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Naïve Bayesian Models for Vero Cell Cytotoxicity

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Abstract

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.

Naive Bayesian models have been trained with publically available data and offer a useful tool for chemical biology and drug discovery to select for small molecules with a high probability of exhibiting acceptably low Vero cell cytotoxicity.

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Abbreviations

ADME/Tox:

Absorption, metabolism, distribution, excretion and toxicity

AID:

Assay Identification number on PubChem BioAssay

ECFP_6:

Extended class fingerprints of maximum diameter 6

FCFP_6:

Molecular function class fingerprints of maximum diameter 6

NPV:

Negative predictive value (filtering rate)

PPV:

Positive predictive value (hit rate)

QSAR:

Quantitative Structure-Activity Relationships

ROC:

Receiver-operator characteristic

SAR:

Structure-Activity Relationship

SMILES:

Simplified molecular-input line-entry system

Vero CC50 :

Vero cell (African green monkey kidney cell) 50% cytotoxicity value

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Author information

Authors and Affiliations

  1. Department of Pharmacology, Physiology and Neuroscience, and Medicine, Rutgers University-New Jersey Medical School, Medical Sciences Building, I-503, 185 South Orange Ave, Newark, NJ, 07103, USA

    Alexander L. Perryman, Jimmy S. Patel & Joel S. Freundlich

  2. 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, Medical Sciences Building, I-503, 185 South Orange Ave, Newark, NJ, 07103, USA

    Riccardo Russo, Eric Singleton, Nancy Connell & Joel S. Freundlich

  3. Collaborations Pharmaceuticals, Inc., Main Campus Drive Lab 3510, Raleigh, North Carolina,, 27606, USA

    Sean Ekins

Authors
  1. Alexander L. Perryman
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  2. Jimmy S. Patel
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  3. Riccardo Russo
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  4. Eric Singleton
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  5. Nancy Connell
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  6. Sean Ekins
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  7. Joel S. Freundlich
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Corresponding author

Correspondence to Joel S. Freundlich.

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Conflicts of Interest

S.E. is the Founder and CEO of Collaborations Pharmaceuticals Inc.

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Perryman, A.L., Patel, J.S., Russo, R. et al. Naïve Bayesian Models for Vero Cell Cytotoxicity. Pharm Res 35, 170 (2018). https://doi.org/10.1007/s11095-018-2439-9

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  • DOI: https://doi.org/10.1007/s11095-018-2439-9

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