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Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014-2015).
J Chem Inf Model. 2016 07 25; 56(7):1332-43.JC

Abstract

The renewed urgency to develop new treatments for Mycobacterium tuberculosis (Mtb) infection has resulted in large-scale phenotypic screening and thousands of new active compounds in vitro. The next challenge is to identify candidates to pursue in a mouse in vivo efficacy model as a step to predicting clinical efficacy. We previously analyzed over 70 years of this mouse in vivo efficacy data, which we used to generate and validate machine learning models. Curation of 60 additional small molecules with in vivo data published in 2014 and 2015 was undertaken to further test these models. This represents a much larger test set than for the previous models. Several computational approaches have now been applied to analyze these molecules and compare their molecular properties beyond those attempted previously. Our previous machine learning models have been updated, and a novel aspect has been added in the form of mouse liver microsomal half-life (MLM t1/2) and in vitro-based Mtb models incorporating cytotoxicity data that were used to predict in vivo activity for comparison. Our best Mtb in vivo models possess fivefold ROC values > 0.7, sensitivity > 80%, and concordance > 60%, while the best specificity value is >40%. Use of an MLM t1/2 Bayesian model affords comparable results for scoring the 60 compounds tested. Combining MLM stability and in vitro Mtb models in a novel consensus workflow in the best cases has a positive predicted value (hit rate) > 77%. Our results indicate that Bayesian models constructed with literature in vivo Mtb data generated by different laboratories in various mouse models can have predictive value and may be used alongside MLM t1/2 and in vitro-based Mtb models to assist in selecting antitubercular compounds with desirable in vivo efficacy. We demonstrate for the first time that consensus models of any kind can be used to predict in vivo activity for Mtb. In addition, we describe a new clustering method for data visualization and apply this to the in vivo training and test data, ultimately making the method accessible in a mobile app.

Authors+Show Affiliations

Collaborative Drug Discovery , 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States. Collaborations in Chemistry , 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States.Department of Pharmacology, Physiology and Neuroscience, Rutgers University-New Jersey Medical School , Newark, New Jersey 07103, United States.Molecular Materials Informatics, Inc. , 1900 St. Jacques #302, Montreal, Quebec H3J 2S1, Canada.Division of Hematology and Oncology, Department of Medicine, and Department of Chemistry, College of Arts and Sciences, University of Alabama at Birmingham , 1530 Third Avenue South, Birmingham, Alabama 35294-1240, United States.Department of Pharmacology, Physiology and Neuroscience, Rutgers University-New Jersey Medical School , Newark, New Jersey 07103, United States. Division of Infectious Diseases, 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, United States.

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

27335215

Citation

Ekins, Sean, et al. "Machine Learning Model Analysis and Data Visualization With Small Molecules Tested in a Mouse Model of Mycobacterium Tuberculosis Infection (2014-2015)." Journal of Chemical Information and Modeling, vol. 56, no. 7, 2016, pp. 1332-43.
Ekins S, Perryman AL, Clark AM, et al. Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014-2015). J Chem Inf Model. 2016;56(7):1332-43.
Ekins, S., Perryman, A. L., Clark, A. M., Reynolds, R. C., & Freundlich, J. S. (2016). Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014-2015). Journal of Chemical Information and Modeling, 56(7), 1332-43. https://doi.org/10.1021/acs.jcim.6b00004
Ekins S, et al. Machine Learning Model Analysis and Data Visualization With Small Molecules Tested in a Mouse Model of Mycobacterium Tuberculosis Infection (2014-2015). J Chem Inf Model. 2016 07 25;56(7):1332-43. PubMed PMID: 27335215.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014-2015). AU - Ekins,Sean, AU - Perryman,Alexander L, AU - Clark,Alex M, AU - Reynolds,Robert C, AU - Freundlich,Joel S, Y1 - 2016/07/01/ PY - 2016/6/24/entrez PY - 2016/6/24/pubmed PY - 2017/8/2/medline SP - 1332 EP - 43 JF - Journal of chemical information and modeling JO - J Chem Inf Model VL - 56 IS - 7 N2 - The renewed urgency to develop new treatments for Mycobacterium tuberculosis (Mtb) infection has resulted in large-scale phenotypic screening and thousands of new active compounds in vitro. The next challenge is to identify candidates to pursue in a mouse in vivo efficacy model as a step to predicting clinical efficacy. We previously analyzed over 70 years of this mouse in vivo efficacy data, which we used to generate and validate machine learning models. Curation of 60 additional small molecules with in vivo data published in 2014 and 2015 was undertaken to further test these models. This represents a much larger test set than for the previous models. Several computational approaches have now been applied to analyze these molecules and compare their molecular properties beyond those attempted previously. Our previous machine learning models have been updated, and a novel aspect has been added in the form of mouse liver microsomal half-life (MLM t1/2) and in vitro-based Mtb models incorporating cytotoxicity data that were used to predict in vivo activity for comparison. Our best Mtb in vivo models possess fivefold ROC values > 0.7, sensitivity > 80%, and concordance > 60%, while the best specificity value is >40%. Use of an MLM t1/2 Bayesian model affords comparable results for scoring the 60 compounds tested. Combining MLM stability and in vitro Mtb models in a novel consensus workflow in the best cases has a positive predicted value (hit rate) > 77%. Our results indicate that Bayesian models constructed with literature in vivo Mtb data generated by different laboratories in various mouse models can have predictive value and may be used alongside MLM t1/2 and in vitro-based Mtb models to assist in selecting antitubercular compounds with desirable in vivo efficacy. We demonstrate for the first time that consensus models of any kind can be used to predict in vivo activity for Mtb. In addition, we describe a new clustering method for data visualization and apply this to the in vivo training and test data, ultimately making the method accessible in a mobile app. SN - 1549-960X UR - https://www.unboundmedicine.com/medline/citation/27335215/Machine_Learning_Model_Analysis_and_Data_Visualization_with_Small_Molecules_Tested_in_a_Mouse_Model_of_Mycobacterium_tuberculosis_Infection__2014_2015__ L2 - https://dx.doi.org/10.1021/acs.jcim.6b00004 DB - PRIME DP - Unbound Medicine ER -