Observational studyDrug design, development and therapy2024
Immunosuppression with Generics in Liver and Kidney Transplantation: A Real-World Evidence Study.
Observational study in Drug design, development and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 2 citations in OpenAlex.
- A Retrospective Post-Marketing Surveillance Study on TGlobulin25Iranian journal of medical sciences · 2026Article
- Kidney and Bladder Transplantation: Advances, Barriers, and Emerging Solutions.Medicina (Kaunas, Lithuania) · 2025Review
- A population-based analysis of immunosuppression therapy after organ transplantation.BMC health services research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This study evaluates the use, benefit-risk profile, and economic impact of generic immunosuppressants (tacrolimus-TAC, cyclosporine-CsA, and mycophenolate-MYC) in kidney and liver transplant recipients compared to brand-name drugs. Patients and Methods: A retrospective multicentre observational study, involving four Italian regions, was conducted based on the national transplant Information system and regional healthcare claims data. The analysis focused on incident patients who received kidney and liver transplants between 2013 and 2019 and evaluated the use of generic of CsA, TAC, and MYC during the 30-day period following discharge. For each type of transplant and immunosuppressive agent, the benefit-risk profile of generic vs branded drugs in a two-year window was estimated by multivariate Cox models (HR; 95% CI). Furthermore, the potential cost savings per person associated with one year of treatment using generics were calculated. Results: The utilization of generic drugs showed a significant increase; over the study years, the proportion of users among kidney recipients ranged from 14.2% to 40.5% for TAC, from 36.9% to 56.7% for MYC, and from 18.2% to 94.7% for CsA. A great variability in generic uptake for region was found. A comparable risk-benefit profile between generic and branded formulations was shown for all immunosuppressors considered. Choosing generic immunosuppressants during maintenance could result in yearly savings of around 2000 euros per person for each therapy ingredient. Conclusion: The study shows an increasing proportion of patients using generic immunosuppressive drugs over time suggesting a growing acceptance of generics within the transplant community and reveals comparable risk-benefit profiles between the generic and branded formulations of TAC, CsA, and MYC. A significant variability in the use of generics immunosuppressive agents was found both at the regional level and among transplant centers and future research should delve into regional prescribing variations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.