Therapeutic drug monitoring of drugs for treatment of substance-related disorders
Bibliographic record
Abstract
Introduction The effect of pharmacotherapy of substance-related disorders is moderate at best. Objectives Therapeutic drug monitoring (TDM) could be an instrument to improve the outcomes. TDM is for most of these drugs not established yet. Aims The authors built a literature based rating scale to evaluate the necessity of TDM for these pharmacological agents. Methods A literature research was performed for TDM related properties of acamprosate, bupropion, buprenorphine, clomethiazole, disulfiram, methadone, naltrexone, and varenicline. A rating scale was established for evaluation. It included 28 items related to five categories (efficacy, toxicity, pharmacokinetics, patient characteristics and cost effectiveness). For comparison, three reference substances with established TDM were similarly assessed: clozapine, lithium and nortriptyline. Results The three reference substances, lithium, clozapine and nortriptyline, achieved scores of 15, 18, and 14 points, respectively. Rating of methadone (19 points), bupropion (14 points), buprenorphine (14 points), disulfiram (13) and naltrexone (12 points in the indication opioid-dependency and 10 points in the indication alcohol dependency) achieved more than 30% of the reachable points, whereas acamprosate (9 points), clomethiazole (9 points), and varenicline (5 points) had fewer points especially in the main characteristics in favor of TDM. Conclusions These results suggest this rating scale is sensitive to detect the appropriateness of TDM for drug treatment. Literature based rating and clinical experience give evidence that TDM has the potential to optimize the pharmacotherapy of substance related-disorders with different rank orders of the single substances.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".