(689d) Computational Design of Drug Delivery Policies

Somayaji, M. R., University of Illinois at Chicago
Kulkarni, K., University of Illinois at Chicago
Xenos, M., University of Illinois at Chicago
Zhang, L., University of Illinois at Chicago
Linninger, A. A., University of Illinois at Chicago

Motivation: The development of systematic drug delivery for brain disorders is a formidable challenge, and there is hardly any successful treatment protocol for the majority of neurodegenerative diseases. Data published by National Institutes of Health suggest that neurodegenerative diseases such as Parkinson's, Alzheimer's, and Huntington's disease affect millions of people worldwide. Targeted delivery of large macromolecules to specific locations in the brain is a challenging task in the treatment of neurodegenerative diseases of the Central Nervous System (CNS) owing to the presence of blood-brain barrier (BBB). In addition, the restriction offered by fibers of the white matter hampers the transport and metabolism of drugs at target areas and pose difficulty to catheter design and placement.

Methodology: In this presentation, we propose three innovations embedded into a hierarchical design procedure. The systematic approach to designing drug targeting faces three challenges and we aim to address them in this presentation: 1. Accurate three-dimensional reconstruction of the patient-specific brain geometry 2. Quantification of achievable treatment volume subject to anisotropy 3. Optimal catheter design and placement The first challenge is to accurately reconstruct the physiologically consistent three-dimensional patient-specific brain geometry and specific substructures of the midbrain considered as infusion sites. Reconstruction step is imperative for quantification of transport processes. State of the art geometric image reconstruction tools are used to render and construct computational grids from novel imaging techniques such MRI and histological data. The second task is to calculate the achievable treatment volume in anisotropic regions of the brain using transport and kinetic inversion problem (TKIP) in-vitro. The TKIP approach extracts the unknown diffusion tensor of the drug in anisotropic regions of the brain. Specifically, the white matter tracts are aligned parallel to each other and confine drug transport along its length. This restriction may lead to drug toxicity in peripheral regions of the brain. By interpreting concentration profiles obtained by advanced imaging techniques in-vivo, we propose to adjust the unknown transport and kinetic properties so that the measured concentration field observed in the image and the model predictions match. We solve with mathematical programming a large-scale transport and kinetic inversion problem for the unknown parameter set that will provide specifications for optimal catheter design.

The third challenge concerns optimal catheter design. Specifically, the optimal placement and orientation of the infusion catheter, its dimensions, shape, and number of drug release ports specific to the target area and the bimolecular properties of the drug are subject to optimization.

Broader Impact: Novel analytical imaging techniques like MRI, functional MRI, Diffusion Tensor Imaging (DTI), Computer Tomography (CT), Positron Emission Tomography (PET), etc, improve medical diagnosis. However, the existing technologies do not directly support the use of the imaging data for devising better treatment options. There appears to be a gap between high quality of imaging techniques and their use in quantitative analysis in the clinical practice. The sketchy understanding of the intracranial dynamics prevents the implementation of effective invasive drug delivery into the brain. The patient specific approach will influence the medical community and the affected patients from brain disorders. This approach integrates state of-the-art imaging techniques and first principles models for transport phenomena in order to provide the medical community with a computer-aided tool to reduce the number of in vivo tests by better capitalizing on the results of fewer experiments with the help of advances computational methods.


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