A Multiple-Model-Informed Drug-Development Approach for Optimal Regimen Selection of an Oncolytic Virus in...

Area of Science:

• Oncology
• Immunotherapy
• Virology
• Computational Biology

Background:

• Genetically engineered oncolytic vaccinia virus (hIL-7/mIL-12-VV) shows antitumor efficacy.
• Combination therapy with anti-PD-1 antibodies requires optimized dosing strategies.
• Preclinical data suggested timing of administration impacts efficacy.

Purpose of the Study:

• To identify an optimal dosing regimen for first-in-human clinical trials of hIL-7/mIL-12-VV and anti-PD-1 antibody.
• To utilize a model-informed drug development (MIDD) approach for dose and regimen selection.
• To predict the interaction between oncolytic virus and immunotherapy in a clinical setting.

Main Methods:

• Developed a quantitative systems pharmacology (QSP) model and an agent-based model (ABM).
• Validated models using literature and preclinical data.
• Simulated multiple dosing scenarios with virtual populations, altering administration intervals.

Main Results:

• QSP and ABM models predicted no antagonistic effect between hIL-7/mIL-12-VV and pembrolizumab.
• Simulations indicated dose-dependent antitumor efficacy is maintained.
• Recommended same-day administration of hIL-7/mIL-12-VV and pembrolizumab, with pembrolizumab following the virus.

Conclusions:

• MIDD strategy successfully guided optimal dose and regimen selection for clinical trials.
• Modeling approaches provided unique insights into combination therapy design.
• The findings support the initiation of first-in-human clinical trials with a defined dosing strategy.