Mechanisms of resistance against allylamine and azole antifungals in Trichophyton: A renewed call for innovative molecular diagnostics in susceptibility testing
Bibliographic record
Abstract
The emergence of antifungal resistance calls for continued research efforts to better guide healthcare providers in treatment selection and outcomes. Unlike bacterial infections, treatment of superficial fungal infections is mainly limited to allylamines (terbinafine) and azoles (itraconazole). Here, we aim to update our current understanding of resistance mechanisms against allylamine and azole antifungals in the Trichophyton genus. Resistance development has been demonstrated in vitro by challenging Trichophyton isolates with allylamines or azoles at levels below the minimum inhibitory concentration (MIC), which corroborates the observation of clinical resistance. Frequently reported mechanisms of resistance include: (I) Alterations of the drug target by single-nucleotide variations (SNVs) of the SQLE/ERG1 and ERG11 genes; in particular, SQLE SNVs (Leu393Phe, Leu393Ser, and Phe397Leu) have been frequently reported in isolates with high terbinafine MICs; (II) overexpression of the target enzyme for azoles (ERG11) and downstream genes in the ergosterol biosynthesis pathway can decrease the effective drug concentration as well as prevent the depletion of ergosterol and the accumulation of toxic sterol intermediates; (III) the up-regulation of drug efflux channels-belonging to the ABC superfamily (PDR1, MDR2, MDR3, MDR4), MFS superfamily (MFS1), or Pma1 (plasma membrane ATPase 1)-can reduce the effective concentrations of terbinafine and azoles. The possibility of multidrug resistance has been shown in Trichophyton strains, of both human and animal origins, harboring multiple resistance mechanisms (e.g., target alteration/overexpression and drug efflux channels). Tackling the issue of antifungal resistance will require an integrated approach with multidisciplinary efforts including surveillance initiatives and antifungal stewardship programs. However, these efforts are hampered by the current limited accessibility of antifungal susceptibility testing as well as the limited choice of antifungals available in routine practice. A better understanding of resistance mechanisms could help develop targeted, molecular-based assays.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".