Mathematical Modeling of Fluconazole Resistance in the Ergosterol Pathway of Candida albicans

Paul K Yu1,2,3, Llewelyn S Moron-Espiritu1,4, Angelyn R Lao1,3

  • 1Systems and Computational Biology Research Unit, Center for Natural Sciences and Environmental Research, De La Salle University, Malate, Manila, National Capital Region, Philippines.

Msystems
|November 16, 2022
PubMed

Insights

Mathematical modeling of the Candida albicans ergosterol pathway reveals that increasing sterol-methyltransferase enzyme levels enhances susceptibility to fluconazole, offering new strategies against fungal infections.

Area of Science:

  • Mycology
  • Biochemistry
  • Computational Biology

Background:

  • Candidiasis, a common fungal infection in critical care, is primarily caused by *Candida albicans*.
  • Fluconazole, an azole antifungal, is the standard treatment but faces increasing resistance.
  • The ergosterol pathway is a key target for antifungal drugs.

Purpose of the Study:

  • To construct a mathematical model of the *Candida albicans* ergosterol pathway.
  • To identify potential drug targets for combating fluconazole-resistant candidiasis.
  • To aid in narrowing down experimental drug target selection.

Main Methods:

  • Development of a mathematical model using ordinary differential equations with mass action kinetics.
  • Simulation of the ergosterol pathway in *Candida albicans*.
  • Analysis of enzyme inhibition and gene overexpression effects on fluconazole susceptibility.

Main Results:

  • Partial inhibition of sterol-methyltransferase and lanosterol pathways can lead to fluconazole resistance.
  • Overexpression of the *ERG6* gene (increasing sterol-methyltransferase) enhances susceptibility to fluconazole.
  • C14α-demethylase is confirmed as a viable fluconazole target; C5-desaturase is not ideal as an adjunct target.

Conclusions:

  • Targeting sterol-methyltransferase, potentially through *ERG6* gene modulation, could be a promising adjunctive strategy with fluconazole.
  • Mathematical modeling provides a valuable tool for predicting drug efficacy and guiding experimental research.
  • Understanding ergosterol pathway dynamics is crucial for developing new antifungals against resistant strains.

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