Terbinafine Resistance in Trichophyton rubrum and Trichophyton indotineae: A Literature Review
Bibliographic record
Abstract
Background/Objectives: Terbinafine has been the gold standard for the management of superficial fungal infections. The etiological agent generally is Trichophyton rubrum (T. rubrum); however, there has been increased reporting of a new terbinafine-resistant strain of the T. mentagrophytes complex (T. mentagrophytes ITS genotype VIII otherwise known as T. indotineae). Here, we review the epidemiology, clinical features, diagnosis, and treatment of T. rubrum and T. indotineae infections. Methods: We conducted a systematic literature search using PubMed, Embase (Ovid), and Web of Science, resulting in 83 qualified studies with data summarized for clinical features, antifungal susceptibility, and terbinafine resistance mechanisms and mutations. Results: Dermatophytosis is most commonly caused by T. rubrum; however, in certain parts of the world, especially in the Indian subcontinent, T. indotineae infections have been reported more frequently. The majority of T. rubrum isolates remain susceptible to terbinafine (over 60% of isolates show MIC50 and MIC90 < 0.5 µg/mL). In contrast, for T. indotineae, 30% of isolates exhibit MIC50 ≥ 0.5 µg/mL and 80% exhibit MIC90 ≥ 0.5 µg/mL. Frequently detected squalene epoxidase (SQLE) mutations in T. rubrum are Phe397Leu/Ile (41.6%) and Leu393Phe (20.8%); in T. indotineae, these include Phe397Leu (33.0%) and Ala448Thr (24.5%). Other potential terbinafine resistance mechanisms in T. rubrum and T. indotineae are discussed. Conclusions: T. rubrum generally remain susceptible in vitro to terbinafine in contrast to T. indotineae. The essential components of an effective antifungal stewardship emphasize accurate clinical and laboratory diagnosis, susceptibility testing, and appropriate antifungal therapy selection with a multidisciplinary approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".