Beyond Cyclosporine: A Systematic Review of Limited Sampling Strategies for Other Immunosuppressants
Bibliographic record
Abstract
Therapeutic drug monitoring has gained much attention in the management of immunosuppressive therapy. Area under the plasma drug concentration-time curve (AUC) is the pharmacokinetic (PK) parameter most commonly used to assess total exposure to a drug. However, estimation of AUC requires multiple blood samples throughout the dosing period, which is often inconvenient and expensive. Limited sampling strategies (LSSs) are therefore developed to estimate AUC and other PK parameters accurately and precisely while minimizing the number of blood samples needed. This greatly reduces costs, labor and inconvenience for both patients and clinical staff. In the therapeutic management of solid organ transplantation, LSSs for cyclosporine are commonplace and have been extensively reviewed. Thus, this systematic review paper focuses on other immunosuppressive agents and categorizes the 24 pertinent citations according to the U.S. Preventive Services Task Force rating scale. Thirteen articles (3 level I, 1 level II-1, 2 level II-2, and 7 level III) involved LSSs for mycophenolate, 7 citations (1 level I and 6 level III) for tacrolimus (TAC), and 3 citations (all level III) for other drugs (sirolimus) or multiple drugs. The 2 main approaches to establishing LSSs, multiple regression and Bayesian analyses, are also reviewed. Important elements to consider for future LSS studies, including proper validation of LSSs, convenient sampling times, and application of LSSs to the appropriate patient population and drug formulation are discussed. Limited sampling strategies are a useful tool to help clinicians make decisions on drug therapy. However, patients' pathophysiology, environmental and genetic factors, and pharmacologic response to therapy, in conjunction with PK profiling tools such as LSSs, should be considered collectively for optimal therapy management.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".