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Thomas JR, Shelton C, Murphy J, Brittain S, Bray MA, Aspesi P, Concannon J, King FJ, Ihry RJ, Ho DJ, Henault M, Hadjikyriacou A, Neri M, Sigoillot FD, Pham HT, Shum M, Barys L, Jones MD, Martin EJ, Blechschmidt A, Rieffel S, Troxler TJ, Mapa FA, Jenkins JL, Jain RK, Kutchukian PS, Schirle M, Renner S. Enhancing the Small-Scale Screenable Biological Space beyond Known Chemogenomics Libraries with Gray Chemical Matter─Compounds with Novel Mechanisms from High-Throughput Screening Profiles. ACS Chem Biol 2024; 19:938-952. [PMID: 38565185 PMCID: PMC11040606 DOI: 10.1021/acschembio.3c00737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 02/28/2024] [Accepted: 03/01/2024] [Indexed: 04/04/2024]
Abstract
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.
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Affiliation(s)
- Jason R. Thomas
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Claude Shelton
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Jason Murphy
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Scott Brittain
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Mark-Anthony Bray
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Peter Aspesi
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - John Concannon
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Frederick J. King
- Novartis
Biomedical Research, San Diego, California 92121, United States
| | - Robert J. Ihry
- Novartis
Biomedical Research, San Diego, California 92121, United States
| | - Daniel J. Ho
- Novartis
Biomedical Research, San Diego, California 92121, United States
| | - Martin Henault
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | | | - Marilisa Neri
- Novartis
Biomedical Research, Basel 4056, Switzerland
| | | | - Helen T. Pham
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Matthew Shum
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Louise Barys
- Novartis
Biomedical Research, Basel 4056, Switzerland
| | - Michael D. Jones
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Eric J. Martin
- Novartis
Biomedical Research, Emeryville, California 94608, United States
| | | | | | | | - Felipa A. Mapa
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Jeremy L. Jenkins
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Rishi K. Jain
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
| | | | - Markus Schirle
- Novartis
Biomedical Research, Cambridge, Massachusetts 02139, United States
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2
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Chen H, King FJ, Zhou B, Wang Y, Canedy CJ, Hayashi J, Zhong Y, Chang MW, Pache L, Wong JL, Jia Y, Joslin J, Jiang T, Benner C, Chanda SK, Zhou Y. Drug target prediction through deep learning functional representation of gene signatures. Nat Commun 2024; 15:1853. [PMID: 38424040 PMCID: PMC10904399 DOI: 10.1038/s41467-024-46089-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 02/14/2024] [Indexed: 03/02/2024] Open
Abstract
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.
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Affiliation(s)
- Hao Chen
- Novartis Biomedical Research, 10675 John Jay Hopkins Drive, San Diego, CA, 92121, USA.
- Department of Computer Science and Engineering, University of California, Riverside, 900 University Avenue, Riverside, CA, 92521, USA.
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
| | - Frederick J King
- Novartis Biomedical Research, 10675 John Jay Hopkins Drive, San Diego, CA, 92121, USA
| | - Bin Zhou
- Novartis Biomedical Research, 10675 John Jay Hopkins Drive, San Diego, CA, 92121, USA
| | - Yu Wang
- Novartis Biomedical Research, 10675 John Jay Hopkins Drive, San Diego, CA, 92121, USA
| | - Carter J Canedy
- Novartis Biomedical Research, 10675 John Jay Hopkins Drive, San Diego, CA, 92121, USA
| | - Joel Hayashi
- Novartis Biomedical Research, 10675 John Jay Hopkins Drive, San Diego, CA, 92121, USA
| | - Yang Zhong
- Novartis Biomedical Research, 10675 John Jay Hopkins Drive, San Diego, CA, 92121, USA
| | - Max W Chang
- Department of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Lars Pache
- NCI Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, 92037, USA
| | - Julian L Wong
- Novartis Biomedical Research, 10675 John Jay Hopkins Drive, San Diego, CA, 92121, USA
| | - Yong Jia
- Novartis Biomedical Research, 10675 John Jay Hopkins Drive, San Diego, CA, 92121, USA
| | - John Joslin
- Novartis Biomedical Research, 10675 John Jay Hopkins Drive, San Diego, CA, 92121, USA
| | - Tao Jiang
- Department of Computer Science and Engineering, University of California, Riverside, 900 University Avenue, Riverside, CA, 92521, USA
| | - Christopher Benner
- Department of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Sumit K Chanda
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, 92037, USA
| | - Yingyao Zhou
- Novartis Biomedical Research, 10675 John Jay Hopkins Drive, San Diego, CA, 92121, USA.
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Li Y, Qin Z, Zhang F, Yang ST. Two-color fluorescent proteins reporting survivin regulation in breast cancer cells for high throughput drug screening. Biotechnol Bioeng 2021; 119:1004-1017. [PMID: 34914099 DOI: 10.1002/bit.28006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 11/25/2021] [Accepted: 12/09/2021] [Indexed: 02/06/2023]
Abstract
Reporter gene assay is widely used for high throughput drug screening and drug action mechanism evaluation. In this study, we developed a robust dual-fluorescent reporter assay to detect drugs repressing the transcription of survivin, a cancer biomarker from the inhibitor of apoptosis family, in breast cancer cells cultured in three-dimensional (3D) microbioreactors. Survivin is overexpressed in numerous malignancies but almost silent in normal tissue cells and is considered a lead target for cancer therapy. Breast cancer MCF-7 cells were engineered to express enhanced green fluorescent protein driven by a survivin promoter and red fluorescent protein driven by a cytomegalovirus promoter as internal control to detect changes in survivin expression in cells as affected by drugs. This 3D dual-fluorescent reporter assay was validated with YM155 and doxorubicin, which were known to downregulate survivin in cancer cells, and further evaluated with two widely used anticancer compounds, cisplatin, and epigallocatechin gallate, to evaluate their effects on survivin expression. The results showed that the 3D dual-fluorescent reporter assay was robust for high throughput screening of drugs targeting survivin in breast cancer cells.
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Affiliation(s)
- You Li
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio, USA
| | - Zhen Qin
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio, USA
| | - Fengli Zhang
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio, USA
| | - Shang-Tian Yang
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio, USA
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Exploring gene knockout strategies to identify potential drug targets using genome-scale metabolic models. Sci Rep 2021; 11:213. [PMID: 33420254 PMCID: PMC7794450 DOI: 10.1038/s41598-020-80561-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 12/11/2020] [Indexed: 01/29/2023] Open
Abstract
Research on new cancer drugs is performed either through gene knockout studies or phenotypic screening of drugs in cancer cell-lines. Both of these approaches are costly and time-consuming. Computational framework, e.g., genome-scale metabolic models (GSMMs), could be a good alternative to find potential drug targets. The present study aims to investigate the applicability of gene knockout strategies to be used as the finding of drug targets using GSMMs. We performed single-gene knockout studies on existing GSMMs of the NCI-60 cell-lines obtained from 9 tissue types. The metabolic genes responsible for the growth of cancerous cells were identified and then ranked based on their cellular growth reduction. The possible growth reduction mechanisms, which matches with the gene knockout results, were described. Gene ranking was used to identify potential drug targets, which reduce the growth rate of cancer cells but not of the normal cells. The gene ranking results were also compared with existing shRNA screening data. The rank-correlation results for most of the cell-lines were not satisfactory for a single-gene knockout, but it played a significant role in deciding the activity of drug against cell proliferation, whereas multiple gene knockout analysis gave better correlation results. We validated our theoretical results experimentally and showed that the drugs mitotane and myxothiazol can inhibit the growth of at least four cell-lines of NCI-60 database.
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Schirle M, Jenkins JL. Contemporary Techniques for Target Deconvolution and Mode of Action Elucidation. PHENOTYPIC DRUG DISCOVERY 2020. [DOI: 10.1039/9781839160721-00083] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
The elucidation of the cellular efficacy target and mechanism of action of a screening hit remain key steps in phenotypic drug discovery. A large number of experimental and in silico approaches have been introduced to address these questions and are being discussed in this chapter with a focus on recent developments. In addition to practical considerations such as throughput and technological requirements, these approaches differ conceptually in the specific compound characteristic that they are focusing on, including physical and functional interactions, cellular response patterns as well as structural features. As a result, different approaches often provide complementary information and we describe a multipronged strategy that is frequently key to successful identification of the efficacy target but also other epistatic nodes and off-targets that together shape the overall cellular effect of a bioactive compound.
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Affiliation(s)
- Markus Schirle
- Chemical Biology and Therapeutics, Novartis Institutes for BioMedical Research Cambridge MA 02139 USA
| | - Jeremy L. Jenkins
- Chemical Biology and Therapeutics, Novartis Institutes for BioMedical Research Cambridge MA 02139 USA
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Wang AC, Pham HT, Lipps JM, Brittain SM, Harrington E, Wang Y, King FJ, Russ C, Pan X, Hoepfner D, Tallarico J, Feng Y, Jain RK, Schirle M, Thomas JR. Previously Uncharacterized Vacuolar-type ATPase Binding Site Discovered from Structurally Similar Compounds with Distinct Mechanisms of Action. ACS Chem Biol 2019; 14:20-26. [PMID: 30461263 DOI: 10.1021/acschembio.8b00656] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Using a comprehensive chemical genetics approach, we identified a member of the lignan natural product family, HTP-013, which exhibited significant cytotoxicity across various cancer cell lines. Correlation of compound activity across a panel of reporter gene assays suggested the vacuolar-type ATPase (v-ATPase) as a potential target for this compound. Additional cellular studies and a yeast haploinsufficiency screen strongly supported this finding. Competitive photoaffinity labeling experiments demonstrated that the ATP6V0A2 subunit of the v-ATPase complex binds directly to HTP-013, and further mutagenesis library screening identified resistance-conferring mutations in ATP6V0A2. The positions of these mutations suggest the molecule binds a novel pocket within the domain of the v-ATPase complex responsible for proton translocation. While other mechanisms of v-ATPase regulation have been described, such as dissociation of the complex or inhibition by natural products including bafilomycin A1 and concanamycin, this work provides detailed insight into a distinct binding pocket within the v-ATPase complex.
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Affiliation(s)
- Andrew C. Wang
- Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Helen T. Pham
- Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Jennifer M. Lipps
- Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Scott M. Brittain
- Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Edmund Harrington
- Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Yuan Wang
- Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Fred J. King
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Carsten Russ
- Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Xuewen Pan
- Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Dominic Hoepfner
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, CH-4056 Basel, Switzerland
| | - John Tallarico
- Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Yan Feng
- Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Rishi K. Jain
- Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Markus Schirle
- Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Jason R. Thomas
- Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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Abstract
Phenotypic screens are increasingly utilized in drug discovery for multiple purposes such as lead and/or tool compound finding, and target discovery. Using potent and selective chemical tool compounds against well-defined targets in phenotypic screens can help elucidate biological processes modulating assay phenotypes. Unfortunately the identification of such tools from large heterogeneous bioactivity databases is nontrivial and there is repeated use of published unselective compounds as phenotypic tools. Here we describe a computational model, the compound-target tool score (TS), which is an evidence-based quantitative confidence metric that can be used to systematically rank tool compounds for targets. The identified selective and nonselective tool compounds have applications in phenotypic assays for target hypothesis validation as well as assay development.
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8
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Wang Y, Cornett A, King FJ, Mao Y, Nigsch F, Paris CG, McAllister G, Jenkins JL. Evidence-Based and Quantitative Prioritization of Tool Compounds in Phenotypic Drug Discovery. Cell Chem Biol 2016; 23:862-874. [PMID: 27427232 DOI: 10.1016/j.chembiol.2016.05.016] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Revised: 04/29/2016] [Accepted: 05/13/2016] [Indexed: 01/07/2023]
Abstract
The use of potent and selective chemical tools with well-defined targets can help elucidate biological processes driving phenotypes in phenotypic screens. However, identification of selective compounds en masse to create targeted screening sets is non-trivial. A systematic approach is needed to prioritize probes, which prevents the repeated use of published but unselective compounds. Here we performed a meta-analysis of integrated large-scale, heterogeneous bioactivity data to create an evidence-based, quantitative metric to systematically rank tool compounds for targets. Our tool score (TS) was then tested on hundreds of compounds by assessing their activity profiles in a panel of 41 cell-based pathway assays. We demonstrate that high-TS tools show more reliably selective phenotypic profiles than lower-TS compounds. Additionally we highlight frequently tested compounds that are non-selective tools and distinguish target family polypharmacology from cross-family promiscuity. TS can therefore be used to prioritize compounds from heterogeneous databases for phenotypic screening.
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Affiliation(s)
- Yuan Wang
- Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA.
| | - Allen Cornett
- Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Fred J King
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, CA 92121, USA
| | - Yi Mao
- Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA
| | - Florian Nigsch
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Novartis Campus, Basel 4056, Switzerland
| | - C Gregory Paris
- Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Gregory McAllister
- Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Jeremy L Jenkins
- Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA.
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Fukuda Y, Sano O, Kazetani K, Yamamoto K, Iwata H, Matsui J. Tubulin is a molecular target of the Wnt-activating chemical probe. BMC BIOCHEMISTRY 2016; 17:9. [PMID: 27207629 PMCID: PMC4873989 DOI: 10.1186/s12858-016-0066-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Accepted: 05/17/2016] [Indexed: 01/07/2023]
Abstract
Background In drug discovery research, cell-based phenotypic screening is an essential method for obtaining potential drug candidates. Revealing the mechanism of action is a key step on the path to drug discovery. However, elucidating the target molecules of hit compounds from phenotypic screening campaigns remains a difficult and troublesome process. Simple and efficient methods for identifying the target molecules are essential. Results 2-Amino-4-(3,4-(methylenedioxy)benzylamino)-6-(3-methoxyphenyl)pyrimidine (AMBMP) was identified as a senescence inducer from a phenotypic screening campaign. The compound is widely used as a Wnt agonist, although its target molecules remain to be clarified. To identify its target proteins, we compared a series of cellular assay results for the compound with our pathway profiling database. The database comprises the activities of compounds from simple assays of cellular reporter genes and cellular proliferations. In this database, compounds were classified on the basis of statistical analysis of their activities, which corresponded to a mechanism of action by the representative compounds. In addition, the mechanisms of action of the compounds of interest could be predicted using the database. Based on our database analysis, the compound was anticipated to be a tubulin disruptor, which was subsequently confirmed by its inhibitory activity of tubulin polymerization. Conclusion These results demonstrate that tubulin is identified for the first time as a target molecule of the Wnt-activating small molecule and that this might have misled the conclusions of some previous studies. Moreover, the present study also emphasizes that our pathway profiling database is a simple and potent tool for revealing the mechanisms of action of hit compounds obtained from phenotypic screenings and off targets of chemical probes. Electronic supplementary material The online version of this article (doi:10.1186/s12858-016-0066-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yasunori Fukuda
- Pharmaceutical Research Division, Takeda Pharmaceutical Company Limited, 2-26-1, Muraokahigashi, Fujisawa, Kanagawa, Japan
| | - Osamu Sano
- Pharmaceutical Research Division, Takeda Pharmaceutical Company Limited, 2-26-1, Muraokahigashi, Fujisawa, Kanagawa, Japan
| | - Kenichi Kazetani
- Pharmaceutical Research Division, Takeda Pharmaceutical Company Limited, 2-26-1, Muraokahigashi, Fujisawa, Kanagawa, Japan
| | - Koji Yamamoto
- Pharmaceutical Research Division, Takeda Pharmaceutical Company Limited, 2-26-1, Muraokahigashi, Fujisawa, Kanagawa, Japan
| | - Hidehisa Iwata
- Pharmaceutical Research Division, Takeda Pharmaceutical Company Limited, 2-26-1, Muraokahigashi, Fujisawa, Kanagawa, Japan.
| | - Junji Matsui
- Pharmaceutical Research Division, Takeda Pharmaceutical Company Limited, 2-26-1, Muraokahigashi, Fujisawa, Kanagawa, Japan.
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10
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Dark chemical matter as a promising starting point for drug lead discovery. Nat Chem Biol 2015; 11:958-66. [DOI: 10.1038/nchembio.1936] [Citation(s) in RCA: 95] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2015] [Accepted: 09/10/2015] [Indexed: 11/08/2022]
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Schirle M, Jenkins JL. Identifying compound efficacy targets in phenotypic drug discovery. Drug Discov Today 2015; 21:82-89. [PMID: 26272035 DOI: 10.1016/j.drudis.2015.08.001] [Citation(s) in RCA: 102] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Revised: 07/10/2015] [Accepted: 08/03/2015] [Indexed: 12/30/2022]
Abstract
The identification of the efficacy target(s) for hits from phenotypic compound screens remains a key step to progress compounds into drug development. In addition to efficacy targets, the characterization of epistatic proteins influencing compound activity often facilitates the elucidation of the underlying mechanism of action; and, further, early determination of off-targets that cause potentially unwanted secondary phenotypes helps in assessing potential liabilities. This short review discusses the most important technologies currently available for characterizing the direct and indirect target space of bioactive compounds following phenotypic screening. We present a comprehensive strategy employing complementary approaches to balance individual technology strengths and weaknesses.
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Affiliation(s)
- Markus Schirle
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Cambridge, MA 02139, USA.
| | - Jeremy L Jenkins
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Cambridge, MA 02139, USA.
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12
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Wassermann AM, Lounkine E, Davies JW, Glick M, Camargo LM. The opportunities of mining historical and collective data in drug discovery. Drug Discov Today 2014; 20:422-34. [PMID: 25463034 DOI: 10.1016/j.drudis.2014.11.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2014] [Revised: 10/21/2014] [Accepted: 11/10/2014] [Indexed: 12/26/2022]
Abstract
Vast amounts of bioactivity data have been generated for small molecules across public and corporate domains. Biological signatures, either derived from systematic profiling efforts or from existing historical assay data, have been successfully employed for small molecule mechanism-of-action elucidation, drug repositioning, hit expansion and screening subset design. This article reviews different types of biological descriptors and applications, and we demonstrate how biological data can outlive the original purpose or project for which it was generated. By comparing 150 HTS campaigns run at Novartis over the past decade on the basis of their active and inactive chemical matter, we highlight the opportunities and challenges associated with cross-project learning in drug discovery.
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Affiliation(s)
- Anne Mai Wassermann
- In Silico Lead Discovery, Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA 02139, USA.
| | - Eugen Lounkine
- In Silico Lead Discovery, Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - John W Davies
- In Silico Lead Discovery, Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Meir Glick
- In Silico Lead Discovery, Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - L Miguel Camargo
- In Silico Lead Discovery, Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA 02139, USA.
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13
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Didiot MC, Agarinis C, Varin T, Wu H, Nelson T, Selinger DW, King F, Schuffenhauer A, Parker CN. Glucocorticoid receptor ligands modulate Cardiovirus encephalomyocarditis virus internal ribosome entry site activity. Assay Drug Dev Technol 2013; 11:355-66. [PMID: 23906347 DOI: 10.1089/adt.2013.523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The use of small molecules to modulate cellular processes is a powerful approach to investigate gene function as a complement to genetic approaches. The discovery and characterization of compounds that modulate translation initiation, the rate-limiting step of protein synthesis, is important both to provide tool compounds to explore this fundamental biological process and to further evaluate protein synthesis as a therapeutic target. While most messenger ribonucleic acids (mRNAs) recruit ribosomes via their 5' cap, some viral and cellular mRNAs initiate protein synthesis via an alternative "cap-independent" mechanism utilizing internal ribosome entry sites (IRES) elements, which are complex mRNA secondary structures, localized within the 5' nontranslated region of the mRNA upstream of the AUG start codon. This report describes the design of a functional, high throughput screen of small molecules miniaturized into a 1,536-well format and performed using the luciferase reporter gene under control of the viral Cardiovirus encephalomyocarditis virus (EMCV) IRES element to identify nontoxic compounds modulating translation initiated from the EMCV IRES. One activating compound, validated in a dose response manner, has previously been shown to bind the glucocorticoid receptor (GR). Subsequent testing of additional GR modulators further supported this as the possible mechanism of action. Detailed characterization of this compound activity supported the notion that this was due to an effect at the level of translation.
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14
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Nigsch F, Hutz J, Cornett B, Selinger DW, McAllister G, Bandyopadhyay S, Loureiro J, Jenkins JL. Determination of minimal transcriptional signatures of compounds for target prediction. EURASIP JOURNAL ON BIOINFORMATICS & SYSTEMS BIOLOGY 2012; 2012:2. [PMID: 22574917 PMCID: PMC3386022 DOI: 10.1186/1687-4153-2012-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2012] [Accepted: 05/10/2012] [Indexed: 11/10/2022]
Abstract
The identification of molecular target and mechanism of action of compounds is a key hurdle in drug discovery. Multiplexed techniques for bead-based expression profiling allow the measurement of transcriptional signatures of compound-treated cells in high-throughput mode. Such profiles can be used to gain insight into compounds' mode of action and the protein targets they are modulating. Through the proxy of target prediction from such gene signatures we explored important aspects of the use of transcriptional profiles to capture biological variability of perturbed cellular assays. We found that signatures derived from expression data and signatures derived from biological interaction networks performed equally well, and we showed that gene signatures can be optimised using a genetic algorithm. Gene signatures of approximately 128 genes seemed to be most generic, capturing a maximum of the perturbation inflicted on cells through compound treatment. Moreover, we found evidence for oxidative phosphorylation to be one of the most general ways to capture compound perturbation.
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Affiliation(s)
- Florian Nigsch
- Developmental and Molecular Pathways, Novartis Institutes for BioMedical Research, Forum 1, Novartis Campus Basel, CH-4056, Basel, Switzerland.
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Didiot MC, Serafini S, Pfeifer MJ, King FJ, Parker CN. Multiplexed reporter gene assays: monitoring the cell viability and the compound kinetics on luciferase activity. ACTA ACUST UNITED AC 2011; 16:786-93. [PMID: 21693766 DOI: 10.1177/1087057111407768] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
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.
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