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 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.

Guiding model‐driven combination dose selection using multi‐objective synergy optimization

“…It is widely accepted that the treatment of advanced cancers requires well‐designed drug combinations 24–29 . Many have pursued combination design through the lens of drug synergy.…”

“…It is widely accepted that the treatment of advanced cancers requires well-designed drug combinations. [24][25][26][27][28][29] Many have pursued combination design through the lens of drug synergy. However, the large number of synergy metrics available, and their "largely arbitrary" use 3 complicates the process of identifying the most synergistic drugs, and the most synergistic doses for preselected drugs.…”

Understanding the Mechanisms and Treatment of Heart Failure: Quantitative Systems Pharmacology Models with a Focus on SGLT2 Inhibitors and Sex-Specific Differences

“…In both academia and the industry, QSP models provide the backbone of virtual clinical trials [ 107 , 108 , 109 ], with the goal of predicting the effectiveness of drugs on individuals with specific medical conditions (e.g., diabetic, or non-diabetic HF patients). They do this through the generation of virtual patient cohorts [ 110 ] that can be leveraged to understand pathophysiological mechanisms and heterogeneous treatment responses.…”

In silico trial for the assessment of givinostat dose adjustment rules based on the management of key hematological parameters in polycythemia vera patients

“…15,16 For example, PK/PD models can be developed based on information from early clinical trials and used to predict the response to treatment protocols designed for the late-stage clinical studies, thereby supporting decision-making in oncology drug development. 17,18 Model-based in silico trials could be of great relevance for adaptive dosing regimens for which a priori understanding of the dose-response relationship is challenging. In these cases, patient status is periodically monitored and dose adjusted accordingly, as in therapeutic drug monitoring practice.…”

“…Indeed, describing the relationship between dose, exposure, and one or more efficacy/safety end points, PK/PD models can be used to predict the outcome of a given treatment regimen 15,16 . For example, PK/PD models can be developed based on information from early clinical trials and used to predict the response to treatment protocols designed for the late‐stage clinical studies, thereby supporting decision‐making in oncology drug development 17,18 …”