BIOMED Master's Thesis Defense

Title:
Development and Optimization of GelMA Hydrogels for Controlled Dual-Drug Release in Chronic Wound Healing

Speaker:
Eva Elizabeth Kraus, Master's Candidate
School of Biomedical Engineering, Science and Health Systems
Drexel University
 
Advisor:
Kara L. Spiller, PhD
URBN Professor of Biomedical Innovation
School of Biomedical Engineering, Science and Health Systems
Drexel University

Details:
Chronic wounds, which fail to progress through the normal healing phases, represent a major clinical challenge, particularly in geriatric diabetic populations. These patients often exhibit hyporesponsiveness to pro-inflammatory stimuli, preventing the initiation of the inflammatory phase necessary for proper tissue regeneration. Without this crucial step, macrophages are unable to differentiate into the pro-inflammatory phenotype required for a healthy regenerative transition. As a result, wound healing is delayed, increasing the risk of infection, amputation, and prolonged hospitalization.

To address this issue, hydrogel-based drug delivery systems offer a promising approach for targeted cytokine delivery to modulate immune responses and promote tissue regeneration. Hydrogels are particularly well-suited for this application due to their diffusion-driven drug release without a rate-limiting membrane, tunable mechanical properties that mimic the extracellular matrix, and ability to be synthesized from natural polymers for enhanced biocompatibility.

This study aimed to develop and verify a predictive model for drug release using gelatin methacryloyl (GelMA) hydrogels, optimizing hydrogel formulations for controlled cytokine delivery in chronic wound healing applications. Design requirements included biocompatibility of material, specific values for sustained cytokine release, manufacturability in a clinically adaptable format, and a final recommendation for a murine-sized GelMA hydrogel for in-vivo testing.

The research was conducted in three aims. The first aim focused on the release kinetics of interferon gamma (IFN-γ), a known polarizer of macrophages to their pro-inflammatory state, and Adenosine Deaminase-1 (ADA-1), which has been proven to be an adjuvant in elderly vaccines. Quantitative drug release was characterized across nine GelMA hydrogels formulations. Experimental studies demonstrated that increased methacrylation and weight percent led to lower diffusion coefficients, thereby slowing drug release. Additionally, an interaction between IFN-γ and ADA-1 was observed, suggesting potential binding or interactive diffusion effects that influenced cytokine concentration gradient.

In the second aim, diffusion coefficients were calculated for each hydrogel formulation using experimental release data, providing critical input parameters for computational modeling. This served as part of the verification process to ensure alignment between experimental observations and design expectations. The results validated that hydrogel composition directly affected molecular diffusion, reinforcing the importance of material properties in achieving controlled drug delivery.

The third and final aim focused on the diffusion coefficients that were integrated into a MATLAB-based predictive model to simulate cytokine release profiles across different hydrogel geometries. The model revealed that thickness, or the height at which the drug must diffuse through, was the dominant factor governing release rates, and surface area directly affected mass flux. An increased thickness saw a greater time of drug release while increasing the surface area, increased mass flux, or amount of drug coming in contact with the wound at a given time. The model also explored the realistic requirement of a clinician needing to cut the hydrogel slab to fit a wound size. The model allows user to identify an area to be removed from the gel to alter surface area which resulted in decreased total drug release but increased localized flux. These results highlighted the need to consider hydrogel modifications in clinical applications. Additionally, simulations identified an optimal hydrogel configuration for an in-vivo murine wound healing model, recommending that a 3 mm-thick GelMA hydrogel with a tuned diffusion coefficient of 1.0695x10-11 m2/s or slower would sufficiently deliver murine IFN-γ for four days. This diffusion coefficient value could be accomplished in many different ways by manipulating the weight volume percentage of GelMA and degree of methacrylation.

The predictive modeling approach verified the experimental data and provided a robust framework for optimizing hydrogel-based cytokine delivery. This study highlights the interplay between hydrogel composition, geometry, and cytokine diffusion, offering valuable insights for the design of chronic wound healing dressings. Future work should focus on experimental verification of the optimized hydrogel formulation for the murine model while including hydrogel degradation kinetics into the model, and the expansion to multi-drug delivery systems. By bridging experimental diffusion studies with computational modeling, this research contributes to the development of biomaterial-based strategies for controlled therapeutic delivery, ultimately improving treatment outcomes for chronic wounds.

BIOMED PhD Thesis Defense

Title:
Leveraging Machine Learning (ML) for Classifying the Pathogenicity of Genetic Variants in Cancer Genomics and Precision Medicine

Speaker:
Ashkan Bigdeli, PhD Candidate
School of Biomedical Engineering, Science and Health Systems
Drexel University

Advisors:
Robert Babak Faryabi, PhD
Associate Professor of Pathology and Laboratory Medicine
Assistant Professor of Cancer Biology
Perelman School of Medicine
University of Pennsylvania
 
Ahmet Sacan, PhD
Teaching Professor
School of Biomedical Engineering, Science and Health Systems
Drexel University

Details:
Accurate classification of genetic variants is crucial for advancing precision medicine, particularly in oncology, where variant interpretation can guide treatment strategies and accelerate clinical research. Existing classification methods remain largely manual, inherently variable, and difficult to scale. To this end, we introduce Azurify, a machine learning-based model developed to classify the pathogenicity of somatic variants across diverse cancer types.

Trained on a broad set of cancer sequencing data and over 15,000 clinically classified variants, Azurify demonstrates robust performance across independent datasets from multiple clinical laboratories, outperforming existing classification tools and generalizing well across platforms. Azurify is designed to be easily deployed and scaled, allowing it to integrate new data as it becomes available, making Azurify adaptable for both clinical support and research environments. We demonstrate Azurify through the accurate molecular profiling of pathogenic mutations in patients with Acute Myeloid Leukemia (AML) and Chronic Lymphocytic Leukemia (CLL).

We additionally applied Azurify to sequencing data from CAR-T–treated B-cell non-Hodgkin lymphoma (B-NHL) patients, identifying key genetic predictors of treatment response and toxicity. These findings underscore the potential of machine learning to inform immunotherapy outcomes, offering a data-driven approach to precision oncology that can support patient stratification and personalized treatment strategies.

The School of Biomedical Engineering, Science and Health Systems is pleased to announce its 7th Annual Immune Modulation & Engineering Symposium (IMES).