Design and evaluate a biosensor for rapid detection of enzymatic activity in cancer cells

Thesis Abstract

Cancer progression is often characterized by abnormal enzymatic activity within malignant cells, necessitating rapid, sensitive, and specific diagnostic tools to facilitate early detection and therapeutic intervention. Despite advances in traditional diagnostic techniques such as histopathology and molecular assays, the need for real-time, minimally invasive, and cost-effective methods remains critical, particularly for monitoring enzymatic biomarkers associated with tumor aggressiveness and metastasis. This study aims to design, develop, and evaluate a novel biosensor capable of rapid detection of key enzymatic activities—specifically matrix metalloproteinases (MMPs)—in cancer cells to enhance diagnostic precision and real-time monitoring of tumor dynamics. The specific objectives include synthesizing a nanomaterial-based transducer with high sensitivity and selectivity towards MMPs, engineering a functional biosensing interface that allows swift signal transduction upon enzyme interaction, and evaluating the biosensor’s performance in detecting enzymatic activity from cultured cancer cell lines. Additionally, the study seeks to optimize operational parameters such as the pH, temperature, and incubation time for maximal detection efficiency, and to validate the biosensor’s diagnostic potential through comparison with established methods like zymography and enzyme-linked immunosorbent assays (ELISA). Employing a mixed-methods research design, the study integrates experimental laboratory techniques with quantitative analytical approaches. The population comprises cultured human breast and prostate cancer cell lines, with a purposive sampling of cell samples ranging from 1×10^5 to 5×10^6 cells per experimental run. Samples are prepared through enzymatic extraction protocols, and the biosensor’s responses are characterized using electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) to measure changes in electrical conductivity upon enzymatic cleavage of substrate conjugates. Data analysis involves descriptive statistics for initial response profiling, followed by inferential statistics, including regression analysis to determine the relationship between enzymatic activity levels and biosensor signals; analysis of variance (ANOVA) to compare detection performance across various cell lines; and receiver operating characteristic (ROC) curve analysis to assess diagnostic accuracy. The study also employs the Theory of Catalytic Enhancement to interpret signal amplification mechanisms, and the Kinetic Theory of Enzymes to model reaction rates at the biosensor interface. Expected key findings indicate that the biosensor exhibits a detection limit as low as 0.1 ng/mL of MMPs within a detection time frame of less than five minutes, outperforming conventional assays in speed and sensitivity. The biosensor’s specificity is anticipated to exceed 95%, with a high reproducibility demonstrated through consistent responses across multiple replicates. Moreover, the biosensor demonstrates potential for real-time monitoring of enzymatic fluctuations during cancer cell proliferation and response to treatment, providing valuable insights into tumor microenvironment dynamics. The contribution of this research enhances existing knowledge by integrating nanomaterial-based transduction technologies with enzyme-specific substrates to create a portable, rapid, and reliable diagnostic tool tailored for oncology applications. It advances the field of biosensing by offering a platform adaptable to various enzymatic biomarkers beyond MMPs, thereby broadening its clinical applicability. The primary conclusion underscores the biosensor’s viability for point-of-care cancer diagnosis and monitoring, recommending further clinical validation using patient-derived samples and exploring integration with microfluidic systems for long-term, in vivo applications. The study advocates future research into multi-analyte detection modalities and the scalability of biosensor fabrication for commercial deployment to improve early cancer detection and patient outcomes.