Dataset Information

Determinants of Intraparenchymal Infusion Distributions: Modeling and Analyses of Human Glioblastoma Trials.

ABSTRACT: Intra-parenchymal injection and delivery of therapeutic agents have been used in clinical trials for brain cancer and other neurodegenerative diseases. The complexity of transport pathways in tissue makes it difficult to envision therapeutic agent distribution from clinical MR images. Computer-assisted planning has been proposed to mitigate risk for inadequate delivery through quantitative understanding of infusion characteristics. We present results from human studies and simulations of intratumoral infusions of immunotoxins in glioblastoma patients. Gd-DTPA and 124I-labeled human serum albumin (124I-HSA) were co-infused with the therapeutic, and their distributions measured in MRI and PET. Simulations were created by modeling tissue fluid mechanics and physiology and suggested that reduced distribution of tracer molecules within tumor is primarily related to elevated loss rates computed from DCE. PET-tracer on the other hand shows that the larger albumin molecule had longer but heterogeneous residence times within the tumor. We found over two orders of magnitude variation in distribution volumes for the same infusion volumes, with relative error ~20%, allowing understanding of even anomalous infusions. Modeling and measurement revealed that key determinants of flow include infusion-induced expansion and loss through compromised BBB. Opportunities are described to improve computer-assisted CED through iterative feedback between simulations and imaging.

SUBMITTER: Brady M

PROVIDER: S-EPMC7559135 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Determinants of Intraparenchymal Infusion Distributions: Modeling and Analyses of Human Glioblastoma Trials.

Brady Martin M Raghavan Raghu R Sampson John J

Pharmaceutics 20200921 9

Intra-parenchymal injection and delivery of therapeutic agents have been used in clinical trials for brain cancer and other neurodegenerative diseases. The complexity of transport pathways in tissue makes it difficult to envision therapeutic agent distribution from clinical MR images. Computer-assisted planning has been proposed to mitigate risk for inadequate delivery through quantitative understanding of infusion characteristics. We present results from human studies and simulations of intratu ...[more]

PMID: 32967184

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Project description:Oncogenic protein tyrosine phosphatases have long been viewed as drug targets of interest, and recently developed allosteric inhibitors of SH2 domain-containing phosphatase-2 (SHP2) have entered clinical trials. However, the ability of phosphatases to regulate many targets directly or indirectly and to both promote and antagonize oncogenic signaling may make the efficacy of phosphatase inhibition challenging to predict. Here we explore the consequences of antagonizing SHP2 in glioblastoma, a recalcitrant cancer where SHP2 has been proposed as a useful drug target. Measuring protein phosphorylation and expression in glioblastoma cells across 40 signaling pathway nodes in response to different drugs and for different oxygen tensions revealed that SHP2 antagonism has network-level, context-dependent signaling consequences that affect cell phenotypes (e.g., cell death) in unanticipated ways. To map specific signaling consequences of SHP2 antagonism to phenotypes of interest, a data-driven computational model was constructed based on the paired signaling and phenotype data. Model predictions aided in identifying three signaling processes with implications for treating glioblastoma with SHP2 inhibitors. These included PTEN-dependent DNA damage repair in response to SHP2 inhibition, AKT-mediated bypass resistance in response to chronic SHP2 inhibition, and SHP2 control of hypoxia-inducible factor expression through multiple MAPKs. Model-generated hypotheses were validated in multiple glioblastoma cell lines, in mouse tumor xenografts, and through analysis of The Cancer Genome Atlas data. Collectively, these results suggest that in glioblastoma, SHP2 inhibitors antagonize some signaling processes more effectively than existing kinase inhibitors but can also limit the efficacy of other drugs when used in combination. SIGNIFICANCE: These findings demonstrate that allosteric SHP2 inhibitors have multivariate and context-dependent effects in glioblastoma that may make them useful components of some combination therapies, but not others.

Dataset Information

Determinants of Intraparenchymal Infusion Distributions: Modeling and Analyses of Human Glioblastoma Trials.

Publications

Determinants of Intraparenchymal Infusion Distributions: Modeling and Analyses of Human Glioblastoma Trials.

Similar Datasets

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