Generic immunosuppression in solid organ transplantation: systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To compare the clinical efficacy and bioequivalence of generic immunosuppressive drugs in patients with solid organ transplants. DESIGN: Systematic review and meta-analysis of all studies comparing generic with innovator immunosuppressive drugs. DATA SOURCES: Medline and Embase from 1980 to September 2014. REVIEW METHODS: A literature search was performed for all studies comparing a generic to an innovator immunosuppressive drug in solid organ transplantation. Two reviewers independently extracted data and assessed quality of studies. Meta-analyses of prespecified outcomes were performed when deemed appropriate. Outcomes included patient survival, allograft survival, acute rejection, adverse events and bioequivalence. RESULTS: 1679 citations were screened, of which 50 studies met eligibility criteria (17 randomized trials, 15 non-randomized interventional studies, and 18 observational studies). Generics were compared with Neoral (cyclosporine) (32 studies), Prograf (tacrolimus) (12 studies), and Cellcept (mycophenolate mofetil) (six studies). Pooled analysis of randomized controlled trials in patients with kidney transplants that reported bioequivalence criteria showed that Neoral (two studies) and Prograf (three studies) were not bioequivalent with generic preparations according to criteria of the European Medicines Agency. The single Cellcept trial also did not meet bioequivalence. Acute rejection was rare but did not differ between groups. For Neoral, the pooled Peto odds ratio was 1.23 (95% confidence interval 0.64 to 2.36) for kidney randomized controlled trials and 0.66 (0.40 to 1.08) for observational studies. For kidney observational studies, the pooled Peto odds ratios were 0.98 (0.37 to 2.60) for Prograf and 0.49 (0.09 to 2.56) for Cellcept. Meta-analyses for non-renal solid organ transplants were not performed because of a lack of data.There were insufficient data reported on patient or graft survival. Pooling of results was limited by inconsistent study methods and reporting of outcomes. Many studies did not report standard criteria used to determine bioequivalence. While rates of acute rejection seemed similar and were relatively rare, few studies were designed to properly compare clinical outcomes. Most studies had short follow-up times and included stable patients without a history of rejection. CONCLUSIONS: High quality data showing bioequivalence and clinical efficacy of generic immunosuppressive drugs in patients with transplants are lacking. Given the serious consequences of rejection and allograft failure, well designed studies on bioequivalence and safety of generic immunosuppression in transplant recipients are needed.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.034 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".