With increasing number of immunocompromised patients as well as drug resistance in fungi, the risk of fatal fungal infections in humans increases as well. The action of echinocandins is based on the inhibition of β-(1,3)-d-glucan synthesis that builds the fungal cell wall. Caspofungin, micafungin, anidulafungin and rezafungin are semi-synthetic cyclic lipopeptides. Their specific chemical structure possess a potential to obtain novel derivatives with better pharmacological properties resulting in more effective treatment, especially in infections caused by Candida and Aspergillus species. In this review we summarise information about echinocandins with closer look on their chemical structure, mechanism of action, drug resistance and usage in clinical practice. We also introduce actual trends in modification of this antifungals as well as new methods of their administration, and additional use in viral and bacterial infections.

This study presents a model investigation into the development of tolerance to polyene antifungal drugs (nystatin and natamycin) in strains of Malassezia pachydermatis. This species, commonly associated with external ear canal infections in dogs, has emerged as increasingly significant in the broader context of growing fungal resistance to treatment. In the experiment, 10 strains of M. pachydermatis were passaged over a period of 105 weeks on media containing sublethal concentrations of nystatin and natamycin. Minimal inhibitory (MIC) and minimal fungicidal concentration (MFC) values were regularly assessed to monitor tolerance development. The results revealed a varied response among the strains: Some were eliminated during the process, while others showed a gradual increase in MIC values, up to fivefold in the case of nystatin. In several strains, acquired resistance remained stable even after passaging in drug-free conditions, whereas others reverted to their original susceptibility. The model demonstrated that resistance does not emerge immediately; significant changes appeared only after 30–45 passages. The authors propose this model as a valuable tool for tracking sequential changes that lead to resistance development. Such an approach may support targeted therapy development and help identify strains predisposed to drug adaptation. These findings hold promise for assessing therapeutic risk in immunosuppressed patients and for building resistance datasets that can support artificial intelligence algorithms in predicting fungal resistance mechanisms.

Highlights: • Integrated oral microbiome–metabolome profiling in ALMS/BBS • Disease-specific oral microbial restructuring beyond obesity • Distinct saliva and GCF microbiome–metabolome networks • Organic, amino acid, and carbohydrate metabolism are associated with oral taxa • Multi-omics reveals functional host–microbiome interactions