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Automated design of ligands to polypharmacological profiles

Author

Listed:
  • Jérémy Besnard

    (College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK)

  • Gian Filippo Ruda

    (College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK)

  • Vincent Setola

    (NIMH Psychoactive Drug Screening Program, The University of North Carolina Chapel Hill School of Medicine)

  • Keren Abecassis

    (College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK)

  • Ramona M. Rodriguiz

    (Mouse Behavioral and Neuroendocrine Analysis Core Facility, Duke University Medical School)

  • Xi-Ping Huang

    (NIMH Psychoactive Drug Screening Program, The University of North Carolina Chapel Hill School of Medicine)

  • Suzanne Norval

    (College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK)

  • Maria F. Sassano

    (The University of North Carolina Chapel Hill School of Medicine)

  • Antony I. Shin

    (Mouse Behavioral and Neuroendocrine Analysis Core Facility, Duke University Medical School)

  • Lauren A. Webster

    (College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK)

  • Frederick R. C. Simeons

    (College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK)

  • Laste Stojanovski

    (College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK)

  • Annik Prat

    (Laboratory of Biochemical Neuroendocrinology, Clinical Research Institute of Montreal (IRCM), affiliated with the University of Montreal, Montreal, Quebec, H2W 1R7, Canada)

  • Nabil G. Seidah

    (Laboratory of Biochemical Neuroendocrinology, Clinical Research Institute of Montreal (IRCM), affiliated with the University of Montreal, Montreal, Quebec, H2W 1R7, Canada)

  • Daniel B. Constam

    (Ecole Polytechnique Fédérale de Lausanne (EPFL) SV ISREC, Station 19, CH-1015 Lausanne, Switzerland)

  • G. Richard Bickerton

    (College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK)

  • Kevin D. Read

    (College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK)

  • William C. Wetsel

    (Mouse Behavioral and Neuroendocrine Analysis Core Facility, Duke University Medical School
    Cell Biology, and Neurobiology, Duke University Medical School)

  • Ian H. Gilbert

    (College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK)

  • Bryan L. Roth

    (NIMH Psychoactive Drug Screening Program, The University of North Carolina Chapel Hill School of Medicine
    The University of North Carolina Chapel Hill School of Medicine)

  • Andrew L. Hopkins

    (College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK)

Abstract

The clinical efficacy and safety of a drug is determined by its activity profile across many proteins in the proteome. However, designing drugs with a specific multi-target profile is both complex and difficult. Therefore methods to design drugs rationally a priori against profiles of several proteins would have immense value in drug discovery. Here we describe a new approach for the automated design of ligands against profiles of multiple drug targets. The method is demonstrated by the evolution of an approved acetylcholinesterase inhibitor drug into brain-penetrable ligands with either specific polypharmacology or exquisite selectivity profiles for G-protein-coupled receptors. Overall, 800 ligand–target predictions of prospectively designed ligands were tested experimentally, of which 75% were confirmed to be correct. We also demonstrate target engagement in vivo. The approach can be a useful source of drug leads when multi-target profiles are required to achieve either selectivity over other drug targets or a desired polypharmacology.

Suggested Citation

  • Jérémy Besnard & Gian Filippo Ruda & Vincent Setola & Keren Abecassis & Ramona M. Rodriguiz & Xi-Ping Huang & Suzanne Norval & Maria F. Sassano & Antony I. Shin & Lauren A. Webster & Frederick R. C. S, 2012. "Automated design of ligands to polypharmacological profiles," Nature, Nature, vol. 492(7428), pages 215-220, December.
    • Handle: RePEc:nat:nature:v:492:y:2012:i:7428:d:10.1038_nature11691
    DOI: 10.1038/nature11691
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    Cited by:

    1. Jason Wallach & Andrew B. Cao & Maggie M. Calkins & Andrew J. Heim & Janelle K. Lanham & Emma M. Bonniwell & Joseph J. Hennessey & Hailey A. Bock & Emilie I. Anderson & Alexander M. Sherwood & Hamilto, 2023. "Identification of 5-HT2A receptor signaling pathways associated with psychedelic potential," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    2. Tuomo Kalliokoski & Christian Kramer & Anna Vulpetti & Peter Gedeck, 2013. "Comparability of Mixed IC50 Data – A Statistical Analysis," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-12, April.
    3. Choo Jun Tan & Siew Chin Neoh & Chee Peng Lim & Samer Hanoun & Wai Peng Wong & Chu Kong Loo & Li Zhang & Saeid Nahavandi, 2019. "Application of an evolutionary algorithm-based ensemble model to job-shop scheduling," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 879-890, February.
    4. Václav Havel & Andrew C. Kruegel & Benjamin Bechand & Scot McIntosh & Leia Stallings & Alana Hodges & Madalee G. Wulf & Mel Nelson & Amanda Hunkele & Michael Ansonoff & John E. Pintar & Christopher Hw, 2024. "Oxa-Iboga alkaloids lack cardiac risk and disrupt opioid use in animal models," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    5. Tia A. Tummino & Christos Iliopoulos-Tsoutsouvas & Joao M. Braz & Evan S. O’Brien & Reed M. Stein & Veronica Craik & Ngan K. Tran & Suthakar Ganapathy & Fangyu Liu & Yuki Shiimura & Fei Tong & Thanh C, 2025. "Virtual library docking for cannabinoid-1 receptor agonists with reduced side effects," Nature Communications, Nature, vol. 16(1), pages 1-19, December.
    6. Danielle Ireland & Christina Rabeler & Sagar Rao & Rudy J Richardson & Eva-Maria S Collins, 2025. "Distinguishing classes of neuroactive drugs based on computational physicochemical properties and experimental phenotypic profiling in planarians," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-38, January.
    7. Richard D Cramer, 2015. "Template CoMFA Generates Single 3D-QSAR Models that, for Twelve of Twelve Biological Targets, Predict All ChEMBL-Tabulated Affinities," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-22, June.

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