Pharmaceutical Research

, Volume 33, Issue 2, pp 433–449 | Cite as

Predicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data

  • Alexander L. Perryman
  • Thomas P. Stratton
  • Sean Ekins
  • Joel S. Freundlich
Research Paper

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.

Methods

Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism).

Results

“Pruning” out the moderately unstable / moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 h.

Conclusions

Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources.

Graphical Abstract

Key Words

Bayesian model machine learning metabolic stability mouse liver microsomal stability translational research 

ABBREVIATIONS

ADME/Tox

Absorption metabolism, distribution, excretion and toxicity

CDD

Collaborative Drug Discovery

FCFP_6

Molecular function class fingerprints of maximum diameter 6

HLM

Human liver microsomal stability

HTS

High Throughput Screens

Mtb

Mycobacterium tuberculosis

PPV

positive predictive value

QSAR

Quantitative Structure-Activity Relationships

ROC

Receiver-operator characteristic

SAR

Structure Activity Relationship

SVM

Support Vector Machine

Supplementary material

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Alexander L. Perryman
    • 1
  • Thomas P. Stratton
    • 2
  • Sean Ekins
    • 3
    • 4
  • Joel S. Freundlich
    • 1
    • 2
  1. 1.Division of Infectious Disease, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging PathogensRutgers University–New Jersey Medical SchoolNewarkUSA
  2. 2.Department of Pharmacology & PhysiologyRutgers University-New Jersey Medical SchoolNewarkUSA
  3. 3.Collaborations in ChemistryFuquay-VarinaUSA
  4. 4.Collaborative Drug DiscoveryBurlingameUSA

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