1
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Godinez WJ, Trifonov V, Fang B, Kuzu G, Pei L, Guiguemde WA, Martin EJ, King FJ, Jenkins JL, Skewes-Cox P. Compound Activity Prediction with Dose-Dependent Transcriptomic Profiles and Deep Learning. J Chem Inf Model 2024; 64:2695-2704. [PMID: 38293736 DOI: 10.1021/acs.jcim.3c01855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
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.
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Affiliation(s)
- William J Godinez
- Novartis Institutes for BioMedical Research, Emeryville, California 94608, United States
| | - Vladimir Trifonov
- Novartis Institutes for BioMedical Research, San Diego, California 92121, United States
| | - Bin Fang
- Novartis Institutes for BioMedical Research, San Diego, California 92121, United States
| | - Guray Kuzu
- Novartis Institutes for BioMedical Research, San Diego, California 92121, United States
| | - Luying Pei
- Novartis Institutes for BioMedical Research, Emeryville, California 94608, United States
| | - W Armand Guiguemde
- Novartis Institutes for BioMedical Research, Emeryville, California 94608, United States
| | - Eric J Martin
- Novartis Institutes for BioMedical Research, Emeryville, California 94608, United States
| | - Frederick J King
- Novartis Institutes for BioMedical Research, San Diego, California 92121, United States
| | - Jeremy L Jenkins
- Novartis Institutes for BioMedical Research, Cambridge, Massachusetts 02139, United States
| | - Peter Skewes-Cox
- Novartis Institutes for BioMedical Research, Emeryville, California 94608, United States
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3
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Shen L, Fang J, Liu L, Yang F, Jenkins JL, Kutchukian PS, Wang H. Pocket Crafter: a 3D generative modeling based workflow for the rapid generation of hit molecules in drug discovery. J Cheminform 2024; 16:33. [PMID: 38515171 PMCID: PMC10958880 DOI: 10.1186/s13321-024-00829-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 03/16/2024] [Indexed: 03/23/2024] Open
Abstract
We present a user-friendly molecular generative pipeline called Pocket Crafter, specifically designed to facilitate hit finding activity in the drug discovery process. This workflow utilized a three-dimensional (3D) generative modeling method Pocket2Mol, for the de novo design of molecules in spatial perspective for the targeted protein structures, followed by filters for chemical-physical properties and drug-likeness, structure-activity relationship analysis, and clustering to generate top virtual hit scaffolds. In our WDR5 case study, we acquired a focused set of 2029 compounds after a targeted searching within Novartis archived library based on the virtual scaffolds. Subsequently, we experimentally profiled these compounds, resulting in a novel chemical scaffold series that demonstrated activity in biochemical and biophysical assays. Pocket Crafter successfully prototyped an effective end-to-end 3D generative chemistry-based workflow for the exploration of new chemical scaffolds, which represents a promising approach in early drug discovery for hit identification.
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Affiliation(s)
- Lingling Shen
- Novartis Biomedical Research, Cambridge, MA, 02139, USA.
| | - Jian Fang
- Novartis Biomedical Research, Cambridge, MA, 02139, USA
| | - Lulu Liu
- Novartis Biomedical Research, Cambridge, MA, 02139, USA
| | - Fei Yang
- Novartis Biomedical Research, Cambridge, MA, 02139, USA
| | | | | | - He Wang
- Novartis Biomedical Research, Cambridge, MA, 02139, USA.
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4
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Li J, Canham SM, Wu H, Henault M, Chen L, Liu G, Chen Y, Yu G, Miller HR, Hornak V, Brittain SM, Michaud GA, Tutter A, Broom W, Digan ME, McWhirter SM, Sivick KE, Pham HT, Chen CH, Tria GS, McKenna JM, Schirle M, Mao X, Nicholson TB, Wang Y, Jenkins JL, Jain RK, Tallarico JA, Patel SJ, Zheng L, Ross NT, Cho CY, Zhang X, Bai XC, Feng Y. Activation of human STING by a molecular glue-like compound. Nat Chem Biol 2024; 20:365-372. [PMID: 37828400 PMCID: PMC10907298 DOI: 10.1038/s41589-023-01434-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 09/02/2023] [Indexed: 10/14/2023]
Abstract
Stimulator of interferon genes (STING) is a dimeric transmembrane adapter protein that plays a key role in the human innate immune response to infection and has been therapeutically exploited for its antitumor activity. The activation of STING requires its high-order oligomerization, which could be induced by binding of the endogenous ligand, cGAMP, to the cytosolic ligand-binding domain. Here we report the discovery through functional screens of a class of compounds, named NVS-STGs, that activate human STING. Our cryo-EM structures show that NVS-STG2 induces the high-order oligomerization of human STING by binding to a pocket between the transmembrane domains of the neighboring STING dimers, effectively acting as a molecular glue. Our functional assays showed that NVS-STG2 could elicit potent STING-mediated immune responses in cells and antitumor activities in animal models.
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Affiliation(s)
- Jie Li
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Stephen M Canham
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA.
| | - Hua Wu
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Martin Henault
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Lihao Chen
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Guoxun Liu
- Novartis Institutes for BioMedical Research, San Diego, CA, USA
| | - Yu Chen
- Novartis Institutes for BioMedical Research, San Diego, CA, USA
| | - Gary Yu
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Howard R Miller
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Viktor Hornak
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | | | | | - Antonin Tutter
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Wendy Broom
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | | | | | | | - Helen T Pham
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | | | - George S Tria
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | | | - Markus Schirle
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Xiaohong Mao
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | | | - Yuan Wang
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | | | - Rishi K Jain
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | | | - Sejal J Patel
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Lianxing Zheng
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Nathan T Ross
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Charles Y Cho
- Novartis Institutes for BioMedical Research, San Diego, CA, USA
| | - Xuewu Zhang
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
- Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Xiao-Chen Bai
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
- Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Yan Feng
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA.
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5
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Li J, Ho DJ, Henault M, Yang C, Neri M, Ge R, Renner S, Mansur L, Lindeman A, Kelly B, Tumkaya T, Ke X, Soler-Llavina G, Shanker G, Russ C, Hild M, Gubser Keller C, Jenkins JL, Worringer KA, Sigoillot FD, Ihry RJ. DRUG-seq Provides Unbiased Biological Activity Readouts for Neuroscience Drug Discovery. ACS Chem Biol 2022; 17:1401-1414. [PMID: 35508359 PMCID: PMC9207813 DOI: 10.1021/acschembio.1c00920] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Unbiased transcriptomic RNA-seq data has provided deep insights into biological processes. However, its impact in drug discovery has been narrow given high costs and low throughput. Proof-of-concept studies with Digital RNA with pertUrbation of Genes (DRUG)-seq demonstrated the potential to address this gap. We extended the DRUG-seq platform by subjecting it to rigorous testing and by adding an open-source analysis pipeline. The results demonstrate high reproducibility and ability to resolve the mechanism(s) of action for a diverse set of compounds. Furthermore, we demonstrate how this data can be incorporated into a drug discovery project aiming to develop therapeutics for schizophrenia using human stem cell-derived neurons. We identified both an on-target activation signature, induced by a set of chemically distinct positive allosteric modulators of the N-methyl-d-aspartate (NMDA) receptor, and independent off-target effects. Overall, the protocol and open-source analysis pipeline are a step toward industrializing RNA-seq for high-complexity transcriptomics studies performed at a saturating scale.
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Affiliation(s)
| | | | | | | | - Marilisa Neri
- Chemical and Biological Therapeutics, Novartis Institutes for BioMedical Research, Basel, 4056, Switzerland
| | | | - Steffen Renner
- Chemical and Biological Therapeutics, Novartis Institutes for BioMedical Research, Basel, 4056, Switzerland
| | | | | | | | | | | | | | | | | | | | - Caroline Gubser Keller
- Chemical and Biological Therapeutics, Novartis Institutes for BioMedical Research, Basel, 4056, Switzerland
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6
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Godinez WJ, Ma EJ, Chao AT, Pei L, Skewes-Cox P, Canham SM, Jenkins JL, Young JM, Martin EJ, Guiguemde WA. Design of potent antimalarials with generative chemistry. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00448-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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7
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Tan C, Ginzberg MB, Webster R, Iyengar S, Liu S, Papadopoli D, Concannon J, Wang Y, Auld DS, Jenkins JL, Rost H, Topisirovic I, Hilfinger A, Derry WB, Patel N, Kafri R. Cell size homeostasis is maintained by CDK4-dependent activation of p38 MAPK. Dev Cell 2021; 56:1756-1769.e7. [PMID: 34022133 DOI: 10.1016/j.devcel.2021.04.030] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 02/08/2021] [Accepted: 04/28/2021] [Indexed: 02/07/2023]
Abstract
While molecules that promote the growth of animal cells have been identified, it remains unclear how such signals are orchestrated to determine a characteristic target size for different cell types. It is increasingly clear that cell size is determined by size checkpoints-mechanisms that restrict the cell cycle progression of cells that are smaller than their target size. Previously, we described a p38 MAPK-dependent cell size checkpoint mechanism whereby p38 is selectively activated and prevents cell cycle progression in cells that are smaller than a given target size. In this study, we show that the specific target size required for inactivation of p38 and transition through the cell cycle is determined by CDK4 activity. Our data suggest a model whereby p38 and CDK4 cooperate analogously to the function of a thermostat: while p38 senses irregularities in size, CDK4 corresponds to the thermostat dial that sets the target size.
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Affiliation(s)
- Ceryl Tan
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1A8, Canada; Cell Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Miriam B Ginzberg
- Cell Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Rachel Webster
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1A8, Canada; Developmental and Stem Cell Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Seshu Iyengar
- Department of Chemical and Physical Sciences, University of Toronto Mississauga, ON L5L 1C6, Canada
| | - Shixuan Liu
- Chemical and Systems Biology, Stanford University, Stanford, CA 94305, USA
| | - David Papadopoli
- Gerald Bronfman Department of Oncology and Lady Davis Institute, McGill University Montreal, QC H4A 3T2, Canada
| | - John Concannon
- Novartis Institutes for BioMedical Research, Cambridge, MA 02139, USA
| | - Yuan Wang
- Novartis Institutes for BioMedical Research, Cambridge, MA 02139, USA
| | - Douglas S Auld
- Novartis Institutes for BioMedical Research, Cambridge, MA 02139, USA
| | - Jeremy L Jenkins
- Novartis Institutes for BioMedical Research, Cambridge, MA 02139, USA
| | - Hannes Rost
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1A8, Canada
| | - Ivan Topisirovic
- Gerald Bronfman Department of Oncology and Lady Davis Institute, McGill University Montreal, QC H4A 3T2, Canada
| | - Andreas Hilfinger
- Department of Chemical and Physical Sciences, University of Toronto Mississauga, ON L5L 1C6, Canada
| | - W Brent Derry
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1A8, Canada; Developmental and Stem Cell Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Nish Patel
- Cell Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Ran Kafri
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1A8, Canada; Cell Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada.
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8
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Cho H, Shen Q, Zhang LH, Okumura M, Kawakami A, Ambrose J, Sigoillot F, Miller HR, Gleim S, Cobos-Correa A, Wang Y, Piechon P, Roma G, Eggimann F, Moore C, Aspesi P, Mapa FA, Burks H, Ross NT, Krastel P, Hild M, Maimone TJ, Fisher DE, Nomura DK, Tallarico JA, Canham SM, Jenkins JL, Forrester WC. CYP27A1-dependent anti-melanoma activity of limonoid natural products targets mitochondrial metabolism. Cell Chem Biol 2021; 28:1407-1419.e6. [PMID: 33794192 DOI: 10.1016/j.chembiol.2021.03.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 01/24/2021] [Accepted: 03/09/2021] [Indexed: 01/18/2023]
Abstract
Three limonoid natural products with selective anti-proliferative activity against BRAF(V600E) and NRAS(Q61K)-mutation-dependent melanoma cell lines were identified. Differential transcriptome analysis revealed dependency of compound activity on expression of the mitochondrial cytochrome P450 oxidase CYP27A1, a transcriptional target of melanogenesis-associated transcription factor (MITF). We determined that CYP27A1 activity is necessary for the generation of a reactive metabolite that proceeds to inhibit cellular proliferation. A genome-wide small interfering RNA screen in combination with chemical proteomics experiments revealed gene-drug functional epistasis, suggesting that these compounds target mitochondrial biogenesis and inhibit tumor bioenergetics through a covalent mechanism. Our work suggests a strategy for melanoma-specific targeting by exploiting the expression of MITF target gene CYP27A1 and inhibiting mitochondrial oxidative phosphorylation in BRAF mutant melanomas.
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Affiliation(s)
- Hyelim Cho
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Qiong Shen
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Lydia H Zhang
- Department of Chemistry, University of California, Berkeley, Berkeley, CA 94720, USA; Novartis-Berkeley Center for Proteomics and Chemistry Technologies, Berkeley, CA 94720, USA
| | - Mikiko Okumura
- Department of Chemistry, University of California, Berkeley, Berkeley, CA 94720, USA; Novartis-Berkeley Center for Proteomics and Chemistry Technologies, Berkeley, CA 94720, USA
| | - Akinori Kawakami
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Jessi Ambrose
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Frederic Sigoillot
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Howard R Miller
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Scott Gleim
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Amanda Cobos-Correa
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Ying Wang
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Philippe Piechon
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Guglielmo Roma
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Fabian Eggimann
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Charles Moore
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Peter Aspesi
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Felipa A Mapa
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Heather Burks
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Nathan T Ross
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Philipp Krastel
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Marc Hild
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Thomas J Maimone
- Department of Chemistry, University of California, Berkeley, Berkeley, CA 94720, USA; Novartis-Berkeley Center for Proteomics and Chemistry Technologies, Berkeley, CA 94720, USA
| | - David E Fisher
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Daniel K Nomura
- Department of Chemistry, University of California, Berkeley, Berkeley, CA 94720, USA; Novartis-Berkeley Center for Proteomics and Chemistry Technologies, Berkeley, CA 94720, USA; Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA; Department of Nutritional Sciences and Toxicology, University of California, Berkeley, CA 94720, USA; Innovative Genomics Institute, Berkeley, CA 94720, USA
| | - John A Tallarico
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA; Novartis-Berkeley Center for Proteomics and Chemistry Technologies, Berkeley, CA 94720, USA
| | - Stephen M Canham
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA; Novartis-Berkeley Center for Proteomics and Chemistry Technologies, Berkeley, CA 94720, USA
| | - Jeremy L Jenkins
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA.
| | - William C Forrester
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA.
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9
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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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10
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Ferrero E, Brachat S, Jenkins JL, Marc P, Skewes-Cox P, Altshuler RC, Gubser Keller C, Kauffmann A, Sassaman EK, Laramie JM, Schoeberl B, Borowsky ML, Stiefl N. Ten simple rules to power drug discovery with data science. PLoS Comput Biol 2020; 16:e1008126. [PMID: 32853229 PMCID: PMC7451597 DOI: 10.1371/journal.pcbi.1008126] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Affiliation(s)
- Enrico Ferrero
- Computational Sciences Council, Novartis Institutes for BioMedical Research, Basel, Switzerland
- * E-mail:
| | - Sophie Brachat
- Computational Sciences Council, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Jeremy L. Jenkins
- Computational Sciences Council, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America
| | - Philippe Marc
- Computational Sciences Council, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Peter Skewes-Cox
- Computational Sciences Council, Novartis Institutes for BioMedical Research, Emeryville, California, United States of America
| | - Robert C. Altshuler
- Computational Sciences Council, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America
| | - Caroline Gubser Keller
- Computational Sciences Council, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Audrey Kauffmann
- Computational Sciences Council, Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Erik K. Sassaman
- Computational Sciences Council, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America
| | - Jason M. Laramie
- Computational Sciences Council, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America
| | - Birgit Schoeberl
- Computational Sciences Council, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America
| | - Mark L. Borowsky
- Computational Sciences Council, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America
| | - Nikolaus Stiefl
- Computational Sciences Council, Novartis Institutes for BioMedical Research, Basel, Switzerland
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11
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Canham SM, Wang Y, Cornett A, Auld DS, Baeschlin DK, Patoor M, Skaanderup PR, Honda A, Llamas L, Wendel G, Mapa FA, Aspesi P, Labbé-Giguère N, Gamber GG, Palacios DS, Schuffenhauer A, Deng Z, Nigsch F, Frederiksen M, Bushell SM, Rothman D, Jain RK, Hemmerle H, Briner K, Porter JA, Tallarico JA, Jenkins JL. Systematic Chemogenetic Library Assembly. Cell Chem Biol 2020; 27:1124-1129. [PMID: 32707038 DOI: 10.1016/j.chembiol.2020.07.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 06/03/2020] [Accepted: 07/02/2020] [Indexed: 12/22/2022]
Abstract
Chemogenetic libraries, collections of well-defined chemical probes, provide tremendous value to biomedical research but require substantial effort to ensure diversity as well as quality of the contents. We have assembled a chemogenetic library by data mining and crowdsourcing institutional expertise. We are sharing our approach, lessons learned, and disclosing our current collection of 4,185 compounds with their primary annotated gene targets (https://github.com/Novartis/MoaBox). This physical collection is regularly updated and used broadly both within Novartis and in collaboration with external partners.
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Affiliation(s)
- Stephen M Canham
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA.
| | - Yuan Wang
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Allen Cornett
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Douglas S Auld
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA.
| | - Daniel K Baeschlin
- Novartis Institute for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Maude Patoor
- Novartis Institute for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Philip R Skaanderup
- Novartis Institute for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Ayako Honda
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Luis Llamas
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Greg Wendel
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Felipa A Mapa
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Peter Aspesi
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Nancy Labbé-Giguère
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Gabriel G Gamber
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Daniel S Palacios
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Ansgar Schuffenhauer
- Novartis Institute for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Zhan Deng
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Florian Nigsch
- Novartis Institute for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Mathias Frederiksen
- Novartis Institute for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Simon M Bushell
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Deborah Rothman
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Rishi K Jain
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Horst Hemmerle
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Karin Briner
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Jeffery A Porter
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - John A Tallarico
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Jeremy L Jenkins
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA.
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12
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Daga N, Eicher S, Kannan A, Casanova A, Low SH, Kreibich S, Andritschke D, Emmenlauer M, Jenkins JL, Hardt WD, Greber UF, Dehio C, von Mering C. Growth-restricting effects of siRNA transfections: a largely deterministic combination of off-target binding and hybridization-independent competition. Nucleic Acids Res 2019; 46:9309-9320. [PMID: 30215772 PMCID: PMC6182159 DOI: 10.1093/nar/gky798] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 09/10/2018] [Indexed: 01/17/2023] Open
Abstract
Perturbation of gene expression by means of synthetic small interfering RNAs (siRNAs) is a powerful way to uncover gene function. However, siRNA technology suffers from sequence-specific off-target effects and from limitations in knock-down efficiency. In this study, we assess a further problem: unintended effects of siRNA transfections on cellular fitness/proliferation. We show that the nucleotide compositions of siRNAs at specific positions have reproducible growth-restricting effects on mammalian cells in culture. This is likely distinct from hybridization-dependent off-target effects, since each nucleotide residue is seen to be acting independently and additively. The effect is robust and reproducible across different siRNA libraries and also across various cell lines, including human and mouse cells. Analyzing the growth inhibition patterns in correlation to the nucleotide sequence of the siRNAs allowed us to build a predictor that can estimate growth-restricting effects for any arbitrary siRNA sequence. Competition experiments with co-transfected siRNAs further suggest that the growth-restricting effects might be linked to an oversaturation of the cellular miRNA machinery, thus disrupting endogenous miRNA functions at large. We caution that competition between siRNA molecules could complicate the interpretation of double-knockdown or epistasis experiments, and potential interactions with endogenous miRNAs can be a factor when assaying cell growth or viability phenotypes.
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Affiliation(s)
- Neha Daga
- Institute of Molecular Life Sciences, University of Zurich, CH-8057 Zurich, Switzerland.,Swiss Institute of Bioinformatics, University of Zurich, CH-8057 Zurich, Switzerland
| | - Simone Eicher
- Biozentrum, University of Basel, CH-4056 Basel, Switzerland
| | - Abhilash Kannan
- Institute of Molecular Life Sciences, University of Zurich, CH-8057 Zurich, Switzerland
| | - Alain Casanova
- Biozentrum, University of Basel, CH-4056 Basel, Switzerland
| | - Shyan H Low
- Biozentrum, University of Basel, CH-4056 Basel, Switzerland
| | - Saskia Kreibich
- Institute of Microbiology, Department of Biology, ETH Zurich, CH-8093 Zurich, Switzerland
| | - Daniel Andritschke
- Institute of Microbiology, Department of Biology, ETH Zurich, CH-8093 Zurich, Switzerland
| | | | - Jeremy L Jenkins
- Chemical Biology and Therapeutics, Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Wolf-Dietrich Hardt
- Institute of Microbiology, Department of Biology, ETH Zurich, CH-8093 Zurich, Switzerland
| | - Urs F Greber
- Institute of Molecular Life Sciences, University of Zurich, CH-8057 Zurich, Switzerland
| | | | - Christian von Mering
- Institute of Molecular Life Sciences, University of Zurich, CH-8057 Zurich, Switzerland.,Swiss Institute of Bioinformatics, University of Zurich, CH-8057 Zurich, Switzerland
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13
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Nolin E, Gans S, Llamas L, Bandyopadhyay S, Brittain SM, Bernasconi-Elias P, Carter KP, Loureiro JJ, Thomas JR, Schirle M, Yang Y, Guo N, Roma G, Schuierer S, Beibel M, Lindeman A, Sigoillot F, Chen A, Xie KX, Ho S, Reece-Hoyes J, Weihofen WA, Tyskiewicz K, Hoepfner D, McDonald RI, Guthrie N, Dogra A, Guo H, Shao J, Ding J, Canham SM, Boynton G, George EL, Kang ZB, Antczak C, Porter JA, Wallace O, Tallarico JA, Palmer AE, Jenkins JL, Jain RK, Bushell SM, Fryer CJ. Discovery of a ZIP7 inhibitor from a Notch pathway screen. Nat Chem Biol 2019; 15:179-188. [PMID: 30643281 DOI: 10.1038/s41589-018-0200-7] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Accepted: 11/14/2018] [Indexed: 12/15/2022]
Abstract
The identification of activating mutations in NOTCH1 in 50% of T cell acute lymphoblastic leukemia has generated interest in elucidating how these mutations contribute to oncogenic transformation and in targeting the pathway. A phenotypic screen identified compounds that interfere with trafficking of Notch and induce apoptosis via an endoplasmic reticulum (ER) stress mechanism. Target identification approaches revealed a role for SLC39A7 (ZIP7), a zinc transport family member, in governing Notch trafficking and signaling. Generation and sequencing of a compound-resistant cell line identified a V430E mutation in ZIP7 that confers transferable resistance to the compound NVS-ZP7-4. NVS-ZP7-4 altered zinc in the ER, and an analog of the compound photoaffinity labeled ZIP7 in cells, suggesting a direct interaction between the compound and ZIP7. NVS-ZP7-4 is the first reported chemical tool to probe the impact of modulating ER zinc levels and investigate ZIP7 as a novel druggable node in the Notch pathway.
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Affiliation(s)
- Erin Nolin
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Sara Gans
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Luis Llamas
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | | | | | | | - Kyle P Carter
- Department of Chemistry and Biochemistry and BioFrontiers Institute, University of Colorado, Boulder, CO, USA
| | | | - Jason R Thomas
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Markus Schirle
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Yi Yang
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Ning Guo
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Guglielmo Roma
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Sven Schuierer
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Martin Beibel
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Alicia Lindeman
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | | | - Amy Chen
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Kevin X Xie
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Samuel Ho
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | | | | | | | | | | | | | - Abhishek Dogra
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Haibing Guo
- Novartis Institutes for Biomedical Research, Shanghai, China
| | - Jian Shao
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Jian Ding
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | | | - Geoff Boynton
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | | | - Zhao B Kang
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | | | | | - Owen Wallace
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | | | - Amy E Palmer
- Department of Chemistry and Biochemistry and BioFrontiers Institute, University of Colorado, Boulder, CO, USA
| | | | - Rishi K Jain
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Simon M Bushell
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA.
| | - Christy J Fryer
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA.
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14
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Cho H, Geno E, Patoor M, Reid A, McDonald RI, Hild M, Jenkins JL. Correction to Indolyl-Pyridinyl-Propenone-Induced Methuosis through the Inhibition of PIKFYVE. ACS Omega 2018; 3:9034. [PMID: 31459036 PMCID: PMC6644806 DOI: 10.1021/acsomega.8b01656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Indexed: 06/10/2023]
Abstract
[This corrects the article DOI: 10.1021/acsomega.8b00202.].
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15
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Cho H, Geno E, Patoor M, Reid A, McDonald R, Hild M, Jenkins JL. Indolyl-Pyridinyl-Propenone-Induced Methuosis through the Inhibition of PIKFYVE. ACS Omega 2018; 3:6097-6103. [PMID: 30221232 PMCID: PMC6130785 DOI: 10.1021/acsomega.8b00202] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Accepted: 02/23/2018] [Indexed: 06/08/2023]
Abstract
Methuosis is a form of nonapoptotic cell death characterized by the accumulation of macropinosome-derived vacuoles. Herein, we identify PIKFYVE, a class III phosphoinositide (PI) kinase, as the protein target responsible for the methuosis-inducing activity of indolyl-pyridinyl-propenones (3-(5-methoxy-2-methyl-1H-indol-3-yl)-1-(4-pyridinyl)-2-propen-1-one). We further characterize the effects of chemical substitutions at the 2- and 5-indolyl positions on cytoplasmic vacuolization and PIKFYVE binding and inhibitory activity. Our study provides a better understanding of the mechanism of methuosis-inducing indolyl-pyridinyl-propenones.
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Affiliation(s)
- Hyelim Cho
- Chemical
Biology and Therapeutics, Novartis Institutes
for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Erin Geno
- Chemical
Biology and Therapeutics, Novartis Institutes
for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Maude Patoor
- Chemical
Biology and Therapeutics, Novartis Institutes
for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Adam Reid
- Department
of Chemistry, Johns Hopkins University, 3400 N. Charles Street, Baltimore, Maryland 21218, United States
| | - Rick McDonald
- Chemical
Biology and Therapeutics, Novartis Institutes
for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Marc Hild
- Chemical
Biology and Therapeutics, Novartis Institutes
for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Jeremy L. Jenkins
- Chemical
Biology and Therapeutics, Novartis Institutes
for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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16
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Liu S, Ginzberg MB, Patel N, Hild M, Leung B, Li Z, Chen YC, Chang N, Wang Y, Tan C, Diena S, Trimble W, Wasserman L, Jenkins JL, Kirschner MW, Kafri R. Size uniformity of animal cells is actively maintained by a p38 MAPK-dependent regulation of G1-length. eLife 2018; 7:26947. [PMID: 29595474 PMCID: PMC5876018 DOI: 10.7554/elife.26947] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2017] [Accepted: 12/22/2017] [Indexed: 01/09/2023] Open
Abstract
Animal cells within a tissue typically display a striking regularity in their size. To date, the molecular mechanisms that control this uniformity are still unknown. We have previously shown that size uniformity in animal cells is promoted, in part, by size-dependent regulation of G1 length. To identify the molecular mechanisms underlying this process, we performed a large-scale small molecule screen and found that the p38 MAPK pathway is involved in coordinating cell size and cell cycle progression. Small cells display higher p38 activity and spend more time in G1 than larger cells. Inhibition of p38 MAPK leads to loss of the compensatory G1 length extension in small cells, resulting in faster proliferation, smaller cell size and increased size heterogeneity. We propose a model wherein the p38 pathway responds to changes in cell size and regulates G1 exit accordingly, to increase cell size uniformity.
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Affiliation(s)
- Shixuan Liu
- Cell Biology Program, The Hospital for Sick Children, Toronto, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | | | - Nish Patel
- Cell Biology Program, The Hospital for Sick Children, Toronto, Canada
| | - Marc Hild
- Novartis Institutes for BioMedical Research, Cambridge, United States
| | - Bosco Leung
- Cell Biology Program, The Hospital for Sick Children, Toronto, Canada
| | - Zhengda Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, United States
| | - Yen-Chi Chen
- Department of Statistics, University of Washington, Seattle, United States
| | - Nancy Chang
- Cell Biology Program, The Hospital for Sick Children, Toronto, Canada
| | - Yuan Wang
- Novartis Institutes for BioMedical Research, Cambridge, United States
| | - Ceryl Tan
- Cell Biology Program, The Hospital for Sick Children, Toronto, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - Shulamit Diena
- Cell Biology Program, The Hospital for Sick Children, Toronto, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - William Trimble
- Cell Biology Program, The Hospital for Sick Children, Toronto, Canada
| | - Larry Wasserman
- Department of Statistics, Carnegie Mellon University, Pittsburgh, United States
| | - Jeremy L Jenkins
- Novartis Institutes for BioMedical Research, Cambridge, United States
| | - Marc W Kirschner
- Department of Systems Biology, Harvard Medical School, Boston, United States
| | - Ran Kafri
- Cell Biology Program, The Hospital for Sick Children, Toronto, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Canada
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17
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Moutsatsos IK, Hossain I, Agarinis C, Harbinski F, Abraham Y, Dobler L, Zhang X, Wilson CJ, Jenkins JL, Holway N, Tallarico J, Parker CN. Jenkins-CI, an Open-Source Continuous Integration System, as a Scientific Data and Image-Processing Platform. SLAS Discov 2016; 22:238-249. [PMID: 27899692 PMCID: PMC5322829 DOI: 10.1177/1087057116679993] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
High-throughput screening generates large volumes of heterogeneous data that require a diverse set of computational tools for management, processing, and analysis. Building integrated, scalable, and robust computational workflows for such applications is challenging but highly valuable. Scientific data integration and pipelining facilitate standardized data processing, collaboration, and reuse of best practices. We describe how Jenkins-CI, an “off-the-shelf,” open-source, continuous integration system, is used to build pipelines for processing images and associated data from high-content screening (HCS). Jenkins-CI provides numerous plugins for standard compute tasks, and its design allows the quick integration of external scientific applications. Using Jenkins-CI, we integrated CellProfiler, an open-source image-processing platform, with various HCS utilities and a high-performance Linux cluster. The platform is web-accessible, facilitates access and sharing of high-performance compute resources, and automates previously cumbersome data and image-processing tasks. Imaging pipelines developed using the desktop CellProfiler client can be managed and shared through a centralized Jenkins-CI repository. Pipelines and managed data are annotated to facilitate collaboration and reuse. Limitations with Jenkins-CI (primarily around the user interface) were addressed through the selection of helper plugins from the Jenkins-CI community.
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Affiliation(s)
| | - Imtiaz Hossain
- 2 Centre for Proteomic Chemistry, NIBR, Postfach, Basel, Switzerland
| | - Claudia Agarinis
- 3 Developmental and Molecular Pathways, NIBR, Postfach, Basel, Switzerland
| | | | - Yann Abraham
- 5 The Janssen Pharmaceutical Companies of Johnson & Johnson, Beerse, Vlaanderen, Belgium
| | - Luc Dobler
- 6 République et Canton du Jura, Switzerland
| | - Xian Zhang
- 2 Centre for Proteomic Chemistry, NIBR, Postfach, Basel, Switzerland
| | | | - Jeremy L Jenkins
- 1 Developmental and Molecular Pathways, NIBR, Cambridge, MA, USA
| | - Nicholas Holway
- 7 Scientific Computing, NIBR Informatics, Novartis, Postfach, Basel, Switzerland
| | - John Tallarico
- 1 Developmental and Molecular Pathways, NIBR, Cambridge, MA, USA
| | - Christian N Parker
- 3 Developmental and Molecular Pathways, NIBR, Postfach, Basel, Switzerland
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18
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Paricharak S, IJzerman AP, Jenkins JL, Bender A, Nigsch F. Data-Driven Derivation of an "Informer Compound Set" for Improved Selection of Active Compounds in High-Throughput Screening. J Chem Inf Model 2016; 56:1622-30. [PMID: 27487177 DOI: 10.1021/acs.jcim.6b00244] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Despite the usefulness of high-throughput screening (HTS) in drug discovery, for some systems, low assay throughput or high screening cost can prohibit the screening of large numbers of compounds. In such cases, iterative cycles of screening involving active learning (AL) are employed, creating the need for smaller "informer sets" that can be routinely screened to build predictive models for selecting compounds from the screening collection for follow-up screens. Here, we present a data-driven derivation of an informer compound set with improved predictivity of active compounds in HTS, and we validate its benefit over randomly selected training sets on 46 PubChem assays comprising at least 300,000 compounds and covering a wide range of assay biology. The informer compound set showed improvement in BEDROC(α = 100), PRAUC, and ROCAUC values averaged over all assays of 0.024, 0.014, and 0.016, respectively, compared to randomly selected training sets, all with paired t-test p-values <10(-15). A per-assay assessment showed that the BEDROC(α = 100), which is of particular relevance for early retrieval of actives, improved for 38 out of 46 assays, increasing the success rate of smaller follow-up screens. Overall, we showed that an informer set derived from historical HTS activity data can be employed for routine small-scale exploratory screening in an assay-agnostic fashion. This approach led to a consistent improvement in hit rates in follow-up screens without compromising scaffold retrieval. The informer set is adjustable in size depending on the number of compounds one intends to screen, as performance gains are realized for sets with more than 3,000 compounds, and this set is therefore applicable to a variety of situations. Finally, our results indicate that random sampling may not adequately cover descriptor space, drawing attention to the importance of the composition of the training set for predicting actives.
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Affiliation(s)
- Shardul Paricharak
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge , Lensfield Road, CB2 1EW, Cambridge, United Kingdom.,Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University , P.O. Box 9502, 2300 RA Leiden, The Netherlands.,Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research , Novartis Pharma AG, Novartis Campus, 4056 Basel, Switzerland
| | - Adriaan P IJzerman
- Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University , P.O. Box 9502, 2300 RA Leiden, The Netherlands
| | - Jeremy L Jenkins
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research , Cambridge, Massachusetts 02139, United States
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge , Lensfield Road, CB2 1EW, Cambridge, United Kingdom
| | - Florian Nigsch
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research , Novartis Pharma AG, Novartis Campus, 4056 Basel, Switzerland
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19
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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20
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Riniker S, Wang Y, Jenkins JL, Landrum GA. Using information from historical high-throughput screens to predict active compounds. J Chem Inf Model 2014; 54:1880-91. [PMID: 24933016 DOI: 10.1021/ci500190p] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Modern high-throughput screening (HTS) is a well-established approach for hit finding in drug discovery that is routinely employed in the pharmaceutical industry to screen more than a million compounds within a few weeks. However, as the industry shifts to more disease-relevant but more complex phenotypic screens, the focus has moved to piloting smaller but smarter chemically/biologically diverse subsets followed by an expansion around hit compounds. One standard method for doing this is to train a machine-learning (ML) model with the chemical fingerprints of the tested subset of molecules and then select the next compounds based on the predictions of this model. An alternative approach would be to take advantage of the wealth of bioactivity information contained in older (full-deck) screens using so-called HTS fingerprints, where each element of the fingerprint corresponds to the outcome of a particular assay, as input to machine-learning algorithms. We constructed HTS fingerprints using two collections of data: 93 in-house assays and 95 publicly available assays from PubChem. For each source, an additional set of 51 and 46 assays, respectively, was collected for testing. Three different ML methods, random forest (RF), logistic regression (LR), and naïve Bayes (NB), were investigated for both the HTS fingerprint and a chemical fingerprint, Morgan2. RF was found to be best suited for learning from HTS fingerprints yielding area under the receiver operating characteristic curve (AUC) values >0.8 for 78% of the internal assays and enrichment factors at 5% (EF(5%)) >10 for 55% of the assays. The RF(HTS-fp) generally outperformed the LR trained with Morgan2, which was the best ML method for the chemical fingerprint, for the majority of assays. In addition, HTS fingerprints were found to retrieve more diverse chemotypes. Combining the two models through heterogeneous classifier fusion led to a similar or better performance than the best individual model for all assays. Further validation using a pair of in-house assays and data from a confirmatory screen--including a prospective set of around 2000 compounds selected based on our approach--confirmed the good performance. Thus, the combination of machine-learning with HTS fingerprints and chemical fingerprints utilizes information from both domains and presents a very promising approach for hit expansion, leading to more hits. The source code used with the public data is provided.
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Affiliation(s)
- Sereina Riniker
- Novartis Institutes for BioMedical Research, Novartis Pharma AG , Novartis Campus, 4056 Basel, Switzerland
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22
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Jaeger S, Min J, Nigsch F, Camargo M, Hutz J, Cornett A, Cleaver S, Buckler A, Jenkins JL. Causal Network Models for Predicting Compound Targets and Driving Pathways in Cancer. ACTA ACUST UNITED AC 2014; 19:791-802. [PMID: 24518063 DOI: 10.1177/1087057114522690] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2013] [Accepted: 01/14/2014] [Indexed: 02/06/2023]
Abstract
Gene-expression data are often used to infer pathways regulating transcriptional responses. For example, differentially expressed genes (DEGs) induced by compound treatment can help characterize hits from phenotypic screens, either by correlation with known drug signatures or by pathway enrichment. Pathway enrichment is, however, typically computed with DEGs rather than "upstream" nodes that are potentially causal of "downstream" changes. Here, we present graph-based models to predict causal targets from compound-microarray data. We test several approaches to traversing network topology, and show that a consensus minimum-rank score (SigNet) beat individual methods and could highly rank compound targets among all network nodes. In addition, larger, less canonical networks outperformed linear canonical interactions. Importantly, pathway enrichment using causal nodes rather than DEGs recovers relevant pathways more often. To further validate our approach, we used integrated data sets from the Cancer Genome Atlas to identify driving pathways in triple-negative breast cancer. Critical pathways were uncovered, including the epidermal growth factor receptor 2-phosphatidylinositide 3-kinase-AKT-MAPK growth pathway andATR-p53-BRCA DNA damage pathway, in addition to unexpected pathways, such as TGF-WNT cytoskeleton remodeling, IL12-induced interferon gamma production, and TNFR-IAP (inhibitor of apoptosis) apoptosis; the latter was validated by pooled small hairpin RNA profiling in cancer cells. Overall, our approach can bridge transcriptional profiles to compound targets and driving pathways in cancer.
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Affiliation(s)
- Savina Jaeger
- Co-first authors Oncology Translational Medicine, Novartis, Cambridge, MA, USA
| | - Junxia Min
- Co-first authors ONC Target Discovery, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA
| | - Florian Nigsch
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Basel, Switzerland
| | - Miguel Camargo
- Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA
| | - Janna Hutz
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA Pfizer, Cambridge, MA, USA
| | - Allen Cornett
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA
| | - Stephen Cleaver
- NIBR IT, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA
| | - Alan Buckler
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA
| | - Jeremy L Jenkins
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Inc., Cambridge, MA, USA
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Urban L, Maciejewski M, Lounkine E, Whitebread S, Jenkins JL, Hamon J, Fekete A, Muller PY. Translation of off-target effects: prediction of ADRs by integrated experimental and computational approach. Toxicol Res (Camb) 2014. [DOI: 10.1039/c4tx00077c] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Adverse drug reactions (ADRs) are associated with most drugs, often discovered late in drug development and sometimes only during extended course of clinical use.
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Affiliation(s)
- Laszlo Urban
- Preclinical Safety Profiling
- Center for Proteomic Chemistry
- Novartis Institutes for Biomedical Research
- Cambridge, USA
| | - Mateusz Maciejewski
- Preclinical Safety Profiling
- Center for Proteomic Chemistry
- Novartis Institutes for Biomedical Research
- Cambridge, USA
| | - Eugen Lounkine
- Preclinical Safety Profiling
- Center for Proteomic Chemistry
- Novartis Institutes for Biomedical Research
- Cambridge, USA
| | - Steven Whitebread
- Preclinical Safety Profiling
- Center for Proteomic Chemistry
- Novartis Institutes for Biomedical Research
- Cambridge, USA
| | - Jeremy L. Jenkins
- Developmental and Molecular Pathways
- Novartis Institutes for Biomedical Research
- Cambridge, USA
| | - Jacques Hamon
- Basel Screening Operations
- Center for Proteomic Chemistry
- Novartis Institutes for Biomedical Research
- Basel, Switzerland
| | - Alexander Fekete
- Preclinical Safety Profiling
- Center for Proteomic Chemistry
- Novartis Institutes for Biomedical Research
- Cambridge, USA
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25
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Hutz JE, Nelson T, Wu H, McAllister G, Moutsatsos I, Jaeger SA, Bandyopadhyay S, Nigsch F, Cornett B, Jenkins JL, Selinger DW. The multidimensional perturbation value: a single metric to measure similarity and activity of treatments in high-throughput multidimensional screens. ACTA ACUST UNITED AC 2012. [PMID: 23204073 DOI: 10.1177/1087057112469257] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Screens using high-throughput, information-rich technologies such as microarrays, high-content screening (HCS), and next-generation sequencing (NGS) have become increasingly widespread. Compared with single-readout assays, these methods produce a more comprehensive picture of the effects of screened treatments. However, interpreting such multidimensional readouts is challenging. Univariate statistics such as t-tests and Z-factors cannot easily be applied to multidimensional profiles, leaving no obvious way to answer common screening questions such as "Is treatment X active in this assay?" and "Is treatment X different from (or equivalent to) treatment Y?" We have developed a simple, straightforward metric, the multidimensional perturbation value (mp-value), which can be used to answer these questions. Here, we demonstrate application of the mp-value to three data sets: a multiplexed gene expression screen of compounds and genomic reagents, a microarray-based gene expression screen of compounds, and an HCS compound screen. In all data sets, active treatments were successfully identified using the mp-value, and simulations and follow-up analyses supported the mp-value's statistical and biological validity. We believe the mp-value represents a promising way to simplify the analysis of multidimensional data while taking full advantage of its richness.
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Affiliation(s)
- Janna E Hutz
- Novartis Institutes for Biomedical Research, Cambridge, MA 02139, USA.
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Petrone PM, Simms B, Nigsch F, Lounkine E, Kutchukian P, Cornett A, Deng Z, Davies JW, Jenkins JL, Glick M. Rethinking molecular similarity: comparing compounds on the basis of biological activity. ACS Chem Biol 2012; 7:1399-409. [PMID: 22594495 DOI: 10.1021/cb3001028] [Citation(s) in RCA: 113] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Since the advent of high-throughput screening (HTS), there has been an urgent need for methods that facilitate the interrogation of large-scale chemical biology data to build a mode of action (MoA) hypothesis. This can be done either prior to the HTS by subset design of compounds with known MoA or post HTS by data annotation and mining. To enable this process, we developed a tool that compares compounds solely on the basis of their bioactivity: the chemical biological descriptor "high-throughput screening fingerprint" (HTS-FP). In the current embodiment, data are aggregated from 195 biochemical and cell-based assays developed at Novartis and can be used to identify bioactivity relationships among the in-house collection comprising ~1.5 million compounds. We demonstrate the value of the HTS-FP for virtual screening and in particular scaffold hopping. HTS-FP outperforms state of the art methods in several aspects, retrieving bioactive compounds with remarkable chemical dissimilarity to a probe structure. We also apply HTS-FP for the design of screening subsets in HTS. Using retrospective data, we show that a biodiverse selection of plates performs significantly better than a chemically diverse selection of plates, both in terms of number of hits and diversity of chemotypes retrieved. This is also true in the case of hit expansion predictions using HTS-FP similarity. Sets of compounds clustered with HTS-FP are biologically meaningful, in the sense that these clusters enrich for genes and gene ontology (GO) terms, showing that compounds that are bioactively similar also tend to target proteins that operate together in the cell. HTS-FP are valuable not only because of their predictive power but mainly because they relate compounds solely on the basis of bioactivity, harnessing the accumulated knowledge of a high-throughput screening facility toward the understanding of how compounds interact with the proteome.
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Affiliation(s)
- Paula M. Petrone
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Benjamin Simms
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Florian Nigsch
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Eugen Lounkine
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Peter Kutchukian
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Allen Cornett
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Zhan Deng
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - John W. Davies
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Jeremy L. Jenkins
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Meir Glick
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
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Abstract
The advent of in silico compound target prediction offers a potential paradigm shift in how large compound collections are understood and used strategically in high-throughput screens (HTS). Specifically, phenotypic HTS hits may be annotated both with known targets and predicted targets using large-scale QSAR models, enabling a more sophisticated hit assessment. Efforts in massive bioactivity data integration and standardization is empowering such compound-target annotations. These approaches differ fundamentally from the traditional role of QSAR in lead optimization and binding affinity predictions to global, probabilistic target predictions for thousands of human proteins.
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Affiliation(s)
- Jeremy L Jenkins
- Developmental and Molecular Pathways, Quantitative Biology, Novartis Institutes for BioMedical Research, 220 Massachusetts Ave., Cambridge, MA 02139 phone: 617-871-7155.
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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 J Bioinform Syst Biol 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Lounkine E, Nigsch F, Jenkins JL, Glick M. Activity-Aware Clustering of High Throughput Screening Data and Elucidation of Orthogonal Structure–Activity Relationships. J Chem Inf Model 2011; 51:3158-68. [DOI: 10.1021/ci2004994] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Eugen Lounkine
- Novartis Institutes for Biomedical Research, 250 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
| | - Florian Nigsch
- Novartis Institutes for Biomedical Research, Novartis Campus, Forum 1, CH-4056 Basel, Switzerland
| | - Jeremy L. Jenkins
- Novartis Institutes for Biomedical Research, 250 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
| | - Meir Glick
- Novartis Institutes for Biomedical Research, 250 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
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30
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Nigsch F, Lounkine E, McCarren P, Cornett B, Glick M, Azzaoui K, Urban L, Marc P, Müller A, Hahne F, Heard DJ, Jenkins JL. Computational methods for early predictive safety assessment from biological and chemical data. Expert Opin Drug Metab Toxicol 2011; 7:1497-511. [DOI: 10.1517/17425255.2011.632632] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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31
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Koutsoukas A, Simms B, Kirchmair J, Bond PJ, Whitmore AV, Zimmer S, Young MP, Jenkins JL, Glick M, Glen RC, Bender A. From in silico target prediction to multi-target drug design: current databases, methods and applications. J Proteomics 2011; 74:2554-74. [PMID: 21621023 DOI: 10.1016/j.jprot.2011.05.011] [Citation(s) in RCA: 186] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2011] [Revised: 04/10/2011] [Accepted: 05/06/2011] [Indexed: 01/31/2023]
Abstract
Given the tremendous growth of bioactivity databases, the use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. Applications span a wide range, from the 'designed polypharmacology' of compounds to mode-of-action analysis. In this review, we firstly survey databases that can be used for ligand-based target prediction and which have grown tremendously in size in the past. We furthermore outline methods for target prediction that exist, both based on the knowledge of bioactivities from the ligand side and methods that can be applied in situations when a protein structure is known. Applications of successful in silico target identification attempts are discussed in detail, which were based partly or in whole on computational target predictions in the first instance. This includes the authors' own experience using target prediction tools, in this case considering phenotypic antibacterial screens and the analysis of high-throughput screening data. Finally, we will conclude with the prospective application of databases to not only predict, retrospectively, the protein targets of a small molecule, but also how to design ligands with desired polypharmacology in a prospective manner.
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Affiliation(s)
- Alexios Koutsoukas
- Unilever Centre for Molecular Sciences Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
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32
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Sukuru SCK, Nigsch F, Quancard J, Renatus M, Chopra R, Brooijmans N, Mikhailov D, Deng Z, Cornett A, Jenkins JL, Hommel U, Davies JW, Glick M. A lead discovery strategy driven by a comprehensive analysis of proteases in the peptide substrate space. Protein Sci 2011; 19:2096-109. [PMID: 20799349 DOI: 10.1002/pro.490] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
We present here a comprehensive analysis of proteases in the peptide substrate space and demonstrate its applicability for lead discovery. Aligned octapeptide substrates of 498 proteases taken from the MEROPS peptidase database were used for the in silico analysis. A multiple-category naïve Bayes model, trained on the two-dimensional chemical features of the substrates, was able to classify the substrates of 365 (73%) proteases and elucidate statistically significant chemical features for each of their specific substrate positions. The positional awareness of the method allows us to identify the most similar substrate positions between proteases. Our analysis reveals that proteases from different families, based on the traditional classification (aspartic, cysteine, serine, and metallo), could have substrates that differ at the cleavage site (P1-P1') but are similar away from it. Caspase-3 (cysteine protease) and granzyme B (serine protease) are previously known examples of cross-family neighbors identified by this method. To assess whether peptide substrate similarity between unrelated proteases could reliably translate into the discovery of low molecular weight synthetic inhibitors, a lead discovery strategy was tested on two other cross-family neighbors--namely cathepsin L2 and matrix metallo proteinase 9, and calpain 1 and pepsin A. For both these pairs, a naïve Bayes classifier model trained on inhibitors of one protease could successfully enrich those of its neighbor from a different family and vice versa, indicating that this approach could be prospectively applied to lead discovery for a novel protease target with no known synthetic inhibitors.
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Affiliation(s)
- Sai Chetan K Sukuru
- Lead Discovery Informatics, Lead Finding Platform, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA
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Doddareddy MR, van Westen GJP, van der Horst E, Peironcely JE, Corthals F, Ijzerman AP, Emmerich M, Jenkins JL, Bender A. Chemogenomics: Looking at biology through the lens of chemistry. Stat Anal Data Min 2009. [DOI: 10.1002/sam.10046] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Scheiber J, Jenkins JL, Bender A, Milik M, Mikhailov D, Sukuru SCK, Cornett B, Whitebread S, Urban L, Davies JW, Glick M. SPREAD-exploiting chemical features that cause differential activity behavior. Stat Anal Data Min 2009. [DOI: 10.1002/sam.10036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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35
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Sukuru SCK, Jenkins JL, Beckwith RE, Scheiber J, Bender A, Mikhailov D, Davies JW, Glick M. Plate-Based Diversity Selection Based on Empirical HTS Data to Enhance the Number of Hits and Their Chemical Diversity. ACTA ACUST UNITED AC 2009; 14:690-9. [DOI: 10.1177/1087057109335678] [Citation(s) in RCA: 55] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Typically, screening collections of pharmaceutical companies contain more than a million compounds today. However, for certain high-throughput screening (HTS) campaigns, constraints posed by the assay throughput and/or the reagent costs make it impractical to screen the entire deck. Therefore, it is desirable to effectively screen subsets of the collection based on a hypothesis or a diversity selection. How to select compound subsets is a subject of ongoing debate. The authors present an approach based on extended connectivity fingerprints to carry out diversity selection on a per plate basis (instead of a per compound basis). HTS data from 35 Novartis screens spanning 5 target classes were investigated to assess the performance of this approach. The analysis shows that selecting a fingerprint-diverse subset of 250K compounds, representing 20% of the screening deck, would have achieved significantly higher hit rates for 86% of the screens. This measure also outperforms the Murcko scaffold-based plate selection described previously, where only 49% of the screens showed similar improvements. Strikingly, the 2-fold improvement in average hit rates observed for 3 of 5 target classes in the data set indicates a target bias of the plate (and thus compound) selection method. Even though the diverse subset selection lacks any target hypothesis, its application shows significantly better results for some targets—namely, G-protein-coupled receptors, proteases, and protein-protein interactions—but not for kinase and pathway screens. The synthetic origin of the compounds in the diverse subset appears to influence the screening hit rates. Natural products were the most diverse compound class, with significantly higher hit rates compared to the compounds from the traditional synthetic and combinatorial libraries. These results offer empirical guidelines for plate-based diversity selection to enhance hit rates, based on target class and the library type being screened. ( Journal of Biomolecular Screening 2009:690-699)
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Affiliation(s)
- Sai Chetan K. Sukuru
- Lead Discovery Informatics, Center for Proteomic Chemistry Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Jeremy L. Jenkins
- Lead Discovery Informatics, Center for Proteomic Chemistry Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Rohan E.J. Beckwith
- Hit to Lead Optimization, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Josef Scheiber
- Lead Discovery Informatics, Center for Proteomic Chemistry Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Andreas Bender
- Lead Discovery Informatics, Center for Proteomic Chemistry Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, Leiden/Amsterdam Center for Drug Research, Division of Medicinal Chemistry, Leiden, The Netherlands
| | - Dmitri Mikhailov
- Lead Discovery Informatics, Center for Proteomic Chemistry Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - John W. Davies
- Lead Discovery Informatics, Center for Proteomic Chemistry Novartis Institutes for BioMedical Research, Cambridge, Massachusetts
| | - Meir Glick
- Lead Discovery Informatics, Center for Proteomic Chemistry Novartis Institutes for BioMedical Research, Cambridge, Massachusetts,
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Scheiber J, Jenkins JL, Sukuru SCK, Bender A, Mikhailov D, Milik M, Azzaoui K, Whitebread S, Hamon J, Urban L, Glick M, Davies JW. Mapping Adverse Drug Reactions in Chemical Space. J Med Chem 2009; 52:3103-7. [DOI: 10.1021/jm801546k] [Citation(s) in RCA: 128] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Josef Scheiber
- Lead Discovery Informatics, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland, Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Jeremy L. Jenkins
- Lead Discovery Informatics, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland, Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Sai Chetan K. Sukuru
- Lead Discovery Informatics, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland, Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Andreas Bender
- Lead Discovery Informatics, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland, Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Dmitri Mikhailov
- Lead Discovery Informatics, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland, Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Mariusz Milik
- Lead Discovery Informatics, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland, Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Kamal Azzaoui
- Lead Discovery Informatics, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland, Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Steven Whitebread
- Lead Discovery Informatics, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland, Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Jacques Hamon
- Lead Discovery Informatics, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland, Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Laszlo Urban
- Lead Discovery Informatics, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland, Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Meir Glick
- Lead Discovery Informatics, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland, Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - John W. Davies
- Lead Discovery Informatics, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, Preclinical Safety Profiling, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland, Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
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Bender A, Mikhailov D, Glick M, Scheiber J, Davies JW, Cleaver S, Marshall S, Tallarico JA, Harrington E, Cornella-Taracido I, Jenkins JL. Use of Ligand Based Models for Protein Domains To Predict Novel Molecular Targets and Applications To Triage Affinity Chromatography Data. J Proteome Res 2009; 8:2575-85. [DOI: 10.1021/pr900107z] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Andreas Bender
- Center for Proteomic Chemistry, Lead Discovery Informatics, Developmental and Molecular Pathways, and Global Discovery Chemistry, Chemogenetics and Proteomics, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139
| | - Dmitri Mikhailov
- Center for Proteomic Chemistry, Lead Discovery Informatics, Developmental and Molecular Pathways, and Global Discovery Chemistry, Chemogenetics and Proteomics, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139
| | - Meir Glick
- Center for Proteomic Chemistry, Lead Discovery Informatics, Developmental and Molecular Pathways, and Global Discovery Chemistry, Chemogenetics and Proteomics, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139
| | - Josef Scheiber
- Center for Proteomic Chemistry, Lead Discovery Informatics, Developmental and Molecular Pathways, and Global Discovery Chemistry, Chemogenetics and Proteomics, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139
| | - John W. Davies
- Center for Proteomic Chemistry, Lead Discovery Informatics, Developmental and Molecular Pathways, and Global Discovery Chemistry, Chemogenetics and Proteomics, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139
| | - Stephen Cleaver
- Center for Proteomic Chemistry, Lead Discovery Informatics, Developmental and Molecular Pathways, and Global Discovery Chemistry, Chemogenetics and Proteomics, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139
| | - Stephen Marshall
- Center for Proteomic Chemistry, Lead Discovery Informatics, Developmental and Molecular Pathways, and Global Discovery Chemistry, Chemogenetics and Proteomics, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139
| | - John A. Tallarico
- Center for Proteomic Chemistry, Lead Discovery Informatics, Developmental and Molecular Pathways, and Global Discovery Chemistry, Chemogenetics and Proteomics, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139
| | - Edmund Harrington
- Center for Proteomic Chemistry, Lead Discovery Informatics, Developmental and Molecular Pathways, and Global Discovery Chemistry, Chemogenetics and Proteomics, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139
| | - Ivan Cornella-Taracido
- Center for Proteomic Chemistry, Lead Discovery Informatics, Developmental and Molecular Pathways, and Global Discovery Chemistry, Chemogenetics and Proteomics, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139
| | - Jeremy L. Jenkins
- Center for Proteomic Chemistry, Lead Discovery Informatics, Developmental and Molecular Pathways, and Global Discovery Chemistry, Chemogenetics and Proteomics, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139
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Bender A, Jenkins JL, Scheiber J, Sukuru SCK, Glick M, Davies JW. How similar are similarity searching methods? A principal component analysis of molecular descriptor space. J Chem Inf Model 2009; 49:108-19. [PMID: 19123924 DOI: 10.1021/ci800249s] [Citation(s) in RCA: 197] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Different molecular descriptors capture different aspects of molecular structures, but this effect has not yet been quantified systematically on a large scale. In this work, we calculate the similarity of 37 descriptors by repeatedly selecting query compounds and ranking the rest of the database. Euclidean distances between the rank-ordering of different descriptors are calculated to determine descriptor (as opposed to compound) similarity, followed by PCA for visualization. Four broad descriptor classes are identified, which are circular fingerprints; circular fingerprints considering counts; path-based and keyed fingerprints; and pharmacophoric descriptors. Descriptor behavior is much more defined by those four classes than the particular parametrization. Using counts instead of the presence/absence of fingerprints significantly changes descriptor behavior, which is crucial for performance of topological autocorrelation vectors, but not circular fingerprints. Four-point pharmacophores (piDAPH4) surprisingly lead to much higher retrieval rates than three-point pharmacophores (28.21% vs 19.15%) but still similar rank-ordering of compounds (retrieval of similar actives). Looking into individual rankings, circular fingerprints seem more appropriate than path-based fingerprints if complex ring systems or branching patterns are present; count-based fingerprints could be more suitable in databases with a large number of repeated subunits (amide bonds, sugar rings, terpenes). Information-based selection of diverse fingerprints for consensus scoring (ECFP4/TGD fingerprints) led only to marginal improvement over single fingerprint results. While it seems to be nontrivial to exploit orthogonal descriptor behavior to improve retrieval rates in consensus virtual screening, those descriptors still each retrieve different actives which corroborates the strategy of employing diverse descriptors individually in prospective virtual screening settings.
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Affiliation(s)
- Andreas Bender
- Center for Proteomic Chemistry, Lead Discovery Informatics, Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA.
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Scheiber J, Chen B, Milik M, Sukuru SCK, Bender A, Mikhailov D, Whitebread S, Hamon J, Azzaoui K, Urban L, Glick M, Davies JW, Jenkins JL. Gaining Insight into Off-Target Mediated Effects of Drug Candidates with a Comprehensive Systems Chemical Biology Analysis. J Chem Inf Model 2009; 49:308-17. [DOI: 10.1021/ci800344p] [Citation(s) in RCA: 132] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Josef Scheiber
- Lead Discovery Informatics and Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, and Preclinical Safety Profiling and Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Bin Chen
- Lead Discovery Informatics and Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, and Preclinical Safety Profiling and Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Mariusz Milik
- Lead Discovery Informatics and Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, and Preclinical Safety Profiling and Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Sai Chetan K. Sukuru
- Lead Discovery Informatics and Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, and Preclinical Safety Profiling and Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Andreas Bender
- Lead Discovery Informatics and Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, and Preclinical Safety Profiling and Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Dmitri Mikhailov
- Lead Discovery Informatics and Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, and Preclinical Safety Profiling and Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Steven Whitebread
- Lead Discovery Informatics and Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, and Preclinical Safety Profiling and Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Jacques Hamon
- Lead Discovery Informatics and Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, and Preclinical Safety Profiling and Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Kamal Azzaoui
- Lead Discovery Informatics and Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, and Preclinical Safety Profiling and Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Laszlo Urban
- Lead Discovery Informatics and Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, and Preclinical Safety Profiling and Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Meir Glick
- Lead Discovery Informatics and Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, and Preclinical Safety Profiling and Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - John W. Davies
- Lead Discovery Informatics and Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, and Preclinical Safety Profiling and Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
| | - Jeremy L. Jenkins
- Lead Discovery Informatics and Preclinical Safety Profiling, CPC, Novartis Institutes for Biomedical Research, 250 Massachussetts Avenue, Cambridge, Massachusetts 02139, and Preclinical Safety Profiling and Molecular Libraries Informatics, CPC, Novartis Pharma AG, Forum 1, 4002 Basel, Switzerland
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Abstract
Understanding the safety of newly developed compounds is a key task in each early drug discovery project. In early stages, pharmaceutical companies address this task by using so-called preclinical safety profiling, in which compounds are screened in inexpensive large-scale assays to understand possible liabilities. This process generates a large amount of binding data on various compounds against a panel of targets - usually thousands or tens of thousands of compounds profiled against approximately 100 different targets. This data matrix is highly valuable and elicits further analysis. After briefly introducing the nature of safety profiling data, we describe several computational methods used internally at Novartis to analyze it. We showcase protocols that can be used to understand compound promiscuity on a chemical structure level and protocols to evaluate the promiscuity of targets used in safety profiling. We also describe a method to quickly determine the chemical similarity of compounds active against different targets. Next, it is shown what protocols can be used to evaluate global chemical similarity of targets. The above approaches can be used either to optimize the composition of a panel of targets or to better understand certain toxicities. Finally, we will explain a simple method to elucidate hidden patterns in safety profiling data.
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Affiliation(s)
- Josef Scheiber
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Basel, Switzerland
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Jacoby E, Boettcher A, Mayr LM, Brown N, Jenkins JL, Kallen J, Engeloch C, Schopfer U, Furet P, Masuya K, Lisztwan J. Knowledge-based virtual screening: application to the MDM4/p53 protein-protein interaction. Methods Mol Biol 2009; 575:173-94. [PMID: 19727615 DOI: 10.1007/978-1-60761-274-2_7] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Chemogenomics knowledge-based drug discovery approaches aim to extract the knowledge gained from one target and to apply it for the discovery of ligands and hopefully drugs of a new target which is related to the parent target by homology or conserved molecular recognition. Herein, we demonstrate the potential of knowledge-based virtual screening by applying it to the MDM4-p53 protein-protein interaction where the MDM2-p53 protein-protein interaction constitutes the parent reference system; both systems are potentially relevant to cancer therapy. We show that a combination of virtual screening methods, including homology based similarity searching, QSAR (Quantitative Structure-Activity Relationship) methods, HTD (High Throughput Docking), and UNITY pharmacophore searching provide a successful approach to the discovery of inhibitors. The virtual screening hit list is of the magnitude of 50,000 compounds picked from the corporate compound library of approximately 1.2 million compounds. Emphasis is placed on the facts that such campaigns are only feasible because of the now existing HTCP (High throughput Cherry-Picking) automation systems in combination with robust MTS (Medium Throughput Screening) fluorescence-based assays. Given that the MDM2-p53 system constitutes the reference system, it is not surprising that significantly more and stronger hits are found for this interaction compared to the MDM4-p53 system. Novel, selective and dual hits are discovered for both systems. A hit rate analysis will be provided compared to the full HTS (High-throughput Screening).
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Affiliation(s)
- Edgar Jacoby
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Basel, Switzerland
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Nigsch F, Bender A, Jenkins JL, Mitchell JBO. Ligand-Target Prediction Using Winnow and Naive Bayesian Algorithms and the Implications of Overall Performance Statistics. J Chem Inf Model 2008; 48:2313-25. [DOI: 10.1021/ci800079x] [Citation(s) in RCA: 81] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Florian Nigsch
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom; Lead Discovery Informatics, Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139; and Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Andreas Bender
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom; Lead Discovery Informatics, Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139; and Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Jeremy L. Jenkins
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom; Lead Discovery Informatics, Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139; and Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - John B. O. Mitchell
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom; Lead Discovery Informatics, Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139; and Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
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Bender A, Scheiber J, Glick M, Davies JW, Azzaoui K, Hamon J, Urban L, Whitebread S, Jenkins JL. Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure. ChemMedChem 2008; 2:861-73. [PMID: 17477341 DOI: 10.1002/cmdc.200700026] [Citation(s) in RCA: 222] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Preclinical Safety Pharmacology (PSP) attempts to anticipate adverse drug reactions (ADRs) during early phases of drug discovery by testing compounds in simple, in vitro binding assays (that is, preclinical profiling). The selection of PSP targets is based largely on circumstantial evidence of their contribution to known clinical ADRs, inferred from findings in clinical trials, animal experiments, and molecular studies going back more than forty years. In this work we explore PSP chemical space and its relevance for the prediction of adverse drug reactions. Firstly, in silico (computational) Bayesian models for 70 PSP-related targets were built, which are able to detect 93% of the ligands binding at IC(50) < or = 10 microM at an overall correct classification rate of about 94%. Secondly, employing the World Drug Index (WDI), a model for adverse drug reactions was built directly based on normalized side-effect annotations in the WDI, which does not require any underlying functional knowledge. This is, to our knowledge, the first attempt to predict adverse drug reactions across hundreds of categories from chemical structure alone. On average 90% of the adverse drug reactions observed with known, clinically used compounds were detected, an overall correct classification rate of 92%. Drugs withdrawn from the market (Rapacuronium, Suprofen) were tested in the model and their predicted ADRs align well with known ADRs. The analysis was repeated for acetylsalicylic acid and Benperidol which are still on the market. Importantly, features of the models are interpretable and back-projectable to chemical structure, raising the possibility of rationally engineering out adverse effects. By combining PSP and ADR models new hypotheses linking targets and adverse effects can be proposed and examples for the opioid mu and the muscarinic M2 receptors, as well as for cyclooxygenase-1 are presented. It is hoped that the generation of predictive models for adverse drug reactions is able to help support early SAR to accelerate drug discovery and decrease late stage attrition in drug discovery projects. In addition, models such as the ones presented here can be used for compound profiling in all development stages.
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Affiliation(s)
- Andreas Bender
- Lead Finding Platform, Novartis Institutes for BioMedical Research Inc. 250 Massachusetts Ave., Cambridge, Massachusetts 02139, USA.
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Bender A, Bojanic D, Davies JW, Crisman TJ, Mikhailov D, Scheiber J, Jenkins JL, Deng Z, Hill WAG, Popov M, Jacoby E, Glick M. Which aspects of HTS are empirically correlated with downstream success? Curr Opin Drug Discov Devel 2008; 11:327-337. [PMID: 18428086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
High-throughput screening (HTS) is a well-established hit-finding approach used in the pharmaceutical industry. In this article, recent experience at Novartis with respect to factors influencing the success of HTS campaigns is discussed. An inherent measure of HTS quality could be defined by the assay Z and Z' factors, the number of hits and their biological potencies; however, such measures of quality do not always correlate with the advancement of hits to the later stages of drug discovery. Also, for many target classes, such as kinases, it is easy to identify hits, but, as a result of selectivity, intellectual property and other issues, the projects do not result in lead declarations. In this article, HTS success is defined as the fraction of HTS campaigns that advance into the later stages of drug discovery, and the major influencing factors are examined. Interestingly, screening compounds in individual wells or in mixtures did not have a major impact on the HTS success and, equally interesting, there was no difference in the progression rates of biochemical and cell-based assays. Particular target types, assay technologies, structure-activity relationships and powder availability had a much greater impact on success as defined above. In addition, significant mutual dependencies can be observed - while one assay format works well with one target type, this situation might be completely reversed for a combination of the same readout technology with a different target type. The results and opinions presented here should be regarded as groundwork, and a plethora of factors that influence the fate of a project, such as biophysical measurements, chemical attractiveness of the hits, strategic reasons and safety pharmacology, are not covered here. Nonetheless, it is hoped that this information will be used industry-wide to improve success rates in terms of hits progressing into exploratory chemistry and beyond. The support that can be obtained from new in silico approaches to phase transitions are also described, along with the gaps they are designed to fill.
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Affiliation(s)
- Andreas Bender
- Lead Finding Platform, Novartis Institutes for Biomedical Research, Cambridge, MA 02139, USA
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Bender A, Young DW, Jenkins JL, Serrano M, Mikhailov D, Clemons PA, Davies JW. Chemogenomic data analysis: prediction of small-molecule targets and the advent of biological fingerprint. Comb Chem High Throughput Screen 2008; 10:719-31. [PMID: 18045083 DOI: 10.2174/138620707782507313] [Citation(s) in RCA: 83] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Chemogenomics comprises a systematic relationship between targets and ligands that are used as target modulators in living systems such as cells or organisms. In recent years, data on small molecule-bioactivity relationships have become increasingly available, and consequently so have the number of approaches used to translate bioactivity data into knowledge. This review will focus on two aspects of chemogenomics. Firstly, in cases such as cell-based screens, the question of which target(s) a compound is modulating in order to cause the observed phenotype is crucial. In silico target prediction tools can suggest likely biological targets of small molecules via data mining in target-annotated chemical databases. This review presents some of the current tools available for this task and shows some sample applications relevant to a pharmaceutical industry setting. These applications are the prediction of false-positives in cell-based reporter gene assays, the prediction of targets by linking bioassay data with protein domain annotations, and the direct prediction of adverse reactions. Secondly, in recent years a shift from structure-derived chemical descriptors to biological descriptors has occurred. Here, the effect of a compound on a number of biological endpoints is used to make predictions about other properties, such as putative targets, associated adverse reactions, and pathways modulated by the compound. This review further summarizes these "performance" descriptors and their applications, focusing on gene expression profiles and high-content screening data. The advent of such biological fingerprints suggests that the field of drug discovery is currently at a crossroads, where single target bioassay results are supplanted by multidimensional biological fingerprints that reflect a new awareness of biological networks and polypharmacology.
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Affiliation(s)
- Andreas Bender
- Lead Discovery Informatics, Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Ave., Cambridge, MA 02139, USA.
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46
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Scheiber J, Jenkins JL, Bender A, Whitebread S, Hamon J, Urban L, Azzaoui K, Glick M, Davies JW. Side effect profile prediction - early addressing of big pharma's worst nightmare. Chem Cent J 2008. [PMCID: PMC4236057 DOI: 10.1186/1752-153x-2-s1-s4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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Crisman TJ, Bender A, Milik M, Jenkins JL, Scheiber J, Sukuru SCK, Fejzo J, Hommel U, Davies JW, Glick M. “Virtual Fragment Linking”: An Approach To Identify Potent Binders from Low Affinity Fragment Hits. J Med Chem 2008; 51:2481-91. [DOI: 10.1021/jm701314u] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Thomas J. Crisman
- Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, and Center for Proteomic Chemistry, Novartis Pharma AG, Postfach, CH-4002 Basel, Switzerland
| | - Andreas Bender
- Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, and Center for Proteomic Chemistry, Novartis Pharma AG, Postfach, CH-4002 Basel, Switzerland
| | - Mariusz Milik
- Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, and Center for Proteomic Chemistry, Novartis Pharma AG, Postfach, CH-4002 Basel, Switzerland
| | - Jeremy L. Jenkins
- Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, and Center for Proteomic Chemistry, Novartis Pharma AG, Postfach, CH-4002 Basel, Switzerland
| | - Josef Scheiber
- Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, and Center for Proteomic Chemistry, Novartis Pharma AG, Postfach, CH-4002 Basel, Switzerland
| | - Sai Chetan K. Sukuru
- Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, and Center for Proteomic Chemistry, Novartis Pharma AG, Postfach, CH-4002 Basel, Switzerland
| | - Jasna Fejzo
- Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, and Center for Proteomic Chemistry, Novartis Pharma AG, Postfach, CH-4002 Basel, Switzerland
| | - Ulrich Hommel
- Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, and Center for Proteomic Chemistry, Novartis Pharma AG, Postfach, CH-4002 Basel, Switzerland
| | - John W. Davies
- Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, and Center for Proteomic Chemistry, Novartis Pharma AG, Postfach, CH-4002 Basel, Switzerland
| | - Meir Glick
- Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, and Center for Proteomic Chemistry, Novartis Pharma AG, Postfach, CH-4002 Basel, Switzerland
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You TH, Lee MK, Jenkins JL, Alzate O, Dean DH. Blocking binding of Bacillus thuringiensis Cry1Aa to Bombyx mori cadherin receptor results in only a minor reduction of toxicity. BMC Biochem 2008; 9:3. [PMID: 18218126 PMCID: PMC2245940 DOI: 10.1186/1471-2091-9-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2007] [Accepted: 01/24/2008] [Indexed: 11/24/2022]
Abstract
Background Bacillus thuringiensis Cry1Aa insecticidal protein is the most active known B. thuringiensis toxin against the forest insect pest Lymantria dispar (gypsy moth), unfortunately it is also highly toxic against the non-target insect Bombyx mori (silk worm). Results Surface exposed hydrophobic residues over domains II and III were targeted for site-directed mutagenesis. Substitution of a phenylalanine residue (F328) by alanine reduced binding to the Bombyx mori cadherin by 23-fold, reduced biological activity against B. mori by 4-fold, while retaining activity against Lymantria dispar. Conclusion The results identify a novel receptor-binding epitope and demonstrate that virtual elimination of binding to cadherin BR-175 does not completely remove toxicity in the case of B. mori.
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Affiliation(s)
- Taek H You
- Department of Biochemistry, The Ohio State University, Columbus, OH 43210, USA.
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Crisman TJ, Parker CN, Jenkins JL, Scheiber J, Thoma M, Kang ZB, Kim R, Bender A, Nettles JH, Davies JW, Glick M. Understanding false positives in reporter gene assays: in silico chemogenomics approaches to prioritize cell-based HTS data. J Chem Inf Model 2007; 47:1319-27. [PMID: 17608469 DOI: 10.1021/ci6005504] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
High throughput screening (HTS) data is often noisy, containing both false positives and negatives. Thus, careful triaging and prioritization of the primary hit list can save time and money by identifying potential false positives before incurring the expense of followup. Of particular concern are cell-based reporter gene assays (RGAs) where the number of hits may be prohibitively high to be scrutinized manually for weeding out erroneous data. Based on statistical models built from chemical structures of 650 000 compounds tested in RGAs, we created "frequent hitter" models that make it possible to prioritize potential false positives. Furthermore, we followed up the frequent hitter evaluation with chemical structure based in silico target predictions to hypothesize a mechanism for the observed "off target" response. It was observed that the predicted cellular targets for the frequent hitters were known to be associated with undesirable effects such as cytotoxicity. More specifically, the most frequently predicted targets relate to apoptosis and cell differentiation, including kinases, topoisomerases, and protein phosphatases. The mechanism-based frequent hitter hypothesis was tested using 160 additional druglike compounds predicted by the model to be nonspecific actives in RGAs. This validation was successful (showing a 50% hit rate compared to a normal hit rate as low as 2%), and it demonstrates the power of computational models toward understanding complex relations between chemical structure and biological function.
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Affiliation(s)
- Thomas J Crisman
- Lead Discovery Center, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA
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Azzaoui K, Hamon J, Faller B, Whitebread S, Jacoby E, Bender A, Jenkins JL, Urban L. Modeling Promiscuity Based on in vitro Safety Pharmacology Profiling Data. ChemMedChem 2007; 2:874-80. [PMID: 17492703 DOI: 10.1002/cmdc.200700036] [Citation(s) in RCA: 149] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This study describes a method for mining and modeling binding data obtained from a large panel of targets (in vitro safety pharmacology) to distinguish differences between promiscuous and selective compounds. Two naïve Bayes models for promiscuity and selectivity were generated and validated on a test set as well as publicly available drug databases. The model shows a higher score (lower promiscuity) for marketed drugs than for compounds in early development or compounds that failed during clinical development. Such models can be used in triaging high-throughput screening data or for lead optimization.
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Affiliation(s)
- Kamal Azzaoui
- CPC/LFP/MLI, Novartis Institutes for Biomedical Research, Novartis Pharma AG, Postfach, 4002 Basel, Switzerland.
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