Drug Optimization for Cystic Fibrosis Patients Based on Disease Pathways Crosstalk

Abstract

Cystic fibrosis (CF) is a common autosomal recessive disease characterized by pancreatic insufficiency and progressive deterioration of lung function. It has been shown that CF is caused by the presence of mutations in both alleles at the cystic fibrosis transmembrane conductance regulator (CFTR) gene. The severity of CF disease reflects the change of molecular mechanism, including DNA mutations on CFTR gene and polymorphic variations in disease modifier genes. Better understanding the differences among different CF severity group is helpful for improving therapeutic plans for patients. In this paper, the authors present a computational network biology approach to screen precision drugs for CF patients, which is based on the intensity of drugs impact on the pathway crosstalk mediated by differential methylation genes. The results indicate that ivacaftor, tezacaftor, and lumacaftor are applicable to all severity cohorts, gefitinib, sorafenib, sunitinib, and imatinib mesylate have the potential to treat intermediary CF patients, and tamoxifen may be useful to mild and sever CF patients.