Abstract 42: Target inhibitory networks and drug response modeling

Abstract

Abstract Cancer is a complex disease caused by multiple factors, which hamper effective drug discovery. Drug combination or multi target agents provide an alternate way to effectively modify disease networks. Synergistic drug pairs have special potential for treatment since they allow a desired effect to be achieved with lower total dose of administered medicine and usually with fewer side effects. However, the major challenge has been the prediction of chemotherapeutic efficacy based on the biological profile of the tumor. Because of the lack of gene expression data treated with drug combinations, in this study, we present a computational approach to identify effective drug combinations by exploiting high throughput data. We constructed tissue specific network pharmacology based on large-scale screening data on drug treatment efficacies of 130 drugs under clinical and preclinical investigation and drug-target binding affinities. We evaluated CNS cancer subset and applied logic based network algorithm to predict effective drug combinations based on drug-target interactions and single drug sensitivity profiles. Cancer cell based target inhibition network analysis in two case studies using glioma cell lines, (U87 and U251) identified distinct cell line survival pathways (p < 0.001), including cell proliferation, adhesion and growth factor signaling. We estimated pairwise drug synergy scores for all the target genes and identified several synergistic pairs with potential clinical relevance. Target inhibition modeling allowed systematic exploration of functional interactions between drugs and their targets to maximally inhibit multiple survival pathways. Citation Format: Uma Shankavaram, Kevin Camphausen. Target inhibitory networks and drug response modeling. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Integrating Clinical Genomics and Cancer Therapy; Jun 13-16, 2015; Salt Lake City, UT. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(1_Suppl):Abstract nr 42.