel high-throughput screening (HTS)-compatible assay based on the AlphaScreen technology to identify inhibitors of the RNF8-Ubc13 protein-protein interaction, along with a follow-up strategy for subsequent validation. We have adapted the AlphaScreen assay to a 384-well format and demonstrate its reliability, reproducibility, and suitability for automated HTS campaigns. In addition, we have established a biochemical orthogonal homogeneous time-resolved fluorescence (HTRF) assay in HTS format and a cellular microscopy-based assay allowing verification of the primary hits. This strategy will be useful for drug screening programs aimed at RNF8-Ubc13 modulation.

Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development

Thousands of (Quantitative) Structure-Activity Relationships (Q)SAR models have been described in peer-reviewed publications; however, this way of sharing seldom makes models available for the use by the research community outside of the developer's laboratory. Conversely, on-line models allow broad dissemination and application representing the most effective way of sharing the scientific knowledge. Approaches for sharing and providing on-line access to models range from web services created by individual users and laboratories to integrated modeling environments and model repositories. This emerging transition from the descriptive and informative, but "static", and for the most part, non-executable print format to interactive, transparent and functional delivery of "living" models is expected to have a transformative effect on modern experimental research in areas of scientific and regulatory use of (Q)SAR models.

A Novel Assay for Screening Inhibitors Targeting HIV Integrase LEDGF/p75 Interaction Based on Ni(2+) Coated Magnetic Agarose Beads

HIV-1 integrase (IN) plays an essential role in viral replication and thus serves as an important target for chemotherapeutic intervention against HIV-1 infection. However, the current three clinical IN inhibitors, raltegravir, elvitegravir and dolutegravir share the same inhibitory mechanism, resulting in a common clinical resistance profile which have emerged in infected patients receiving treatment. Therefore, it is important to develop small molecule inhibitors that impair IN function with distinct mechanisms of action. In this work, a magnetic-beads based biochemical assay targeting the protein-protein interaction (PPI) between HIV IN and the cellular cofactor LEDGF/p75 was developed for identification of HIV-1 IN inhibitors. Furthermore, a library containing 1000 US. Food and Drug Administration (FDA)-approved drugs currently used for human medication was screened to identify inhibitors targeting the PPI. The assay was proved to be quite robust and with the novel assay we successfully identified dexlansoprazole (IC₅₀ of 4.8 μM), a FDA-approved proton pump inhibitor, as a potential inhibitor for the PPI between IN and LEDGF/p75, which bound to the LEDGF/p75 partner with a kinetic dissociation (Kd) constant of 330 nM ± 2.6 nM.

Development of a scintillation proximity binding assay for high-throughput screening of hematopoietic prostaglandin D2 synthase

Prostaglandin D2 synthase (PGDS) catalyzes the isomerization of prostaglandin H2 (PGH2) to prostaglandin D2 (PGD2). PGD2 produced by hematopoietic prostaglandin D2 synthase (H-PGDS) in mast cells and Th2 cells is proposed to be a mediator of allergic and inflammatory responses. Consequently, inhibitors of H-PGDS represent potential therapeutic agents for the treatment of inflammatory diseases such as asthma. Due to the instability of the PGDS substrate PGH2, an in-vitro enzymatic assay is not feasible for large-scale screening of H-PGDS inhibitors. Herein, we report the development of a competition binding assay amenable to high-throughput screening (HTS) in a scintillation proximity assay (SPA) format. This assay was used to screen an in-house compound library of approximately 280,000 compounds for novel H-PGDS inhibitors. The hit rate of the H-PGDS primary screen was found to be 4%. This high hit rate suggests that the active site of H-PGDS can accommodate a large diversity of chemical scaffolds. For hit prioritization, these initial hits were rescreened at a lower concentration in SPA and tested in the LAD2 cell assay. 116 compounds were active in both assays with IC50s ranging from 6 to 807 nM in SPA and 82 nM to 10 μM in the LAD2 cell assay.

BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry

The increasing volume of biomedical data in chemistry and life sciences requires the development of new methods and approaches for their handling. Here, we briefly discuss some challenges and opportunities of this fast growing area of research with a focus on those to be addressed within the BIGCHEM project. The article starts with a brief description of some available resources for “Big Data” in chemistry and a discussion of the importance of data quality. We then discuss challenges with visualization of millions of compounds by combining chemical and biological data, the expectations from mining the “Big Data” using advanced machine‐learning methods, and their applications in polypharmacology prediction and target de‐convolution in phenotypic screening. We show that the efficient exploration of billions of molecules requires the development of smart strategies. We also address the issue of secure information sharing without disclosing chemical structures, which is critical to enable bi‐party or multi‐party data sharing. Data sharing is important in the context of the recent trend of “open innovation” in pharmaceutical industry, which has led to not only more information sharing among academics and pharma industries but also the so‐called “precompetitive” collaboration between pharma companies. At the end we highlight the importance of education in “Big Data” for further progress of this area.