872. Voriconazole Therapeutic Drug Monitoring (TDM): How Common is Autoinduction?
Bibliographic record
Abstract
Abstract Background Therapeutic drug monitor (TDM) guided optimized dosing of Voriconazole allows optimal drug exposure in the management of mold infection (MI). In addition to already known nuances in pharmacokinetics such as CYP2C19 genetic polymorphism and the role of drug interactions both necessitating TDM, case reports have suggested that auto-induction may occur after initially achieving a therapeutic level. We assessed whether the levels of Voriconazole can change over time and become subtherapeutic due to auto-induction. Methods We prospectively enrolled, adults ≥ 18 y.o of age, on Voriconazole for the treatment of MI at the University Of Alberta Hospital. After achieving an initial therapeutic margin, (1–5.5mg/l), we monitored Voriconazole levels twice a month, using high-performance liquid chromatography, until discontinuation or at 12 weeks of therapy. We calculated the incidence of Voriconazole sub-therapeutic concentrations (auto-induction) defined as drop of Voriconazole level below one, with previous concentrations between the therapeutic margins of 1–5.5 mg/L. Adjustment of Voriconazole dosing in case of auto-induction was at the discretion of the treating physician. The excess Voriconazole dose adjustment was calculated in patients where dosing was increased. Results Between January 2021 and April 2022, we enrolled 12 patients. Median age (IQR) was 62 (52–73), and 25 % were female. Patient characteristics are in table 1. Auto-induction was observed in 6/10 (60%) who completed 12 weeks follow up blood work. Median time to auto-induction was of 46 days (39–55). Voriconazole dosing was increased in 4/6 patients with auto-induction. Of the four patients with dose adjustment, the cumulative Voriconazole dose was 13% higher than expected, which correspond to 5,300 mg excess Voriconazole per patient to maintain therapeutic levels. Conclusion Auto-induction is common in patients treated with Voriconazole. Future studies are needed to assess if undetected auto-induction affects outcomes. Funding: AVIR Pharma. Disclosures Carlos Cervera, Associate Professor, Astra-Zeneca: Advisor/Consultant|AVIR Pharma: Grant/Research Support|AVIR Pharma: Honoraria|Lilly: Advisor/Consultant|Merck: Advisor/Consultant|Merck: Grant/Research Support|Merck: Honoraria|Sunovion: Advisor/Consultant|Takeda: Advisor/Consultant|Takeda: Honoraria|VerityPharma: Advisor/Consultant Dima Kabbani, MD, MSc, AVIR Pharma: Grant/Research Support|AVIR Pharma: Honoraria|GSK: Honoraria|Merck: Grant/Research Support.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".