Literature DB >> 27825920

A generic viral dynamic model to systematically characterize the interaction between oncolytic virus kinetics and tumor growth.

Melanie I Titze1, Julia Frank2, Michael Ehrhardt2, Sigrun Smola3, Norbert Graf2, Thorsten Lehr4.   

Abstract

Oncolytic viruses (OV) represent an encouraging new therapeutic concept for treatment of human cancers. OVs specifically replicate in tumor cells and initiate cell lysis whilst tumor cells act as endogenous bioreactors for virus amplification. This complex bidirectional interaction between tumor and oncolytic virus hampers the establishment of a straight dose-concentration-effect relation. We aimed to develop a generic mathematical pharmacokinetic/pharmacodynamics (PK/PD) model to characterize the relationship between tumor cell growth and kinetics of different OVs. U87 glioblastoma cell growth and titer of Newcastle disease virus (NDV), reovirus (RV) and parvovirus (PV) were systematically determined in vitro. PK/PD analyses were performed using non-linear mixed effects modeling. A viral dynamic model (VDM) with a common structure for the three different OVs was developed which simultaneously described tumor growth and virus replication. Virus specific parameters enabled a comparison of the kinetics and tumor killing efficacy of each OV. The long-term interactions of tumor cells with NDV and RV were simulated to predict tumor reoccurrence. Various treatment scenarios (single and multiple dosing with same OV, co-infection with different OVs and combination with hypothetical cytotoxic compounds) were simulated and ranked for efficacy using a newly developed treatment rating score. The developed VDM serves as flexible tool for the systematic cross-characterization of tumor-virus relationships and supports preselection of the most promising treatment regimens for follow-up in vivo analyses.
Copyright © 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Glioblastoma; Mathematical model; Non-linear mixed effects modeling; Pharmacokinetic/pharmacodynamics modeling; Treatment score

Mesh:

Year:  2016        PMID: 27825920     DOI: 10.1016/j.ejps.2016.11.003

Source DB:  PubMed          Journal:  Eur J Pharm Sci        ISSN: 0928-0987            Impact factor:   4.384


  2 in total

Review 1.  Opportunities and challenges for applying model-informed drug development approaches to gene therapies.

Authors:  Artur Belov; Kimberly Schultz; Richard Forshee; Million A Tegenge
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2021-03-05

2.  Modeling the Spatiotemporal Dynamics of Oncolytic Viruses and Radiotherapy as a Treatment for Cancer.

Authors:  Eman Simbawa; Najwa Al-Johani; Salma Al-Tuwairqi
Journal:  Comput Math Methods Med       Date:  2020-04-12       Impact factor: 2.238

  2 in total

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