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Quantitative Estimate Index for Early-Stage Scr...

Quantitative Estimate Index for Early-Stage Screening of Compounds Targeting Protein-Protein Interactions

Tokyo Bioinformatics Meeting 第5回研究会の発表資料

GitHub :
https://github.com/ohuelab/QEPPI

Reference :
Kosugi T, Ohue M. Quantitative Estimate Index for Early-Stage Screening of Compounds Targeting Protein-Protein Interactions. International Journal of Molecular Sciences. 2021; 22(20):10925. https://doi.org/10.3390/ijms222010925

くろたんく

November 13, 2021
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     BOEUIVT —2&%NBZCFBTJNJMBSJOEFYUP2&11* Int. J. Mol. Sci. 2021, 22, 10925 4 of 15 (a) QED (b) QEPPI Figure 2. Distribution of QED and QEPPI in the PPI-targeting compounds dataset and FDA-approved drug dataset. Each filled area extends to represent the entire data range, with optional lines at the median. The QED score was calculated for both datasets (a). The QEPPI score was calculated for both datasets (b). Figure 2a shows that PPI-targeting compounds exhibit a lower distribution of QED scores compared to conventional drugs, suggesting that QED is not an appropriate measure for PPI-targeting compounds, as it typically represents oral drug-like properties rather than drug-likeness. Figure 2b shows that PPI-targeting compounds have a higher distribution of QEPPI scores compared to conventional drugs, and a QEPPI threshold of 0.5 is sufficient to identify approximately 75% of PPI-targeting compounds. Furthermore, PPI-target drugs Int. J. Mol. Sci. 2021, 22, 10925 (a) QED (b) QEPPI Figure 2. Distribution of QED and QEPPI in the PPI-targeting compounds dataset and FDA-approv filled area extends to represent the entire data range, with optional lines at the median. The QED sc both datasets (a). The QEPPI score was calculated for both datasets (b). Figure 2a shows that PPI-targeting compounds exhibit a lo scores compared to conventional drugs, suggesting that QED is no for PPI-targeting compounds, as it typically represents oral drug-l drug-likeness. Figure 2b shows that PPI-targeting compounds h of QEPPI scores compared to conventional drugs, and a QEPPI th to identify approximately 75% of PPI-targeting compounds. Furth have been removed from the FDA dataset based on the literat
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    QQr  properties more similar to those of standard drugs than those of PPI modulators currently on the market. We also applied QEPPI to the above datasets. The distribution of the QEPPI is shown in Figure 5. Our application of QEPPI to the 30 clinical candidates used by Truong et al. showed a median value of approximately 0.59, which is higher than that of commercially available PPI modulators, Figure 5. Although the physicochemical properties of the PPI-targeting compounds registered in iPPI-DB and FDA-approved drugs are different, as shown in Figure 1 and Table 1, the QEPPI modeled from iPPI-DB shows potential to be adapted to more recent PPI modulators. Figure 5. Distribution of QEPPI with respect to compounds in the clinical phase or approved PPI- targeting compounds dataset. The Truong clinical and Truong approved datasets represent clinical and FDA-approved PPI-targeting compound data, respectively. The iPPI-DB and Soga datasets represent positive and negative controls, respectively. The jitter overlaid on the boxplots shows the QEPPI scores for all samples in each data set. In addition, we also looked at when the PPI-targeted compounds included in the Truong approved data were marketed and when the PPI-targeted compounds included in the Truong clinical data were used in clinical trials. Figure 6 shows the QEPPI of PPI- targeting marketed drugs and compounds in clinical trials within the last 30 years (in detail Supplementary Table S4). Figure 6a shows the PPI-targeting drugs on the market, year the drug was first marketed (as identified in DrugBank), QEPPI value, and target PPI for each drug. PPI-targeting drugs launched in the 1990s showed lower QEPPI scores, $MJOJDBMUSJBM "QQSPWFE OPO11* $POUSPM 1BDMJUBYFM 2&11*  .JDSPUVCVMF  .BSLFUTUBSUJO 3PNJEFQTJO 2&11*  )%"$1*,  .BSLFUTUBSUJO 5FNTJSPMJNVT 2&11*  N503  .BSLFUTUBSUJO .BSLFUFEFYBNQMFT 7FSDJSOPO 2&11*  $$3  1IBTF *EBTBOVUMJO 2&11*  Q.EN  1IBTF "QBCFUBMPOF 2&11*  #&5  1IBTF $MJOJDBMUSJBMFYBNQMFT *OU+.PM4DJ  'JHVSF
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