Distribution of a Dominant-negative Inhibitor of Tumor Necrosis Factor

Objective/Rationale:
The blood-brain barrier (BBB) allows only a small fraction of systemically administered small-molecule drugs and even fewer biopharmaceuticals developed for disorders of the central nervous system (CNS) to reach therapeutic concentrations in the brain. Therefore, bypassing the BBB by direct infusion into the CNS offers a unique means of controlled and targeted delivery of therapies for treating complex CNS disorders such as Parkinson’s disease.

Project Description:
Inhibition of the pro-inflammatory cytokine, tumor necrosis factor (TNF), holds promise as a therapy for PD. However, clinical development is largely hindered by an inability of anti-TNF therapies to cross the BBB and effectively inhibit the elevated TNF activity within the CNS. We propose to advance the therapeutic development of Dominant-negative (DN)-TNF (Xencor) for PD by characterizing its distribution upon chronic infusion into the brain tissue, and also establish the parameters required for scaling of therapy delivery to the human brain. Specifically, we will employ the SynchroMed II® implantable infusion system and manipulate a variety of infusion parameters to determine optimal distribution of DN-TNF in the healthy versus degenerative brain. A novel computational method will be used to analyze the data generated from in vivo imaging and the analysis of biological fluids collected throughout the study.

Relevance to Diagnosis/Treatment of Parkinson’s Disease:
This proposal builds on the discovery that CNS-targeted continuous delivery of DN-TNF significantly attenuates the loss of dopaminergic neurons and behavioral deficits in pre-clinical models of PD. Given the need for CNS-directed delivery and the importance of identifying delivery parameters to effectively distribute the therapy for maximal clinical effect, optimizing the CNS infusion of DN-TNF presents a critical step towards further pre-clinical and clinical development.

Anticipated Outcome:
Upon completion of the proposed work, we expect to have a computational model based on empirical data that will help optimize the distribution of DN-TNF within the brain tissue as a function of the rate, duration, and site of infusion. Furthermore, the computational model would enable the fine-tuning of delivery parameters for an appropriate scale-up of drug distribution from a model brain to the human brain.