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Predictive Models for Human Cytochrome P450 3A7 Selective Inhibitors and Substrates

Xu, Tuan ; Kabir, Md ; Sakamuru, Srilatha ; Shah, Pranav ; Padilha, Elias C. ; Ngan, Deborah K. ; Xia, Menghang ; Xu, Xin ; Simeonov, Anton ; Huang, Ruili

Journal of chemical information and modeling, 2023-02, Vol.63 (3), p.846-855 [Periódico revisado por pares]

United States: American Chemical Society

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  • Título:
    Predictive Models for Human Cytochrome P450 3A7 Selective Inhibitors and Substrates
  • Autor: Xu, Tuan ; Kabir, Md ; Sakamuru, Srilatha ; Shah, Pranav ; Padilha, Elias C. ; Ngan, Deborah K. ; Xia, Menghang ; Xu, Xin ; Simeonov, Anton ; Huang, Ruili
  • Assuntos: Adult ; Adults ; Area Under Curve ; Correlation coefficients ; Cytochrome ; Cytochrome P-450 CYP3A - metabolism ; Cytochromes P450 ; Datasets ; Drugs ; Fetuses ; Humans ; Infant, Newborn ; Inhibitors ; Machine Learning and Deep Learning ; Optimization ; Performance evaluation ; Prediction models ; Prescription drugs ; Structural analysis ; Substrate inhibition
  • É parte de: Journal of chemical information and modeling, 2023-02, Vol.63 (3), p.846-855
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
    R.H. designed the study. M.K., S.S., P.S., and E.P. conducted the experiments. M.X. and X.X. directed the generation of experimental data. T.X. performed statistical analysis of all data. T.X., D.N., and R.H. wrote the manuscript. R.H. and A.S. directed the project. All authors reviewed the manuscript.
    Author contributions
  • Descrição: Inappropriate use of prescription drugs is potentially more harmful in fetuses/neonates than in adults. Cytochrome P450 (CYP) 3A subfamily undergoes developmental changes in expression, such as a transition from CYP3A7 to CYP3A4 shortly after birth, which provides a potential way to distinguish medication effects on fetuses/neonates and adults. The purpose of this study was to build first-in-class predictive models for both inhibitors and substrates of CYP3A7/CYP3A4 using chemical structure analysis. Three metrics were used to evaluate model performance: area under the receiver operating characteristic curve (AUC-ROC), balanced accuracy (BA), and Matthews correlation coefficient (MCC). The performance varied for each CYP3A7/CYP3A4 inhibitor/substrate model depending on the data set type, model type, rebalancing method, and specific feature set. For the active inhibitor/substrate data set, the optimal models achieved AUC-ROC values ranging from 0.77 ± 0.01 to 0.84 ± 0.01. For the selective inhibitor/substrate data set, the optimal models achieved AUC-ROC values ranging from 0.72 ± 0.02 to 0.79 ± 0.04. The predictive power of the optimal models was validated by compounds with known potencies as CYP3A7/CYP3A4 inhibitors or substrates. In addition, we identified structural features significant for CYP3A7/CYP3A4 selective or common inhibitors and substrates. In summary, the top performing models can be further applied as a tool to rapidly evaluate the safety and efficacy of new drugs separately for fetuses/neonates and adults. The significant structural features could guide the design of new therapeutic drugs as well as aid in the optimization of existing medicine for fetuses/neonates.
  • Editor: United States: American Chemical Society
  • Idioma: Inglês
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