Autor(es)

Bonner, Mitchell ; Barrera Patino, Claudia P. ; Borsatto, Andrew Ramos ; Soares, Jennifer M. ; Blanco, Kate C. ; Bagnato, Vanderlei S.

• Tipo de documento: Artigo Científico
• Fonte: ANTIBIOTICS-BASEL; v. 14, n. 8, p. 22-pg., 2025-08-15.

Resumo

Background/Objectives: The progression of antibiotic resistance is increasingly recognized as a dynamic and time-dependent phenomenon, challenging conventional diagnostics that define resistance as a binary trait. Methods: Biomolecules have fingerprints in Fourier-transform infrared spectroscopy (FTIR). The targeting of specific molecular groups, combined with principal component analysis (PCA) and machine learning algorithms (ML), enables the identification of bacteria resistant to antibiotics. Results: In this work, we investigate how effective classification depends on the use of different numbers of principal components, spectral regions, and defined resistance thresholds. Additionally, we explore how the time-dependent behavior of certain spectral regions (different biomolecules) may demonstrate behaviors that, independently, do not capture a complete picture of resistance development. FTIR spectra were obtained from Staphylococcus aureus exposed to azithromycin, trimethoprim/sulfamethoxazole, and oxacillin at sequential time points during resistance induction. Combining spectral windows substantially improved model performance, with accuracy reaching up to 96%, depending on the antibiotic and number of components. Early resistance patterns were detected as soon as 24 h post-exposure, and the inclusion of all three biochemical windows outperformed single-window models. Each spectral region contributed distinctively, reflecting biochemical remodeling associated with specific resistance mechanisms. Conclusions: These results indicate that antibiotic resistance should be viewed as a temporally adaptive trajectory rather than a static state. FTIR-based biochemical profiling, when integrated with ML, enables projection of phenotypic transitions and supports real-time therapeutic decision-making. This strategy represents a shift toward adaptive antimicrobial management, with the potential to personalize interventions based on dynamic resistance monitoring through spectral biomarkers. (AU)

• Processo FAPESP: 14/50857-8 - INCT 2014 - de Óptica Básica e Aplicada às Ciências da Vida
• Beneficiário: Vanderlei Salvador Bagnato
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 13/07276-1 - CEPOF - Centro de Pesquisa em Óptica e Fotônica
• Beneficiário: Vanderlei Salvador Bagnato
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 23/17384-8 - Identificação da Resistência de Bactérias a Antibióticos por Meio de Absorção de Grupos Bioquímicos Específicos com Implementação de Algoritmos de Aprendizado de Máquina.
• Beneficiário: Claudia Patricia Barrera Patiño
• Modalidade de apoio: Bolsas no Brasil - Programa Capacitação - Treinamento Técnico
• Processo FAPESP: 24/00100-0 - Combate da resistência antimicrobiana em bactérias multirresistentes por ação fotodinâmica
• Beneficiário: Jennifer Machado Soares
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado