High-throughput Screening Experiments Research Articles

Automated Design of Efficient and Functionally Diverse Enzyme Repertoires

Substantial improvements in enzyme activity demand multiple mutations at spatially proximal positions in the active site. Such mutations, however, often exhibit unpredictable epistatic (non-additive) effects on activity. Here we describe FuncLib, an automated method for designing multipoint mutations at enzyme active sites using phylogenetic analysis and Rosetta design calculations. We applied FuncLib to two unrelated enzymes, a phosphotriesterase and an acetyl-CoA synthetase. All designs were active, and most showed activity profiles that significantly differed from the wild-type and from one another. Several dozen designs with only 3-6 active-site mutations exhibited 10- to 4,000-fold higher efficiencies with a range of alternative substrates, including hydrolysis of the toxic organophosphate nerve agents soman and cyclosarin and synthesis of butyryl-CoA. FuncLib is implemented as a web server (http://FuncLib.weizmann.ac.il); it circumvents iterative, high-throughput experimental screens and opens the way to designing highly efficient and diverse catalytic repertoires.

BET Inhibition-Induced GSK3β Feedback Enhances Lymphoma Vulnerability to PI3K Inhibitors

The phosphatidylinositol 3 kinase (PI3K)-glycogen synthase kinase β (GSK3β) axis plays a central role in MYC-driven lymphomagenesis, and MYC targeting with bromodomain and extraterminal protein family inhibitors (BETi) is a promising treatment strategy in lymphoma. In a high-throughput combinatorial drug screening experiment, BETi enhance the antiproliferative effects of PI3K inhibitors in a panel of diffuse large B cell lymphoma (DLBCL) and Burkitt lymphoma cell lines. BETi or MYC silencing upregulates several PI3K pathway genes and induces GSK3β S9 inhibitory phosphorylation, resulting in increased β-catenin protein abundance. Furthermore, BETi or MYC silencing increases GSK3β S9 phosphorylation levels and β-catenin protein abundance through downregulating the E2 ubiquitin conjugating enzymes UBE2C and UBE2T. In a mouse xenograft DLBCL model, BETi decrease MYC, UBE2C, and UBE2T and increase phospho-GSK3β S9 levels, enhancing the anti-proliferative effect of PI3K inhibitors. Our study reveals prosurvival feedbacks induced by BETi involving GSK3β regulation, providing a mechanistic rationale for combination strategies.

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.

Discovery of Mcl-1 inhibitors from integrated high throughput and virtual screening

Protein-protein interactions (PPIs) represent important and promising therapeutic targets that are associated with the regulation of various molecular pathways, particularly in cancer. Although they were once considered “undruggable,” the recent advances in screening strategies, structure-based design, and elucidating the nature of hot spots on PPI interfaces, have led to the discovery and development of successful small-molecule inhibitors. In this report, we are describing an integrated high-throughput and computational screening approach to enable the discovery of small-molecule PPI inhibitors of the anti-apoptotic protein, Mcl-1. Applying this strategy, followed by biochemical, biophysical, and biological characterization, nineteen new chemical scaffolds were discovered and validated as Mcl-1 inhibitors. A novel series of Mcl-1 inhibitors was designed and synthesized based on the identified difuryl-triazine core scaffold and structure-activity studies were undertaken to improve the binding affinity to Mcl-1. Compounds with improved in vitro binding potency demonstrated on-target activity in cell-based studies. The obtained results demonstrate that structure-based analysis complements the experimental high-throughput screening in identifying novel PPI inhibitor scaffolds and guides follow-up medicinal chemistry efforts. Furthermore, our work provides an example that can be applied to the analysis of available screening data against numerous targets in the PubChem BioAssay Database, leading to the identification of promising lead compounds, fuelling drug discovery pipelines.

Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening

Data generated by high-throughput screening (HTS) technologies are prone to spatial bias. Traditionally, bias correction methods used in HTS assume either a simple additive or, more recently, a simple multiplicative spatial bias model. These models do not, however, always provide an accurate correction of measurements in wells located at the intersection of rows and columns affected by spatial bias. The measurements in these wells depend on the nature of interaction between the involved biases. Here, we propose two novel additive and two novel multiplicative spatial bias models accounting for different types of bias interactions. We describe a statistical procedure that allows for detecting and removing different types of additive and multiplicative spatial biases from multiwell plates. We show how this procedure can be applied by analyzing data generated by the four HTS technologies (homogeneous, microorganism, cell-based, and gene expression HTS), the three high-content screening (HCS) technologies (area, intensity, and cell-count HCS), and the only small-molecule microarray technology available in the ChemBank small-molecule screening database. The proposed methods are included in the AssayCorrector program, implemented in R, and available on CRAN.

High-performance virtual screening by targeting a high-resolution RNA dynamic ensemble.

Dynamic ensembles hold great promise in advancing RNA-targeted drug discovery. Here, we subjected the transactivation response element (TAR) RNA from human immunodeficiency virus type-1 to experimental high-throughput screening against ~100,000 drug-like small molecules. Results were augmented with 170 known TAR-binding molecules and used to generate sub-libraries optimized for evaluating enrichment when virtually screening (VS) a dynamic ensemble of TAR determined by combining NMR spectroscopy data and molecular dynamics (MD) simulations. Ensemble-based VS scores molecules with an area under the receiver operator characteristic curve of ~0.85-0.94 and with ~40-75% of all hits falling within the top 2% of scored molecules. The enrichment decreased significantly for ensembles generated from the same MD simulations without input NMR data and for other control ensembles. The results demonstrate that experimentally determined RNA ensembles can significantly enrich libraries with true hits, and that the degree of enrichment is dependent on the accuracy of the ensemble.

Benchmark Study Based on 2P2IDB to Gain Insights into the Discovery of Small-Molecule PPI Inhibitors.

Protein-protein interactions (PPIs) have been regarded as novel and highly promising drug targets in drug discovery. Numerous new experimental techniques and computational approaches have been developed to assist the design of PPI modulators during the past two decades. However, identification and optimization of small-molecule inhibitors targeting PPIs is still a particularly challenging task due to the "undruggable" profiles of PPI interfaces. Nowadays, in silico screening, especially docking-based virtual screening, has emerged as an effective method to complement experimental high-throughput screening in identifying novel and potent small-molecule PPI inhibitors. Here, on the basis of the 2P2IDB database, we explored the structural features of the known small-molecule PPI inhibitors and analyzed the characteristics of the PPI binding pockets. More importantly, we evaluated the sampling power and screening power of six popular docking programs for PPI targets. Our results indicate that the chlorinated conjugate group and amidelike linkage are two types of privileged fragments of PPI inhibitors; the average druggability of the binding sites of the PPI targets in 2P2IDB is slightly worse than that of traditional ones; both academic and commercial docking programs exhibit an acceptable accuracy on pose prediction for PPI inhibitors, but their screening powers for identifying PPI inhibitors are still not satisfactory. It is expected that our work can provide valuable guidance on the construction of PPI-focused library, the determination of druggable PPI binding pocket, and the selection of docking program for the screening of small-molecule PPI inhibitors.

The Importance of Non-neuronal Cell Types in hiPSC-Based Disease Modeling and Drug Screening.

Current applications of human induced pluripotent stem cell (hiPSC) technologies in patient-specific models of neurodegenerative and neuropsychiatric disorders tend to focus on neuronal phenotypes. Here, we review recent efforts toward advancing hiPSCs toward non-neuronal cell types of the central nervous system (CNS) and highlight their potential use for the development of more complex in vitro models of neurodevelopment and disease. We present evidence from previous works in both rodents and humans of the importance of these cell types (oligodendrocytes, microglia, astrocytes) in neurological disease and highlight new hiPSC-based models that have sought to explore these relationships in vitro. Lastly, we summarize efforts toward conducting high-throughput screening experiments with hiPSCs and propose methods by which new screening platforms could be designed to better capture complex relationships between neural cell populations in health and disease.

Program-Guided Design of High-Throughput Enzyme Screening Experiments and Automated Data Analysis/Evaluation.

The open source LARA software suite is designed for guiding manual or automated high-throughput screening experiments. Process planning, data reading, analysis, and visualization are herein explained in a step-by-step guide using exemplary dataset.

Abstract B06: HTSvis: An user-friendly application for analysis of arrayed high-throughput experiments by interactive data representations

Abstract The presented application HTSvis provides an user-interface tool for interactive analysis of arrayed high-throughput screening experiments. Arrayed formats are broadly used for cell-based screens which help to reveal mechanisms of complex diseases such as cancer. Technological advances allow to screen tens of thousands experimental conditions in a single experiments resulting in large datasets with complicated annotation files. Software packages for standardized analysis with a minimized programming effort have been developed accordingly. Even though such pipelines provide comprehensive analysis options, interpretation of the results remains a challenge as the user has to manually compare files and possibly re-run the analysis to prosecute certain hypotheses. Here we present HTSvis, a web application that visualizes data from arrayed screens. The data can be read-in as result files of the established R package CellHTS2 or as any spreadsheet table. This enables the user to feed in data which has been statistically analyzed whilst preserving flexibility to analyze data independent of the CellHTS2 package. The application provides several informative data representations like heatmaps, scatter plots and tables. Within the application, the data can be browsed in an ad hoc manner via the implemented user interface. This allows the user to assess data quality, compare experiments, identify measurements of interest and to verify generated hypotheses in an exploratory manner. The direct allocation of data points to experimental conditions is preserved in all representations facilitating an intuitive interpretation. HTSvis is easy to install, data input is straight forward and no programming skills are required for the usage. The application is compatible with common web browsers and can be set up on local computers as well as on server clusters which allows parallel access for multiple users. Summarized, HTSvis provides a helpful tool for interactive data analysis and interpretation for both, users with and without programming skills. Thereby, the application also promotes the cooperation and exchange of ideas between bioinformaticians and experimental biologists or between collaborators. Citation Format: Christian Scheeder, Florian Heigwer, Michael Boutros. HTSvis: An user-friendly application for analysis of arrayed high-throughput experiments by interactive data representations [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr B06.

The use of high-throughput screening techniques to evaluate mitochondrial toxicity

Toxicologists and chemical regulators depend on accurate and effective methods to evaluate and predict the toxicity of thousands of current and future compounds. Robust high-throughput screening (HTS) experiments have the potential to efficiently test large numbers of chemical compounds for effects on biological pathways. HTS assays can be utilized to examine chemical toxicity across multiple mechanisms of action, experimental models, concentrations, and lengths of exposure. Many agricultural, industrial, and pharmaceutical chemicals classified as harmful to human and environmental health exert their effects through the mechanism of mitochondrial toxicity. Mitochondrial toxicants are compounds that cause a decrease in the number of mitochondria within a cell, and/or decrease the ability of mitochondria to perform normal functions including producing adenosine triphosphate (ATP) and maintaining cellular homeostasis. Mitochondrial dysfunction can lead to apoptosis, necrosis, altered metabolism, muscle weakness, neurodegeneration, decreased organ function, and eventually disease or death of the whole organism. The development of HTS techniques to identify mitochondrial toxicants will provide extensive databases with essential connections between mechanistic mitochondrial toxicity and chemical structure. Computational and bioinformatics approaches can be used to evaluate compound databases for specific chemical structures associated with toxicity, with the goal of developing quantitative structure-activity relationship (QSAR) models and mitochondrial toxicophores. Ultimately these predictive models will facilitate the identification of mitochondrial liabilities in consumer products, industrial compounds, pharmaceuticals and environmental hazards.

Detecting and removing multiplicative spatial bias in high-throughput screening technologies.

Considerable attention has been paid recently to improve data quality in high-throughput screening (HTS) and high-content screening (HCS) technologies widely used in drug development and chemical toxicity research. However, several environmentally- and procedurally-induced spatial biases in experimental HTS and HCS screens decrease measurement accuracy, leading to increased numbers of false positives and false negatives in hit selection. Although effective bias correction methods and software have been developed over the past decades, almost all of these tools have been designed to reduce the effect of additive bias only. Here, we address the case of multiplicative spatial bias. We introduce three new statistical methods meant to reduce multiplicative spatial bias in screening technologies. We assess the performance of the methods with synthetic and real data affected by multiplicative spatial bias, including comparisons with current bias correction methods. We also describe a wider data correction protocol that integrates methods for removing both assay and plate-specific spatial biases, which can be either additive or multiplicative. The methods for removing multiplicative spatial bias and the data correction protocol are effective in detecting and cleaning experimental data generated by screening technologies. As our protocol is of a general nature, it can be used by researchers analyzing current or next-generation high-throughput screens. The AssayCorrector program, implemented in R, is available on CRAN. makarenkov.vladimir@uqam.ca. Supplementary data are available at Bioinformatics online.

Identification of Potent and Selective CYP1A1 Inhibitors via Combined Ligand and Structure-Based Virtual Screening and Their in Vitro Validation in Sacchrosomes and Live Human Cells.

Target structure-guided virtual screening (VS) is a versatile, powerful, and inexpensive alternative to experimental high-throughput screening (HTS). To discover potent CYP1A1 enzyme inhibitors for cancer chemoprevention, a commercial library of 50 000 small molecules was utilized for VS guided by both ligand and structure-based strategies. For experimental validation, 300 ligands were proposed based on combined analysis of fitness scores from ligand based e-pharmacophore screening and docking score, prime MMGB/SA binding affinity and interaction pattern analysis from structure-based VS. These 300 compounds were screened, at 10 μM concentration, for in vitro inhibition of CYP1A1-Sacchrosomes (yeast-derived microsomal enzyme) in the ethoxyresorufin-O-de-ethylase assay. Thirty-two compounds displayed >50% inhibition of CYP1A1 enzyme activity at 10 μM. 2-Phenylimidazo-[1,2-a]quinoline (5121780, 119) was found to be the most potent with 97% inhibition. It also inhibited ∼95% activity of CYP1B1 and CYP1A2, the other two CYP1 enzymes. The compound 5121780 (119) showed high selectivity toward inhibition of CYP1 enzymes with respect to CYP2 and CYP3 enzymes (i.e., there was no detectable inhibition of CYP2D6/CYP2C9/CYP2C19 and CYP3A4 at 10 μM). It was further investigated in live CYP-expressing human cell system, which confirmed that compound 5121780 (119) potently inhibited CYP1A1, CYP1A2, CYP1B1 enzymes with IC50 values of 269, 30, and 56 nM, respectively. Like in Sacchrosomes, inhibition of CYP2D6/CYP2C9/CYP2C19 and CYP3A4 enzymes, expressed within live human cells, could hardly be detected at 10 μM. The compound 119 rescued CYP1A1 overexpressing HEK293 cells from CYP1A1 mediated benzo[a]pyrene (B[a]P) toxicity and also overcame cisplatin resistance in CYP1B1 overexpressing HEK293 cells. Molecular dynamics simulations of 5121780 (119) with CYP1 enzymes was performed to understand the interaction pattern to CYP isoforms. Results indicate that VS can successfully be used to identify promising CYP1A1 inhibitors, which may have potential in the development of novel cancer chemo-preventive agents.

Efficient Management of High-Throughput Screening Libraries with SAVANAH

High-throughput screening (HTS) has become an indispensable tool for the pharmaceutical industry and for biomedical research. A high degree of automation allows for experiments in the range of a few hundred up to several hundred thousand to be performed in close succession. The basis for such screens are molecular libraries, that is, microtiter plates with solubilized reagents such as siRNAs, shRNAs, miRNA inhibitors or mimics, and sgRNAs, or small compounds, that is, drugs. These reagents are typically condensed to provide enough material for covering several screens. Library plates thus need to be serially diluted before they can be used as assay plates. This process, however, leads to an explosion in the number of plates and samples to be tracked. Here, we present SAVANAH, the first tool to effectively manage molecular screening libraries across dilution series. It conveniently links (connects) sample information from the library to experimental results from the assay plates. All results can be exported to the R statistical environment or piped into HiTSeekR (http://hitseekr.compbio.sdu.dk) for comprehensive follow-up analyses. In summary, SAVANAH supports the HTS community in managing and analyzing HTS experiments with an emphasis on serially diluted molecular libraries.

Combining thermodynamic modeling and 3D printing of elemental powder blends for high-throughput investigation of high-entropy alloys – Towards rapid alloy screening and design

High-entropy alloys have gained high interest of both academia and industry in recent years due to their excellent properties and large variety of possible alloy systems. However, so far prediction of phase constitution and stability is based on empirical rules that can only be applied to selected alloy systems. In the current study, we introduce a methodology that enables high-throughput theoretical and experimental alloy screening and design. As a basis for thorough thermodynamic calculations, a new database was compiled for the Co–Cr–Fe–Mn–Ni system and used for Calphad and Scheil simulations. For bulk sample production, laser metal deposition (LMD) of an elemental powder blend was applied to build up the equiatomic CoCrFeMnNi Cantor alloy as a first demonstrator. This production approach allows high flexibility in varying the chemical composition and, thus, renders itself suitable for high-throughput alloy production. The microstructure, texture, and mechanical properties of the material processed were characterized using optical microscopy, EBSD, EDX, XRD, hardness and compression testing. The LMD-produced alloy revealed full density, strongly reduced segregation compared to conventionally cast material, pronounced texture, and excellent mechanical properties. Phase constitution and elemental distribution were correctly predicted by simulations. The applicability of the introduced methodology to high-entropy alloys and extension to compositionally complex alloys is discussed.

The virtual cell based assay: Current status and future perspectives

In order to replace the use of animals in toxicity testing, there is a need to predict in vivo toxic doses from concentrations that cause toxicological effects in relevant in vitro systems. The Virtual Cell Based Assay (VCBA) estimates time-dependent concentration of a test chemical in the cell and cell culture for a given in vitro system. The concentrations in the different compartments of the cell and test system are derived from ordinary differential equations, physicochemical parameters of the test chemical and properties of the cell line. The VCBA has been developed for a range of cell lines including BALB/c 3T3 cells, HepG2, HepaRG, lung A459 cells, and cardiomyocytes. The model can be used to design and refine in vitro experiments and extrapolate in vitro effective concentrations to in vivo doses that can be applied in risk assessment. In this paper, we first discuss potential applications of the VCBA: i) design of in vitro High Throughput Screening (HTS) experiments; ii) hazard identification (based on acute systemic toxicity); and iii) risk assessment. Further extension of the VCBA is discussed in the second part, exploring potential application to i) manufactured nanomaterials, ii) additional cell lines and endpoints, and considering iii) other opportunities.

First protein drug target’s appraisal of lead-likeness descriptors to unfold the intervening chemical space

Despite the advances in combinatorial chemistry, high throughput and virtual screening experiments, plethora of clinical studies disquiet due to lead and drug-likeness attritions. For mitigation, the knowledge of physicochemical properties are really useful for guiding and selection of compounds from libraries dictated by certain rule of thumbs. However, robust bio-technological and instrumental innovations have created exponential increase in novel compounds and databases which compelled rethinking of the evaluation procedures. Known descriptive molecular property filters proposed by Lipinski, Verber and Hann are not efficient enough to encompass long array of compounds. Moreover, these filters do not take into account the specificity of biological target. In this pursuit, we have tried to appraise eight molecular properties for two major classes of biological targets viz membrane proteins and ion channels binding ligands. These molecular properties were utilized to search for the specific attributes that can be identified as an intervening space for dictating the biological activity.

Prediction of luciferase inhibitors by the high-performance MIEC-GBDT approach based on interaction energetic patterns.

High-throughput screening (HTS) is widely applied in many fields ranging from drug discovery to clinical diagnostics and toxicity assessment. Firefly luciferase is commonly used as a reporter to monitor the effect of chemical compounds on the activity of a specific target or pathway in HTS. However, the false positive rate of luciferase-based HTS is relatively high because many artifacts or promiscuous compounds that have direct interaction with the luciferase reporter enzyme are usually identified as active compounds (hits). Therefore, it is necessary to develop a rapid screening method to identify these compounds that can inhibit the luciferase activity directly. In this study, a virtual screening (VS) classification model called MIEC-GBDT (MIEC: Molecular Interaction Energy Components; GBDT: Gradient Boosting Decision Tree) was developed to distinguish luciferase inhibitors from non-inhibitors. The MIECs calculated by Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) free energy decomposition were used to energetically characterize the binding pattern of each small molecule at the active site of luciferase, and then the GBDT algorithm was employed to construct the classifiers based on MIECs. The predictions to the test set show that the optimized MIEC-GBDT model outperformed molecular docking and MM/GBSA rescoring. The best MIEC-GBDT model based on the MIECs with the energy terms of ΔGele, ΔGvdW, ΔGGB, and ΔGSA achieves the prediction accuracies of 87.2% and 90.3% for the inhibitors and non-inhibitors in the test sets, respectively. Moreover, the energetic analysis of the vital residues suggests that the energetic contributions of the vital residues to the binding of inhibitors are quite different from those to the binding of non-inhibitors. These results suggest that the MIEC-GBDT model is reliable and can be used as a powerful tool to identify potential interference compounds in luciferase-based HTS experiments.

MYC-Dependent PI3K and MCL-1 Feedbacks Attenuate BET Inhibitors Activity in Diffuse Large B-Cell Lymphoma

MYC overexpression is a poor prognostic predictor in Diffuse Large B-Cell Lymphoma (DLBCL). MYC-targeting with bromodomain and extraterminal protein family (BET) inhibitors is a promising strategy for the treatment of MYC-driven cancers, including lymphomas. However, preclinical and emerging data from early clinical trials demonstrated a modest antiproliferative activity in vitro and in vivo. We hypothesized that BET inhibition may induce feedback survival mechanisms preventing or attenuating cell death that could be exploited for designing future, more effective, combination strategies. In a high-throughput combinatorial drug screening experiment, we found that phosphatidylinositol 3-kinase (PI3K) pathway inhibitors enhanced the antiproliferative effects of BET inhibitors (JQ1, I-BET 151, CPI-203) with a strong class effect. JQ1 upregulated the mRNA expression of several upstream components of the PI3K pathway, including PIK3CA, PIK3R1, PDK1 in a large panel of DLBCL and Burkitt lymphoma cell lines. These effects translated in increased pathway activation as demonstrated by increased levels of the phosphorylated forms of downstream targets GSK3α/β, TSC2, P70S6K, and by increased concentrations of chemokines known to be regulated by PI3K in cell culture supernatants (CCL3 and CCL4). This effect was reversed by submicromolar doses of the PI3K inhibitor BKM-120. MYC silencing recapitulated the effects of BET inhibitors on PI3K pathway gene expression, activation and chemokine secretion. These data indicate that BET inhibition induces PI3K activation by a MYC-dependent feedback. We also observed transcriptional upregulation of the antiapoptotic gene Myeloid Leukemia 1 (MCL-1) following BET inhibition or MYC depletion, suggesting a second MYC-dependent mechanism. RNAi-mediated MCL-1 silencing or co-treatment with a small molecule MCL-1 inhibitor (UMI-77) enhanced the effects of BET inhibitors in DLBCL cell lines by inducing apoptosis. Using SILAC-based quantitative mass spectrometry, we found that BET inhibitors at submicromolar doses downregulated several E2 ubiquitin conjugating enzymes including UBE2C. RNAi mediated UBE2C knockdown induced MCL-1 upregulation in DLBCL cells. The enhanced in vitro effect of combining BETi and PI3Ki was reproduced in TMD8 mouse xenografts. To our knowledge, this is the first study demonstrating MYC-dependent regulation of the PI3K pathway, MCL-1 and the ubiquitin system upon BET inhibition. Our study revealed previously unknown mechanisms of action of BET inhibitors uncovering novel MYC-dependent survival feedback loops, and providing a framework for future combination strategies. DisclosuresZelenetz:Gilead Sciences: Research Funding.

DRABAL: novel method to mine large high-throughput screening assays using Bayesian active learning.

Background Mining high-throughput screening (HTS) assays is key for enhancing decisions in the area of drug repositioning and drug discovery. However, many challenges are encountered in the process of developing suitable and accurate methods for extracting useful information from these assays. Virtual screening and a wide variety of databases, methods and solutions proposed to-date, did not completely overcome these challenges. This study is based on a multi-label classification (MLC) technique for modeling correlations between several HTS assays, meaning that a single prediction represents a subset of assigned correlated labels instead of one label. Thus, the devised method provides an increased probability for more accurate predictions of compounds that were not tested in particular assays.ResultsHere we present DRABAL, a novel MLC solution that incorporates structure learning of a Bayesian network as a step to model dependency between the HTS assays. In this study, DRABAL was used to process more than 1.4 million interactions of over 400,000 compounds and analyze the existing relationships between five large HTS assays from the PubChem BioAssay Database. Compared to different MLC methods, DRABAL significantly improves the F1Score by about 22%, on average. We further illustrated usefulness and utility of DRABAL through screening FDA approved drugs and reported ones that have a high probability to interact with several targets, thus enabling drug-multi-target repositioning. Specifically DRABAL suggests the Thiabendazole drug as a common activator of the NCP1 and Rab-9A proteins, both of which are designed to identify treatment modalities for the Niemann–Pick type C disease.ConclusionWe developed a novel MLC solution based on a Bayesian active learning framework to overcome the challenge of lacking fully labeled training data and exploit actual dependencies between the HTS assays. The solution is motivated by the need to model dependencies between existing experimental confirmatory HTS assays and improve prediction performance. We have pursued extensive experiments over several HTS assays and have shown the advantages of DRABAL. The datasets and programs can be downloaded from https://figshare.com/articles/DRABAL/3309562.Graphical abstract. Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0177-8) contains supplementary material, which is available to authorized users.