Abstract
Background
Sirolimus (rapamycin), an antibiotic discovered in the 1970s, can effectively reduce allograft rejection, and plays an important role in the fields of immunosuppressive therapy and organ transplantation. Therefore, this study aimed to analyze adverse drug reactions (ADRs) associated with sirolimus using the FDA Adverse Event Reporting System (FAERS) database to inform clinical risk minimization strategies and guide safer therapeutic use.
Methods
ADRs associated with sirolimus were retrospectively extracted from the FAERS database. Following the removal of duplicate entries, the final analytical dataset comprised 8,150,023 unique reports. Statistical analyses for signal detection of disproportionate reporting were performed using established pharmacovigilance metrics.
Results
A total of 4,821 FAERS reports associated with sirolimus as the primary suspect drug were identified. Injury, poisoning and procedural complications (ROR = 1.61) \are the most common reports, followed by investigation (ROR = 2.19), infections and infestations (ROR = 1.74). The top ADRs matched known profiles, including kidney transplant rejection (ROR = 230.41). The analysis also revealed signals not prominently described in current prescribing information, including ovarian adenoma (ROR = 56.6), which should be interpreted cautiously as a hypothesis-generating pharmacovigilance signal rather than evidence of causality.
Conclusion
This analysis details the ADR profile of sirolimus, revealing both known and potentially novel clinically significant adverse event signals that augment its known safety profile. Ongoing pharmacovigilance and targeted investigations are necessary to confirm these pharmacovigilance signals and inform optimal safety management strategies for sirolimus.
Keywords: adverse drug reactions, FAERS database, pharmacovigilance signals, rapamycin, sirolimus
1. Introduction
Advances in surgical technology have established organ transplantation as the primary therapeutic intervention for end-stage organ failure, significantly enhancing patient quality of life (1–3). Despite considerable success, immune rejection remains a significant barrier to the long-term survival of transplant recipients (4, 5). Timely intervention is critical for maintaining graft function, with the administration of immunosuppressive agents (ISAs) being essential for preventing rejection (6, 7).
Sirolimus, also known as rapamycin, an antibiotic discovered in the 1970s from Streptomyces hygroscopicus on Easter Island (Rapanui), can effectively mitigate allograft rejection following organ transplantation by inhibiting mTOR (mechanistic target of rapamycin) signaling pathway to block the activation of T cells and B cells, which plays an important role in the fields of immunosuppressive therapy and organ transplantation, especially in renal transplantation (8, 9). Furthermore, recent research indicates that sirolimus plays a significant role in adjuvant treatment of tumors, prolonging the survival of cancer patients, preventing stent stenosis after coronary angioplasty and prolonging life (10–12).
Though the therapeutic benefits of sirolimus have been fully proved, its expanding clinical application needs to be highly vigilant about its safety. Although its efficacy, it is associated with recognized adverse drug reactions (ADRs), including gastrointestinal disorders, pulmonary toxicity, proteinuria, peripheral edema, and increased risk of rejection (13–15). However, the existing evidence describing these adverse events usually comes from studies which are lacking large-scale, real-world pharmacovigilance data. The FDA Adverse Event Reporting System (FAERS) database provides a powerful resource to solve these limitations.It facilitates systematic signal detection through quantitative disproportionality analysis, enabling the identification of potential associations between specific drugs (such as sirolimus) and specific ADRs with greater statistical power (16, 17).
Therefore, this study utilized the FAERS database to describe ADRs related to sirolimus, aiming to provide evidence-based insights for optimizing risk-benefit assessment, and to provide information for clinical and regulatory decision-making of sirolimus treatment.
2. Methods
2.1. Data source
This retrospective pharmacovigilance study utilized data from the FAERS database (https://open.fda.gov/data/). FAERS represents a comprehensive repository of spontaneous adverse event reports submitted to the FDA. Data extraction and deduplication were performed according to the FDA’s recommended methodology (18). The final analytical cohort comprised 8,150,023 unique reports spanning the period from 2004 to 2024. This extensive dataset provides a robust foundation for investigating ADRs associated with sirolimus.
2.2. Data extraction
A systematic extraction process that identified sirolimus-related ADRs was carried out, as described in our previous work (19). To ensure data integrity, duplicate reports were resolved using the FDA-recommended deduplication strategy: for each unique CASEID, the report with the most recent FDA_DT was retained; when CASEID and FDA_DT were identical, the record with the highest PRIMARYID was selected. Reports lacking essential demographic information were excluded to improve data quality. For disproportionality analysis, the background comparator included all deduplicated reports within the FAERS database, and reports listing sirolimus as the primary suspect (PS) drug constituted the exposure group.
The preliminary filtering produced 4,821 case reports that sirolimus was definitely used as PS drug. From these reports, 16,173 unique Preferred Terms (PTs) coded by terms in the Medical Dictionary of Regulatory Activities (MedDRA) were identified as related to sirolimus exposure. PTs provide standardized nomenclature for adverse events, which is essential for consistent pharmacovigilance analysis. Because a FAERS report may contain multiple PT, the report level count and PT level count are analyzed separately throughout the study to avoid ambiguity. The time to onset was determined by subtracting START_DT from EVENT_DT. Reports with missing, invalid, or contradictory dates were excluded from onset-time analyses.
2.3. Statistical analysis
Disproportionality analysis was performed using established pharmacovigilance methods to quantify signal strength and identify significant adverse event associations. The Reporting Odds Ratio (ROR) with 95% confidence intervals (CI) assessed association strength between sirolimus and specific adverse drug reactions (ADRs). The Proportional Reporting Ratio (PRR) compared sirolimus-related ADR frequencies against background reporting rates for all other drugs in the FAERS database. To enhance signal robustness, the Bayesian Confidence Propagation Neural Network (BCPNN) method was implemented, generating Information Components (IC) with Bayesian confidence intervals to evaluate dependency probabilities. Additionally, the Empirical Bayesian Geometric Mean (EBGM) quantified disproportionate reporting while adjusting for database-wide covariate patterns, with EBGM05 (lower 90% CI bound) used as the significance threshold. Signal detection criteria were defined as follows: ROR lower 95% confidence interval > 1; PRR ≥ 2 with χ² ≥ 4; IC025 > 0; and EBGM05 > 2. Signals meeting all four criteria were considered statistically significant disproportionate reporting signals. All statistical analyses were performed using R software (version 4.3.2). Figure 1 illustrates the analytical workflow.
Figure 1.
Flow diagram for the selection of sirolimus-related adverse events from FAERS.
3. Results
3.1. General features on adverse drug reactions
Table 1; Figure 2 summarize the general characteristics of patients reporting sirolimus-associated ADRs. A total of 4,821 FAERS reports associated with sirolimus as the primary suspect drug were documented, peaking in 2005. Most reports involved males (55.11%) and patients aged 18–60 years (40.03%). Geographically, the majority originated from the Americas (81.13%). The primary reporter source was consumers (29.72%). The most frequently reported outcome was other serious medical events (42.47%), followed by hospitalization (39.25%).
Table 1.
Baseline characteristics of patients reporting adverse events related to Sirolimus from the FAERS database.
| Variable | N (%) |
|---|---|
| Gender | |
| female | 1802 (37.38%) |
| male | 2657 (55.11%) |
| unknown | 362 (7.51%) |
| Age | |
| <18 years | 290 (6.02%) |
| 18~60 years | 1930 (40.03%) |
| >=60 years | 1418 (29.41%) |
| unknow | 1183 (24.54%) |
| Reporter | |
| Consumer | 1433 (29.72%) |
| Physician | 1274 (26.43%) |
| unknown | 1100 (22.82%) |
| Other health-professional | 639 (13.25%) |
| Pharmacist | 369 (7.65%) |
| Lawyer | 6 (0.12%) |
| Reported countries | |
| United States | 2089 (81.13%) |
| Canada | 124 (4.82%) |
| France | 82 (3.18%) |
| other | 74 (2.87%) |
| United Kingdom | 54 (2.10%) |
| Argentina | 42 (1.63%) |
| Germany | 31 (1.20%) |
| Spain | 25 (0.97%) |
| China | 18 (0.70%) |
| Brazil | 13 (0.50%) |
| Colombia | 12 (0.47%) |
| Italy | 11 (0.43%) |
| Route | |
| oral | 2946 (61.11%) |
| other | 1875 (38.89%) |
| Outcomes | |
| hospitalization | 2180 (39.25%) |
| other serious | 2359 (42.47%) |
| death | 699 (12.59%) |
| life threatening | 198 (3.56%) |
| disability | 105 (1.89%) |
| required intervention to Prevent Permanent Impairment/Damage | 7 (0.13%) |
| congenital anomaly | 6 (0.11%) |
The percentages were calculated based on report-level counts. Other serious refers to medically significant events that do not meet predefined regulatory seriousness categories such as death, life-threatening condition, hospitalization, disability, congenital anomaly, or required intervention to prevent permanent impairment/damage.
Figure 2.
Basic characteristics of adverse events related to sirolimus from the FAERS database.
3.2. Signal detection for sirolimus-related ADRs based on SOC levels
The analysis of various SOCs associated with sirolimus-related ADRs is shown in Table 2; Figure 3. Our statistical analysis shows that injury, poisoning and procedural complications (2,367cases) are the most common reports, followed by investigation (2,129 cases), general disorders and administration site conditions (2,074 cases), infections and infestations (1,483 cases), gastrointestinal disorders (1,066 cases), respiratory, thoracic and mediastinal disorders (1,000 cases), renal and urinary disorders (856 cases), nervous system disorders (661 cases), and immune system disorders (635 cases).
Table 2.
Adverse drug reactions of sirolimus at the SOC level in the FAERS database (ordered by ROR).
| SOC | Case reports | ROR(95% CI) | PRR (95% CI) | chisq | IC (IC025) | EBGM (EBGM05) |
|---|---|---|---|---|---|---|
| immune system disorders | 635 | 3.5(3.23, 3.79) | 3.4(3.14, 3.68) | 1086.11 | 1.76(1.65) | 3.4(3.18) |
| renal and urinary disorders | 856 | 2.84(2.65, 3.05) | 2.75(2.59, 2.92) | 967.73 | 1.46(1.36) | 2.74(2.59) |
| investigations | 2129 | 2.19(2.09, 2.29) | 2.03(1.95, 2.11) | 1189.74 | 1.02(0.96) | 2.03(1.95) |
| infections and infestations | 1483 | 1.74(1.65, 1.84) | 1.67(1.61, 1.74) | 423.52 | 0.74(0.66) | 1.67(1.6) |
| blood and lymphatic system disorders | 472 | 1.66(1.52, 1.82) | 1.65(1.5, 1.82) | 121.49 | 0.72(0.59) | 1.64(1.52) |
| injury, poisoning and procedural complications | 2367 | 1.61(1.54, 1.68) | 1.52(1.46, 1.58) | 469.2 | 0.61(0.54) | 1.52(1.47) |
| hepatobiliary disorders | 219 | 1.42(1.25, 1.63) | 1.42(1.24, 1.63) | 27.32 | 0.5(0.31) | 1.42(1.27) |
| metabolism and nutrition disorders | 468 | 1.3(1.18, 1.42) | 1.29(1.17, 1.42) | 31.15 | 0.37(0.23) | 1.29(1.19) |
| cardiac disorders | 569 | 1.27(1.16, 1.38) | 1.26(1.16, 1.36) | 30.68 | 0.33(0.21) | 1.26(1.17) |
| respiratory, thoracic and mediastinal disorders | 1000 | 1.26(1.18, 1.34) | 1.24(1.17, 1.32) | 49.31 | 0.31(0.22) | 1.24(1.18) |
| vascular disorders | 435 | 1.19(1.09, 1.31) | 1.19(1.08, 1.31) | 13.28 | 0.25(0.11) | 1.19(1.1) |
| neoplasms benign, malignant and unspecified (incl cysts and polyps) | 430 | 0.95(0.86, 1.05) | 0.95(0.86, 1.05) | 1.03 | -0.07(-0.21) | 0.95(0.88) |
| gastrointestinal disorders | 1066 | 0.72(0.68, 0.77) | 0.74(0.7, 0.78) | 108.6 | -0.44(-0.53) | 0.74(0.7) |
| general disorders and administration site conditions | 2074 | 0.66(0.63, 0.69) | 0.71(0.68, 0.74) | 310.21 | -0.5(-0.57) | 0.71(0.68) |
| ear and labyrinth disorders | 47 | 0.64(0.48, 0.86) | 0.64(0.48, 0.86) | 9.25 | -0.63(-1.04) | 0.64(0.51) |
| reproductive system and breast disorders | 81 | 0.58(0.46, 0.72) | 0.58(0.47, 0.72) | 25.05 | -0.79(-1.1) | 0.58(0.48) |
| congenital, familial and genetic disorders | 29 | 0.55(0.38, 0.8) | 0.55(0.38, 0.8) | 10.42 | -0.85(-1.37) | 0.55(0.41) |
| eye disorders | 161 | 0.47(0.4, 0.55) | 0.48(0.41, 0.56) | 93.94 | -1.07(-1.29) | 0.48(0.42) |
| nervous system disorders | 661 | 0.44(0.4, 0.47) | 0.46(0.43, 0.5) | 459.72 | -1.12(-1.23) | 0.46(0.43) |
| musculoskeletal and connective tissue disorders | 391 | 0.42(0.38, 0.47) | 0.44(0.4, 0.49) | 299.09 | -1.19(-1.34) | 0.44(0.4) |
| pregnancy, puerperium and perinatal conditions | 30 | 0.41(0.29, 0.59) | 0.41(0.29, 0.58) | 25.57 | -1.29(-1.79) | 0.41(0.3) |
| skin and subcutaneous tissue disorders | 359 | 0.38(0.34, 0.42) | 0.4(0.36, 0.44) | 349.4 | -1.33(-1.49) | 0.4(0.36) |
| endocrine disorders | 12 | 0.28(0.16, 0.49) | 0.28(0.16, 0.49) | 22.16 | -1.83(-2.62) | 0.28(0.17) |
| psychiatric disorders | 199 | 0.2(0.17, 0.22) | 0.21(0.18, 0.24) | 650.12 | -2.28(-2.48) | 0.21(0.18) |
SOC, System Organ Class; ROR, reporting odds ratio; PRR, proportional reporting ratio; Chisq, Chi-Square; IC, Information Component; EBGM, Empirical Bayesian Geometric Mean.
Figure 3.
Distribution of system organ classes (SOCs) for sirolimus-related adverse events.
When we selected SOCs that met all predefined signal detection criteria (ROR, PRR, IC, and EBGM thresholds) and arranged them in descending order of case, immune system disorders (ROR = 3.5, PRR = 3.4, χ2 = 1086.11, IC = 1.76, EBGM = 3.4), renal and urinary disorders (ROR = 2.84, PRR = 2.75, χ2 = 967.73, IC = 1.46, EBGM = 2.74), investigations (ROR = 2.19, PRR = 2.03, χ2 = 1189.74, IC = 1.02, EBGM = 2.03), infections and infestations (ROR = 1.74, PRR = 1.67, χ2 = 423.52, IC = 0.74, EBGM = 1.67), blood and lymphatic system disorders (ROR = 1.66, PRR = 1.65, χ2 = 121.49, IC = 0.72, EBGM = 1.64), injury, poisoning and procedural complications (ROR = 1.61, PRR = 1.52, χ2 = 469.2, IC = 0.61, EBGM = 1.52), hepatobiliary disorders (ROR = 1.42, PRR = 1.42, χ2 = 27.32, IC = 0.5, EBGM = 1.42), metabolism and nutrition disorders (ROR = 1.3, PRR = 1.29, χ2 = 31.15, IC = 0.37, EBGM = 1.29), cardiac disorders (ROR = 1.27, PRR = 1.26, χ2 = 30.68, IC = 0.33, EBGM = 1.26), respiratory, thoracic and mediastinal disorders (ROR = 1.26, PRR = 1.24, χ2 = 49.31, IC = 0.31, EBGM = 1.24), vascular disorders (ROR = 1.19, PRR = 1.19, χ2 = 13.28, IC = 0.25, EBGM = 1.19), were found to maybe affected. These SOC-level signals should be interpreted as disproportionate reporting associations rather than confirmed causal toxicities.
3.3. Signal detection for sirolimus-related ADRs based on PT levels
Table 3; Figures 4, 5 offer an overview of specific PTs associated with sirolimus. Our statistical analysis shows that kidney transplant rejection (292 reports) represented one of the most frequently reported PT-level signals, followed by complications of transplanted kidney (136 cases), transplant rejection (103 cases), and immunosuppressant drug level increased (97 cases).
Table 3.
Top clinical adverse reactions of sirolimus ranked by case reports in the FAERS database.
| SOC | PT | Case reports | ROR (95% CI) | PRR (95% CI) | chisq | IC (IC025) | EBGM (EBGM05) |
|---|---|---|---|---|---|---|---|
| renal and urinary disorders | ureteric fistula | 3 | 303.08(92.65, 991.48) | 303.03 (93.49, 982.24) | 823.4 | 8.11(6.62) | 276.38(102.52) |
| urinary fistula | 8 | 248.14(120.78, 509.82) | 248.02 (120.1, 512.2) | 1823.75 | 7.84(6.86) | 229.89(125.85) | |
| perinephric collection | 8 | 216.06(105.52, 442.38) | 215.95 (104.57, 445.97) | 1601.21 | 7.66(6.68) | 202.08(110.95) | |
| urinoma | 5 | 122.35(50.06, 299.03) | 122.32 (49.65, 301.34) | 579 | 6.88(5.7) | 117.75(55.75) | |
| glomerulonephritis proliferative | 7 | 69.39(32.81, 146.78) | 69.36 (32.93, 146.07) | 461.43 | 6.08(5.07) | 67.88(36.27) | |
| renal necrosis | 4 | 63.92(23.75, 172.03) | 63.9 (23.52, 173.63) | 242.72 | 5.97(4.69) | 62.65(27.36) | |
| renal vessel disorder | 3 | 46.74(14.95, 146.18) | 46.74 (15, 145.68) | 132.3 | 5.53(4.1) | 46.06(17.74) | |
| investigations | immunosuppressant drug level decreased | 39 | 120.84(87.73, 166.45) | 120.55 (88.1, 164.95) | 4452.53 | 6.86(6.4) | 116.12(88.83) |
| x-ray limb abnormal | 3 | 120.46(38.02, 381.67) | 120.43 (37.89, 382.78) | 342.17 | 6.86(5.41) | 116.01(44.2) | |
| ultrasound kidney abnormal | 5 | 117.75(48.21, 287.6) | 117.72 (47.78, 290.01) | 557.66 | 6.83(5.65) | 113.49(53.76) | |
| immunosuppressant drug level increased | 97 | 114.48(93.43, 140.27) | 113.8 (93.54, 138.44) | 10465.71 | 6.78(6.49) | 109.84(92.67) | |
| biopsy kidney abnormal | 6 | 102.14(45.29, 230.36) | 102.11 (45.72, 228.07) | 581.73 | 6.63 (5.54) | 98.91(50.09) | |
| culture wound positive | 4 | 47.82(17.81, 128.38) | 47.81 (17.94, 127.39) | 180.55 | 5.56 (4.28) | 47.1(20.61) | |
| drug level fluctuating | 10 | 41.28(22.12, 77.05) | 41.26 (22.04, 77.25) | 387.69 | 5.35 (4.49) | 40.73(24.16) | |
| injury, poisoning and procedural complications | renal lymphocele | 4 | 305.56(109.44, 853.18) | 305.49 (110.25, 846.51) | 1106.06 | 8.12 (6.79) | 278.42(117.92) |
| complications of transplanted kidney | 136 | 233.14(195.74, 277.69) | 231.19 (193.8, 275.79) | 29028.41 | 7.75 (7.5) | 215.36(186.05) | |
| complications of transplant surgery | 19 | 129.77(81.99, 205.38) | 129.62 (82.58, 203.45) | 2328.5 | 6.96 (6.31) | 124.5(84.79) | |
| vascular graft complication | 4 | 79.29(29.39, 213.92) | 79.27(29.17, 215.39) | 301.51 | 6.27(4.99) | 77.34(33.71) | |
| graft complication | 9 | 54.02(27.94, 104.42) | 53.99(27.73, 105.13) | 460.12 | 5.73(4.83) | 53.09(30.58) | |
| transplant dysfunction | 24 | 50.37(33.65, 75.42) | 50.3(33.33, 75.91) | 1141.41 | 5.63(5.06) | 49.52(35.33) | |
| complications of transplanted liver | 5 | 47.75(19.74, 115.5) | 47.73(19.76, 115.3) | 225.33 | 5.56(4.39) | 47.03(22.46) | |
| immune system disorders | kidney transplant rejection | 292 | 230.41(204.39,259.73) | 226.26(201.16, 254.5) | 61078.27 | 7.72(7.55) | 211.08(190.95) |
| heart transplant rejection | 31 | 110.64(77.31, 158.35) | 110.43(77.6, 157.15) | 3247.2 | 6.74(6.23) | 106.7(79.05) | |
| liver transplant rejection | 44 | 90.53(67.06, 122.21) | 90.29(67.29, 121.15) | 3776.29 | 6.46(6.03) | 87.78(68.29) | |
| pancreas transplant rejection | 4 | 72.84(27.03, 196.31) | 72.82(26.8, 197.87) | 276.9 | 6.15(4.87) | 71.19(31.05) | |
| chronic allograft nephropathy | 15 | 70.17(42.05, 117.08) | 70.1(42.11, 116.69) | 999.4 | 6.1(5.38) | 68.59(44.69) | |
| transplant rejection | 103 | 47.8(39.33, 58.11) | 47.51(39.05, 57.8) | 4619.87 | 5.55(5.27) | 46.81(39.76) | |
| vascular disorders | lymphocele | 36 | 189.24(135.14, 265) | 188.82(135.31, 263.48) | 6343.29 | 7.48(7) | 178.14(134.4) |
| respiratory, thoracic and mediastinal disorders | alveolar proteinosis | 7 | 49.61(23.51, 104.69) | 49.59(23.55, 104.44) | 328.08 | 5.61(4.6) | 48.83(26.14) |
| neoplasms benign, malignant and unspecified (incl cysts and polyps) | ovarian adenoma | 3 | 56.6(18.07, 177.31) | 56.59(18.16, 176.38) | 160.91 | 5.8(4.37) | 55.6(21.39) |
SOC, System Organ Class; PT, Preferred term; ROR, reporting odds ratio; PRR, proportional reporting ratio; Chisq, Chi-Square; IC, Information Component; EBGM, Empirical Bayesian Geometric Mean.
Figure 4.
Top 30 preferred terms (PTs) of adverse events and their affiliated system organ classes (SOCs) reported for sirolimus.
Figure 5.
Logarithmic representation of Reporting Odds Ratios (ROR) for different adverse events linked to sirolimus.
Among these, kidney transplant rejection was notably prevalent (ROR = 230.41, PRR = 226.26, χ2 = 61078.27, IC = 7.72, EBGM = 211.08). Renal lymphocele had the highest reporting odds ratio (ROR = 305.56, PRR = 305.49, χ2 = 1106.06, IC = 8.12, EBGM = 278.42), indicating a strong disproportionality signal associated with sirolimus.
The analysis also revealed adverse reactions not prominently mentioned in the prescribing information, such as rare but notable ovarian adenoma (ROR = 56.6, PRR = 56.59, χ2= 160.91, IC = 5.8, EBGM = 55.6), which enhances the comprehensive understanding of the safety of sirolimus.
3.4. Subgroup analysis
Subgroup analyses revealed that males had a higher frequency of reported ADRs, particularly for kidney transplant rejection and complications of transplanted kidney (Table 4). Furthermore, the same trend has taken place among females (Table 5).
Table 4.
Subgroup analysis of signal strength of reports of male at the preferred term level in the FAERS database.
| SOC | PT | Case reports | ROR (95% CI) | PRR (95% CI) | chisq | IC (IC025) | EBGM (EBGM05) |
|---|---|---|---|---|---|---|---|
| renal and urinary disorders | perinephric collection | 4 | 134.46(48.78, 370.65) | 134.4(48.5, 372.42) | 494.89 | 6.97(5.66) | 125.65(53.79) |
| ureteric stenosis | 6 | 34.63(15.44, 77.67) | 34.61(15.5, 77.3) | 192.37 | 5.09(4.01) | 34.01(17.31) | |
| ureteric fistula | 3 | 383.16(110.9, 1323.77) | 383.03(111.42, 1316.74) | 952.59 | 8.32(6.76) | 319.36(113.18) | |
| urinary fistula | 5 | 171.09(68.52, 427.21) | 171(68.07, 429.6) | 775.75 | 7.3(6.09) | 157.06(73.04) | |
| urinoma | 3 | 78.73(24.81, 249.85) | 78.71(24.76, 250.18) | 221.07 | 6.24(4.79) | 75.64(28.78) | |
| renal necrosis | 3 | 69.25(21.88, 219.14) | 69.22(21.78, 220.01) | 194.68 | 6.06(4.62) | 66.84(25.49) | |
| glomerulonephritis proliferative | 5 | 55.7(22.89, 135.57) | 55.67(23.04, 134.48) | 260.88 | 5.76(4.58) | 54.13(25.72) | |
| investigations | immunosuppressant drug level decreased | 21 | 77.97(50.38, 120.68) | 77.79(50.54, 119.73) | 1529.81 | 6.22(5.61) | 74.79(51.9) |
| immunosuppressant drug level increased | 56 | 74.27(56.82, 97.07) | 73.81(56.1, 97.11) | 3873.32 | 6.15(5.77) | 71.11(56.84) | |
| drug level fluctuating | 6 | 34.84(15.54, 78.14) | 34.82(15.59, 77.77) | 193.58 | 5.1(4.02) | 34.22(17.41) | |
| ultrasound kidney abnormal | 3 | 122.29(38.05, 392.96) | 122.24(37.71, 396.23) | 339.11 | 6.85(5.38) | 114.97(43.29) | |
| biopsy kidney abnormal | 3 | 55.26(17.53, 174.18) | 55.25(17.38, 175.61) | 155.31 | 5.75(4.31) | 53.72(20.56) | |
| culture wound positive | 3 | 47.11(14.98, 148.13) | 47.09(15.11, 146.77) | 132.1 | 5.52(4.09) | 45.99(17.63) | |
| culture stool positive | 3 | 44.55(14.18, 139.99) | 44.54(14.29, 138.82) | 124.78 | 5.44(4.01) | 43.55(16.71) | |
| sputum culture positive | 8 | 38.43(19.08, 77.41) | 38.4(18.96, 77.76) | 285.68 | 5.24(4.28) | 37.66(20.96) | |
| injury, poisoning and procedural complications | complications of transplanted kidney | 59 | 105.02(80.74, 136.59) | 104.33(80.86, 134.61) | 5726.75 | 6.63(6.25) | 99(79.45) |
| complications of transplant surgery | 7 | 60.16(28.34, 127.7) | 60.12(28.55, 126.61) | 394.56 | 5.87(4.85) | 58.32(31.07) | |
| vascular graft complication | 3 | 70.09(22.14, 221.87) | 70.07(22.05, 222.72) | 197.04 | 6.08(4.63) | 67.63(25.79) | |
| complications of transplanted liver | 3 | 35.26(11.25, 110.49) | 35.25(11.31, 109.87) | 98.03 | 5.11(3.68) | 34.63(13.32) | |
| seroma | 5 | 31.83(13.15, 77.05) | 31.81(13.17, 76.84) | 146.79 | 4.97(3.8) | 31.31(14.94) | |
| immune system disorders | kidney transplant rejection | 199 | 185.3(159.99, 214.61) | 181.23(158, 207.88) | 32588.1 | 7.37(7.16) | 165.65(146.49) |
| heart transplant rejection | 14 | 70.48(41.33, 120.2) | 70.37(41.45, 119.46) | 923.52 | 6.09(5.34) | 67.91(43.45) | |
| liver transplant rejection | 21 | 55.22(35.77, 85.25) | 55.09(35.79, 84.79) | 1084.21 | 5.74(5.13) | 53.58(37.26) | |
| chronic allograft nephropathy | 8 | 42.83(21.25, 86.34) | 42.8(21.14, 86.67) | 319.43 | 5.39(4.43) | 41.88(23.3) | |
| lung transplant rejection | 4 | 28.49(10.61, 76.48) | 28.48(10.69, 75.88) | 104.5 | 4.81(3.53) | 28.08(12.29) | |
| infections and infestations | urinary tract infection pseudomonal | 5 | 37.14(15.32, 90) | 37.12(15.37, 89.67) | 172.37 | 5.19(4.02) | 36.43(17.37) |
| urinary tract infection enterococcal | 4 | 29.48(10.98, 79.15) | 29.46(11.06, 78.49) | 108.33 | 4.86(3.58) | 29.03(12.7) | |
| vascular disorders | lymphocele | 23 | 158.28(103.41, 242.28) | 157.88(102.58, 242.99) | 3312.41 | 7.19(6.59) | 145.93(102.2) |
| microangiopathy | 5 | 28.86(11.93, 69.81) | 28.84(11.94, 69.67) | 132.4 | 4.83(3.66) | 28.43(13.58) | |
| respiratory, thoracic and mediastinal disorders | pulmonary necrosis | 3 | 35.7(11.39, 111.87) | 35.69(11.45, 111.24) | 99.29 | 5.13(3.7) | 35.05(13.48) |
SOC, System Organ Class; PT, Preferred term; ROR, reporting odds ratio; PRR, proportional reporting ratio; Chisq, Chi-Square; IC, Information Component; EBGM, Empirical Bayesian Geometric Mean.
Table 5.
Subgroup analysis of signal strength of reports of female at the preferred term level in the FAERS database.
| SOC | PT | Case reports | ROR (95% CI) | PRR (95% CI) | chisq | IC (IC025) | EBGM (EBGM05) |
|---|---|---|---|---|---|---|---|
| renal and urinary disorders | perinephric collection | 4 | 330.97(119.67, 915.34) | 330.77(119.37, 916.56) | 1221.15 | 8.26(6.94) | 307.21(131.15) |
| renal tubular atrophy | 4 | 61.68(22.98, 165.56) | 61.65(23.14, 164.26) | 235.29 | 5.93(4.65) | 60.79(26.61) | |
| glomerulosclerosis | 3 | 53.77(17.22, 167.95) | 53.75(17.25, 167.53) | 153.39 | 5.73(4.3) | 53.1(20.47) | |
| ureteric stenosis | 3 | 46.42(14.88, 144.86) | 46.4(14.89, 144.62) | 131.85 | 5.52(4.1) | 45.92(17.72) | |
| renal tubular necrosis | 32 | 38.56(27.21, 54.66) | 38.38(26.97, 54.62) | 1154.89 | 5.25(4.75) | 38.05(28.42) | |
| kidney fibrosis | 5 | 35.16(14.58, 84.8) | 35.13(14.54, 84.86) | 164.45 | 5.12(3.96) | 34.85(16.68) | |
| investigations | immunosuppressant drug level decreased | 13 | 141.44(81.36, 245.87) | 141.16(81.54, 244.37) | 1751.7 | 7.09(6.33) | 136.71(86.07) |
| immunosuppressant drug level increased | 38 | 162.87(117.7, 225.36) | 161.94(118.35, 221.59) | 5857.62 | 7.29(6.82) | 156.1(118.95) | |
| drug level fluctuating | 4 | 52.15(19.45, 139.82) | 52.12(19.56, 138.87) | 198.16 | 5.69(4.41) | 51.51(22.57) | |
| drug level below therapeutic | 17 | 47.62(29.51, 76.84) | 47.5(29.68, 76.03) | 765.41 | 5.55(4.88) | 46.99(31.48) | |
| Pco2 decreased | 4 | 38.16(14.26, 102.15) | 38.14(14.31, 101.62) | 143.38 | 5.24(3.97) | 37.81(16.59) | |
| injury, poisoning and procedural complications | complications of transplanted kidney | 47 | 326.7(242.64, 439.88) | 324.4(241.77, 435.27) | 14090.1 | 8.24(7.81) | 301.71(235.23) |
| complications of transplant surgery | 5 | 127.31(52.3, 309.94) | 127.22(52.66, 307.33) | 608.14 | 6.95(5.77) | 123.59(58.7) | |
| transplant dysfunction | 23 | 132.32(87.32, 200.5) | 131.87(87.38, 199.02) | 2898.28 | 7(6.41) | 127.97(90.38) | |
| graft complication | 6 | 120.11(53.34, 270.42) | 120(53.73, 268.03) | 688.83 | 6.87(5.78) | 116.77(59.21) | |
| graft loss | 3 | 75.03(23.96, 235.01) | 75(24.06, 233.76) | 215.28 | 6.2(4.77) | 73.73(28.36) | |
| transplant failure | 10 | 66.56(35.62, 124.35) | 66.46(35.5, 124.44) | 634.95 | 6.03(5.17) | 65.46(38.8) | |
| immune system disorders | kidney transplant rejection | 90 | 279.41(225.47, 346.25) | 275.64(222.18, 341.96) | 23145.42 | 8.02(7.71) | 259.09(216.53) |
| heart transplant rejection | 9 | 181.08(92.91, 352.95) | 180.84(92.87, 352.13) | 1544.66 | 7.44(6.52) | 173.58(99.31) | |
| liver transplant rejection | 15 | 114.41(68.48, 191.16) | 114.16(68.58, 190.03) | 1639.03 | 6.8(6.08) | 111.23(72.39) | |
| chronic allograft nephropathy | 5 | 90.78(37.43, 220.2) | 90.72(37.55, 219.15) | 434.48 | 6.47(5.3) | 88.86(42.34) | |
| transplant rejection | 50 | 82.14(62.03, 108.78) | 81.53(61.97, 107.27) | 3903.54 | 6.32(5.92) | 80.03(63.27) | |
| infections and infestations | bacterial pyelonephritis | 3 | 79.18(25.27, 248.12) | 79.14(25.39, 246.66) | 227.28 | 6.28(4.85) | 77.73(29.89) |
| pneumonia streptococcal | 4 | 37.33(13.95, 99.92) | 37.31(14, 99.41) | 140.13 | 5.21(3.94) | 37(16.23) | |
| clostridium colitis | 5 | 34.32(14.23, 82.76) | 34.29(14.19, 82.84) | 160.32 | 5.09(3.93) | 34.03(16.29) | |
| vascular disorders | lymphocele | 12 | 206.77(115.8, 369.2) | 206.4(114.64, 371.6) | 2340.52 | 7.62(6.82) | 196.99(121.28) |
| arteriovenous fistula | 3 | 74.17(23.68, 232.28) | 74.14(23.79, 231.08) | 212.79 | 6.19(4.76) | 72.9(28.05) | |
| venoocclusive disease | 4 | 35.63(13.31, 95.35) | 35.61(13.36, 94.88) | 133.45 | 5.14(3.87) | 35.33(15.5) | |
| respiratory, thoracic and mediastinal disorders | alveolar proteinosis | 5 | 111.48(45.87, 270.97) | 111.4(46.11, 269.11) | 533.22 | 6.76(5.59) | 108.61(51.66) |
| neoplasms benign, malignant and unspecified (incl cysts and polyps) | ovarian adenoma | 3 | 80.66(25.73, 252.82) | 80.62(25.87, 251.27) | 231.57 | 6.31(4.88) | 79.16(30.43) |
SOC, System Organ Class; PT, Preferred term; ROR, reporting odds ratio; PRR, proportional reporting ratio; Chisq, Chi-Square; IC, Information Component; EBGM, Empirical Bayesian Geometric Mean.
3.5. Time to onset of ADRs
The time to onset of ADRs varied significantly among patients (Figure 6). Most reported adverse events occurred within the first 60 days after sirolimus initiation. Male reports were numerically more common than female reports; however, FAERS data do not permit estimation of true incidence rates.
Figure 6.
Time to onset of adverse drug reactions for sirolimus-related adverse events.
4. Discussion
Sirolimus played an essential role in organ transplantation and treatment of tumors (9). Current research on sirolimus predominantly focuses on preclinical mechanisms, controlled clinical trials, and literature syntheses, with limited real-world pharmacovigilance evidence (20, 21). To our knowledge, this represents one of the few comprehensive pharmacovigilance analyses evaluating sirolimus-associated adverse event signals using the FAERS database. This pharmacovigilance approach provides critical insights to inform evidence-based risk mitigation strategies and optimize therapeutic decision-making for clinicians and regulators. Importantly, disproportionality analyses based on spontaneous reporting systems are intended for signal detection rather than causal inference. Consequently, elevated ROR or PRR values should not be interpreted as direct evidence that sirolimus caused specific adverse events. Several highly reported PTs, including renal transplant rejection and complications, may be affected by indications, disease severity, modification of immunosuppressive regimen, drug non-compliance or clinical progress related to transplantation. Therefore, these signals should be carefully interpreted in a wider context of transplantation.
Other serious medical events accounted for 42.47% of reports, followed by hospitalization (39.25%). These findings emphasize the possible threat of some ADRs related to sirolimus, underscoring the necessity for health care providers to closely follow patients and deal with serious ADRs promptly. The incidence of this serious consequence is consistent with data from other studies, which also reported significant rates of other serious and hospitalizations among sirolimus recipients (22, 23).
Disproportionality analysis identified significant ADRs associated with sirolimus across various SOCs including injury, poisoning and procedural complications, investigations, infections and infestations, respiratory, thoracic and mediastinal disorders mainly, which are consistent with previous reports (24, 25). The appearance of these symptoms emphasizes the need for doctors to closely monitor patients and manage these side effects.
At the PT level, our study showed that the ADRs with high frequency occurrence included transplant rejection, complications of transplanted kidney, and immunosuppressant drug level fluctuation, as has been reported (23, 26). Because transplant rejection-related PTs may reflect underlying disease progression, insufficient immunosuppression, treatment modification, or confounding by indication, these findings should be interpreted cautiously within the clinical transplantation context. Furthermore, being different from the reported improvement signal associated with sirolimus on neoplasms benign, malignant and unspecified (incl cysts and polyps), a signal of ovarian adenoma associated with sirolimus was identified in this study. Nevertheless, this finding may also reflect reporting artifacts, surveillance bias in transplant recipients, MedDRA coding variability, or spurious disproportionality. Therefore, this observation should be regarded as hypothesis-generating and requires further validation in prospective studies.
There are some limitations in our research. As FAERS relies on spontaneous reporting, data are subject to underreporting, selective reporting biases (e.g., overrepresentation of severe or novel events), and variable data completeness. The absence of detailed clinical records—including comorbidities, precise dosing regimens, treatment duration, concomitant medications, and surgical histories—precludes assessment of causality and confounds risk quantification for specific adverse events. These limitations may influence signal strength and interpretation. Nevertheless, our findings identify clinically significant safety signals that warrant targeted pharmacoepidemiological investigation. These results provide hypothesis-generating evidence to guide future prospective studies and enhance risk mitigation strategies for sirolimus therapy.
5. Conclusion
This analysis details the ADR profile of sirolimus, revealing both known and potentially novel clinically significant adverse event signals that augment its known safety profile. Ongoing pharmacovigilance and targeted investigations are necessary to confirm these pharmacovigilance signals and inform optimal safety management strategies for sirolimus.
Acknowledgments
The authors would like to thank the FDA for access to FAERS database which contributed in this study.
Funding Statement
The author(s) declared that financial support was received for this work and/or its publication. This study was supported by the Natural Science Foundation of Jilin Province, China (Grant No. YDZJ202401658ZYTS) to BL.
Footnotes
Edited by: Mohammad Hossein Karimi, Shiraz University of Medical Sciences, Iran
Reviewed by: Amelia Filippelli, University of Salerno, Italy
Juan Chen, The First Affiliated Hospital of China Medical University, China
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
Ethical approval was not required for the study involving humans in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and the institutional requirements.
Author contributions
DY: Conceptualization, Data curation, Formal analysis, Writing – original draft. YJ: Conceptualization, Data curation, Formal analysis, Writing – original draft. CX: Conceptualization, Methodology, Project administration, Software, Writing – original draft. LH: Conceptualization, Methodology, Software, Writing – original draft. SL: Conceptualization, Investigation, Software, Writing – original draft. XS: Conceptualization, Investigation, Supervision, Writing – original draft. BL: Conceptualization, Writing – original draft, Writing – review & editing.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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The author(s) declared that generative AI was not used in the creation of this manuscript.
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References
- 1. Nathan PC, Lau C, Ng VL, Miserachs M, Teoh CW, Solomon M, et al. Outcomes after solid organ transplantation in survivors of childhood, adolescent and young adult cancer: A population-based study. J Natl Cancer Inst. (2025) 117(9):1809–16. doi: 10.1093/jnci/djaf106. PMID: [DOI] [PubMed] [Google Scholar]
- 2. El Rafei A, Cogswell R, Atik Zuckermann A, Allen LA. Review of the global activity of heart transplant. Circ Heart Fail. (2025) 18(7):e012272. doi: 10.1161/CIRCHEARTFAILURE.124.012272. PMID: [DOI] [PubMed] [Google Scholar]
- 3. Basile G, Pecoraro A, Gallioli A, Territo A, Berquin C, Robalino J, et al. Robotic kidney transplantation. Nat Rev Urol. (2024) 21:521–33. doi: 10.1038/s41585-024-00865-z. PMID: [DOI] [PubMed] [Google Scholar]
- 4. Tatapudi VS, Mattoo A, Schiff T, Mehta SA, Skolnik EY, Montgomery RA. Xenotransplantation: Current understanding of the mechanism of immune-mediated injury. J Am Soc Nephrol. (2025) 36(10):2030–40. doi: 10.1681/ASN.0000000745. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Sun Z, Yi Z, Wei C, Wang W, Ren T, Cravedi P, et al. LILRB3 genetic variation is associated with kidney transplant failure in African American recipients. Nat Med. (2025) 31:1677–87. doi: 10.1038/s41591-025-03568-z. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Ushijima K, Sanada Y, Otomo S, Ogaki K, Wakiya T, Okada N, et al. Night-time chronotherapy with methylprednisolone prevents an acute rejection in pediatric patients with liver transplantation: A randomized clinical trial. Clin Pharmacol Ther. (2025) 118(2):386–93. doi: 10.1002/cpt.3640. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Rim JG, Hellkamp AS, Neely ML, Reynolds JM, Belperio JA, Budev M, et al. Basiliximab induction immunosuppression and lung transplant outcomes: Propensity analysis in a multicenter cohort. J Heart Lung Transplant. (2025) 44:950–60. doi: 10.1016/j.healun.2024.11.033. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Ehx G, Ritacco C, Hannon M, Dubois S, Delens L, Willems E, et al. Comprehensive analysis of the immunomodulatory effects of rapamycin on human T cells in graft-versus-host disease prophylaxis. Am J Transplant. (2021) 21:2662–74. doi: 10.1111/ajt.16505. PMID: [DOI] [PubMed] [Google Scholar]
- 9. Saunders RN, Metcalfe MS, Nicholson ML. Rapamycin in transplantation: a review of the evidence. Kidney Int. (2001) 59:3–16. doi: 10.1046/j.1523-1755.2001.00460.x. PMID: [DOI] [PubMed] [Google Scholar]
- 10. Xu C, Yu C, Zhang J, Zhang Z, Zhang J, Meng Y, et al. YTHDF1 differentiates the contributing roles of mTORC1 in aging. Mol Cell. (2025) 85:2194–2210.e8. doi: 10.1016/j.molcel.2025.05.003. PMID: [DOI] [PubMed] [Google Scholar]
- 11. Gurgoglione FL, Gattuso D, Greco A, Benatti G, Niccoli G, Cortese B. Predictors and long-term prognostic significance of bailout stenting during percutaneous coronary interventions with sirolimus-coated balloon: A subanalysis of the Eastbourne study. Am J Cardiol. (2025) 239:68–74. doi: 10.1016/j.amjcard.2024.12.015. PMID: [DOI] [PubMed] [Google Scholar]
- 12. Aiello G, Sabino C, Pernici D, Audano M, Antonica F, Gianesello M, et al. Transient rapamycin treatment during developmental stage extends lifespan in Mus musculus and Drosophila melanogaster. EMBO Rep. (2022) 23:e55299. doi: 10.15252/embr.202255299. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Kommaraju K, Alsharif M, Scott JP, Pennington KM. Calcineurin inhibitor replacement with mammalian target of rapamycin (mTOR) inhibitor following lung transplantation. Clin Transplant. (2025) 39:e70211. doi: 10.1111/ctr.70211. PMID: [DOI] [PubMed] [Google Scholar]
- 14. Nisbet R, Pang C, Evans N, Belhadj A, Khalifa M, Papadopoulou A, et al. Oral sirolimus therapy for patients with complex low-flow vascular malformations. J Vasc Surg Venous Lymphat Disord. (2025) 13(5):102261. doi: 10.1016/j.jvsv.2025.102261. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Shahidi S, Vahdat S, Atatpour A, Meshkinfar S. The side effect profile of sirolimus and its relationship with some variables: A retrospective study of Iranian renal transplant patients. J Res Med Sci. (2023) 28:58. doi: 10.4103/jrms.jrms_531_22. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Huang D, Shi J. Real-world case supporting drug-induced autoimmune hepatitis: Updated insights and analysis of the FAERS data. Liver Int. (2025) 45:e70055. doi: 10.1111/liv.70055. PMID: [DOI] [PubMed] [Google Scholar]
- 17. Nie W, Wu X, Xia Y, Zheng L, Lu H. Reported psychiatric adverse events among isotretinoin users: Monitoring priorities from a 20-year FAERS database study. J Am Acad Dermatol. (2025) 93(1):64–72. doi: 10.1016/j.jaad.2025.02.072. PMID: [DOI] [PubMed] [Google Scholar]
- 18. Sakaeda T, Tamon A, Kadoyama K, Okuno Y. Data mining of the public version of the FDA adverse event reporting system. Int J Med Sci. (2013) 10:796–803. doi: 10.7150/ijms.6048. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Jiao Y, Liu ZC, Liu YH, Zhou SY, Liu B. Unveiling the hidden risks: analysis of adverse drug reactions associated with probiotics from the FAERS database - quality improvement study. Int J Surg. (2025) 111:5262–7. doi: 10.1097/JS9.0000000000002592. PMID: [DOI] [PubMed] [Google Scholar]
- 20. Andres AM, Talayero P, Alcolea Sanchez A, Sanchez Galán A, Serradilla Rodríguez J, Bueno Jimenez A, et al. Delayed introduction of sirolimus in paediatric intestinal transplant recipients: indications and long-term benefits. Transpl Int. (2021) 34:1895–907. doi: 10.1111/tri.13959. PMID: [DOI] [PubMed] [Google Scholar]
- 21. Naik MG, Jürgensen JS, Arns W, Basic E, Budde K, Eitner F, et al. Determinants of successful use of sirolimus in renal transplant patients. Transplant Proc. (2020) 52:3103–11. doi: 10.1016/j.transproceed.2020.02.159. PMID: [DOI] [PubMed] [Google Scholar]
- 22. Sutherland AI, Akhtar MZ, Zilvetti M, Brockmann J, Ruse S, Fuggle SV, et al. Alemtuzumab and sirolimus in renal transplantation: six-year results of a single-arm prospective pilot study. Am J Transplant. (2014) 14:677–84. doi: 10.1111/ajt.12572. PMID: [DOI] [PubMed] [Google Scholar]
- 23. Smith MP, Newstead CG, Ahmad N, Lewington AJ, Tibble S, Lodge JP, et al. Poor tolerance of sirolimus in a steroid avoidance regimen for renal transplantation. Transplantation. (2008) 85:636–9. doi: 10.1097/TP.0b013e3181613e65. PMID: [DOI] [PubMed] [Google Scholar]
- 24. Zaza G, Tomei P, Ria P, Granata S, Boschiero L, Lupo A. Systemic and nonrenal adverse effects occurring in renal transplant patients treated with mTOR inhibitors. Clin Dev Immunol. (2013) 2013:403280. doi: 10.1155/2013/403280. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Reigada CH, de Ataide EC, de Araújo FR, Ramos AP, Stucchi RS, Eescanhoela CA, et al. Sirolimus as rescue therapy after liver transplantation. Transplant Proc. (2012) 44:2434–7. doi: 10.1016/j.transproceed.2012.07.003. PMID: [DOI] [PubMed] [Google Scholar]
- 26. Halleck F, Duerr M, Waiser J, Huber L, Matz M, Brakemeier S, et al. An evaluation of sirolimus in renal transplantation. Expert Opin Drug Metab Toxicol. (2012) 8:1337–56. doi: 10.1517/17425255.2012.719874. PMID: [DOI] [PubMed] [Google Scholar]
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Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.