Establishing combination PAC-1 and TRAIL regimens for treating ovarian cancer based on patient-specific pharmacokinetic profiles using in silico clinical trials

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 tumour necrosis factor-related apoptosis-inducing ligand (TRAIL) and procaspase activating compound (PAC-1) therapies in granulosa cell tumours (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 (PopPK) 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 pharmacokinetic 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 pharmacokinetic 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.

A practical guide for the generation of model-based virtual clinical trials

“…Beyond FIH dose selection, there are many other questions that can be explored in a virtual clinical trial. For instance, virtual clinical trials can be used to identify predictive biomarkers that separate responders from non-responders (Wang et al, 2020;Jenner et al, 2021b;Cardinal et al, 2022). It should be noted, though, that such stratification is only based on inter-individual variability criteria that are already built into the model, and thus VP simulations cannot identify novel factors that may affect treatment safety or efficacy.…”

“…Virtual populations (VPs) and in silico clinical trials (also called virtual clinical trials) are relatively new mathematical modeling techniques that are being increasingly used in the fields of quantitative systems pharmacology and mathematical oncology (Scott, 2012;Kim et al, 2016;Polasek and Rostami-Hodjegan, 2020;Wang et al, 2020;Jenner et al, 2021b;Zahid et al, 2021;Cardinal et al, 2022). Traditionally, modeling and simulation have focused on understanding biological processes and systems, sometimes with the aim of supporting drug development (Holford et al, 2000;Holford et al, 2010;Scott, 2012;Kim et al, 2016;Polasek and Rostami-Hodjegan, 2020;Wang et al, 2020;Jenner et al, 2021b;Zahid et al, 2021;Cardinal et al, 2022). In silico clinical trials extend the tools and scope of modeling and simulation with the goal of predicting the heterogeneous effects of drugs on populations of individuals.…”