Drug susceptibility of matrix-encapsulatedCandida albicansnano-biofilms
| Autor: | Anand K. Ramasubramanian, Celia Macias Gupta, C. Mauli Agrawal, Kai P. Leung, Jose L. Lopez-Ribot, Anand Srinivasan |
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| Rok vydání: | 2013 |
| Předmět: |
Antifungal drug
Biofilm Bioengineering Context (language use) biochemical phenomena metabolism and nutrition Biology Pharmacology biology.organism_classification Applied Microbiology and Biotechnology Corpus albicans Microbiology chemistry.chemical_compound chemistry Amphotericin B medicine Caspofungin Cell encapsulation Candida albicans Biotechnology medicine.drug |
| Zdroj: | Biotechnology and Bioengineering. 111:418-424 |
| ISSN: | 0006-3592 |
| Popis: | The rise in the use of biomedical devices and implants has seen a concomitant surge in the advent of device-related nosocomial (hospital-acquired) infections of bacterial and fungal origins. The most common nosocomial fungal infection is candidiasis caused mainly by Candida albicans biofilms. Candidiasis is associated with an unacceptably high mortality rate, and there is an urgent need for the discovery of new antifungal drugs that prevent or control biofilm formation. To this end, we recently developed an ultra-high-throughput microarray platform consisting of nano-scale biofilms of C. albicans encapsulated in collagen or alginate hydrogel matrices for antifungal drug screening. Here, we report that the choice of matrix influences the apparent susceptibility of C. albicans to the common antifungal drugs, amphotericin B, and caspofungin. While amphotericin B is equally effective against biofilms grown in collagen and alginate matrices, caspofungin is effective only against biofilms grown only in alginate, but not in collagen. We demonstrate differences in the distribution of the drugs in the two matrices may contribute to the susceptibility of C. albicans nano-biofilms. In a larger context, our results highlight the importance of the choice of matrix as a parameter in 3D cell encapsulation, and suggest a screening strategy to predict drug performance in vivo. |
| Databáze: | OpenAIRE |
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