Disseminated fusariosis is treated with amphotericin B and voriconazole. To determine adequate therapy, the minimal inhibitory concentration (MIC) is used. However, MIC analysis is based on visual observation and requires a long period of fungal incubation. The measure of the minimal profile change concentration (MPCC) using MALDI-TOF MS is a quick spectral method that has presented good results in determining the antimicrobial resistance of yeasts. However, there is a lack of information on filamentous fungi. In the present work, 13 Fusarium spp. clinical isolates and two reference strains were used. MIC was obtained according to the M38-A2 protocol of the Clinical Laboratory Standards Institute, while MPPC was obtained following the initial steps of the M38-A2 protocol. Both Biotyper and the Rstudio environment were used to analyze mass spectra. For some fungal strains, the data obtained from the software MALDI Biotyper Compass 4.1 led to fuzzy heatmaps resulting in difficult interpretation, while heatmaps obtained using Rstudio tools generated better MPCC resolutions. Herein, 86.6% of the AMB MPCC values were highly correlated with the gold-standard AMB MIC. MALDI-TOF MS is a prominent tool used to determine MPCCs quicker, cost-effectively, and more accurately for Fusarium spp. strains. However, better statistical analyses could help measure the technique’s limit detection.

Fungal resistance is a public health concern due to the limited availability of antifungal resources and the complexities associated with treating persistent fungal infections. Azoles are thus far the primary line of defense against fungi. Specifically, azoles inhibit the conversion of lanosterol to ergosterol, producing defective sterols and impairing fluidity in fungal plasmatic membranes. Studies on azole resistance have emphasized specific point mutations in CYP51/ERG11 proteins linked to resistance. Although very insightful, the traditional approach to studying azole resistance is time-consuming and prone to errors during meticulous alignment evaluation. It relies on a reference-based method using a specific protein sequence obtained from a wild-type (WT) phenotype. Therefore, this study introduces a machine learning (ML)-based approach utilizing molecular descriptors representing the physiochemical attributes of CYP51/ERG11 protein isoforms. This approach aims to unravel hidden patterns associated with azole resistance. The results highlight that descriptors related to amino acid composition and their combination of hydrophobicity and hydrophilicity effectively explain the slight differences between the resistant non-wild-type (NWT) and WT (nonresistant) protein sequences. This study underscores the potential of ML to unravel nuanced patterns in CYP51/ERG11 sequences, providing valuable molecular signatures that could inform future endeavors in drug development and computational screening of resistant and nonresistant fungal lineages.