TY - JOUR
T1 - Naïve Bayesian Models for Vero Cell Cytotoxicity
AU - Perryman, Alexander L.
AU - Patel, Jimmy S.
AU - Russo, Riccardo
AU - Singleton, Eric
AU - Connell, Nancy
AU - Ekins, Sean
AU - Freundlich, Joel S.
N1 - Funding Information:
J.S.F., S.E., and N.C. were supported by Award Number U19AI109713 NIH/NIAID for the BCenter to develop therapeutic countermeasures to high-threat bacterial agents,^ from the National Institutes of Health: Centers of Excellence for Translational Research (CETR). We thank Tim O’Driscoll at BIOVIA for providing J.S.F with Discovery Studio and Pipeline Pilot. We also thank Jodi Shaulsky at BIOVIA and Katalin Nadassy (formerly at Accelrys) for assistance with setting up and maintaining the license server and Pipeline Pilot server.
Publisher Copyright:
© 2018, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2018/9/1
Y1 - 2018/9/1
N2 - Purpose: To advance translational research of potential therapeutic small molecules against infectious microbes, the compounds must display a relative lack of mammalian cell cytotoxicity. Vero cell cytotoxicity (CC50) is a common initial assay for this metric. We explored the development of naïve Bayesian models that can enhance the probability of identifying non-cytotoxic compounds. Methods: Vero cell cytotoxicity assays were identified in PubChem, reformatted, and curated to create a training set with 8741 unique small molecules. These data were used to develop Bayesian classifiers, which were assessed with internal cross-validation, external tests with a set of 193 compounds from our laboratory, and independent validation with an additional diverse set of 1609 unique compounds from PubChem. Results: Evaluation with independent, external test and validation sets indicated that cytotoxicity Bayesian models constructed with the ECFP_6 descriptor were more accurate than those that used FCFP_6 fingerprints. The best cytotoxicity Bayesian model displayed predictive power in external evaluations, according to conventional and chance-corrected statistics, as well as enrichment factors. Conclusions: The results from external tests demonstrate that our novel cytotoxicity Bayesian model displays sufficient predictive power to help guide translational research. To assist the chemical tool and drug discovery communities, our curated training set is being distributed as part of the Supplementary Material. [Figure not available: see fulltext.].
AB - Purpose: To advance translational research of potential therapeutic small molecules against infectious microbes, the compounds must display a relative lack of mammalian cell cytotoxicity. Vero cell cytotoxicity (CC50) is a common initial assay for this metric. We explored the development of naïve Bayesian models that can enhance the probability of identifying non-cytotoxic compounds. Methods: Vero cell cytotoxicity assays were identified in PubChem, reformatted, and curated to create a training set with 8741 unique small molecules. These data were used to develop Bayesian classifiers, which were assessed with internal cross-validation, external tests with a set of 193 compounds from our laboratory, and independent validation with an additional diverse set of 1609 unique compounds from PubChem. Results: Evaluation with independent, external test and validation sets indicated that cytotoxicity Bayesian models constructed with the ECFP_6 descriptor were more accurate than those that used FCFP_6 fingerprints. The best cytotoxicity Bayesian model displayed predictive power in external evaluations, according to conventional and chance-corrected statistics, as well as enrichment factors. Conclusions: The results from external tests demonstrate that our novel cytotoxicity Bayesian model displays sufficient predictive power to help guide translational research. To assist the chemical tool and drug discovery communities, our curated training set is being distributed as part of the Supplementary Material. [Figure not available: see fulltext.].
KW - Bayesian model
KW - machine learning
KW - predicting mammalian cytotoxicity
KW - translational research
KW - vero cell CC
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U2 - 10.1007/s11095-018-2439-9
DO - 10.1007/s11095-018-2439-9
M3 - Article
C2 - 29959603
AN - SCOPUS:85049157944
SN - 0724-8741
VL - 35
JO - Pharmaceutical Research
JF - Pharmaceutical Research
IS - 9
M1 - 170
ER -