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

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• 作者：Melanie I. Titze^a ; Julia Frank^b ; Michael Ehrhardt^b ; Sigrun Smola^c ; Norbert Graf^b ; Thorsten Lehr^a ; ^{thorsten.lehr@mx.uni-saarland.de}
• 关键词：MD ; multiple dose ; NDV ; Newcastle disease virus ; OV ; oncolytic virus ; PV ; parvovirus ; RV ; reovirus ; SD ; single dose ; VDM ; viral dynamic model ; VPC ; visual predictive check
• 刊名：European Journal of Pharmaceutical Sciences
• 出版年：2017
• 出版时间：15 January 2017
• 年：2017
• 卷：97
• 期：Complete
• 页码：38-46
• 全文大小：1392 K
• 卷排序：97

文摘

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.