Vibrational Spectroscopy for Antimicrobial Resistance and Drug Quality Investigation

• Grant number: 25/03135-1
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: July 01, 2025
• End date: February 29, 2028
• Field of knowledge: Physical Sciences and Mathematics - Physics - Atomic and Molecular Physics
• Principal Investigator: Denise Maria Zezell
• Grantee: Vinicius Pereira dos Anjos
• Host Institution: Instituto de Pesquisas Energéticas e Nucleares (IPEN). Secretaria de Desenvolvimento Econômico (São Paulo - Estado). São Paulo , SP, Brazil
• Associated research grant: 20/07065-4 - Multicenter program using PSMA radioligants for the diagnosis and therapy of patients with prostate cancer, AP.NPOP

Abstract

Cancer patients are at a higher risk of infections primarily due to two factors: malignancy-related immunosuppression and the immunosuppressive effects of their treatments. Certain types of cancer, such as prostate cancer, can lead to neutropenia, which impairs the body's ability to fight infections effectively. This makes these patients particularly vulnerable to bacterial infections, which may also pose issues related to antibiotic resistance. Rapid and accurate detection of antibiotics is a critical challenge in healthcare (not only in oncology). However, a new spectroscopy-based approach could offer a solution contributing to antimicrobial stewardship. This ongoing research involves obtaining the spectra ("fingerprints") of beta-lactam antibiotics using Raman and IR techniques. The antibiotics evaluated include Ampicillin, Amoxicillin, Ceftazidime, and Meropenem (a widely used antibiotic in oncology treatments). To enhance the antibiotic signal when diluted in water, nanoparticles will be synthesized using the LASiS technique (Laser Ablation in Liquid Solution) and employed as mediators for signal amplification in FTIR (SEIRA effect). DLS and UV-Vis will characterize the nanoparticles. These analyses will provide a better understanding of the nanoparticles' properties and how they influence the SEIRA spectrum of each molecule. The results aim to show that spectroscopic techniques associated with modern mathematical models of data analysis and processing, such as the use of artificial intelligence - especially convolutional neural networks (CNNs) and advanced deep learning approaches -are capable of providing unique spectra for each type of antibiotic evaluated, allowing their precise identification. Additionally, nanoparticles synthesized via the LASiS technique may enhance the sensitivity and reproducibility of the technique. The outcomes of this study are intended to suggest that FTIR and Raman spectroscopic techniques are promising options for identifying and quantifying different types of antibiotics in samples from various sources. (AU)