Exploring novel furochochicine derivatives as promising JAK2 inhibitors in HeLa cells: Integrating docking, QSAR-ML, MD simulations, and experiments

Abstract

Cervical cancer, largely driven by high-risk human papillomavirus (HPV), remains a global health challenge. Janus tyrosine kinase 2 (JAK2) has emerged as a promising therapeutic target for HPV-induced malignancies. This study employed both in silico and in vitro approaches to discover novel JAK2 inhibitors from a library of 76 furochochicine (FCC) derivatives. Twenty-nine compounds were selected via virtual screening, synthesized, and tested for cytotoxicity against HeLa cells. Four FCCs showed potent cytotoxicity with selectivity indices (SI) greater than 3. These cytotoxicity data were used to construct QSAR models with machine learning; eXtreme Gradient Boosting (XGB) yielded the best performance (RMSE = 0.177, R² = 0.831, MAPE = 2.93 %) and was used to predict additional FCC derivatives. FCC90 emerged as a lead compound with strong predictive accuracy (MAPE = 1.43 %) and selectivity (SI = 3.25). JAK2 kinase assays revealed strong inhibition by FCC6, FCC27, and FCC90 (IC₅₀ = 9.10-27.34 nM), with FCC6 and FCC27 surpassing ruxolitinib. Flow cytometry confirmed apoptosis and sub-G1 cell cycle arrest. Molecular dynamics simulations supported the stability of FCC-JAK2 complexes. Furthermore, all active compounds met extended Rule of Five (eRo5) criteria. These findings highlight the potential of FCC derivatives as JAK2 inhibitors for cervical cancer therapy.

Keywords: Experimental validation; JAK2 inhibitors; Machine learning-based QSAR.

Graphical abstract

Fig. 1

Schematic overview of the integrated in silico and in vitro workflow for identifying JAK2 inhibitors from furochochicine (FCC) derivatives. Purple boxes represent in silico procedures, including virtual screening, QSAR-based machine learning, and molecular dynamics simulations. Orange boxes denote in vitro experimental validation steps, such as synthesis, cytotoxicity assays, JAK2 kinase assays, and flow cytometry.

Fig. 2

GOLD fitness scores of seventy-six FCC derivatives docked into the ATP-binding site of the JAK2 protein. Black bars indicate compounds with GOLD fitness scores surpassing that of the reference inhibitor, ruxolitinib. (B) Superimposition of the screened FCC compounds (gray line structure) and ruxolitinib (green line structure) within the binding site. (C) Heat map summarizing the predicted interactions between screened FCC derivatives and the JAK2 protein, based on 2D ligand–protein interaction diagrams generated using Discovery Studio Visualizer 2.5.

Scheme 1

Synthesis of target compounds.

Fig. 3

(A) Variance Inflation Factor (VIF) calculation for the selected molecular descriptors. All descriptors exhibit VIF values below the threshold of 10, indicating low to moderate multicollinearity. (B) Correlation matrix of the molecular descriptors and pIC₅₀. The matrix highlights the pairwise correlations between pIC₅₀ and descriptors such as Count.AromaticBonds, XLogP, RNCS, MOMI-YZ, and Count.HBD1. Positive correlations are shown in green, while negative correlations are indicated in pink. The color intensity reflects the magnitude of the correlation, with values ranging from −1.00–1.00.

Fig. 4

Comparison of predicted versus experimental pIC₅₀ values across different machine learning models. Each plot shows the correlation between predicted and experimental pIC₅₀ values for the training set (blue) and test set (red) using ANN (Artificial Neural Network), SVM (Support Vector Machine), RF (Random Forest), BR (Bagging Regressor), GB (Gradient Boosting), and XGB (eXtreme Gradient Boosting). The R² values for both the training and test sets are indicated in each plot. The gray dotted line represents a 20 % prediction error.

Fig. 5

Predicted pIC₅₀ values of an additional 18 FCC derivatives using the XGB-based QSAR model. Blue dots represent the training set, red dots correspond to the test set, and green dots represent the additional FCC compounds predicted by the model. The top three candidates (FCC80, FCC82, and FCC90) are highlighted in yellow circles.

Fig. 6

(A) 2D structures and dose–response cytotoxicity curves of the top three candidates—FCC80, FCC82, and FCC90—prioritized by the XGBoost (XGB)-based QSAR model, tested against HeLa cells. (B) Summary of experimental and predicted cytotoxicity data, including IC₅₀ values (mean ± SD, n = 3), experimental and predicted pIC₅₀ values, and mean absolute percentage error (MAPE), confirming the predictive performance of the QSAR model.

Fig. 7

JAK2 kinase assay of hit FCCs against JAK2. Data are represented as means ± SEM of three independent experiments. IC₅₀ values are presented in blue text.

Fig. 8

Effect of Ruxolitinib and potent FCC compounds (FCC6, FCC27, and FCC90) on the cell cycle distribution in HeLa cells. (A) The DNA content in each cell cycle phase was analyzed by flow cytometry with propidium iodide (PI). The histogram represented cell cycle distribution. (B) Representative histograms from flow cytometry analysis of PI-stained HeLa cells, showing the distribution across the G₀/G₁, S, and G₂/M phases (B) Quantitative analysis of cell cycle phase distribution (n = 3; mean ± SEM; * p < 0.05, ** p < 0.01, *** p < 0.001 vs. untreated control).

Fig. 9

Apoptosis induction and cell death analysis of HeLa cells treated with compounds FCC6, FCC27, and FCC90 compared to control and Ruxolitinib. (A) Representative dot plots of Annexin V/PI staining. Each plot shows the distribution of cells in the different stages of apoptosis based on Annexin V-FITC and PI fluorescence intensities. (B) Quantification of cell populations in different apoptosis stages (live, early apoptotic, late apoptotic, and necrotic) (n = 3; mean ± SEM; * p < 0.05, ** p < 0.01, *** p < 0.001 vs. untreated control).

Fig. 10

Decomposition of the free energy from the average of three independent molecular dynamics simulations over the final 200 ns for FCC6, FCC27, and FCC90 in complex with the JAK2. The left and middle panels show the per-residue decomposition of the binding free energy (in kcal/mol) and key residues involved in stabilizing the complexes. The right panels illustrate the binding modes of FCC6, FCC27, and FCC90, highlighting significant hydrogen bonding interactions (dashed lines) The lowest and highest energies range from red to light gray, respectively. H-bonds between the ligands and JAK2 are shown as dotted lines.