GLOBALLY OPTIMIZING THERAPEUTIC COMBINATIONS USING QUANTITATIVE PARABOLIC OPTIMIZATION PLATFORM (QPOP)

Abstract

Rationally designing drug combinations remains a challenge due to the complex molecular networks that contribute to feedback mechanisms of drug resistance. Acknowledging this knowledge in gap, this thesis utilized quantitative parabolic optimization platform (QPOP) to rationally converge onto synergistic, optimal drug combinations. Based on experimentally determined phenotypic outputs, QPOP identifies the most optimal drug combinations without any prior knowledge of the system-of-interest using a second-order linear regression model. We have applied this platform on two systems-of-interest, hepatocellular carcinoma (HCC) and multiple myeloma (MM), where we have identified optimal drug combinations that are efficacious against glucose uptake in HCC and Bortezomib resistance in MM. These findings have been validated at the in vitro, in vivo as well as ex vivo levels. The pertinent role of QPOP as an optimization platform on the specified system of interest highlights the importance of precision/personalized medicine.