Proliferation inhibition of cisplatin-resistant ovarian cancer cells using drugs screened by integrating a metabolic model and transcriptomic data

The proposed strategy was successful to identify drugs effective on the viability of resistant cancer cells. This strategy can enhance the potency of treatments for drug-resistant cancer cells and provides the possibility of using existing drugs.

An algorithm (transcriptional regulated flux balance analysis [TRFBA]) integrating a generic human metabolic model with transcriptomic data was used to identify genes affecting the growth of drug-resistant cancer cells. Drugs that inhibit activation of the target genes were found and their effect on the proliferation was experimentally evaluated.

Experimental assessments demonstrated that TRFBA improves the prediction of cancer cell growth in comparison with previous algorithms. The algorithm was then used to propose the system-oriented strategy to search drugs effective in limiting the growth rate of the cisplatin-resistant A2780 epithelial ovarian cancer cell. Experimental evaluations resulted in the selection of azathioprine, terbinafine, hydralazine and sodium valproate that appropriately inhibit the proliferation of resistant cancer cells while minimally affecting normal cells. Furthermore, experimental data indicate that the selected drugs are synergistic and can be used in combination therapies. Conclusions: The proposed strategy was successful to identify drugs effective on the viability of resistant cancer cells. This strategy can enhance the potency of treatments for drug-resistant cancer cells and provides the possibility of using existing drugs.