Establishing combination PAC‐1 and TRAIL regimens for treating ovarian cancer based on patient‐specific pharmacokinetic profiles using in silico clinical trials. Issue 2 (15th June 2022) - Cardinal, Olivia; Burlot, Chloé; Fu, Yangxin; Crosley, Powel; Hitt, Mary; Craig, Morgan; Jenner, Adrianne L.
Abstract: Ovarian cancer is commonly diagnosed in its late stages, and new treatment modalities are needed to improve patient outcomes and survival. We have recently established the synergistic effects of combination tumor necrosis factor‐related apoptosis‐inducing ligand (TRAIL) and procaspase activating compound (PAC‐1) therapies in granulosa cell tumors (GCT) of the ovary, a rare form of ovarian cancer, using a mathematical model of the effects of both drugs in a GCT cell line. Here, to understand the mechanisms of combined TRAIL and PAC‐1 therapy, study the viability of this treatment strategy, and accelerate preclinical translation, we leveraged our mathematical model in combination with population pharmacokinetics (PKs) models of both TRAIL and PAC‐1 to expand a realistic heterogeneous cohort of virtual patients and optimize treatment schedules. Using this approach, we investigated treatment responses in this virtual cohort and determined optimal therapeutic schedules based on patient‐specific PK characteristics. Our results showed that schedules with high initial doses of PAC‐1 were required for therapeutic efficacy. Further analysis of individualized regimens revealed two distinct groups of virtual patients within our cohort: one with high PAC‐1 elimination and one with normal PAC‐1 elimination. In the high elimination group, high weekly doses of both PAC‐1 and TRAIL were necessary for therapeutic efficacy; however, virtual patients in this group were predicted to have a worse prognosis when compared to those in the normal elimination group. Thus, PAC‐1 PK characteristics, particularly clearance, can be used to identify patients most likely to respond to combined PAC‐1 and TRAIL therapy. This work underlines the importance of quantitative approaches in preclinical oncology.