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Structure-Based and Property-Compliant Library Design of 11β-HSD1 Adamantyl Amide Inhibitors

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Chemical Library Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 685))

Abstract

Multiproperty lead optimization that satisfies multiple biological endpoints remains a challenge in the pursuit of viable drug candidates. Optimization of a given lead compound to one having a desired set of molecular attributes often involves a lengthy iterative process that utilizes existing information, tests hypotheses, and incorporates new data. Within the context of a data-rich corporate setting, computational tools and predictive models have provided the chemists a means for facilitating and streamlining this iterative design process. This chapter discloses an actual library design scenario for following up a lead compound that inhibits 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) enzyme. The application of computational tools and predictive models in the targeted library design of adamantyl amide 11β-HSD1 inhibitors is described. Specifically, the multiproperty profiling using our proprietary PGVL (Pfizer Global Virtual Library) Hub is discussed in conjunction with the structure-based component of the library design using our in-house docking tool AGDOCK. The docking simulations were based on a piecewise linear potential energy function in combination with an efficient evolutionary programming search engine. The library production protocols and results are also presented.

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Acknowledgments

The authors would like to thank Simon Bailey, Martin Edwards, and Michael McAllister for their valuable advice, encouragement, and guidance. Specifically, the authors are grateful to Stanley Kupchinsky for the synthesis of the starting adamantyl amide lead and to the Discovery Computation group at PGRD La Jolla for the development of PGVL and AGDOCK, under the leadership of Atsuo Kuki and Peter Rose, respectively. Thanks are especially due to the following colleagues who developed and performed our project assays, specifically, Jacques Ermolieff (11β-HSD1 enzyme assays); Andrea Fanjul (11β-HSD1 cellular assays); Nora Wallace, Christine Taylor, and Rob Foti (HLM assays); and Veronica Zelesky, Kevin Whalen, and Walter Mitchell (HHEP assays). This work was supported by the 11β-HSD1 project team and the Pfizer Diabetes Therapeutic Area management.

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Authors and Affiliations

  1. Cancer Crystallography & Computational Chemistry, La Jolla Laboratories, Pfizer Inc., San Diego, CA, USA

    Genevieve D. Paderes

  2. Oncology Medicinal Chemistry, La Jolla Laboratories, Pfizer Inc., San Diego, CA, USA

    Klaus Dress, Buwen Huang, Jeff Elleraas & Tom Pauly

  3. Oncology, La Jolla Laboratories, Pfizer Inc., San Diego, CA, USA

    Paul A. Rejto

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  1. Genevieve D. Paderes
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  2. Klaus Dress
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  3. Buwen Huang
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  4. Jeff Elleraas
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  5. Paul A. Rejto
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  6. Tom Pauly
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Editors and Affiliations

  1. , Department of Pharmacology, University of California, La Jolla, 92093, California, USA

    Joe Zhongxiang Zhou

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Paderes, G.D., Dress, K., Huang, B., Elleraas, J., Rejto, P.A., Pauly, T. (2011). Structure-Based and Property-Compliant Library Design of 11β-HSD1 Adamantyl Amide Inhibitors. In: Zhou, J. (eds) Chemical Library Design. Methods in Molecular Biology, vol 685. Humana Press. https://doi.org/10.1007/978-1-60761-931-4_10

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  • DOI: https://doi.org/10.1007/978-1-60761-931-4_10

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  • Publisher Name: Humana Press

  • Print ISBN: 978-1-60761-930-7

  • Online ISBN: 978-1-60761-931-4

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