Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.

Journal: Pharmaceutical research
Published Date: Feb 1, 2016

Abstract

PURPOSE: Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability.

Authors

  • Alexander L Perryman
    Division of Infectious Disease, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University-New Jersey Medical School, Newark, New Jersey, 07103, USA.
  • Thomas P Stratton
    Department of Pharmacology & Physiology, Rutgers University-New Jersey Medical School, Medical Sciences Building, I-503, 185 South Orange Ave., Newark, New Jersey, 07103, USA.
  • Sean Ekins
    Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526, USA; Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA; Collaborations Pharmaceuticals, Inc., 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526, USA; Phoenix Nest, Inc., P.O. Box 150057, Brooklyn, NY 11215, USA; Hereditary Neuropathy Foundation, 401 Park Avenue South, 10th Floor, New York, NY 10016, USA. Electronic address: ekinssean@yahoo.com.
  • Joel S Freundlich
    Division of Infectious Disease, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University-New Jersey Medical School, Newark, New Jersey, 07103, USA. freundjs@rutgers.edu.

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