Malisa Sarntinoranont, Ph.D., Professor, Associate Dean for Academic Affairs in the College, is a leading force in interdisciplinary engineering, integrating mechanical and biomedical research at the Soft Tissue Mechanics and Drug Delivery Laboratory.
The Sarntinoranont group is developing exciting research tools for soft tissue mechanics associated with tumors and drug delivery in the body. Their work has significant implications for personalized cancer treatment and patient monitoring.
Advances in Tumor Microenvironment Simulation
In recent years, Sarntinoranont and her team have developed novel computational models for studying glioblastomas (GBM), which are soft tumors originating from cerebral glial cells. Glial cells, also known as neuroglia, are a class of cells within the central nervous system (CNS) that, unlike neurons, do not communicate through electrical signals. Neuroglia support neuron function, maintain homeostasis, and form myelin – the insulating sheath around nerves that enhances signal transmission.
Because of their critical functions, cancerous neuroglia are the most common source of primary brain tumors. Aggressive gliomas, such as GBM, are called “high-grade” and have poor prognoses; the five-year survival rate is less than 5% in adults and even lower in pediatric and elderly patients.
GBM possess unique properties that complicate treatment and contribute to their malignancy. The tumors consist of a dense “core mass” surrounded by a diffuse layer of invasive single cells that extend several few centimeters beyond the core. The outer cells are highly motile, contributing to metastasis, and they can generate or divert local blood vessels for nutrients. The sequestered vessels often leak into the surrounding space, increasing the tumor’s interstitial fluid pressure – a characteristic phenomenon of high-grade GBM which complicates treatment.
To address this challenge, the Sarntinoranont group is developing computational models of tumor-bearing brains. So far, they have reported the individual and cumulative effects of leakiness and growth for in silico and animal models of GBM. Using diagnostic, magnetic resonance imaging scans, future models will investigate the role of radiation therapy on the interplay between a tumor’s mechanical environment and tumor recurrence.
Tools to Quantify Structural Protein Alignment in Engineered Tissues
In tissue engineering, new tools are needed for future manufacturing lines. The team developed an integrated workflow to quantify structural protein alignment in engineered tissue using polarized Raman spectroscopy (PRS), immunofluorescence imaging, and computational modeling.
PRS is a nondestructive, label-free technique used to examine molecular organization in hydrated tissues. In PRS, polarized light is delivered to the sample, and the resulting Raman signal varies with the orientation of protein bonds; these orientation-dependent differences in scattered intensity are examined to quantify alignment.
In earlier studies, alignment had been measured with fluorescence microscopy and other techniques, but these approaches are restricted by limited penetration depth, the requirement for fluorescent labeling, and need to dehydrate the samples. Because it can be performed on intact, fully hydrated samples without staining, PRS enables reliable monitoring of engineered tissues as they remodel over time.
Understanding protein alignment is essential because the organization of structural proteins strongly influences tissue mechanics, function, and maturation. Accurate assessment of structural organization also supports the long-term goal of fabricating engineered tissues suitable for future regenerative medicine and organ/tissue-replacement applications.
This project is a collaboration between Sarntinoranont, Ghatu Subhash, Ph.D., and Chelsey Simmons, Ph.D.
Computational Modeling of Spinal Drug Delivery
Concurrently, the Sarntinoranont group investigates the interplay of fluid dynamics and drug delivery in the spinal canal. Computational models provide a critical step in refining protocols for targeted treatments in the central nervous system. The team is developing drug delivery models derived from subject-specific imaging to simulate the mixing effects of cerebrospinal fluid (CSF) motion.
These simulations incorporate factors such as spinal cord eccentricity and physiological pulsations to analyze drug distribution patterns, aiming to optimize therapeutic transport within the spinal canal.
Advances in Brain Clearance Modeling
It all starts with an MRI image – the simulation software transforms the 2D scan into a geometrically accurate model for different parts of the brain.
Following the development of new tracer-based MRI imaging techniques, the team has managed to segment the internal network of perivascular spaces crossing the brain. Inclusion of this network will greatly improve the ability of models to properly account for brain clearance – which has been hypothesized as one of the reasons for sleep. As such, full-brain models are being developed that are capable of modeling the effect of the perivascular network on clearance. A new project is also investigating the role of perivascular space on clearance around brain tumors. These new models can be applied to better understand pathways of waste products and drugs as they are eliminated from the brain.
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UF Mechanical & Aerospace Engineering