Understanding the Role of Activation Loop Mutants in Drug Efficacy for FLT3-ITD

Figures

• 

  Figure 1. Interaction dynamics of FLT3 kinase domain and its mutants with small molecules. (A) Distribution of interaction scores for over 5.7 million unique FLT3-KD-small molecule pairs sourced from the ChEMBL database. ConPLex platform with a threshold score of >0.8 was used to identify interactions between small molecules and FLT3 kinase domains. (B) Trend analysis of interaction scores, revealing a descending order from FLT3-KD > FLT3-KD-Y842C > FLT3-KD-Y842F. (C) Specific interaction scores for FLT3 inhibitors.

• 

  Figure 2. Differential apoptotic responses and binding energy analyses of FLT3-ITD Y842 mutants under drug treatments. (A) Measurement of apoptotic cells using the annexin V-7-AAD kit after treating cells with specific inhibitors for 48 h prior to processing and analysis (n = 5). (B) Binding energy, represented as negative values, is plotted against various drug–mutant pairs. (C) Calculation of relative apoptotic cells by subtracting the number of apoptotic cells observed in DMSO-treated controls from those treated with specific inhibitors.

• 

  Figure 3. Assessment of various drug sensitivity metrics. (A) IC50 values were determined using GraphPad Prism 9 (n = 5), derived from interpolated values at 50 and subsequently transformed to a negative log10 scale. (B) The area under the curve (AUC) was computed from the same dataset, with a baseline response set at 10. (C) Drug Sensitivity Score 1 (DSS1) was determined using the Xepto50 software (version 0.0.2). (D) The Xepto50 score was derived from the normalized AUC at a specific interval on a logarithmic concentration axis.