Development and assessment of a rapid biosensor for antimicrobial resistance detection in clinical isolates

Thesis Abstract

The rapid emergence and proliferation of antimicrobial-resistant pathogens pose a significant challenge to effective clinical management of infectious diseases, necessitating innovative diagnostic tools capable of delivering timely and accurate resistance profiles. Current standard methods, such as culture-based susceptibility testing, are often laborious and time-consuming, impeding prompt decision-making and contributing to the spread of resistant strains. This study aims to develop and thoroughly assess a novel, portable biosensor capable of detecting antimicrobial resistance markers directly from clinical isolates within a reduced timeframe. The specific objectives include (i) designing a biosensor prototype based on innovative nanomaterials and biorecognition elements; (ii) optimizing the biosensor's functional interface for specificity and sensitivity toward prevalent resistance determinants, such as blaCTX-M and mecA genes; (iii) evaluating the biosensor's performance against conventional phenotypic and genotypic methods using clinical isolates; and (iv) determining its operational reliability, reproducibility, and potential for point-of-care deployment in hospital settings. The methodology employs a mixed-methods approach, combining engineering design, microbiological testing, and statistical analysis. The biosensor development follows a design science paradigm, with iterative phases of prototype fabrication and technical validation. The study population comprises 300 clinical bacterial isolates collected from major hospital laboratories, representing key pathogens such as Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus. Stratified random sampling ensures proportional representation across resistance profiles. Data collection involves molecular characterization of isolates via PCR and sequencing to confirm resistance gene presence, alongside biosensor readouts obtained through electrochemical impedance spectroscopy. The performance metrics, including sensitivity, specificity, reproducibility, and detection limit, are analyzed using descriptive statistics, paired t-tests, and receiver operating characteristic (ROC) curves. A comparative analysis with conventional methods provides a basis for evaluating the biosensor’s diagnostic accuracy. Analytical modeling employs regression analysis to identify predictors of biosensor performance, and cost-effectiveness analysis assesses operational feasibility. Expected findings suggest that the biosensor will demonstrate high sensitivity (>95%) and specificity (>98%) in detecting targeted resistance determinants within 30 minutes, representing a significant improvement over traditional methods that often require 24-72 hours. It is anticipated that the biosensor's reproducibility will meet acceptable standards (coefficient of variation <10%), and its detection limits will be sufficient for clinical pathogen samples. The study is projected to reveal that the biosensor provides reliable resistance detection suitable for point-of-care use, potentially transforming antimicrobial stewardship practices and infection control protocols. The contribution to knowledge includes providing empirical evidence on the feasibility of nanomaterial-based biosensors for rapid antimicrobial resistance diagnostics, enhancing understanding of biosensor engineering tailored for clinical microbiology applications, and offering a validated translational tool for healthcare settings. The main conclusion underscores the biosensor's potential to accelerate resistance detection, reduce diagnostic turnaround times, and improve patient outcomes. Based on these findings, recommendations include further field validation across diverse healthcare environments, integration with electronic health records for real-time data sharing, and exploration of multiplexing capabilities to detect multiple resistance markers simultaneously. Future research directions suggested involve scaling production, assessing long-term operational stability, and expanding the platform to include viral and fungal resistance screening, with the ultimate goal of establishing a comprehensive rapid diagnostic ecosystem for antimicrobial resistance management.