Machine learning-driven QSAR modeling combined with single cell transcriptomics identifies novel drug targets for...

Area of Science:

• Oncology
• Computational Biology
• Drug Discovery

Background:

• Non-small cell lung cancer (NSCLC) is a major cause of cancer mortality, with frequent metastasis to the brain and bones.
• Limited treatment options and drug resistance in metastatic NSCLC necessitate novel therapeutic targets.
• Identifying conserved druggable targets across primary and metastatic sites is crucial for precision oncology.

Purpose of the Study:

• To identify conserved druggable targets in metastatic non-small cell lung cancer (NSCLC).
• To discover novel lead compounds for NSCLC using a machine learning-driven drug discovery framework.
• To provide a foundation for developing new therapies for both primary and metastatic NSCLC.

Main Methods:

• Single-cell RNA sequencing (scRNA-seq) analyzed gene expression in primary NSCLC and matched metastases.
• Ingenuity Pathway Analysis prioritized therapeutic targets.
• Machine learning (ML) models, including quantitative structure-activity relationship (QSAR), screened for potential drug ligands.
• Virtual screening, molecular docking, and molecular dynamics simulations optimized lead compounds.

Main Results:

• ARPC2, PSMB4, and RAC2 were identified as consistently overexpressed targets in primary and metastatic NSCLC.
• ML-based QSAR models achieved high predictive performance (AUROC > 0.97).
• Virtual screening identified high-affinity drug candidates, with subsequent simulations confirming structural stability and interactions for ARPC2, PSMB4, and RAC2 complexes.

Conclusions:

• An integrated approach using single-cell transcriptomics and ML identified conserved druggable targets for metastatic NSCLC.
• Promising lead compounds were discovered, offering potential for novel therapeutic strategies.
• The findings support further experimental validation for treating primary tumors and metastatic lesions.