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Published on: October 3, 2025
Using computational strategies to predict potential drugs for nasopharyngeal carcinoma
Ming-Ying Lan1, Wu-Lung R Yang, Kuan-Ting Lin
1Division of Rhinology, Department of Otolaryngology Head and Neck Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan.
Background:
Nasopharyngeal carcinoma (NPC) is a unique cancer. Refinement of current therapy by discovering potential drugs may be approached by several computational strategies.
Methods:
We collected NPC genes from published microarray data and the literature. The NPC disease network was constructed via a protein-protein interaction (PPI) network. The Connectivity Map (CMap) was used to predict potential chemicals, and support vector machines (SVMs) were further utilized to classify the effectiveness of tested drugs against NPC using their gene expression from CMap.
Results:
A highly interconnected network was obtained. Several chemically sensitive genes were identified and 87 drugs were predicted with the potential for treating NPC by SVM, in which nearly half of them have anticancer effects according to the literature. The 2 top-ranked drugs, thioridazine and vorinostat, were demonstrated to be effective in inhibiting NPC cells.
Conclusion:
This in silico approach provides a promising strategy for screening potential therapeutic drugs for NPC treatment.
Insights
Computational drug discovery identified 87 potential treatments for nasopharyngeal carcinoma (NPC). Two top drugs, thioridazine and vorinostat, effectively inhibited NPC cells, offering a promising new strategy for cancer therapy.
Area of Science:
- Oncology
- Computational Biology
- Pharmacology
Background:
- Nasopharyngeal carcinoma (NPC) is a distinct cancer requiring novel therapeutic strategies.
- Computational approaches offer potential for identifying new drug candidates for NPC treatment.
Purpose of the Study:
- To discover novel therapeutic drugs for nasopharyngeal carcinoma (NPC) using computational methods.
- To identify and validate potential drug candidates for NPC treatment through network analysis and machine learning.
Main Methods:
- Constructed a nasopharyngeal carcinoma (NPC) disease network using protein-protein interaction (PPI) data.
- Employed the Connectivity Map (CMap) to predict potential therapeutic chemicals.
- Utilized support vector machines (SVMs) to classify drug effectiveness against NPC based on gene expression data.
Main Results:
- A highly interconnected NPC network was generated, revealing key disease-related genes.
- Identified 87 drugs with potential for treating NPC, with nearly half possessing known anticancer properties.
- Thioridazine and vorinostat emerged as top-ranked drugs, demonstrating significant inhibition of NPC cell growth in vitro.
Conclusions:
- The in silico strategy effectively screened for potential therapeutic drugs for NPC.
- This computational approach presents a promising avenue for accelerating drug discovery in NPC treatment.
- Validated top drug candidates offer a foundation for further preclinical and clinical investigations.