Real-World Renal Safety of Voclosporin: A Disproportionality and Stratified Analysis of the FAERS Database Short running title: Renal Safety of Voclosporin: A FAERS Analysis

Real-World Renal Safety of Voclosporin: A Disproportionality and Stratified Analysis of the FAERS Database Short running title: Renal Safety of Voclosporin: A FAERS Analysis | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 24 March 2026 V1 Latest version Share on Real-World Renal Safety of Voclosporin: A Disproportionality and Stratified Analysis of the FAERS Database Short running title: Renal Safety of Voclosporin: A FAERS Analysis Authors : Wei Zhou , Lirui Sun , Yue Chen , and Yajuan Liu 0009-0002-8355-3841 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177434317.73051169/v1 144 views 68 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background: Voclosporin, a novel calcineurin inhibitor approved for the treatment of lupus nephritis, has proven efficacious in clinical trials; however, its real-world renal safety profile remains incompletely characterized. This study aimed to evaluate renal adverse event signals associated with voclosporin using the FDA Adverse Event Reporting System (FAERS) and to investigate the impact of drug exposure duration and reporter type on signal detection. Methods： Data from the first quarter of 2021 to the fourth quarter of 2025 were extracted from the FAERS database. After data cleaning and deduplication, reports listing voclosporin as the primary suspected drug were assigned to the exposed group, while reports involving other drugs (regardless of role code) served as the comparator group. Renal adverse events were defined using 32 MedDRA preferred terms, and a composite endpoint of proteinuria consisting of 9 terms was constructed. Four algorithms were employed for signal detection: reporting odds ratio (ROR), proportional reporting ratio (PRR), empirical Bayes geometric mean (EBGM05), and information component with Bayesian credible interval (IC025). Stratified analyses were performed by treatment duration (acute ≤30 days, subacute 31–90 days, chronic >90 days, unknown), as well as by gender and reporter type (consumer vs. health professional). Sensitivity analyses included excluding the unknown duration group and stratifying by reporter type. Results： A total of 10,128 voclosporin primary suspect reports and 41,887 other drug reports were included. The study population was predominantly female (85.1%), with 98.9% of reports originating from the United States, and the rate of missing age data was only 3.6%. Overall signal detection revealed positive signals for decreased glomerular filtration rate (EBGM05=1.09, IC025=0.126) and increased urine protein/creatinine ratio (UPCR; EBGM05=1.03, IC025=0.041). For proteinuria (a=186), the overall ROR was 0.45 (95% CI 0.38–0.53), indicating a lower reporting rate in the voclosporin group; it was not among the top 10 signals. However, a borderline positive signal was observed in the unknown duration group (ROR 1.19, 95% CI 1.00–1.42). Time‑stratified analysis showed that all positive signals were confined to reports with unknown treatment duration: UPCR ROR=1.42 (1.14–1.77), and protein urine present ROR=1.59 (1.01–2.52); no signals were observed in known duration groups. Gender stratification revealed that UPCR was positive in females (ROR=1.33, 1.07–1.66), while no signal was detected in males. Reporter type stratification demonstrated a strong positive signal for the composite endpoint in health professional reports (ROR=1.84, 1.56–2.19), whereas consumer reports showed no signal (0.90, 0.76–1.07). Sensitivity analyses excluding the unknown duration group eliminated all signals. Conclusions： Voclosporin is associated with weak but statistically significant renal adverse events. The signals are entirely dependent on reports with missing drug exposure duration and are significantly influenced by reporter type. Missing data on treatment duration severely limits the assessment of time‑risk trends. Clinical monitoring of urine protein/creatinine ratio and glomerular filtration rate is recommended. These findings highlight an urgent need for improved documentation of treatment duration in pharmacovigilance databases. Title Page Title: Real-World Renal Safety of Voclosporin: A Disproportionality and Stratified Analysis of the FAERS Database Short running title: Renal Safety of Voclosporin: A FAERS Analysis Authors: Wei Zhou, Lirui Sun,Yue Chen, Yajuan Liu Department of Pharmacy, The First Hospital of Jilin University, Jilin University, Changchun, China [1]¿p#1 Corresponding author: Yajuan Liu Address: Department of Pharmacy, The First Hospital of Jilin University, 71 Xinmin Street, Changchun 130021, China Email: [email protected] Phone: +86 13944858877 Funding statement: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Conflict of interest disclosure: The authors declare no competing interests. Ethics approval statement: Ethical approval was not required for this study, as it used publicly available, anonymized data from the FDA Adverse Event Reporting System (FAERS). Patient consent statement: Not applicable. Permission to reproduce material from other sources: Not applicable. Prior postings and presentations: None. Abstract Background: Voclosporin, a novel calcineurin inhibitor approved for the treatment of lupus nephritis, has proven efficacious in clinical trials; however, its real-world renal safety profile remains incompletely characterized. This study aimed to evaluate renal adverse event signals associated with voclosporin using the FDA Adverse Event Reporting System (FAERS) and to investigate the impact of drug exposure duration and reporter type on signal detection. Methods： Data from the first quarter of 2021 to the fourth quarter of 2025 were extracted from the FAERS database. After data cleaning and deduplication, reports listing voclosporin as the primary suspected drug were assigned to the exposed group, while reports involving other drugs (regardless of role code) served as the comparator group. Renal adverse events were defined using 32 MedDRA preferred terms, and a composite endpoint of proteinuria consisting of 9 terms was constructed. Four algorithms were employed for signal detection: reporting odds ratio (ROR), proportional reporting ratio (PRR), empirical Bayes geometric mean (EBGM05), and information component with Bayesian credible interval (IC025). Stratified analyses were performed by treatment duration (acute ≤30 days, subacute 31–90 days, chronic (consumer vs. health professional). Sensitivity analyses included excluding the unknown duration group and stratifying by reporter type. Results： A total of 10,128 voclosporin primary suspect reports and 41,887 other drug reports were included. The study population was predominantly female (85.1%), with 98.9% of reports originating from the United States, and the rate of missing age data was only 3.6%. Overall signal detection revealed positive signals for decreased glomerular filtration rate (EBGM05=1.09, IC025=0.126) and increased urine protein/creatinine ratio (UPCR; EBGM05=1.03, IC025=0.041). For proteinuria (a=186), the overall ROR was 0.45 (95% CI 0.38–0.53), indicating a lower reporting rate in the voclosporin group; it was not among the top 10 signals. However, a borderline positive signal was observed in the unknown duration group (ROR 1.19, 95% CI 1.00–1.42). Time‑stratified analysis showed that all positive signals were confined to reports with unknown treatment duration: UPCR ROR=1.42 (1.14–1.77), and protein urine present ROR=1.59 (1.01–2.52); no signals were observed in known duration groups. Gender stratification revealed that UPCR was positive in females (ROR=1.33, 1.07–1.66), while no signal was detected in males. Reporter type stratification demonstrated a strong positive signal for the composite endpoint in health professional reports (ROR=1.84, 1.56–2.19), whereas consumer reports showed no signal (0.90, 0.76–1.07). Sensitivity analyses excluding the unknown duration group eliminated all signals. Conclusions： Voclosporin is associated with weak but statistically significant renal adverse events. The signals are entirely dependent on reports with missing drug exposure duration and are significantly influenced by reporter type. Missing data on treatment duration severely limits the assessment of time‑risk trends. Clinical monitoring of urine protein/creatinine ratio and glomerular filtration rate is recommended. These findings highlight an urgent need for improved documentation of treatment duration in pharmacovigilance databases. Keywords： Voclosporin; FAERS; Renal Safety; Proteinuria; Disproportionality Analysis; Pharmacovigilance Key Points This comprehensive FAERS analysis (2021–2025) detected weak but statistically significant renal safety signals for voclosporin, including decreased glomerular filtration rate and increased urine protein/creatinine ratio. • All positive signals were confined to reports with missing drug duration, highlighting a major limitation of spontaneous reporting databases. • Signal strength was markedly influenced by reporter type: strong signals were observed in health professional reports (ROR 1.84) whereas consumer reports showed no signal (ROR 0.90). • The composite proteinuria endpoint showed a significant signal (ROR 1.25, 95%CI 1.10–1.41) in the unknown duration group, while no signals were observed in acute, subacute, or chronic strata. • These findings support focused clinical monitoring of UPCR and eGFR in patients receiving voclosporin and underscore the need for improved data completeness in pharmacovigilance. Plain Language Summary Voclosporin is a medication used to treat lupus nephritis, a kidney disease caused by lupus. Using the FDA’s adverse event reporting database (FAERS) from 2021 to 2025, we investigated kidney-related side effects of voclosporin in real-world clinical practice. We found weak but statistically significant signals linking voclosporin to decreased kidney function and proteinuria (excess protein in urine). Importantly, all of these signals were only found in reports that did not record how long patients had been taking the medication before the side effect occurred. The strength of the signals was also strongly influenced by who reported them: reports from health professionals showed clear signals, while consumer reports showed none. When we removed reports with missing treatment duration, all signals disappeared, demonstrating that the lack of this information is a major obstacle to understanding whether kidney injury happens early or late in treatment. Our findings suggest that healthcare providers should regularly monitor urine protein/creatinine ratio and kidney function in patients taking voclosporin, and that safety databases need to improve how they record medication start and end dates. 1. Introduction Lupus nephritis is one of the most common and severe complications of systemic lupus erythematosus, affecting up to 60% of patients and substantially increasing the risks of end‑stage renal disease and mortality 1 . Voclosporin, a novel calcineurin inhibitor, was approved by the US Food and Drug Administration (FDA) in January 2021 for the treatment of adult patients with active lupus nephritis in combination with mycophenolate mofetil and corticosteroids 2 . Pivotal phase III trials (AURORA 1 and 2) demonstrated that voclosporin significantly improves renal response rates with a generally manageable safety profile 3,4 . As a class, calcineurin inhibitors have long been a clinical concern due to their nephrotoxicity, which includes acute kidney injury, chronic kidney disease, and proteinuria 5 . Owing to limited sample sizes, short follow‑up periods, and stringent eligibility criteria, clinical trials may not fully capture the safety profile of drugs in widespread real‑world use. Spontaneous reporting databases, such as the FDA Adverse Event Reporting System (FAERS), serve as essential tools for post‑marketing pharmacovigilance and can provide information on rare or delayed adverse events not covered by clinical trials 6 . To date, large‑scale real‑world studies specifically evaluating the renal safety of voclosporin are lacking. In particular, the temporal patterns of adverse event occurrence and the influence of potential confounding factors have not been systematically assessed. Using FAERS data from 2021 to 2025, this study employed disproportionality analyses to: (1) detect signals of voclosporin‑associated renal adverse events; (2) evaluate the temporal distribution of these signals through stratification by treatment duration; (3) investigate the impact of sex and reporter type on signal detection; and (4) elucidate the critical role of missing data via sensitivity analyses, thereby providing evidence to support rational clinical use and improvements in database quality. 2. Methods 2.1 Data Source and Study Design This study was a retrospective case/non‑case pharmacovigilance study. Data were obtained from the FDA Adverse Event Reporting System (FAERS) covering the period from the first quarter of 2021 (when voclosporin was approved) through the fourth quarter of 2025. Quarterly FAERS data files (ASCII format) were downloaded and imported into R version 4.5.0 for processing. The database comprises seven core tables: DEMO (demographic and administrative information), DRUG (drug details), REAC (adverse event terms), INDI (indications), OUTC (patient outcomes), THER (drug start and end dates), and RPSR (report sources). Data deduplication was performed in accordance with official FDA guidelines. For each CASEID, the record with the most recent FDA_DT was retained. When CASEID and FDA_DT were identical, the report with the highest PRIMARYID was kept. Reports flagged for deletion in the quarterly data packages were systematically excluded. [1]¿p#1 2.2 Target Drug Identification and Group Definition Reports were identified by searching the drugname or prod_ai fields in the DRUG table for the terms ”voclosporin” or ”lupkynis” (case‑insensitive). Reports listing voclosporin as the primary suspected drug (role_cod = ”PS”) were classified as the exposure group. The comparator group comprised all reports involving other drugs, regardless of role code, as earlier analysis indicated that other drugs rarely had primary suspect reports. [1]¿p#1 2.3 Definition of Renal Adverse Events Renal adverse events were defined using 32 preferred terms (PTs) from the Medical Dictionary for Regulatory Activities (MedDRA) version 28.1, covering renal function parameters, proteinuria, acute kidney injury, chronic kidney disease, tubular disorders, and other renal symptoms (see Table S1 in the Supplementary Material). Additionally, a composite proteinuria endpoint was constructed from nine PTs directly reflecting glomerular barrier damage: proteinuria, urine protein/creatinine ratio increased, protein urine present, urine albumin/creatinine ratio increased, urine protein/creatinine ratio abnormal, protein urine, albuminuria, urine albumin/creatinine ratio abnormal, and urine protein/creatinine ratio decreased (Table S2). [1]¿p#1 2.4 Stratification by Treatment Duration The time from treatment initiation to adverse event onset (event_dt – start_dt) was calculated using the THER table. Values that were missing or negative were categorized as ”unknown.” Based on clinical relevance, treatment duration was stratified into four categories: acute (≤30 days), subacute (31–90 days), chronic (>90 days), and unknown. 2.5 Statistical Analysis 2.5.1 Disproportionality Analysis For each preferred term (PT), a 2 × 2 contingency table was constructed as follows: Target AE a c Non-target AEs b d • a = number of reports with the target AE for the target drug • b = number of reports without the target AE for the target drug • c = number of reports with the target AE for other drugs • d = number of reports without the target AE for other drugs Four algorithms were applied: • Reporting Odds Ratio (ROR): ROR = (a/c) / (b/d). The 95% confidence interval (CI) was calculated using the Haldane–Anscombe continuity correction (adding 0.5 to all cells) 7 . A positive signal was defined as a ≥ 3 and the lower limit of the 95% CI > 1. • Proportional Reporting Ratio (PRR): PRR = [a/(a+b)] / [c/(c+d)], with the chi‑square (χ²) test using Yates’ correction. According to the MHRA 8 criterion, a signal was considered positive when a ≥ 3, PRR ≥ 2, and χ² ≥ 4. • Empirical Bayes Geometric Mean (EBGM): The multi‑item gamma Poisson shrinker (MGPS) method was used, with EBGM05 > 2 indicating a positive signal 9 . • Information Component (IC) with Bayesian Confidence Propagation Neural Network (BCPNN) 10 : A positive signal was defined as IC025 > 0. 2.5.2 Stratified Analyses Stratified analyses were performed by treatment duration, sex, and reporter type (consumer vs. health professional, the latter including health professionals, physicians, and pharmacists). Within each stratum, the ROR and 95% confidence interval (CI) were calculated for the core PTs (proteinuria, urine protein/creatinine ratio increased, protein urine present) and for the composite proteinuria endpoint. 2.5.3 Sensitivity Analyses Two sensitivity analyses were performed: Reports with unknown treatment duration were excluded, and the RORs for the core preferred terms (PTs) were recalculated. The ROR for the composite proteinuria endpoint was calculated after stratification by reporter type. For the comparison of demographic and clinical characteristics (Table 1) between the voclosporin and other drug groups, categorical variables were analyzed using the chi‑square test (or Fisher’s exact test when expected cell counts were <5). A two‑sided P‑value < 0.05 was considered statistically significant. The overall P-values for each variable (age group, sex, reporter type, reporting country, and serious outcomes) were calculated from the corresponding contingency tables. All statistical analyses were conducted using R version 4.5.0. This study is reported in accordance with the REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance (READUS‑PV) guidelines 11 . 3. Results 3.1 Study Population Characteristics A total of 10,128 voclosporin primary suspect reports and 41,887 reports for other drugs were included.The demographic and clinical characteristics of the study population are presented in Table 1. The study population was predominantly female (85.1%), with 98.9% of reports originating from the United States, and the rate of missing age data was only 3.6%. In the voclosporin group, consumer reports accounted for 76.9%, while health professionals, physicians, and pharmacists together accounted for 21.2% (health professional 9.9%, physician 11.1%, pharmacist 0.2%). The hospitalization rate was 17.0%, and other serious events (primarily laboratory abnormalities) constituted 82.1%. Statistically significant differences were observed between the two groups in the distribution of age, sex, reporter type, reporting country, and serious outcomes (all P < 0.001). Table 1. Demographic and clinical characteristics of the study population [1]¿p#1 Age group, n (%) <0.001 * <2 years 1 (0.0%) 15 (0.0%) 2-11 years 12 (0.1%) 0 (0.0%) 12-17 years 167 (1.6%) 1083 (2.6%) 18-64 years 8951 (88.4%) 36530 (87.2%) 65-85 years 637 (6.3%) 2389 (5.7%) missing 360 (3.6%) 1870 (4.5%) Sex, n (%) <0.001 male 1384 (13.7%) 5238 (12.5%) female 8616 (85.1%) 35552 (84.9%) missing 128 (1.3%) 1097 (2.6%) Reporter type, n (%) <0.001 Consumer 7789 (76.9%) 31287 (74.7%) Health Professional 1001 (9.9%) 6386 (15.2%) Pharmacist 25 (0.2%) 94 (0.2%) Physician 1124 (11.1%) 3485 (8.3%) missing 189 (1.9%) 635 (1.5%) Reporting country, n (%) <0.001 Japan 60 (0.6%) 804 (1.9%) Spain 38 (0.4%) 132 (0.3%) United States of America 10016 (98.9%) 40829 (97.5%) Other countries 14 (0.1%) 122 (0.3%) Serious outcomes, n (%) <0.001 Death 74 (0.7%) 379 (0.9%) Disability 7 (0.1%) 15 (0.0%) Hospitalization 1723 (17.0%) 10425 (24.9%) Life-Threatening 9 (0.1%) 164 (0.4%) Other 8315 (82.1%) 30904 (73.8%) * Note: Data are presented as n (%). P-values were calculated using the chi-square test (or Fisher’s exact test when expected cell counts <5). Abbreviations: n, number of reports. * P-values <0.001 are shown as ’<0.001’. * [1]¿p#1 3.2 Overall Renal Safety Signals The results of the signal detection analysis for renal-related preferred terms (PTs) are presented in Table 2. Positive signals were observed for: Decreased glomerular filtration rate (a = 175, EBGM05 = 1.09, IC025 = 0.126) Increased urine protein/creatinine ratio (UPCR; a = 126, EBGM05 = 1.03, IC025 = 0.041) For proteinuria (a = 186), the overall ROR was 0.45 (95% CI 0.38–0.53), indicating a lower reporting rate in the voclosporin group compared to other drugs, consequently it was not among the top 10 signals. However, when considered together with the borderline signal observed in the unknown duration group in the stratified analysis (ROR 1.19, 95% CI 1.00–1.42), this finding suggests a potential weak association between proteinuria and voclosporin that is contingent on missing data. No signals were detected for the remaining PTs. Table 2. Top 10 renal adverse event signals for voclosporin Blood creatinine abnormal 6 2.8 (1-7.7) 2.8 (3) 0.731 -0.379 Glomerular filtration rate abnormal 9 2.4 (1.1-5.3) 2.3 (3) 0.817 -0.244 Urine odour abnormal 5 2 (0.7-5.5) 1.9 (1) 0.558 -0.734 Chronic kidney disease 9 1.9 (0.9-4.1) 1.9 (2) 0.726 -0.409 Urine protein/creatinine ratio abnormal 13 1.6 (0.8-2.9) 1.5 (1) 0.736 -0.404 Protein urine present 27 1.5 (1-2.3) 1.5 (3) 0.867 -0.188 Renal disorder 15 1.5 (0.8-2.6) 1.4 (1) 0.738 -0.405 Glomerular filtration rate decreased 175 1.4 (1.2-1.7) 1.4 (14) 1.09 0.126 Urine protein/creatinine ratio increased 126 1.3 (1.1-1.6) 1.3 (8) 1.027 0.041 Renal pain 4 1.3 (0.4-3.7) 1.2 (0) 0.376 -1.244 *Note: a: number of voclosporin reports with the adverse event; ROR_CI: reporting odds ratio with 95% confidence interval; PRR_chi : proportional reporting ratio with chi-square value; EBGM05: empirical Bayes geometric mean 5th percentile; IC025: information component 95% lower bound. Positive signals are shown in bold.Proteinuria (a=186) was not among the top 10 signals (ROR 0.45, 95% CI 0.38–0.53). 3.3 Time‑Stratified Analysis of Proteinuria‑Related PTs The RORs and 2 × 2 contingency tables for the core proteinuria‑related PTs across different treatment duration strata are presented in Table 3. All positive signals were confined exclusively to reports with unknown treatment duration: • For UPCR, the ROR in the unknown duration group was 1.42 (95% CI 1.14–1.77), while no signals were observed in the known duration groups. • Proteinuria exhibited a borderline positive signal in the unknown duration group (ROR 1.19, 95% CI 1.00–1.42) , with no signals detected in the known duration groups. • For protein urine present, the ROR in the unknown duration group was 1.59 (95% CI 1.01–2.52) , and no signals were observed in the known duration groups. The proportion of events occurring in the unknown duration group was remarkably high: 114 of 126 (90.5%) for UPCR, 162 of 186 (87.1%) for proteinuria, and 24 of 27 (88.9%) for protein urine present. Table 3. Time‑stratified analysis of core proteinuria‑related PTs [1]¿p#1 proteinuria Acute 7 506 7 808 1.6 (0.58-4.42) Subacute 4 409 7 303 0.44 (0.14-1.44) Chronic 13 624 11 496 0.93 (0.42-2.07) Unknown 162 8,403 642 39,613 1.19 (1.00-1.42) urine protein/creatinine ratio increased Acute 0 513 2 813 0.32 (0.02-6.61) Subacute 7 406 3 307 1.62 (0.45-5.81) Chronic 5 632 4 503 0.97 (0.28-3.4) Unknown 114 8,451 380 39,875 1.42 (1.15-1.75) protein urine present Acute 0 513 2 813 0.32 (0.02-6.61) Subacute 2 411 0 310 3.77 (0.18-78.87) Chronic 1 636 2 505 0.48 (0.06-3.62) Unknown 24 8,541 72 40,183 1.59 (1.00-2.51) Note: a: number of voclosporin reports with the adverse event; b: number of voclosporin reports without the adverse event; c: number of other drug reports with the adverse event; d: number of other drug reports without the adverse event; ROR_CI: reporting odds ratio with 95% confidence interval. All RORs calculated using Haldane–Anscombe continuity correction. Positive signals are shown in bold. [1]¿p#1 Proteinuria in the unknown duration group is a borderline positive signal (lower limit = 1.00). 3.4 Stratified Analysis of the Composite Proteinuria Endpoint The RORs for the composite proteinuria endpoint across different treatment duration strata are presented in Table 4. In the unknown duration group, the ROR was 1.25 (95% CI 1.10–1.41) , while no signals were observed in the known duration groups. A total of 337 events occurred in the unknown duration group, accounting for 92% of all composite endpoint events. Table 4. [1]¿p#1 Time‑stratified analysis of the composite proteinuria endpoint Acute 8 505 11 804 1.18 (0.48-2.87) Subacute 14 399 10 300 1.04 (0.46-2.33) Chronic 20 617 17 490 0.93 (0.49-1.78) Unknown 337 8,228 1,282 38,973 1.25 (1.1-1.41) Note: a: number of voclosporin reports with the composite endpoint; b: number of voclosporin reports without the composite endpoint; c: number of other drug reports with the composite endpoint; d: number of other drug reports without the composite endpoint; ROR_CI: reporting odds ratio with 95% confidence interval. All RORs calculated using Haldane–Anscombe continuity correction. Positive signal (Unknown group) is shown in bold . 3.5 Gender‑Stratified Analysis The gender‑stratified forest plot (Figure 1) illustrates the signal distribution across sexes. A positive signal for UPCR was observed in females (ROR 1.33, 95% CI 1.07–1.66) , but not in males (ROR 1.35, 95% CI 0.82–2.22). No signal was detected for proteinuria in either sex (females: ROR 1.14, 95% CI 0.95–1.36; males: ROR 1.15, 95% CI 0.77–1.71). Estimates for the missing sex group were unstable owing to the small sample size and are not reported. 3.6 Sensitivity Analyses 3.6.1 Exclusion of the Unknown Duration Group After excluding reports with unknown treatment duration, the RORs for the core PTs were no longer statistically significant (Table S3): • Proteinuria: ROR = 1.00 (95% CI 0.57–1.75) • UPCR: ROR = 1.38 (95% CI 0.59–3.21) • Protein urine present: ROR = 0.81 (95% CI 0.20–3.29) The number of events in the known duration groups decreased substantially, with 24 events for proteinuria and 12 events for UPCR. 3.6.2 Stratification by Reporter Type For the composite proteinuria endpoint, a strong positive signal was observed in the health professional group (ROR = 1.84, 95% CI 1.56–2.19) , whereas no signal was detected in the consumer group (ROR = 0.90, 95% CI 0.76–1.07) or in the other/missing group (ROR = 0.82, 95% CI 0.45–1.49) (Table S4). 4. Discussion 4.1 Main Findings This comprehensive FAERS analysis (2021–2025), incorporating time‑stratified, reporter‑type stratified, and sensitivity analyses, characterized the renal safety signals associated with voclosporin. Weak but statistically significant positive signals-namely decreased glomerular filtration rate and increased urine protein/creatinine ratio-were detected, while proteinuria overall showed a lower reporting rate in the voclosporin group (ROR 0.45, 95% CI 0.38–0.53). Importantly, all positive signals were entirely contingent on reports with missing time‑on‑drug data. Signal strength was markedly influenced by reporter type: a strong signal was observed in health professional reports (ROR 1.84), whereas no signal was detected in consumer reports (ROR 0.90). Sensitivity analyses further confirmed that all positive signals disappeared after excluding reports with unknown treatment duration, and reporter‑type stratification demonstrated that the signal intensity in the health professional group substantially exceeded that in other groups. 4.2 The Critical Implication of Missing Time‑on‑Drug Data The pivotal finding of this study is that all positive signals disappeared after excluding reports with unknown treatment duration. This is directly supported by the sensitivity analysis (Table S3), where the RORs for proteinuria, UPCR, and protein urine present all became non‑significant and the number of events dropped dramatically.This underscores that the substantial lack of time‑on‑drug information in the FAERS database—over 87% of proteinuria events lacked this critical variable—is a fundamental obstacle to time‑to‑event risk analysis. Although demographic data, such as age, exhibited high completeness (with a missing rate of only 3.6%), the absence of this core exposure variable still precludes differentiation between acute and chronic nephrotoxicity and precludes assessment of cumulative effects. These results strongly suggest that spontaneous reporting systems should mandate the recording of drug start and end dates and encourage complete documentation by reporters. 4.3 Substantial Influence of Reporter Type Bias The marked disparity between health professional and consumer reports (ROR 1.84 vs. 0.90) underscores a non‑negligible reporter bias. Health professionals may be more accurate in diagnosing and attributing adverse events, making their reports more likely to reflect genuine drug‑event associations. Conversely, consumer reports may be subject to signal attenuation due to factors such as self‑medication, concomitant drug use, or insufficient recognition of symptoms. This finding, as demonstrated in this study, suggests that reporter‑type stratification should be routinely incorporated into pharmacovigilance analyses, and that priority be given to health professional reports when resources are limited. Moreover, relying solely on consumer reports may underestimate drug risks—a potential bias that warrants attention. Of note, the missing reporter type group accounted for only 1.9% of voclosporin reports (Table 1), and its small sample size precluded reliable signal estimation. 4.4 Insights from Gender‑Stratified Analysis Gender‑stratified analysis revealed a positive signal for UPCR in females (ROR 1.33, 95% CI 1.07–1.66), whereas no signal was detected in males. Although the interaction did not reach statistical significance—possibly due to the smaller sample size in males—this observation warrants attention. Females constitute the vast majority of patients with lupus nephritis (approximately 85–90%) 12 , and previous studies have suggested that females may be more susceptible to calcineurin inhibitor‑induced nephrotoxicity 13 . While these findings should be interpreted with caution, they provide potential insights for individualized monitoring strategies. 4.5 Dilution Effect and Composite Endpoint Construction The signal for the composite endpoint was weaker than that for UPCR alone (ROR 1.25 vs. 1.42), further confirming the presence of a ”dilution effect.” When multiple preferred terms with potentially heterogeneous biological mechanisms are combined, strong individual signals may be attenuated. Therefore, composite endpoints should be constructed based on robust mechanistic rationale and should always be interpreted alongside analyses of individual preferred terms. [1]¿p#1 4.6 Robustness of Multi‑Algorithm Validation The consistent results obtained across four signal detection algorithms—ROR, PRR, EBGM, and IC—reinforce the credibility of the findings. Cross‑validation using multiple algorithms should be a routine requirement in pharmacovigilance research. 4.7 Limitations This study has limitations inherent to spontaneous reporting databases, including underreporting, reporting bias, and the inability to infer causality. Potential confounding factors, such as concomitant medication use and disease severity, were not adjusted for. Data on treatment duration and reporter type remained partially missing, and the impact of these missing data was revealed through sensitivity analyses. Specifically, the proportion of reports with missing treatment duration was >87% for proteinuria‑related events, and reporter type was missing in 1.9% of voclosporin reports (Table 1). Furthermore, MedDRA terms may not capture all manifestations of renal injury. 4.8 Clinical Implications and Future Directions Although the detected signals were of modest strength, the positive findings for increased urine protein/creatinine ratio and decreased glomerular filtration rate remain clinically relevant. Regular monitoring of urine protein/creatinine ratio and glomerular filtration rate is recommended for patients receiving voclosporin. Future studies should leverage electronic health records or prescription databases with complete treatment duration information to externally validate these findings and further investigate the potential mechanisms by which sex and reporter type may modify the risk of renal injury. Prospective cohort studies with complete exposure data are needed to assess the time course of voclosporin-induced renal events. [1]¿p#1 5. Conclusions Based on FAERS data from 2021 to 2025, this study found that voclosporin was associated with weak but statistically significant renal adverse events (UPCR and GFR decreased). These signals were entirely contingent on reports with missing time‑on‑drug data and were markedly influenced by reporter type. Proteinuria overall showed a reduced reporting rate (ROR 0.45), but a borderline signal emerged in the unknown duration group.The lack of treatment duration information represents a major obstacle to time‑to‑event risk analysis. In clinical practice, attention should be paid to changes in urine protein/creatinine ratio and glomerular filtration rate, and efforts to improve the completeness of medication information in pharmacovigilance databases are warranted. 6. Supplementary Material • Table S1. List of 32 Preferred Terms for Renal Adverse Events • Table S2. List of 9 Preferred Terms for the Composite Proteinuria Endpoint • Table S3. Sensitivity Analysis Results After Excluding the Unknown Duration Group • Table S4. Stratified Analysis of the Composite Proteinuria Endpoint by Reporter Type • Figure S1. Gender‑Stratified Forest Plot • READUS‑PV Checklist 7. References 1. Almaani S, Meara A, Rovin BH. Update on lupus nephritis. Clin J Am Soc Nephrol. 2017;12(5):825-835. doi:10.2215/CJN.05780616 2. US Food and Drug Administration. LUPKYNIS (voclosporin) prescribing information. Published January 2021. Accessed March 15, 2026. https://www.accessdata.fda.gov/drugsatfda_docs/label/2021/213716s000lbl.pdf 3. Rovin BH, Teng YKO, Ginzler EM, et al. Efficacy and safety of voclosporin versus placebo for lupus nephritis (AURORA 1): a double-blind, randomised, multicentre, placebo-controlled, phase 3 trial. Lancet. 2021;397(10289):2070-2080. doi:10.1016/S0140-6736(21)00578-X 4. Saxena A, Ginzler EM, Gibson K, et al. Safety and efficacy of long-term voclosporin treatment for lupus nephritis in the phase 3 AURORA 2 clinical trial. Rheumatology. 2022;61(8):3230-3240. doi:10.1093/rheumatology/keab843 5. Naesens M, Kuypers DR, Sarwal M. Calcineurin inhibitor nephrotoxicity. Clin J Am Soc Nephrol. 2009;4(2):481-508. doi:10.2215/CJN.04800908 6. Berlin C, Blanch C, Lewis DJ, et al. Are all quantitative postmarketing signal detection methods equal? Performance characteristics of logistic regression and the multi-item gamma Poisson shrinker. Pharmacoepidemiol Drug Saf. 2021;30(6):721-730. doi:10.1002/pds.5215 7. van Puijenbroek EP, Bate A, Leufkens HG, Lindquist M, Orre R, Egberts AC. A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions. Pharmacoepidemiol Drug Saf. 2002;11(1):3-10. doi:10.1002/pds.668 8. Evans SJ, Waller PC, Davis S. Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiol Drug Saf. 2001;10(6):483-486. doi:10.1002/pds.677 9. Szarfman A, Machado SG, O‘Neill RT. Use of screening algorithms and computer systems to efficiently signal higher-than-expected combinations of drugs and events in the US FDA’s spontaneous reports database. Drug Saf. 2002;25(6):381-392. doi:10.2165/00002018-200225060-00001 10. Bate A, Lindquist M, Edwards IR, et al. A Bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol. 1998;54(4):315-321. doi:10.1007/s002280050466 11. Fusaroli M, Salvo F, Begaud B, et al. The Reporting of a Disproportionality Analysis for Drug Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Development and Statement. Drug Saf. 2024;47(6):575-584. doi:10.1007/s40264-024-01421-9 12. Hermansen MLF, Lindhardsen J, Torp-Pedersen C, Faurschou M, Jacobsen S. Incidence of systemic lupus erythematosus and lupus nephritis in Denmark: a nationwide cohort study. J Rheumatol. 2016;43(7):1335-1339. doi:10.3899/jrheum.151221 13. Venuto RC, Meaney CJ, Chang S, et al. Association of extrarenal adverse effects of posttransplant immunosuppression with sex and ABCB1 haplotypes. Medicine. 2015;94(37):e1315. doi:10.1097/MD.0000000000001315 Declarations Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Conflict of Interest: The authors declare no competing interests. Author Contributions: Wei Zhou contributed to study conception and design, data acquisition, statistical analysis, and drafted the manuscript. Lirui Sun and Yue Chen contributed to data interpretation, software programming (R code development), and critical revision of the manuscript.Yajuan Liu supervised the study, contributed to methodology, and reviewed the manuscript. All authors approved the final version and agree to be accountable for all aspects of the work. Data Availability Statement: The data supporting this study are from the publicly available FAERS database (Q1 2021–Q4 2025). Detailed R code for data cleaning, analysis, and visualization is available as supplementary material or from the corresponding author upon request. The study protocol is available from the corresponding author. This study is reported following the READUS-PV guidelines, and the completed checklist is included in the supplementary materials. This study was not pre-registered. Ethics Approval: Not applicable. This study used publicly available, anonymized data from the FDA Adverse Event Reporting System. Artificial Intelligence Generated Content: No generative AI tools were used in the design, analysis, or writing of this manuscript beyond language editing assistance. Information & Authors Information Version history V1 Version 1 24 March 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords ：voclosporin disproportionality analysis faers pharmacovigilance proteinuria renal safety Authors Affiliations Wei Zhou The First Hospital of Jilin University Department of Pharmacy View all articles by this author Lirui Sun The First Hospital of Jilin University Department of Pharmacy View all articles by this author Yue Chen The First Hospital of Jilin University Department of Pharmacy View all articles by this author Yajuan Liu 0009-0002-8355-3841 [email protected] The First Hospital of Jilin University Department of Pharmacy View all articles by this author Metrics & Citations Metrics Article Usage 144 views 68 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Wei Zhou, Lirui Sun, Yue Chen, et al. Real-World Renal Safety of Voclosporin: A Disproportionality and Stratified Analysis of the FAERS Database Short running title: Renal Safety of Voclosporin: A FAERS Analysis. Authorea . 24 March 2026. 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