Addressing the Metabolic Stability of Antituberculars through Machine Learning
Open accessAbstract
We present the first prospective application of our mouse liver microsomal (MLM) stability Bayesian model. CD117, an antitubercular thienopyrimidine tool compound that suffers from metabolic instability (MLM t 1/2 < 1 min), was utilized to assess the predictive power of our new MLM stability model. The S-substituent was removed, a set of commercial reagents was utilized to construct a virtual library of 411 analogues, and our MLM stability model was applied to prioritize 13 analogues for synthesis and biological profiling. In MLM stability assays, all 13 analogues had superior metabolic stability to the parent compound, and six new analogues had acceptable MLM t 1/2 values greater than or equal to 60 min. It is noteworthy that whole-cell efficacy and lack of relative mammalian cell cytotoxicity could not be predicted simultaneously. These results support the utility of our new MLM stability model in chemical tool and drug discovery optimization efforts.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
After 4-fold cross validation and ranked normalized scores of metrics, DNNs ranked higher than all the other machine learning methods across all datasets.
“After 4-fold cross validation and ranked normalized scores of metrics, DNNs ranked higher than all the other machine learning methods across all datasets48.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [48]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Palomino, J.-C., Martin, A., Camacho, M., Guerra, H., Swings, J., & Portaels, F. (2002) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.46.8.2720-2722.2002 · Cited by 1,229 · see citing works
Walters, W. P., Stahl, M. T., & Murcko, M. A. (1998) · Drug Discovery Today · doi:10.1016/s1359-6446(97)01163-x · Cited by 996 · see citing works
Ghose, A. K., Viswanadhan, V. N., & Wendoloski, J. J. (1998) · The Journal of Physical Chemistry A · doi:10.1021/jp980230o · Cited by 796 · see citing works
MetaSite: Understanding Metabolism in Human Cytochromes from the Perspective of the Chemist
Cruciani, G., Carosati, E., Boeck, B. D., Ethirajulu, K., Mackie, C. E., Howe, T. J., & Vianello, R. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050529c · Cited by 497 · see citing works
The Practice of Medicinal Chemistry
(2015) · Elsevier eBooks · doi:10.1016/c2012-0-03066-9 · Cited by 383 · see citing works
Probing the Probes: Fitness Factors For Small Molecule Tools
Workman, P., & Collins, I. (2010) · Chemistry & Biology · doi:10.1016/j.chembiol.2010.05.013 · Cited by 300 · see citing works
Mouse Model of Necrotic Tuberculosis Granulomas Develops Hypoxic Lesions
Harper, J. S., Skerry, C. M., Davis, S. L., Tasneen, R., Weir, M., Kramnik, I., Bishai, W. R., Pomper, M. G., Nuermberger, E. L., & Jain, S. K. (2011) · The Journal of Infectious Diseases · doi:10.1093/infdis/jir786 · Cited by 256 · see citing works
Kramnik, I., & Beamer, G. (2015) · Seminars in Immunopathology · doi:10.1007/s00281-015-0538-9 · Cited by 164 · see citing works
Irwin, S. M., Driver, E., Lyon, E. R., Schrupp, C., Ryan, G. J., Juarrero, M. G., Basaraba, R. J., Nuermberger, E. L., & Lenaerts, A. J. M. (2015) · Disease Models & Mechanisms · doi:10.1242/dmm.019570 · Cited by 161 · see citing works
Comprehensive physicochemical, pharmacokinetic and activity profiling of anti-TB agents
Lakshminarayana, S. B., Huat, T. B., Ho, P. C., Manjunatha, U. H., Dartois, V. A., Dick, T., & Rao, S. P. S. (2014) · Journal of Antimicrobial Chemotherapy · doi:10.1093/jac/dku457 · Cited by 151 · see citing works
Heterogeneous disease progression and treatment response in a C3HeB/FeJ mouse model of tuberculosis
Lanoix, J., Lenaerts, A. J. M., & Nuermberger, E. L. (2015) · Disease Models & Mechanisms · doi:10.1242/dmm.019513 · Cited by 128 · see citing works
Bayesian Models Leveraging Bioactivity and Cytotoxicity Information for Drug Discovery
Ekins, S., Reynolds, R. C., Kim, H., Koo, M.-S., Ekonomidis, M., Talaue, M. T., Paget, S. D., Woolhiser, L. K., Lenaerts, A. J. M., Bunin, B. A., et al. (2013) · Chemistry & Biology · doi:10.1016/j.chembiol.2013.01.011 · Cited by 97 · see citing works
Self-citationComputational databases, pathway and cheminformatics tools for tuberculosis drug discovery
Ekins, S., Freundlich, J. S., Choi, I., Sarker, M., & Talcott, C. (2010) · Trends in Microbiology · doi:10.1016/j.tim.2010.10.005 · Cited by 83 · see citing works
Self-citationApplications of High Throughput Microsomal Stability Assay in Drug Discovery
Di, L., Kerns, E. H., Ma, X., et al. (2008) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/138620708784911429 · Cited by 69 · see citing works
Vilchèze, C., Baughn, A. D., Tufariello, J. M., et al. (2011) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.00266-11 · Cited by 67 · see citing works
Self-citationThompson, A. M., O’Connor, P. D., Marshall, A. J., et al. (2017) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.7b00034 · Cited by 61 · see citing works
Ekins, S., Freundlich, J. S., Hobrath, J. V., White, E. L., & Reynolds, R. C. (2013) · Pharmaceutical Research · doi:10.1007/s11095-013-1172-7 · Cited by 57 · see citing works
Self-citationPredicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data
Perryman, A. L., Stratton, T. P., Ekins, S., & Freundlich, J. S. (2016) · Pharmaceutical Research · doi:10.1007/s11095-015-1800-5 · Cited by 50 · see citing works
Self-citationHu, Y., Unwalla, R. J., Denny, R. A., Bikker, J. A., Di, L., & Humblet, C. C. (2009) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-009-9309-9 · Cited by 49 · see citing works
Cooper, C. B. (2013) · Journal of Medicinal Chemistry · doi:10.1021/jm400381v · Cited by 45 · see citing works
Li, S.-G., Vilchèze, C., Chakraborty, S., et al. (2015) · Tetrahedron Letters · doi:10.1016/j.tetlet.2015.02.129 · Cited by 30 · see citing works
Self-citationEkins, S., Perryman, A. L., Clark, A. M., Reynolds, R. C., & Freundlich, J. S. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00004 · Cited by 26 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Global Tuberculosis Report; WHO: Geneva, 2016. [Google Scholar]
Cited by
Sorted by citations
Exploiting machine learning for end-to-end drug discovery and development
Ekins, S., Puhl, A. C., Zorn, K. M., Lane, T. R., Russo, D. P., Klein, J. J., Hickey, A. J., & Clark, A. M. (2019) · Nature Materials · doi:10.1038/s41563-019-0338-z · Cited by 578 · see citing works
Self-citationNaïve Bayesian Models for Vero Cell Cytotoxicity
Perryman, A. L., Patel, J. S., Russo, R., Singleton, E., Connell, N., Ekins, S., & Freundlich, J. S. (2018) · Pharmaceutical Research · doi:10.1007/s11095-018-2439-9 · Cited by 33 · see citing works
Self-citation
- Authors
- Stratton, T. P., Perryman, A. L., Vilchèze, C., Russo, R., Li, S.-G., Patel, J. S., Singleton, E., Ekins, S., Connell, N., Jacobs, W. R., & Freundlich, J. S.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Albert Einstein College of Medicine (US)
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University−New Jersey Medical School, Newark, New Jersey 07103, United States
- Collaborative Drug Discovery (United States) (US)
- Corresponding author
- Freundlich, Joel S.
- ORCID
- Venue
- ACS Medicinal Chemistry Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-09-22
- Volume
- 8
- Issue
- 10
- Pages
- 1099-1104
- ISSN
- 1948-5875 (ISSN-L)
- PMC
- PMC5642018
- OpenAlex
- W2755230800
- Cited by
- 14
- References
- 22
- Citations by year
- 2025: 1 · 2022: 2 · 2021: 3 · 2020: 3 · 2019: 3 · 2018: 2
- FWCI
- 1.64
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- ACS Medicinal Chemistry Letters (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Center for Advancing Translational Sciences (R44TR000942-02)
- National Institute of Allergy and Infectious Diseases (1U19AI109713, AI026170, U19AI111276)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
- Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
- Keywords
- metabolic stability, CD117, thienopyrimidines, cytotoxicity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
