Integrating QSAR-Machine Learning, Biochemical Assays, and Molecular Dynamics for the Discovery of JAK2 Inhibitors in Cervical Cancer
Clinical Snapshot
PICO Framework
| P — Population | HeLa cells (cervical cancer cell line) and normal fibroblasts as controls |
| I — Intervention | Naphthalene-based derivatives designed as JAK2 inhibitors, including compounds 2q, 2s, D4, and D13 |
| C — Comparator | Normal fibroblasts for selectivity assessment; existing JAK2 inhibitors for potency comparison |
| O — Outcomes | JAK2 kinase inhibition (IC50), cytotoxicity in cancer cells, selectivity index, apoptosis induction, JAK2/STAT3/STAT5 pathway suppression |
Bottom Line
This study presents a promising computational-experimental approach for discovering JAK2 inhibitors targeting cervical cancer. The integration of QSAR machine learning with biochemical validation identified naphthalene-based compounds with nanomolar JAK2 inhibitory activity and selectivity over normal cells. The Categorical Boosting model demonstrated excellent predictive performance, successfully identifying active compounds that met drug-likeness criteria. Lead compounds 2q, 2s, D4, and D13 showed potent JAK2 inhibition and induced apoptosis through JAK2/STAT3/STAT5 pathway suppression. Molecular dynamics simulations provided mechanistic insights into binding interactions. While these findings represent significant progress in cervical cancer drug discovery, the work remains at early preclinical stage. Clinical translation will require extensive safety evaluation, in vivo efficacy studies, and ultimately clinical trials. The QSAR-ML framework offers a valuable tool for accelerating future inhibitor discovery efforts.
Key Findings
P Value: Not specified for individual comparisons
Effect Size: Compounds 2q, 2s, D4, and D13 achieved low nanomolar JAK2 IC50 values
Primary Outcome: JAK2 kinase inhibition with nanomolar potency
Nnt Or Sensitivity: QSAR model sensitivity: R2=0.955 training set, RMSE=0.156 test set, MAPE=2.6-14.4% for active predictions
Confidence Interval: Not reported for biological assays
Clinical Application
Early discovery stage requiring extensive preclinical and clinical development before therapeutic application Relevant to Australian cervical cancer burden; would require TGA approval pathway for novel therapeutics; potential PBS consideration pending clinical efficacy data Patients with HPV-positive cervical cancer expressing dysregulated JAK2 signaling
Abstract
Cervical cancer remains a major global health challenge, where dysregulated JAK2 signaling constitutes a key molecular driver. Nevertheless, selective small-molecule JAK2 inhibitors for HPV-positive cervical cancer are still limited. Here, we integrated biochemical assays, QSAR-machine learning, and molecular dynamics simulations to identify potent JAK2 inhibitors. A series of naphthalene-based derivatives, including hydroxynaphthalenamide and phosphorylated dihydronaphthylamide analogs, were evaluated for cytotoxicity in HeLa cells and JAK2 kinase inhibition. Several compounds exhibited selective cytotoxicity with minimal activity toward normal fibroblasts, among which 2q and 2s showed low-nanomolar JAK2 inhibition and strong apoptosis induction through suppression of the JAK2/STAT3/STAT5 pro-tumorigenic signaling pathway. To accelerate hit identification, a QSAR-machine learning (QSAR-ML) framework was employed to prioritize 13 newly designed derivatives. Among three ensemble boosting models, the Categorical Boosting (CB) model demonstrated the strongest predictive capability, achieving a high R2 of 0.955 for the training set and a low RMSE of 0.156 for the test set. This model successfully identified five active candidates with strong prediction-experiment agreement (MAPE = 2.6-14.4%), with D4 and D13 meeting drug-likeness criteria and displaying potent nanomolar JAK2 inhibition. Finally, 1-μs molecular dynamics simulations revealed that hydrophobic contacts and hydrogen bonding cooperatively stabilize these inhibitors within the JAK2 ATP-binding pocket. Collectively, these findings establish a QSAR-ML-guided strategy for accelerating JAK2 inhibitor discovery and highlight naphthalene-based scaffolds as promising leads for targeted cervical cancer therapy.
References
- 1.Todsaporn, D., Sanachai, K., Suddee, N., Kanjanapanyakom, C., Maitarad, P., Worayuthakarn, R., Aonbangkhen, C., Thasana, N., & Rungrotmongkol, T. (2026). Integrating QSAR-Machine Learning, Biochemical Assays, and Molecular Dynamics for the Discovery of JAK2 Inhibitors in Cervical Cancer. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.6c00414
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