NIH Research Festival
Precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes and drug sensitivity. Most medical treatments have been designed for the “average patient.” As a result of this “one-size-fits-all-approach,” treatments can be very successful for some patients but not for others. Identifying drug combinations or multi target agents based on individual genetic profile provide an alternate way to effectively modify disease networks at the personal level. However, the major challenge has been the prediction of chemotherapeutic efficacy based on the biological profile of the tumor. In this study, we present a computational approach to identify effective drug combinations by exploiting high throughput data from individual cell lines. We modeled two case studies using glioma cell lines, (U87 and U251). We constructed target inhibition networks based on large-scale screening data on drug treatment efficacies of 130 drugs under clinical and preclinical investigation. Using logic based network algorithm, we predicted effective drug combinations from drug-target interactions and single drug sensitivity profiles. Cancer cell based target inhibition network analysis identified distinct cell survival pathways (p < 0001), including cell proliferation, adhesion and growth factor signaling in two glioma cell lines. Our modeling approach allowed systematic exploration of functional interactions between drugs and their targets to maximally inhibit multiple survival pathways of clinical relevance.
Scientific Focus Area: Computational Biology
This page was last updated on Friday, March 26, 2021