Abstract
The pharmaceutical industry occupies a complex position at the intersection of human health and environmental sustainability. While widely perceived as a socially beneficial sector, its operations, including energy-intensive research and development, globalized supply chains, and resource-heavy manufacturing, contribute significantly to greenhouse gas emissions and environmental degradation.
STS Thesis: This study investigates whether current sustainability efforts within the pharmaceutical industry reflect meaningful progress or remain largely symbolic. Using a qualitative analysis of corporate sustainability reports, peer-reviewed literature, and industry publications, this research identifies key structural barriers that limit the effectiveness of sustainability initiatives. Findings indicate that the primary constraints are not a lack of awareness or intent, but systemic factors embedded within the industry. The analysis reveals a persistent gap between stated sustainability commitments and measurable outcomes. Ultimately, this study argues that achieving meaningful sustainability in the pharmaceutical sector will require coordinated structural changes, including standardized reporting systems, regulatory reform, financial incentives for innovation, and greater supply chain accountability.
Capstone Research: Alzheimer’s disease (AD) and Type 2 diabetes (T2D) are highly comorbid diseases with shared disease hallmarks including microvascular dysfunction, insulin resistance, and deposition of toxic Amyloid beta plaques. Pericytes are specialized support cells for endothelial cells and maintain microvascular stability. Pericytes have been shown to detach and extend aberrant processes at a higher rate in hyperglycemic retina. This bridging phenomenon is hypothesized to precede pericyte loss and has not been studied in diabetic brain, AD (retina and brain), or comorbid (retina and brain) murine models. Additionally, existing methods used to assess incidence of bridging are limited in throughput due to the need for manual quantification. This project investigated pericyte bridging across hyperglycemic, AD, and comorbid murine models in retina and brain tissue while developing an automated analysis pipeline for bridging quantification. Though statistical significance could not be analyzed due to limitation in sample size, positive trends between hyperglycemia and bridging incidence were seen. No significant changes in basement membrane protein basal lamina were observed across disease conditions. Additionally, results from the developed analysis pipeline were comparable to results from manual quantification. The classification accuracy of the machine learning model was roughly 90% with an average precision of 97%. Ultimately this work presents preliminary data assessing pericyte bridging in novel disease models and tissue while introducing a new workflow that can be further optimized to improve analytical capacity and minimize bias.
Both of these works, although not directly related in subject matter, ultimately converge on a shared objective: reducing inefficiency and waste within the broader healthcare and pharmaceutical landscape. My STS thesis addresses waste at a systemic level, highlighting structural barriers, while my capstone research targets inefficiency at the experimental and analytical level, developing an automated pipeline that reduces the time, labor, and potential bias associated with manual quantification of biological data. By addressing both systemic and methodological inefficiencies, this combined work contributes to a more sustainable and effective approach to advancing medical science.
