A high throughput cell stretch device for investigating mechanobiology in vitro.

Mechanobiology is a rapidly advancing field, with growing evidence that mechanical signaling plays key roles in health and disease. To accelerate mechanobiology-based drug discovery, novel in vitro systems are needed that enable mechanical perturbation of cells in a format amenable to high throughput screening. Here, both a mechanical stretch device and 192-well silicone flexible linear stretch plate were designed and fabricated to meet high throughput technology needs for cell stretch-based applications. To demonstrate the utility of the stretch plate in automation and screening, cell dispensing, liquid handling, high content imaging, and high throughput sequencing platforms were employed. Using this system, an assay was developed as a biological validation and proof-of-concept readout for screening. A mechano-transcriptional stretch response was characterized using focused gene expression profiling measured by RNA-mediated oligonucleotide Annealing, Selection, and Ligation with Next-Gen sequencing. Using articular chondrocytes, a gene expression signature containing stretch responsive genes relevant to cartilage homeostasis and disease was identified. The possibility for integration of other stretch sensitive cell types (e.g., cardiovascular, airway, bladder, gut, and musculoskeletal), in combination with alternative phenotypic readouts (e.g., protein expression, proliferation, or spatial alignment), broadens the scope of high throughput stretch and allows for wider adoption by the research community. This high throughput mechanical stress device fills an unmet need in phenotypic screening technology to support drug discovery in mechanobiology-based disease areas.

Enhancing the Small-Scale Screenable Biological Space beyond Known Chemogenomics Libraries with Gray Chemical Matter─Compounds with Novel Mechanisms from High-Throughput Screening Profiles.

Phenotypic assays have become an established approach to drug discovery. Greater disease relevance is often achieved through cellular models with increased complexity and more detailed readouts, such as gene expression or advanced imaging. However, the intricate nature and cost of these assays impose limitations on their screening capacity, often restricting screens to well-characterized small compound sets such as chemogenomics libraries. Here, we outline a cheminformatics approach to identify a small set of compounds with likely novel mechanisms of action (MoAs), expanding the MoA search space for throughput limited phenotypic assays. Our approach is based on mining existing large-scale, phenotypic high-throughput screening (HTS) data. It enables the identification of chemotypes that exhibit selectivity across multiple cell-based assays, which are characterized by persistent and broad structure activity relationships (SAR). We validate the effectiveness of our approach in broad cellular profiling assays (Cell Painting, DRUG-seq, and Promotor Signature Profiling) and chemical proteomics experiments. These experiments revealed that the compounds behave similarly to known chemogenetic libraries, but with a notable bias toward novel protein targets. To foster collaboration and advance research in this area, we have curated a public set of such compounds based on the PubChem BioAssay dataset and made it available for use by the scientific community.

Drug target prediction through deep learning functional representation of gene signatures.

Many machine learning applications in bioinformatics currently rely on matching gene identities when analyzing input gene signatures and fail to take advantage of preexisting knowledge about gene functions. To further enable comparative analysis of OMICS datasets, including target deconvolution and mechanism of action studies, we develop an approach that represents gene signatures projected onto their biological functions, instead of their identities, similar to how the word2vec technique works in natural language processing. We develop the Functional Representation of Gene Signatures (FRoGS) approach by training a deep learning model and demonstrate that its application to the Broad Institute's L1000 datasets results in more effective compound-target predictions than models based on gene identities alone. By integrating additional pharmacological activity data sources, FRoGS significantly increases the number of high-quality compound-target predictions relative to existing approaches, many of which are supported by in silico and/or experimental evidence. These results underscore the general utility of FRoGS in machine learning-based bioinformatics applications. Prediction networks pre-equipped with the knowledge of gene functions may help uncover new relationships among gene signatures acquired by large-scale OMICs studies on compounds, cell types, disease models, and patient cohorts.

Compound Activity Prediction with Dose-Dependent Transcriptomic Profiles and Deep Learning.

Predicting compound activity in assays is a long-standing challenge in drug discovery. Computational models based on compound-induced gene expression signatures from a single profiling assay have shown promise toward predicting compound activity in other, seemingly unrelated, assays. Applications of such models include predicting mechanisms-of-action (MoA) for phenotypic hits, identifying off-target activities, and identifying polypharmacologies. Here, we introduce transcriptomics-to-activity transformer (TAT) models that leverage gene expression profiles observed over compound treatment at multiple concentrations to predict the compound activity in other biochemical or cellular assays. We built TAT models based on gene expression data from a RASL-seq assay to predict the activity of 2692 compounds in 262 dose-response assays. We obtained useful models for 51% of the assays, as determined through a realistic held-out set. Prospectively, we experimentally validated the activity predictions of a TAT model in a malaria inhibition assay. With a 63% hit rate, TAT successfully identified several submicromolar malaria inhibitors. Our results thus demonstrate the potential of transcriptomic responses over compound concentration and the TAT modeling framework as a cost-efficient way to identify the bioactivities of promising compounds across many assays.

Integrated CRISPR screening and drug profiling identifies combination opportunities for EGFR, ALK, and BRAF/MEK inhibitors.

Anti-tumor efficacy of targeted therapies is variable across patients and cancer types. Even in patients with initial deep response, tumors are typically not eradicated and eventually relapse. To address these challenges, we present a systematic screen for targets that limit the anti-tumor efficacy of EGFR and ALK inhibitors in non-small cell lung cancer and BRAF/MEK inhibitors in colorectal cancer. Our approach includes genome-wide CRISPR screens with or without drugs targeting the oncogenic driver ("anchor therapy"), and large-scale pairwise combination screens of anchor therapies with 351 other drugs. Interestingly, targeting of a small number of genes, including MCL1, BCL2L1, and YAP1, sensitizes multiple cell lines to the respective anchor therapy. Data from drug combination screens with EGF816 and ceritinib indicate that dasatinib and agents disrupting microtubules act synergistically across many cell lines. Finally, we show that a higher-order-combination screen with 26 selected drugs in two resistant EGFR-mutant lung cancer cell lines identified active triplet combinations.

EndoBind detects endogenous protein-protein interactions in real time.

We present two high-throughput compatible methods to detect the interaction of ectopically expressed (RT-Bind) or endogenously tagged (EndoBind) proteins of interest. Both approaches provide temporal evaluation of dimer formation over an extended duration. Using examples of the Nrf2-KEAP1 and the CRAF-KRAS-G12V interaction, we demonstrate that our method allows for the detection of signal for more than 2 days after substrate addition, allowing for continuous monitoring of endogenous protein-protein interactions in real time.

Dual ALK and CDK4/6 Inhibition Demonstrates Synergy against Neuroblastoma.

Purpose: Anaplastic lymphoma kinase (ALK) is the most frequently mutated oncogene in the pediatric cancer neuroblastoma. We performed an in vitro screen for synergistic drug combinations that target neuroblastomas with mutations in ALK to determine whether drug combinations could enhance antitumor efficacy.Experimental Design: We screened combinations of eight molecularly targeted agents against 17 comprehensively characterized human neuroblastoma-derived cell lines. We investigated the combination of ceritinib and ribociclib on in vitro proliferation, cell cycle, viability, caspase activation, and the cyclin D/CDK4/CDK6/RB and pALK signaling networks in cell lines with representative ALK status. We performed in vivo trials in CB17 SCID mice bearing conventional and patient-derived xenograft models comparing ceritinib alone, ribociclib alone, and the combination, with plasma pharmacokinetics to evaluate for drug-drug interactions.Results: The combination of ribociclib, a dual inhibitor of cyclin-dependent kinase (CDK) 4 and 6, and the ALK inhibitor ceritinib demonstrated higher cytotoxicity (P = 0.008) and synergy scores (P = 0.006) in cell lines with ALK mutations as compared with cell lines lacking mutations or alterations in ALK Compared with either drug alone, combination therapy enhanced growth inhibition, cell-cycle arrest, and caspase-independent cell death. Combination therapy achieved complete regressions in neuroblastoma xenografts with ALK-F1174L and F1245C de novo resistance mutations and prevented the emergence of resistance. Murine ribociclib and ceritinib plasma concentrations were unaltered by combination therapy.Conclusions: This preclinical combination drug screen with in vivo validation has provided the rationale for a first-in-children trial of combination ceritinib and ribociclib in a molecularly selected pediatric population. Clin Cancer Res; 23(11); 2856-68. ©2016 AACR.

Dark chemical matter as a promising starting point for drug lead discovery.

High-throughput screening (HTS) is an integral part of early drug discovery. Herein, we focused on those small molecules in a screening collection that have never shown biological activity despite having been exhaustively tested in HTS assays. These compounds are referred to as 'dark chemical matter' (DCM). We quantified DCM, validated it in quality control experiments, described its physicochemical properties and mapped it into chemical space. Through analysis of prospective reporter-gene assay, gene expression and yeast chemogenomics experiments, we evaluated the potential of DCM to show biological activity in future screens. We demonstrated that, despite the apparent lack of activity, occasionally these compounds can result in potent hits with unique activity and clean safety profiles, which makes them valuable starting points for lead optimization efforts. Among the identified DCM hits was a new antifungal chemotype with strong activity against the pathogen Cryptococcus neoformans but little activity at targets relevant to human safety.

Chemical and biological properties of frequent screening hits.

High-throughput screening (HTS) has become an important technology for the drug discovery process. It has been noted that certain compounds frequently appear as hits in many screening campaigns. By mining an HTS database covering large chemical space and diverse biological functions, we identified many novel chemical features, as well as several biological processes that were associated with a significant portion of frequent hits. However, we also noted that several marketed drugs also contained characteristics that commonly were associated with frequent hits. This observation suggested that current generally employed strategies for triaging compounds may result in the removal of compounds with desirable properties. Therefore, we developed a novel strategy that overlaid chemical scaffolds and biological processes, along with empirical hit frequency data, in order to provide a more functional frequent hit triage strategy; the risk of removing biologically relevant frequent hits was reduced compared to the typical empirical hit frequency-based filtering strategy.

Multiplexed reporter gene assays: monitoring the cell viability and the compound kinetics on luciferase activity.

High-throughput screening assays with multiple readouts enable one to monitor multiple assay parameters. By capturing as much information about the underlying biology as possible, the detection of true actives can be improved. This report describes an extension to standard luciferase reporter gene assays that enables multiple parameters to be monitored from each sample. The report describes multiplexing luciferase assays with an orthogonal readout monitoring cell viability using reduction of resazurin. In addition, this technical note shows that by using the luciferin substrate in live cells, an assay time course can be recorded. This enables the identification of nonactive or unspecific compounds that act by inhibiting luciferase, as well as compounds altering gene expression or cell growth.

Seeing the light: luminescent reporter gene assays.

The luminescent reporter gene assay (LRGA) is arguably the most prominent type of reporter gene assay used in biomolecular and pharmaceutical development laboratories. Part of this popularity is due to the high signal associated with luciferases, the foundation of this technology. This feature makes them ideally suited for high throughput screening applications where potentially millions of chemical compounds can be analyzed in a given assay. Recent technical advancements that enhance signal stability of the luciferases along with development and commercialization of multiple forms of luciferases, their respective substrates, and improvements in expression vectors for reporter gene assay (RGA) applications have broadened their use. While the practical challenges related to the application of luminescent technology in a laboratory setting have been overcome, there remains much to do in laying a systematic approach towards the construction of RGAs, which are essential to the elucidation of the basic biology for genes of interest. This mini-review aims at giving a birds-eye view of the available luciferases, substrates and other luminescent technologies available and provides a general blueprint as well as practical considerations for constructing and interfacing RGAs with chemical biology and functional genomics for the elucidation of fundamental biological questions and for biomedical research.

Chemical-text hybrid search engines.

As the amount of chemical literature increases, it is critical that researchers be enabled to accurately locate documents related to a particular aspect of a given compound. Existing solutions, based on text and chemical search engines alone, suffer from the inclusion of "false negative" and "false positive" results, and cannot accommodate diverse repertoire of formats currently available for chemical documents. To address these concerns, we developed an approach called Entity-Canonical Keyword Indexing (ECKI), which converts a chemical entity embedded in a data source into its canonical keyword representation prior to being indexed by text search engines. We implemented ECKI using Microsoft Office SharePoint Server Search, and the resultant hybrid search engine not only supported complex mixed chemical and keyword queries but also was applied to both intranet and Internet environments. We envision that the adoption of ECKI will empower researchers to pose more complex search questions that were not readily attainable previously and to obtain answers at much improved speed and accuracy.

Large-scale annotation of small-molecule libraries using public databases.

While many large publicly accessible databases provide excellent annotation for biological macromolecules, the same is not true for small chemical compounds. Commercial data sources also fail to encompass an annotation interface for large numbers of compounds and tend to be cost prohibitive to be widely available to biomedical researchers. Therefore, using annotation information for the selection of lead compounds from a modern day high-throughput screening (HTS) campaign presently occurs only under a very limited scale. The recent rapid expansion of the NIH PubChem database provides an opportunity to link existing biological databases with compound catalogs and provides relevant information that potentially could improve the information garnered from large-scale screening efforts. Using the 2.5 million compound collection at the Genomics Institute of the Novartis Research Foundation (GNF) as a model, we determined that approximately 4% of the library contained compounds with potential annotation in such databases as PubChem and the World Drug Index (WDI) as well as related databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and ChemIDplus. Furthermore, the exact structure match analysis showed 32% of GNF compounds can be linked to third party databases via PubChem. We also showed annotations such as MeSH (medical subject headings) terms can be applied to in-house HTS databases in identifying signature biological inhibition profiles of interest as well as expediting the assay validation process. The automated annotation of thousands of screening hits in batch is becoming feasible and has the potential to play an essential role in the hit-to-lead decision making process.

Learning from the data: mining of large high-throughput screening databases.

High-throughput screening (HTS) campaigns in pharmaceutical companies have accumulated a large amount of data for several million compounds over a couple of hundred assays. Despite the general awareness that rich information is hidden inside the vast amount of data, little has been reported for a systematic data mining method that can reliably extract relevant knowledge of interest for chemists and biologists. We developed a data mining approach based on an algorithm called ontology-based pattern identification (OPI) and applied it to our in-house HTS database. We identified nearly 1500 scaffold families with statistically significant structure-HTS activity profile relationships. Among them, dozens of scaffolds were characterized as leading to artifactual results stemming from the screening technology employed, such as assay format and/or readout. Four types of compound scaffolds can be characterized based on this data mining effort: tumor cytotoxic, general toxic, potential reporter gene assay artifact, and target family specific. The OPI-based data mining approach can reliably identify compounds that are not only structurally similar but also share statistically significant biological activity profiles. Statistical tests such as Kruskal-Wallis test and analysis of variance (ANOVA) can then be applied to the discovered scaffolds for effective assignment of relevant biological information. The scaffolds identified by our HTS data mining efforts are an invaluable resource for designing SAR-robust diversity libraries, generating in silico biological annotations of compounds on a scaffold basis, and providing novel target family specific scaffolds for focused compound library design.

An efficient rapid system for profiling the cellular activities of molecular libraries.

Rapid quantitative methods for characterizing small molecules, peptides, proteins, or RNAs in a broad array of cellular assays would allow one to discover new biological activities associated with these molecules and also provide a more comprehensive profile of drug candidates early in the drug development process. Here we describe a robotic system, termed the automated compound profiler, capable of both propagating a large number of cell lines in parallel and assaying large collections of molecules simultaneously against a matrix of cellular assays in a highly reproducible manner. To illustrate its utility, we have characterized a set of 1,400 kinase inhibitors in a panel of 35 activated tyrosine-kinase-dependent cellular assays in dose-response format in a single experiment. Analysis of the resulting multidimensional dataset revealed subclusters of both inhibitors and kinases with closely correlated activities. The approach also identified activities for the p38 inhibitor BIRB796 and the dual src/abl inhibitor BMS-354825 and exposed the expected side activities for Glivec/STI571, including cellular inhibition of c-kit and platelet-derived growth factor receptor. This methodology provides a powerful tool for unraveling the cellular biology and molecular pharmacology of both naturally occurring and synthetic chemical diversity.

Profiling the kinome for drug discovery.

The human kinome is made up of 518 distinctive serine/threonine and tyrosine kinases, which are key components of virtually every mammalian signal transduction pathway. Consequently, kinases provide a compelling target family for the development of small molecule inhibitors, which could be used as tools to delineate the mechanism of action for biological processes and potentially be used as therapeutics to treat human diseases such as cancer. A myriad of recent technological advances have accelerated our understanding of kinome function, its relationship to tumorigenic development, and have contributed to the progression of small molecule kinase inhibitors into the clinic. Essential to the continued growth of the field are informatics tools that can assist in interpreting disparate and voluminous data sets and correctly guide decision making processes. These advances are expected to have a dramatic impact on kinase drug development and clinical diagnoses and treatment in the near future.:

Genome-scale functional profiling of the mammalian AP-1 signaling pathway.

Large-scale functional genomics approaches are fundamental to the characterization of mammalian transcriptomes annotated by genome sequencing projects. Although current high-throughput strategies systematically survey either transcriptional or biochemical networks, analogous genome-scale investigations that analyze gene function in mammalian cells have yet to be fully realized. Through transient overexpression analysis, we describe the parallel interrogation of approximately 20,000 sequence annotated genes in cancer-related signaling pathways. For experimental validation of these genome data, we apply an integrative strategy to characterize previously unreported effectors of activator protein-1 (AP-1) mediated growth and mitogenic response pathways. These studies identify the ADP-ribosylation factor GTPase-activating protein Centaurin alpha1 and a Tudor domain-containing hypothetical protein as putative AP-1 regulatory oncogenes. These results provide insight into the composition of the AP-1 signaling machinery and validate this approach as a tractable platform for genome-wide functional analysis.