Pharmacovigilance Study of Drug-Related Renal Vascular Lesions Based on FAERS and Experimental Exploration

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We aimed to map drug signals for renal vascular injury in FAERS and to provide experimental evidence for plausible vascular mechanisms. Methods We extracted 19,231 FAERS reports (Q1 2004–Q4 2024) and applied four disproportionality algorithms (ROR, PRR, MGPS, BCPNN) plus χ² screening and multivariable logistic regression to prioritize suspect agents. Time-to-onset and reporting characteristics were described. Three high-risk drugs (imatinib, rofecoxib, tenofovir disoproxil) were tested in vitro on HUVECs and HEK293A cells using CCK-8 viability and FITC/PI apoptosis assays; short-term intraperitoneal dosing in mice provided preliminary histologic correlation. Results Nineteen drugs met signal criteria. Time-to-onset analysis revealed a bimodal distribution: an “ultra-acute” cluster (median ≈ 1 day; e.g., CAR-T products, contrast agents) and a delayed cluster (months–years; e.g., rofecoxib, tenofovir). In vitro, all three tested drugs produced concentration- and time-dependent reductions in cell viability and increased apoptotic/necrotic fractions, with endothelial (HUVEC) cells more susceptible than renal epithelial (293A) cells. Short-term murine kidney histology showed small-vessel congestion, focal hemorrhage, and perivascular inflammation after drug exposure. Conclusion Combined pharmacovigilance and experimental data identify candidate drugs associated with renal vascular lesions and provide preliminary mechanistic plausibility for direct endothelial injury. These findings support tailored clinical vigilance and prioritization of mechanistic and prospective validation studies. Drug-induced renal vascular disorders Pharmacovigilance FAERS database Signal detection Time-to-onset analysis Adverse drug reactions Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Drug-induced renal vascular lesions are an underrecognized but clinically important subset of nephrotoxicity that encompass varied pathologies such as renal vasculitis, thrombotic microangiopathy, arterial stenosis and embolic events[ 1 , 2 ]. These lesions often herald severe renal dysfunction, lead to hospitalization or life-threatening outcomes, and pose diagnostic and therapeutic challenges because their pathogenesis is heterogeneous—ranging from immune-mediated endothelial injury and coagulation dysregulation to hemodynamic and metabolic perturbations[ 3 – 5 ]. Despite this clinical importance, most published evidence is limited to case series and small cohort studies, while systematic, large-scale pharmacovigilance analyses focusing specifically on nephrovascular adverse events remain scarce[ 6 – 8 ]. This gap hampers timely recognition of suspect medications, the characterization of vulnerable patient subgroups, and the development of targeted monitoring strategies. Spontaneous reporting systems such as the U.S. FDA Adverse Event Reporting System (FAERS) aggregate millions of post-marketing reports from diverse settings and are therefore well suited to detect rare or delayed adverse reactions that are not readily captured in pre-approval trials[ 9 , 10 ]. When combined with robust disproportionality algorithms and multivariable adjustment, FAERS can generate hypothesis-driven signals that prioritize drugs for further mechanistic and clinical evaluation[ 11 – 13 ]. Leveraging this approach, we performed comprehensive signal mining across FAERS (Q1 2004–Q4 2024), applied multiple disproportionality metrics and regression adjustment, and characterized reporting demographics, outcomes and time-to-onset patterns to map the epidemiology and temporal signatures of drug-related renal vascular injury. Importantly, pharmacovigilance signals alone cannot distinguish direct endothelial toxicity from indirect or immune-mediated mechanisms[ 14 , 15 ]. To bridge this translational gap, we selected three drugs labeled as high-risk by FAERS (imatinib, rofecoxib and tenofovir disoproxil) and conducted in vitro cytotoxicity tests in vascular endothelial and nephrogenic cell lines, as well as in vivo administration experiments in C57BL/6N mice, to evaluate the possibility of their causing renal vascular injury. The combined analysis reveals not only a diverse drug spectrum associated with renal vascular lesions in FAERS but also distinct latency patterns—some agents produce ultra-rapid events (median ≈ 1 day) consistent with infusion- or immune-related injury, whereas others show long latencies suggestive of cumulative or hemodynamic mechanisms—and provides preliminary cellular evidence that several implicated drugs can directly compromise endothelial viability under specific exposure conditions. Here we present the FAERS-based detection pipeline, the epidemiologic profile of 19,231 renal vascular lesion reports, prioritized drug signals, time-to-onset stratification. We also experimentally investigated the effects of three drugs that may cause drug-induced renal vascular lesions on renal cells (293A) and vascular endothelial cells (HUVEC), as well as on the kidneys of C57BL/6N mice. These integrated analyses aim to (1) elucidate candidate drug-renal vascular associations meriting heightened clinical vigilance, (2) delineate critical monitoring windows tailored to plausible mechanisms, and (3) inform mechanistic follow-up studies to confirm causality and guide risk mitigation in vulnerable patient populations. 2. Methods We extracted all reports of drug-related renal vascular lesions submitted to FAERS from Q1 2004 through Q4 2024( https://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html ). To ensure uniqueness, each report underwent a three-stage de-duplication procedure: For duplicate case IDs, retain only the record with the most recent fda_dt . If case_id and fda_dt are identical, retain the record with the larger primary_id . A secondary review was performed to remove any remaining duplicates. After de-duplication, a total of N = 19 231 valid reports were included. Each case report comprised the following modules: DEMO : patient demographics and administration data REAC : MedDRA-coded adverse reaction terms DRUG : information on suspect and concomitant drugs/biologics OUTC : patient outcome RPSR : source of the report THER : drug start and end dates INDI : all MedDRA-coded indications/diagnoses associated with the reported drug Renal vascular lesion–related Preferred Terms (PTs) were defined according to the MedDRA hierarchy, covering 36 subtypes (e.g., “RENAL VASCULITIS,” “RENAL ARTERY STENOSIS,” “RENAL EMBOLISM,” “RENAL VESSEL DISORDER,” etc.). Reports with patient age > 120 years or weight > 200 kg were considered implausible and excluded. We applied four statistical disproportionality analyses—Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Multi-item Gamma Poisson Shrinker (MGPS), and Bayesian Confidence Propagation Neural Network (BCPNN)—together with χ² tests (Bonferroni-adjusted) to identify drugs disproportionately associated with renal vascular lesions. These candidate drugs and patient covariates (demographics, comorbidities, etc.) were then entered into a multivariable logistic regression model to determine independent risk factors for drug-related renal vascular injury (Supplementary Table S2 ). All signal calculations were based on standard 2×2 contingency tables (Supplementary Table S1 ). Finally, we performed descriptive analyses to summarize the clinical characteristics and identified risk factors of patients with reported renal vascular lesions. All statistical analyses were conducted using R software. To validate the direct cytotoxicity of high-risk drugs identified from FAERS signals, in vitro experiments were conducted using human umbilical vein endothelial cells (HUVEC) and human embryonic kidney epithelial cells (HEK293A). Both cell lines were obtained from QuiCell and authenticated via STR profiling. Cells were cultured in DMEM supplemented with 10% fetal bovine serum at 37°C in a 5% CO₂ atmosphere. The high-risk drugs imatinib (MedChemExpress, HY-15463), rofecoxib (MedChemExpress, HY-17372), and tenofovir disoproxil (MedChemExpress, HY-13782A) were dissolved in dimethyl sulfoxide (DMSO) to prepare stock solutions. For cell viability assays, cells were seeded in 96-well plates at a density of 10,000 cells per well. After adherence, the culture medium was replaced with fresh medium containing a gradient of concentrations of the test drugs or an equivalent volume of DMSO as a vehicle control, and incubated for 48 hours. Cell viability was assessed using the CCK-8 assay kit (MedChemExpress, HY-K0301). Briefly, 10 µL of CCK-8 reagent was added to each well, followed by incubation for 1 hour. The absorbance at 450 nm was then measured using a microplate reader. Relative cell survival rates were calculated by normalizing the absorbance of drug-treated groups to that of the DMSO control group. C57BL/6N mice were administered imatinib (10 mg/kg/day), rofecoxib (20 mg/kg/day), or tenofovir disoproxil (5 mg/kg/day) via intraperitoneal injection for one week. Subsequently, kidneys were harvested, fixed, sectioned, and subjected to hematoxylin and eosin (H&E) staining. For apoptosis analysis, drug-induced apoptosis/cell death was evaluated using an FITC/PI double-staining apoptosis detection kit (Bestbio, BB-4101). After 48 hours of drug exposure, cells were trypsinized, washed, and subsequently stained with FITC-conjugated Annexin V and propidium iodide (PI) according to the manufacturer's instructions. The stained cells were then analyzed using a flow cytometer, which distinguished viable cells (FITC⁻/PI⁻), early apoptotic cells (FITC⁺/PI⁻), and late apoptotic/necrotic cells (FITC⁺/PI⁺). A JC-1 assay kit (Biosharp, BL711A) was used. HUVEC and HEK293A cells were treated with either DMSO or the respective drugs for 48 hours before analysis. GEO Database Analysis: Public datasets GSE59671 (human aortic smooth muscle cells treated with rofecoxib for 24 h) and GSE137534 (isolated vascular fragments from imatinib-treated mice, 24 h) were retrieved from the GEO database. Data processing and analysis were performed using R. HUVEC and HEK293A cells were treated with DMSO or the following drug concentrations for 48 hours: imatinib (60 µM), rofecoxib (100 µM), or tenofovir disoproxil (100 µM).Total RNA was extracted using the Super FastPure Cell RNA Isolation Kit (Vazyme Biotech, RC102-01).Reverse transcription was performed with the HiScript III All-in-one RT SuperMix Perfect for qPCR (Vazyme Biotech, R333-C1).qPCR was carried out using the SYBR Green Pro Taq HS Pre-mix qPCR Kit (AG11701) on a real-time PCR system. Primer sequences are listed in Supplementary Table S3 . Relative gene expression was calculated using the 2^(-ΔΔCt) method with GAPDH as the internal control. 3. Results 3.1. Baseline characteristics of reports of renal vascular lesions A total of 19,231 reports of drug-related renal vascular lesions were included in the analysis. The cohort was balanced by sex (female 52.8%, n = 10,163; male 46.5%, n = 8,941; unknown 0.7%, n = 127). The median age was 58 years (mean 53.7 ± 20.3 years), and mean body weight was 72.5 ± 25.7 kg (median 70.0 kg). Physicians submitted the largest share of reports (47.4%, n = 9,107), followed by consumers (19.1%, n = 3,671) and other health professionals (12.9%, n = 2,477); smaller contributions came from pharmacists (4.9%), allied health professionals (6.7%) and lawyers (2.6%); registered nurses accounted for < 0.1% (n = 6), and 6.5% of reports lacked reporter-occupation data. Geographically, reports were concentrated in the United States (42.6%, n = 8,186), Japan (13.4%, n = 2,577), France (6.1%, n = 1,175), China (4.9%, n = 950) and Germany (4.3%, n = 819), with smaller proportions from the United Kingdom, Canada, Italy, Brazil and Spain (Table 1 ). Table 1 Baseline characteristics of patients with reported renal vascular lesions in the FAERS database (2004–2024). Data are presented as number (percentage, %) for categorical variables and mean (standard deviation, SD) or median [minimum, maximum] for continuous variables. A total of 19,231 reports were included in the analysis. Characteristics Renal vascular lesion (N = 19231) Gender F 10163 (52.8%) M 8941 (46.5%) Unknown 127 (0.7%) Age Mean (SD) 53.7 (20.3) Median [Min, Max] 58.0 [0, 100] Weight Mean (SD) 72.5 (25.7) Median [Min, Max] 70.0 [0, 200] Occupation of the reporter Physician 9107 (47.4%) Consumer 3671 (19.1%) Other health-professional 2477 (12.9%) Health Professional 1298 (6.7%) Pharmacist 938 (4.9%) Lawyer 492 (2.6%) Registered Nurse 6 (0.0%) Unknown 1242 (6.5%) Country of the reporter United States 8186 (42.6%) Japan 2577 (13.4%) France 1175 (6.1%) China 950 (4.9%) Germany 819 (4.3%) United Kingdom 792 (4.1%) Canada 737 (3.8%) Italy 443 (2.3%) Brazil 238 (1.2%) Spain 162 (0.8%) Temporal analysis from Q1 2004 to Q4 2024 showed an overall upward trend in reporting with interannual fluctuations (Fig. 1 A). Age-stratified data (excluding 640 records with missing age, n = 18,591) indicated the highest reporting frequency in the 50–75 year group (53.4%, n = 9,936). Among outcomes (n = 19,231), hospitalization was most common (43.3%, n = 8,339), followed by death (9.7%, n = 1,866) and life-threatening events (5.9%, n = 1,131). When stratified by body-weight quartiles (excluding 472 records with missing weight, n = 18,759), the lowest quartile (< 57 kg) accounted for the largest share of reports (26.2%, n = 4,921). Country-level age distributions revealed notable variation: median age was highest in Japan and lowest in the United States, with bimodal patterns observed in several European countries (Fig. 2 ). 3.2. Drugs associated with renal vascular lesions We restricted signal detection to drugs with ≥ 50 reports and applied four disproportionality metrics (ROR, PRR, MGPS/EBGM, BCPNN/IC) together with Bonferroni-adjusted χ² tests. Nineteen drugs met pre-specified signal criteria (Table 2 ) and were entered into multivariable logistic regression models to adjust for reporting and patient covariates (Fig. 3 ). The resulting signals spanned multiple therapeutic classes, including nonsteroidal anti-inflammatory agents, tyrosine kinase inhibitors, antiviral combinations, radiographic contrast media, CAR-T products and selected psychotropic and cardiac agents. Notably, rofecoxib and several biologic/CAR-T agents showed strong disproportionality estimates, with imatinib, tenofovir disoproxil (and tenofovir-containing combinations), quetiapine and certain kinase inhibitors also demonstrating robust signals after unadjusted screening. Multivariable adjustment refined these associations and helped prioritize agents for experimental follow-up (Fig. 3 and Table 2 ). Table 2 Nineteen drugs significantly associated with renal vascular disorders identified by disproportionality analysis. Signals were generated based on four algorithms (ROR, PRR, BCPNN, MGPS) and a case report count of ≥ 50. The table presents the number of case reports and the corresponding statistical measures for each drug, including the lower bounds of the 95% confidence interval (CI) for ROR and PRR, the 5th percentile of the Empirical Bayesian Geometric Mean (EBGM05), the 2.5th percentile of the Information Component (IC025), and adjusted p-values. Abbreviations: ROR, reporting odds ratio; PRR, proportional reporting ratio; EBGM, empirical Bayesian geometric mean; IC, information component; Cl, confidence interval. Drug Case Reports ROR.(95.Cl.) PRR (95.Cl.) PRR-χ² EBGM.(EBGM05.) IC.(IC025.) pvalue p_adjust ROFECOXIB 1416 14.82 ( 14.01–15.68 ) 13.69 ( 13.64–13.74 ) 15531.59 12.76 ( 12.17 ) 3.67 ( 3.59 ) 0 0 QUETIAPINE 507 4.47 ( 4.09–4.89 ) 5.91 ( 5.82–5.99 ) 2253.21 4.29 ( 3.98 ) 2.1 ( 1.97 ) 0 0 IMATINIB 164 5.76 ( 4.93–6.74 ) 4.38 ( 4.29–4.46 ) 1294.51 5.55 ( 4.87 ) 2.47 ( 2.24 ) 9.3374E-283 1.1214E-279 OLANZAPINE 151 2.43 ( 2.07–2.86 ) 8.59 ( 8.49–8.68 ) 2544.94 2.4 ( 2.1 ) 1.26 ( 1.03 ) 0 0 ROSIGLITAZONE 151 2.38 ( 2.02–2.79 ) 5.1 ( 4.99–5.22 ) 945.38 2.35 ( 2.05 ) 1.23 ( 0.99 ) 1.0312E-206 1.2385E-203 BOTULINUM TOXIN TYPE A 91 3.02 ( 2.45–3.72 ) 2.58 ( 2.46–2.71 ) 247.98 2.97 ( 2.5 ) 1.57 ( 1.27 ) 1.58545E-55 1.90412E-52 DIGOXIN 81 5.22 ( 4.18–6.51 ) 3.32 ( 3.17–3.46 ) 292.35 5.06 ( 4.21 ) 2.34 ( 2.02 ) 4.88804E-65 5.87054E-62 PAZOPANIB 81 3.85 ( 3.09–4.8 ) 4.07 ( 3.92–4.22 ) 402.22 3.77 ( 3.13 ) 1.91 ( 1.59 ) 8.3955E-89 1.0083E-85 CABOZANTINIB 80 3.45 ( 2.76–4.3 ) 5.59 ( 5.44–5.74 ) 617.24 3.38 ( 2.81 ) 1.76 ( 1.43 ) 2.9555E-135 3.5495E-132 EMTRICITABINE;TENOFOVIR DISOPROXIL 80 4.71 ( 3.77–5.88 ) 2.41 ( 2.25–2.57 ) 124.54 4.58 ( 3.8 ) 2.2 ( 1.87 ) 1.30355E-28 1.56557E-25 TENOFOVIR DISOPROXIL 77 8.21 ( 6.53–10.33 ) 2.36 ( 2.2–2.52 ) 117.81 7.82 ( 6.45 ) 2.97 ( 2.63 ) 3.78494E-27 4.54571E-24 TISAGENLECLEUCEL 76 12.41 ( 9.82–15.68 ) 2.9 ( 2.74–3.06 ) 185.03 11.52 ( 9.47 ) 3.53 ( 3.18 ) 1.0045E-41 1.2064E-38 IOPROMIDE 68 4.42 ( 3.48–5.63 ) 4.06 ( 3.9–4.22 ) 343.2 4.32 ( 3.53 ) 2.11 ( 1.76 ) 5.94098E-76 7.13512E-73 AXICABTAGENE CILOLEUCEL 61 4.35 ( 3.37–5.62 ) 2.57 ( 2.41–2.74 ) 138.45 4.25 ( 3.44 ) 2.09 ( 1.72 ) 1.28008E-31 1.53737E-28 RIBAVIRIN 60 2.59 ( 2–3.34 ) 4.9 ( 4.74–5.07 ) 419.15 2.56 ( 2.07 ) 1.35 ( 0.98 ) 2.62931E-92 3.1578E-89 EFAVIRENZ;EMTRICITABINE;TENOFOVIR DISOPROXIL 57 7.88 ( 6.04–10.29 ) 4.45 ( 4.29–4.62 ) 361.45 7.53 ( 6.02 ) 2.91 ( 2.52 ) 7.67685E-80 9.2199E-77 ALEMTUZUMAB 55 4.35 ( 3.32–5.69 ) 2.6 ( 2.42–2.77 ) 122.87 4.25 ( 3.39 ) 2.09 ( 1.7 ) 3.32761E-28 3.99646E-25 AGALSIDASE BETA 54 3.04 ( 2.32–3.99 ) 3.31 ( 3.12–3.5 ) 169.11 3 ( 2.39 ) 1.58 ( 1.19 ) 3.68348E-38 4.42386E-35 ANTITHYMOCYTE IMMUNOGLOBULIN 54 3.6 ( 2.75–4.72 ) 2.98 ( 2.78–3.19 ) 120.03 3.54 ( 2.82 ) 1.82 ( 1.43 ) 1.69174E-27 2.03178E-24 3.3. Time-to-onset patterns across implicated drugs Time-to-onset distributions for the 19 signaled drugs revealed two distinct temporal patterns (Fig. 4 ). One cluster exhibited ultra-acute onset (median ≈ 1 day), exemplified by iopromide and CAR-T products (tisagenlecleucel, axicabtagene ciloleucel), consistent with immediate infusion-related, inflammatory or hemodynamic mechanisms[ 16 , 17 ]. A second cluster showed delayed onset (median latency often > 100 days), including rofecoxib, tenofovir disoproxil (and tenofovir-containing regimens), several psychotropic agents, olanzapine/ quetiapine, rosiglitazone and digoxin—patterns compatible with cumulative toxicity, long-term hemodynamic effects, or indirect metabolic pathways[ 18 – 21 ]. These latency patterns provide mechanistic clues that may guide clinical vigilance: agents in the ultra-acute cluster warrant close monitoring around the time of administration, whereas drugs in the delayed cluster may require longer-term surveillance for progressive renovascular compromise. 3.4. In vitro and short-term in vivo assessment of endothelial cytotoxicity induced by FAERS-identified high-risk drugs We selected imatinib, rofecoxib and tenofovir disoproxil for experimental validation and treated HEK293A (293A) renal epithelial cells and human umbilical vein endothelial cells (HUVEC) with a concentration gradient for 48 h; outcomes included bright-field morphology, CCK-8 viability, FITC/PI apoptosis/necrosis and JC-1 mitochondrial membrane potential assays (Fig. 5 – 6 ). All three agents produced dose-dependent cytopathic changes and reductions in metabolic viability in both cell types, but morphological disruption (cell rounding, detachment, monolayer loss) and viability loss were consistently greater in HUVEC than in 293A (Fig. 5 A–E). FITC/PI flow cytometry demonstrated drug- and dose-specific increases in early and late apoptotic/necrotic fractions relative to DMSO controls: high-dose imatinib preferentially increased PI-positive late death in HUVEC, rofecoxib showed a mixed early/late apoptotic pattern that varied with dose, and tenofovir disoproxil induced progressive apoptotic enrichment at higher concentrations (Fig. 6 A). JC-1 ratiometric analysis confirmed dose-dependent mitochondrial depolarization with both imatinib and rofecoxib, the decline in red/green ratio being substantially larger in HUVEC than in 293A (Fig. 6 B–C), consistent with mitochondrial dysfunction contributing to endothelial injury. To provide preliminary in vivo correlation, C57BL/6 mice received daily intraperitoneal injections of imatinib, rofecoxib or tenofovir disoproxil for one week and kidneys were examined by H&E (Fig. 5 F). Control kidneys showed intact capillaries and small veins without congestion, perivascular hemorrhage or obvious thrombi (baseline). Imatinib-treated kidneys demonstrated marked congestion of small vessels and capillary clusters, focal capillary dilation and traces of peritubular/interstitial erythrocyte extravasation; no organized fibrin thrombi were identified on H&E, recognizing that H&E has limited sensitivity for early or microthrombi. Rofecoxib treatment produced moderate capillary congestion with scattered focal extravasation. Tenofovir treatment led to perivascular inflammatory cell infiltrates adjacent to small vessels, focal congestion and small hemorrhagic foci, consistent with a perivascular/venular inflammatory response. The in vitro and short-term in vivo data provide concordant evidence that these agents can compromise endothelial integrity under defined exposure conditions, supporting biological plausibility for the FAERS renovascular signals while acknowledging that in vitro toxic concentrations exceed typical clinical plasma levels and that immune-mediated or hemodynamic mechanisms may also contribute in patients. 3.5. GEO-based transcriptional evidence linking drug exposures to vascular adhesion and junctional programs To seek independent transcriptomic support for the experimentally observed vascular effects, we analyzed two public GEO datasets: GSE59671 (human aortic smooth muscle cells, rofecoxib 24 h) and GSE137534 (isolated vascular fragments from imatinib-treated mice, 24 h). In GSE59671, rofecoxib exposure was associated with upregulation of individual transcripts (notably PDE4B, CRISPLD2 and NPR3) and with coordinated downregulation of gene sets involved in cell–cell adhesion and junction organization (e.g., GO:0016338, GO:0045216), as indicated by negative normalized enrichment scores and significant adjusted p/q values(Fig. 7 A-E, Supplementary Table S4 ). GSE137534 (vascular fragments after imatinib) showed transcriptional remodeling of pathways related to adenylate cyclase signaling and integrin-mediated adhesion, consistent with altered vascular signaling and junctional regulation. Collectively, the GEO signatures corroborate our experimental observations by linking rofecoxib and imatinib exposure to perturbation of adhesion/junctional and related signaling programs in vascular cells, thereby providing an independent, dataset-level rationale for endothelial dysfunction as a mechanistic contributor to drug-associated renovascular lesions. 3.6. qPCR validation of GEO-selected candidate genes in 293A and HUVEC cells We selected representative GEO-derived candidates for targeted qPCR validation: PDE4B, CRISPLD2 and NPR3 from the rofecoxib signature, and ADCY family members and KNN4 from the imatinib signature. Rofecoxib exposure reproducibly increased CRISPLD2 expression in both 293A and HUVEC (p < 0.05), in agreement with the GEO observation(Fig. 8 A-B). By contrast, PDE4B and NPR3 did not reach statistical significance in our cell models, suggesting that their modulation may be context-dependent or below the detection threshold in these in vitro systems( Fig. 8 A-B). Imatinib elicited cell-type specific transcriptional responses: both ADCY (adenylate cyclase) family transcripts and KNN4 were downregulated in 293A, whereas in HUVEC ADCY expression decreased but KNN4 expression increased (all significant at p < 0.05) ( Fig. 8 C-D). These divergent directions between epithelial and endothelial cells indicate cell-type specific regulation and reinforce the notion that drug-induced transcriptional remodeling of adhesion and signaling programs can differ across vascular versus parenchymal compartments. The qPCR results provide targeted experimental confirmation for selected GEO signals (notably CRISPLD2 and ADCY-related perturbation) while highlighting gene- and cell-specific effects that warrant further mechanistic follow-up. 4. Discussion In this study, FAERS database and multiple signal detection algorithms were used to screen 19 drug signals significantly associated with renal vascular injury, and the occurrence time sequence characteristics of these signals were analyzed. The results showed that these drugs can be divided into two groups: one is the "hyper-acute" drugs (e.g., CAR-T cell therapy products, iodinated contrast media), whose adverse event onset latency is very short (median about 1 day); The other group is "delayed" drugs (such as non-steroidal anti-inflammatory drug rofecoxib, antiviral drug tenofovir and some psychotropic drugs), showing a long incubation period of several months or even years. Contrast agents are known to rapidly damage renal microcirculation by inducing renal vasoconstriction and red blood cell aggregation, whereas CAR-T therapy is often accompanied by cytokine release syndrome, which leads to systemic inflammation and endothelial activation, thereby inducing renal vascular injury in a very short time[ 22 – 25 ]. Drugs such as rofecoxib and tenofovir often develop vascular lesions after long-term use[ 14 , 15 ]. This may be related to their accumulation in the kidney or their mediating chronic immune damage. In vitro experiments further revealed the difference in toxicity between the above-mentioned high-risk drugs on vascular endothelial cells (HUVEC) and renal epithelial cells (293A). Under the same exposure concentration and time, the survival rate of HUVEC decreased and the proportion of apoptosis was higher than that of 293A cells, which indicated that endothelial cells were more sensitive to these drugs. Specifically, high concentration of imatinib significantly induced PI-stained cell death in HUVEC; Rofecoxib induced HUVEC apoptosis at the early stage. Tenofovir also induced early apoptosis in huvecs in a dose-dependent manner. Mechanistically, imatinib, as a PDGF/VEGF receptor inhibitor, may disrupt the signaling pathways of endothelial cell proliferation and survival and aggravate apoptosis[ 26 ]. Rofecoxib not only has endothelial toxicity, but also induces vasoconstriction and procoagulability by inhibiting prostaglandin I₂ production[ 27 ]. Tenofovir, on the other hand, is known to accumulate in the kidney, causing mitochondrial dysfunction and oxidative stress, leading to apoptosis[ 28 ]. These results preliminarily demonstrated the biological feasibility of some FAERS signaling drugs to directly damage the endothelium under fully exposed conditions. It should be noted that in vitro toxicity experiments often use drug concentrations that are higher than clinical blood concentrations, so these results mainly address the potential for direct endothelial cell damage at high or cumulative exposures, rather than correspond quantitatively to risk in humans. Short-term in vivo administration experiments in mice provided pathological support for the in vitro results. Small vessel congestion, capillary clumps and focal erythrocyte exudation were observed in the kidneys of imatinib-treated mice. Moderate capillary congestion with scattered oozing foci was observed in rofecoxib group. Perivascular infiltration of inflammatory cells and focal hemorrhagic foci were observed in the tenofovir group (Fig. 5 F). These morphological changes suggest that these drugs can cause microvascular structural disruption, local hyperemia, and inflammatory cell exudation at high doses of short-term exposure. Previous studies have shown that long-term rofecoxib treatment can enhance thrombophilia in mice (shortening the occlusion time of the venous model), consistent with our observation of vascular congestion and exudation[ 29 ]. These in vivo results are consistent with the impairment of endothelial barrier function seen in vitro, suggesting that these drugs may promote renal vasculopathy by inducing endothelial cell injury and inflammatory response in vivo. Transcriptome analysis of GEO database further revealed the disorder of drug regulation on vascular adhesion/connection pathway. In HASMC cells, rofecoxib treatment for 24 hours resulted in significant down-regulation of several cell-cell adhesion and cell-matrix adhesion related genes. In the cerebrovascular segment, imatinib also perturbed integrin-mediated signaling pathways. These results suggest that the junctional structure between vascular cells may be relaxed or dissociated after the treatment of these drugs, which in turn weakens the integrity of the endothelial barrier. Our GEO and cellular results suggest that drug-induced downregulation of connexin may provide a molecular basis for the loss of adhesion and susceptibility to apoptosis observed in endothelial cells in vitro. qPCR results verified the expression changes of key molecules in the above transcriptomic findings in the cell model. After rofecoxib treatment, CRISPLD2 was significantly up-regulated in 293A and HUVEC. ADCY was significantly downregulated in both cell types after imatinib treatment, whereas KCNQ4 was significantly upregulated in HUVEC. All of these molecules are involved in signaling pathways related to endothelial function. ADCY encodes adenylate cyclase, and its down-regulation may lead to a decrease in cAMP production, thereby inhibiting the cAMP/PKA pathway, which is important for maintaining endothelial integrity and anti-inflammatory response[ 30 ]. KCNQ4 is a subunit of voltage-gated potassium channel[ 31 ]. Changes in its expression may indirectly affect endothelial tone and signal transduction by regulating membrane potential and intracellular Ca²⁺ homeostasis. As a secreted protein, CRISPLD2 interacts with inflammatory mediators, and its up-regulation or reflection reflects the feedback regulatory response of cells to drug stress[ 32 ]. Taken together, the dysregulation of these molecules further suggests the disruption of intercellular connectivity and signaling networks, providing a molecular basis for drug-induced endothelial dysfunction. Several limitations should be considered with caution. First, FAERS, as a spontaneous adverse event reporting system, has its own problems of reporting bias, incomplete information, and lack of a medication denominator. Secondly, the representativeness of in vitro models is limited. Although HUVEC is a classic endothelial cell model, it cannot reflect the particularity of molecular and microenvironment of renal microvessels, such as glomerular and medullary vessels. 293A cells are embryonical-derived renal epithelial cells, which cannot completely represent the physiological state of mature renal tubules or podocytes. Further validation of the signals and mechanisms in more physiologically relevant systems, such as primary renal microvascular endothelium, co-culture systems, renal organoids, or animal models, is warranted. In addition, the mouse model in this study was only a short-term high-dose exposure and could not fully simulate the chronic cumulative toxicity process. Future studies should consider different doses, different routes of administration, and long-term observation to more comprehensively evaluate the effects of drugs on renal vascular structure and function. Through large-scale FAERS signal analysis combined with in vitro/in vivo experimental verification, this study systematically identified and clarified the association and potential mechanism of multiple drugs with renal vascular injury, and revealed the heterogeneity of related drugs in the time sequence of action. The results of cell and animal experiments demonstrated that the selected representative drugs were able to directly damage the vascular endothelium under certain conditions, providing support for the biological feasibility behind FAERS signaling. Despite the limitations, these findings provide a reference for future research on the mechanisms and risk prediction of drug-induced renal vasculopathy. 5. Conclusion In this integrated pharmacovigilance and experimental study, FAERS signal mining (2004–2024) identified nineteen drugs disproportionately associated with drug-related renal vascular lesions and revealed a bimodal time-to-onset pattern distinguishing hyperacute (e.g., CAR-T, contrast media) from delayed (e.g., rofecoxib, tenofovir disoproxil) presentations. Targeted in vitro and short-term in vivo experiments demonstrated that imatinib, rofecoxib and tenofovir disoproxil can compromise endothelial viability and induce vascular structural changes under defined exposure conditions. Declarations Ethical statement All animal experiments were conducted in strict accordance with the guide for the Care and Use of Laboratory Animals (NIH, USA) and were approved by the Institutional Animal Care and Use Committee of Shanghai Novopathway Biotechnology Co., Ltd. (Approval No.: P0820251216A). Competing Interests The authors declare that there are no actual or potential conflicts of interest related to this study. Funding This work was supported by the National Natural Science Foundation of China [Grant Number 8247114926]. Author Contribution H.H. conceptualized the study and developed the methodology; Y.Y. developed the software; H.H., Y.Y., and X.Y. performed validation and conducted formal analysis; J.J. conducted investigation; J.R. curated data; H.H. wrote the original draft; L.Z. reviewed and edited the manuscript; P.Z. prepared visualizations; Z.Z. supervised the research; L.Z. administered the project; and J.J. acquired funding. All authors reviewed the manuscript. Acknowledgement The authors extend their gratitude to the U.S. FDA for providing access to the FAERS database, which made this pharmacovigilance study possible. We thank all the clinicians, researchers, and patients whose contributions to adverse event reporting underpin the data used in this analysis. 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Supplementary Files SupplementaryTableS1.docx SupplementaryTableS2.docx SupplementaryTableS3.docx SupplementaryTableS4.docx image1.jpg Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8665668","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616805814,"identity":"9a95e7ff-8e28-4e21-bea4-23f5d8992973","order_by":0,"name":"Hongxiang He","email":"","orcid":"","institution":"University of Shanghai for Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hongxiang","middleName":"","lastName":"He","suffix":""},{"id":616805815,"identity":"e1ac9167-5bc6-4167-97a2-17bb8b41b008","order_by":1,"name":"Yili Yang","email":"","orcid":"","institution":"University of Shanghai for Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yili","middleName":"","lastName":"Yang","suffix":""},{"id":616805816,"identity":"0a4ca8e3-8bb2-47ed-9f98-5521b615525d","order_by":2,"name":"Jin Rao","email":"","orcid":"","institution":"Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Rao","suffix":""},{"id":616805817,"identity":"0e785366-ee68-4677-81a2-cac7e4209c40","order_by":3,"name":"Xuan Yin","email":"","orcid":"","institution":"Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Yin","suffix":""},{"id":616805818,"identity":"0e810ef0-c062-4098-90fa-e5f0e6a59a06","order_by":4,"name":"Pinjie Zhang","email":"","orcid":"","institution":"University of Shanghai for Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Pinjie","middleName":"","lastName":"Zhang","suffix":""},{"id":616805819,"identity":"871764c1-7ea6-4a4f-977f-042dd7779947","order_by":5,"name":"Zifeng Zeng","email":"","orcid":"","institution":"Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zifeng","middleName":"","lastName":"Zeng","suffix":""},{"id":616805820,"identity":"35d285b8-6046-424d-a9b0-5d09259ec272","order_by":6,"name":"Junfeng Jiang","email":"","orcid":"","institution":"Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junfeng","middleName":"","lastName":"Jiang","suffix":""},{"id":616805821,"identity":"a04585e5-4345-47c7-b0f2-ef8d10605898","order_by":7,"name":"Li Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYBACA/kzIEqifv/x5gMHPvwgRgsDWIsNY8OZY4kHZ/YQryWNseFGjvFhDjZitDD2Hvxc8OswM+OMnA+HGXgY5PnFDhDQwsyXLD2z7zAbM8/bDYcLLBgMZ85OIKCFjcdAmrfnMA8be+6GwzN4GBIMbhPSwsNj/BuoRYKHIecBUCMxWiR4zKR5fqQZSHDkMBCvxZq3wSbBgOeYATCQJQj7xX7+GePbPH8kEgzYmx9/+PDDRp5fmoAWMGBsgzMliFAOBn+IVTgKRsEoGAUjEgAAwfJFhBuD07cAAAAASUVORK5CYII=","orcid":"","institution":"Naval Medical University","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-01-22 05:53:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8665668/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8665668/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106402188,"identity":"37768fec-f19b-420d-971a-a9b5d8f37484","added_by":"auto","created_at":"2026-04-08 09:11:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":113708,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCharacteristics of drug-related renal vascular lesion reports in FAERS. (A)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eAge distribution of patients; \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(B)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Annual reporting trends (2004 Q1–2024 Q4);\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e (C)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Clinical outcomes of adverse events; \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(D) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eWeight distribution of patients.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8665668/v1/73bf111be9c0fbbd4061614e.png"},{"id":106109845,"identity":"fdef49bc-62f5-46bf-a640-05a4b77794a3","added_by":"auto","created_at":"2026-04-03 14:43:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":162937,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAge distribution of patients in the top 10 reporting countries.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8665668/v1/40c2ced8b0b03dea9601942e.png"},{"id":106109838,"identity":"2be6b36d-fb2d-4b37-9248-f0f829548bc6","added_by":"auto","created_at":"2026-04-03 14:43:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":340255,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMultivariable logistic regression of drugs associated with renal vascular disorders. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eForest plot showing adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for 19 prioritized drugs. P_adj \u0026lt; 0.01 (Bonferroni-corrected) indicates statistical significance.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8665668/v1/492d7ba0a65b619c6e8c5d80.png"},{"id":106401999,"identity":"2d1b61aa-e197-4da5-bf4d-e9531bff096d","added_by":"auto","created_at":"2026-04-08 09:10:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":291046,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTime-to-onset profiles of renal vascular lesions for 19 high-risk drugs. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eViolin plots depict distribution of days from drug initiation to event onset.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8665668/v1/0d7ea8c042fdc46838522cd6.png"},{"id":106402335,"identity":"7f6ee9e9-2684-4035-9dad-e7d743c412e2","added_by":"auto","created_at":"2026-04-08 09:11:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3825847,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIn vitro and short-term in vivo assessment of cytotoxicity induced by FAERS-identified high-risk drugs.(A) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eRepresentative bright-field images of HUVEC and 293A cells after 48 h exposure to imatinib.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(B)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Representative bright-field images of HUVEC and 293A cells after 48 h exposure to rofecoxib.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(C) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eQuantification of cell viability by CCK-8 in HUVEC and 293A cells after 48 h imatinib exposure.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(D)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eQuantification of cell viability by CCK-8 in HUVEC and 293A cells after 48 h rofecoxib exposure.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(E) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eQuantification of cell viability by CCK-8 in HUVEC and 293A cells after 48 h tenofovir disoproxil exposure. For panels C–E, data are mean ± SD of four independent biological replicates (n = 4). Statistical analysis was performed by two-way ANOVA with Bonferroni multiple-comparisons test; significance is indicated as *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001, ****P \u0026lt; 0.0001; ns, not significant.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(F) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eRepresentative H\u0026amp;E-stained kidney sections from C57BL/6N mice after daily intraperitoneal injection of vehicle (Ctrl), imatinib, rofecoxib or tenofovir disoproxil for one week.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8665668/v1/600362d02fcf39dfc3dbbf71.png"},{"id":106401738,"identity":"0b80d075-c323-4873-8bd0-8b5c98ac9ffd","added_by":"auto","created_at":"2026-04-08 09:09:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":653731,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFlow cytometric and fluorescence-ratio assessment of cell death and membrane integrity following drug exposure.(A)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Representative FITC/PI flow cytometry dot plots for HUVEC and 293A cells following 48 h exposure to imatinib, rofecoxib or tenofovir disoproxi.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(B) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eBar graph of Red/Green fluorescence ratio (relative to DMSO control), measured by flow cytometry, in HUVEC and 293A cells 48 h after imatinib exposure.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(C) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eBar graph of Red/Green fluorescence ratio (relative to DMSO control), measured by flow cytometry, in HUVEC and 293A cells 48 h after rofecoxib exposure.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8665668/v1/2521b2b480b7cdacc46bb237.png"},{"id":106109840,"identity":"f35925dc-be3c-4b5f-90b0-84e2d2ec9696","added_by":"auto","created_at":"2026-04-03 14:43:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":332735,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGEO-based transcriptomic analysis reveals drug-associated dysregulation of vascular adhesion and junctional programs.(A) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eVolcano plot of differentially expressed genes in human aortic smooth muscle cells (HASMCs) from GSE59671 after 24 h rofecoxib treatment versus vehicle control.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(B) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eGO enrichment bubble plot showing suppressed biological processes related to vascular adhesion and junction integrity following rofecoxib exposure.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(C–E) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eGene set enrichment analysis (GSEA) demonstrating negative enrichment of regulation of cell–substrate adhesion, cell–cell junction organization, and calcium-independent cell–cell adhesion via plasma membrane cell-adhesion molecules in rofecoxib-treated HASMCs.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(F) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eHeatmap of differentially expressed genes from GSE137534, derived from isolated vascular fragments (representing the blood–brain barrier) of imatinib- versus PBS-treated mice at 3 h and 24 h after MCAO induction.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8665668/v1/2c437e7e5ee975d582601472.png"},{"id":106109847,"identity":"990ef7e8-885f-4443-979a-3afb3301feb8","added_by":"auto","created_at":"2026-04-03 14:43:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":953083,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eqPCR validation of GEO-derived candidate genes in renal epithelial and endothelial cells following drug exposure.293A and HUVEC cells were treated with rofecoxib or imatinib for 48 h, followed by quantitative real-time PCR analysis of selected genes identified from GEO analyses.(A) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eRelative mRNA expression of PDE4B, CRISPLD2, and NPR3 in 293A cells after rofecoxib stimulation.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(B) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eRelative mRNA expression of PDE4B, CRISPLD2, and NPR3 in HUVECs after rofecoxib stimulation.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(C) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eRelative mRNA expression of ADCY and KNN4 in 293A cells after imatinib stimulation.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(D)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eRelative mRNA expression of ADCY and KNN4 in HUVECs after imatinib stimulation.Data are shown as mean ± SEM from four independent biological replicates (n = 4). Statistical analysis was performed using two-way ANOVA followed by Bonferroni multiple-comparisons test. Statistical significance is indicated as *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001, and ****P \u0026lt; 0.0001; ns, not significant.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8665668/v1/7d6de7921585e2ef7a5d5817.png"},{"id":106723712,"identity":"015e8cd7-974c-40e4-a091-8375fcb54d05","added_by":"auto","created_at":"2026-04-12 18:12:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7489345,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8665668/v1/1d876d77-d296-4222-a287-e9a77d643536.pdf"},{"id":106402157,"identity":"24c41039-543f-438a-82c8-9ab940efb63c","added_by":"auto","created_at":"2026-04-08 09:11:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17039,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8665668/v1/a8c212ae54c350210d4f39d7.docx"},{"id":106402436,"identity":"0ebaf595-a887-4776-bb12-762111871ada","added_by":"auto","created_at":"2026-04-08 09:12:01","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19224,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8665668/v1/498e186859b1f5b0502f945c.docx"},{"id":106109844,"identity":"49557a26-149f-48f1-b86b-a2b414dc3e43","added_by":"auto","created_at":"2026-04-03 14:43:19","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17431,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8665668/v1/98227a88e1825d5c6bdcaa12.docx"},{"id":106109846,"identity":"6e2a8bad-0f40-4c91-a1b2-fbe107210c0c","added_by":"auto","created_at":"2026-04-03 14:43:20","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":20899,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-8665668/v1/556eb6ce47c3b4e908a0f5c7.docx"},{"id":106402111,"identity":"42e3cb73-267e-4b7d-80c6-f986fc284bc2","added_by":"auto","created_at":"2026-04-08 09:11:07","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":583056,"visible":true,"origin":"","legend":"","description":"","filename":"image1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8665668/v1/71102755ecc36f20653c6336.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pharmacovigilance Study of Drug-Related Renal Vascular Lesions Based on FAERS and Experimental Exploration","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDrug-induced renal vascular lesions are an underrecognized but clinically important subset of nephrotoxicity that encompass varied pathologies such as renal vasculitis, thrombotic microangiopathy, arterial stenosis and embolic events[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These lesions often herald severe renal dysfunction, lead to hospitalization or life-threatening outcomes, and pose diagnostic and therapeutic challenges because their pathogenesis is heterogeneous\u0026mdash;ranging from immune-mediated endothelial injury and coagulation dysregulation to hemodynamic and metabolic perturbations[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite this clinical importance, most published evidence is limited to case series and small cohort studies, while systematic, large-scale pharmacovigilance analyses focusing specifically on nephrovascular adverse events remain scarce[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This gap hampers timely recognition of suspect medications, the characterization of vulnerable patient subgroups, and the development of targeted monitoring strategies.\u003c/p\u003e \u003cp\u003eSpontaneous reporting systems such as the U.S. FDA Adverse Event Reporting System (FAERS) aggregate millions of post-marketing reports from diverse settings and are therefore well suited to detect rare or delayed adverse reactions that are not readily captured in pre-approval trials[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. When combined with robust disproportionality algorithms and multivariable adjustment, FAERS can generate hypothesis-driven signals that prioritize drugs for further mechanistic and clinical evaluation[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Leveraging this approach, we performed comprehensive signal mining across FAERS (Q1 2004\u0026ndash;Q4 2024), applied multiple disproportionality metrics and regression adjustment, and characterized reporting demographics, outcomes and time-to-onset patterns to map the epidemiology and temporal signatures of drug-related renal vascular injury.\u003c/p\u003e \u003cp\u003eImportantly, pharmacovigilance signals alone cannot distinguish direct endothelial toxicity from indirect or immune-mediated mechanisms[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To bridge this translational gap, we selected three drugs labeled as high-risk by FAERS (imatinib, rofecoxib and tenofovir disoproxil) and conducted in vitro cytotoxicity tests in vascular endothelial and nephrogenic cell lines, as well as in vivo administration experiments in C57BL/6N mice, to evaluate the possibility of their causing renal vascular injury. The combined analysis reveals not only a diverse drug spectrum associated with renal vascular lesions in FAERS but also distinct latency patterns\u0026mdash;some agents produce ultra-rapid events (median\u0026thinsp;\u0026asymp;\u0026thinsp;1 day) consistent with infusion- or immune-related injury, whereas others show long latencies suggestive of cumulative or hemodynamic mechanisms\u0026mdash;and provides preliminary cellular evidence that several implicated drugs can directly compromise endothelial viability under specific exposure conditions.\u003c/p\u003e \u003cp\u003eHere we present the FAERS-based detection pipeline, the epidemiologic profile of 19,231 renal vascular lesion reports, prioritized drug signals, time-to-onset stratification. We also experimentally investigated the effects of three drugs that may cause drug-induced renal vascular lesions on renal cells (293A) and vascular endothelial cells (HUVEC), as well as on the kidneys of C57BL/6N mice. These integrated analyses aim to (1) elucidate candidate drug-renal vascular associations meriting heightened clinical vigilance, (2) delineate critical monitoring windows tailored to plausible mechanisms, and (3) inform mechanistic follow-up studies to confirm causality and guide risk mitigation in vulnerable patient populations.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eWe extracted all reports of drug-related renal vascular lesions submitted to FAERS from Q1 2004 through Q4 2024(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html\u003c/span\u003e\u003cspan address=\"https://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To ensure uniqueness, each report underwent a three-stage de-duplication procedure:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFor duplicate case IDs, retain only the record with the most recent \u003cb\u003efda_dt\u003c/b\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIf \u003cb\u003ecase_id\u003c/b\u003e and \u003cb\u003efda_dt\u003c/b\u003e are identical, retain the record with the larger \u003cb\u003eprimary_id\u003c/b\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eA secondary review was performed to remove any remaining duplicates.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAfter de-duplication, a total of \u003cb\u003eN\u0026thinsp;=\u0026thinsp;19 231\u003c/b\u003e valid reports were included.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eEach case report comprised the following modules:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDEMO\u003c/b\u003e: patient demographics and administration data\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eREAC\u003c/b\u003e: MedDRA-coded adverse reaction terms\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDRUG\u003c/b\u003e: information on suspect and concomitant drugs/biologics\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eOUTC\u003c/b\u003e: patient outcome\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRPSR\u003c/b\u003e: source of the report\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTHER\u003c/b\u003e: drug start and end dates\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eINDI\u003c/b\u003e: all MedDRA-coded indications/diagnoses associated with the reported drug\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eRenal vascular lesion\u0026ndash;related Preferred Terms (PTs) were defined according to the MedDRA hierarchy, covering 36 subtypes (e.g., \u0026ldquo;RENAL VASCULITIS,\u0026rdquo; \u0026ldquo;RENAL ARTERY STENOSIS,\u0026rdquo; \u0026ldquo;RENAL EMBOLISM,\u0026rdquo; \u0026ldquo;RENAL VESSEL DISORDER,\u0026rdquo; etc.).\u003c/p\u003e \u003cp\u003eReports with patient age\u0026thinsp;\u0026gt;\u0026thinsp;120 years or weight\u0026thinsp;\u0026gt;\u0026thinsp;200 kg were considered implausible and excluded. We applied four statistical disproportionality analyses\u0026mdash;Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Multi-item Gamma Poisson Shrinker (MGPS), and Bayesian Confidence Propagation Neural Network (BCPNN)\u0026mdash;together with χ\u0026sup2; tests (Bonferroni-adjusted) to identify drugs disproportionately associated with renal vascular lesions. These candidate drugs and patient covariates (demographics, comorbidities, etc.) were then entered into a multivariable logistic regression model to determine independent risk factors for drug-related renal vascular injury (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). All signal calculations were based on standard 2\u0026times;2 contingency tables (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, we performed descriptive analyses to summarize the clinical characteristics and identified risk factors of patients with reported renal vascular lesions. All statistical analyses were conducted using R software.\u003c/p\u003e \u003cp\u003eTo validate the direct cytotoxicity of high-risk drugs identified from FAERS signals, in vitro experiments were conducted using human umbilical vein endothelial cells (HUVEC) and human embryonic kidney epithelial cells (HEK293A). Both cell lines were obtained from QuiCell and authenticated via STR profiling. Cells were cultured in DMEM supplemented with 10% fetal bovine serum at 37\u0026deg;C in a 5% CO₂ atmosphere.\u003c/p\u003e \u003cp\u003eThe high-risk drugs imatinib (MedChemExpress, HY-15463), rofecoxib (MedChemExpress, HY-17372), and tenofovir disoproxil (MedChemExpress, HY-13782A) were dissolved in dimethyl sulfoxide (DMSO) to prepare stock solutions. For cell viability assays, cells were seeded in 96-well plates at a density of 10,000 cells per well. After adherence, the culture medium was replaced with fresh medium containing a gradient of concentrations of the test drugs or an equivalent volume of DMSO as a vehicle control, and incubated for 48 hours.\u003c/p\u003e \u003cp\u003eCell viability was assessed using the CCK-8 assay kit (MedChemExpress, HY-K0301). Briefly, 10 \u0026micro;L of CCK-8 reagent was added to each well, followed by incubation for 1 hour. The absorbance at 450 nm was then measured using a microplate reader. Relative cell survival rates were calculated by normalizing the absorbance of drug-treated groups to that of the DMSO control group. C57BL/6N mice were administered imatinib (10 mg/kg/day), rofecoxib (20 mg/kg/day), or tenofovir disoproxil (5 mg/kg/day) via intraperitoneal injection for one week. Subsequently, kidneys were harvested, fixed, sectioned, and subjected to hematoxylin and eosin (H\u0026amp;E) staining.\u003c/p\u003e \u003cp\u003eFor apoptosis analysis, drug-induced apoptosis/cell death was evaluated using an FITC/PI double-staining apoptosis detection kit (Bestbio, BB-4101). After 48 hours of drug exposure, cells were trypsinized, washed, and subsequently stained with FITC-conjugated Annexin V and propidium iodide (PI) according to the manufacturer's instructions. The stained cells were then analyzed using a flow cytometer, which distinguished viable cells (FITC⁻/PI⁻), early apoptotic cells (FITC⁺/PI⁻), and late apoptotic/necrotic cells (FITC⁺/PI⁺). A JC-1 assay kit (Biosharp, BL711A) was used. HUVEC and HEK293A cells were treated with either DMSO or the respective drugs for 48 hours before analysis.\u003c/p\u003e \u003cp\u003eGEO Database Analysis: Public datasets GSE59671 (human aortic smooth muscle cells treated with rofecoxib for 24 h) and GSE137534 (isolated vascular fragments from imatinib-treated mice, 24 h) were retrieved from the GEO database. Data processing and analysis were performed using R.\u003c/p\u003e \u003cp\u003eHUVEC and HEK293A cells were treated with DMSO or the following drug concentrations for 48 hours: imatinib (60 \u0026micro;M), rofecoxib (100 \u0026micro;M), or tenofovir disoproxil (100 \u0026micro;M).Total RNA was extracted using the Super FastPure Cell RNA Isolation Kit (Vazyme Biotech, RC102-01).Reverse transcription was performed with the HiScript III All-in-one RT SuperMix Perfect for qPCR (Vazyme Biotech, R333-C1).qPCR was carried out using the SYBR Green Pro Taq HS Pre-mix qPCR Kit (AG11701) on a real-time PCR system. Primer sequences are listed in Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e. Relative gene expression was calculated using the 2^(-ΔΔCt) method with GAPDH as the internal control.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Baseline characteristics of reports of renal vascular lesions\u003c/h2\u003e \u003cp\u003eA total of 19,231 reports of drug-related renal vascular lesions were included in the analysis. The cohort was balanced by sex (female 52.8%, n\u0026thinsp;=\u0026thinsp;10,163; male 46.5%, n\u0026thinsp;=\u0026thinsp;8,941; unknown 0.7%, n\u0026thinsp;=\u0026thinsp;127). The median age was 58 years (mean 53.7\u0026thinsp;\u0026plusmn;\u0026thinsp;20.3 years), and mean body weight was 72.5\u0026thinsp;\u0026plusmn;\u0026thinsp;25.7 kg (median 70.0 kg). Physicians submitted the largest share of reports (47.4%, n\u0026thinsp;=\u0026thinsp;9,107), followed by consumers (19.1%, n\u0026thinsp;=\u0026thinsp;3,671) and other health professionals (12.9%, n\u0026thinsp;=\u0026thinsp;2,477); smaller contributions came from pharmacists (4.9%), allied health professionals (6.7%) and lawyers (2.6%); registered nurses accounted for \u0026lt;\u0026thinsp;0.1% (n\u0026thinsp;=\u0026thinsp;6), and 6.5% of reports lacked reporter-occupation data.\u003c/p\u003e \u003cp\u003eGeographically, reports were concentrated in the United States (42.6%, n\u0026thinsp;=\u0026thinsp;8,186), Japan (13.4%, n\u0026thinsp;=\u0026thinsp;2,577), France (6.1%, n\u0026thinsp;=\u0026thinsp;1,175), China (4.9%, n\u0026thinsp;=\u0026thinsp;950) and Germany (4.3%, n\u0026thinsp;=\u0026thinsp;819), with smaller proportions from the United Kingdom, Canada, Italy, Brazil and Spain (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eBaseline characteristics of patients with reported renal vascular lesions in the FAERS database (2004\u0026ndash;2024).\u003c/b\u003e Data are presented as number (percentage, %) for categorical variables and mean (standard deviation, SD) or median [minimum, maximum] for continuous variables. A total of 19,231 reports were included in the analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRenal vascular lesion (N\u0026thinsp;=\u0026thinsp;19231)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10163 (52.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8941 (46.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.7 (20.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.0 [0, 100]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.5 (25.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.0 [0, 200]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOccupation of the reporter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9107 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsumer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3671 (19.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther health-professional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2477 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Professional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1298 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePharmacist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e938 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLawyer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e492 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegistered Nurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1242 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCountry of the reporter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8186 (42.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2577 (13.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1175 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e950 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e819 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e792 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e737 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e443 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrazil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e238 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTemporal analysis from Q1 2004 to Q4 2024 showed an overall upward trend in reporting with interannual fluctuations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Age-stratified data (excluding 640 records with missing age, n\u0026thinsp;=\u0026thinsp;18,591) indicated the highest reporting frequency in the 50\u0026ndash;75 year group (53.4%, n\u0026thinsp;=\u0026thinsp;9,936). Among outcomes (n\u0026thinsp;=\u0026thinsp;19,231), hospitalization was most common (43.3%, n\u0026thinsp;=\u0026thinsp;8,339), followed by death (9.7%, n\u0026thinsp;=\u0026thinsp;1,866) and life-threatening events (5.9%, n\u0026thinsp;=\u0026thinsp;1,131). When stratified by body-weight quartiles (excluding 472 records with missing weight, n\u0026thinsp;=\u0026thinsp;18,759), the lowest quartile (\u0026lt;\u0026thinsp;57 kg) accounted for the largest share of reports (26.2%, n\u0026thinsp;=\u0026thinsp;4,921).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCountry-level age distributions revealed notable variation: median age was highest in Japan and lowest in the United States, with bimodal patterns observed in several European countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Drugs associated with renal vascular lesions\u003c/h2\u003e \u003cp\u003eWe restricted signal detection to drugs with \u0026ge;\u0026thinsp;50 reports and applied four disproportionality metrics (ROR, PRR, MGPS/EBGM, BCPNN/IC) together with Bonferroni-adjusted χ\u0026sup2; tests. Nineteen drugs met pre-specified signal criteria (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and were entered into multivariable logistic regression models to adjust for reporting and patient covariates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe resulting signals spanned multiple therapeutic classes, including nonsteroidal anti-inflammatory agents, tyrosine kinase inhibitors, antiviral combinations, radiographic contrast media, CAR-T products and selected psychotropic and cardiac agents. Notably, rofecoxib and several biologic/CAR-T agents showed strong disproportionality estimates, with imatinib, tenofovir disoproxil (and tenofovir-containing combinations), quetiapine and certain kinase inhibitors also demonstrating robust signals after unadjusted screening. Multivariable adjustment refined these associations and helped prioritize agents for experimental follow-up (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eNineteen drugs significantly associated with renal vascular disorders identified by disproportionality analysis.\u003c/b\u003e Signals were generated based on four algorithms (ROR, PRR, BCPNN, MGPS) and a case report count of \u0026ge;\u0026thinsp;50. The table presents the number of case reports and the corresponding statistical measures for each drug, including the lower bounds of the 95% confidence interval (CI) for ROR and PRR, the 5th percentile of the Empirical Bayesian Geometric Mean (EBGM05), the 2.5th percentile of the Information Component (IC025), and adjusted p-values. Abbreviations: ROR, reporting odds ratio; PRR, proportional reporting ratio; EBGM, empirical Bayesian geometric mean; IC, information component; Cl, confidence interval.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003cp\u003eReports\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eROR.(95.Cl.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePRR (95.Cl.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePRR-χ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEBGM.(EBGM05.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIC.(IC025.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003epvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep_adjust\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROFECOXIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.82 ( 14.01\u0026ndash;15.68 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.69 ( 13.64\u0026ndash;13.74 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15531.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.76 ( 12.17 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.67 ( 3.59 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQUETIAPINE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.47 ( 4.09\u0026ndash;4.89 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.91 ( 5.82\u0026ndash;5.99 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2253.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.29 ( 3.98 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.1 ( 1.97 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMATINIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.76 ( 4.93\u0026ndash;6.74 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.38 ( 4.29\u0026ndash;4.46 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1294.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.55 ( 4.87 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.47 ( 2.24 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.3374E-283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.1214E-279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOLANZAPINE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.43 ( 2.07\u0026ndash;2.86 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.59 ( 8.49\u0026ndash;8.68 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2544.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.4 ( 2.1 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.26 ( 1.03 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROSIGLITAZONE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.38 ( 2.02\u0026ndash;2.79 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.1 ( 4.99\u0026ndash;5.22 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e945.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.35 ( 2.05 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.23 ( 0.99 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0312E-206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.2385E-203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBOTULINUM TOXIN TYPE A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.02 ( 2.45\u0026ndash;3.72 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.58 ( 2.46\u0026ndash;2.71 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e247.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.97 ( 2.5 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.57 ( 1.27 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.58545E-55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.90412E-52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIGOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.22 ( 4.18\u0026ndash;6.51 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.32 ( 3.17\u0026ndash;3.46 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e292.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.06 ( 4.21 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.34 ( 2.02 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.88804E-65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.87054E-62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAZOPANIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.85 ( 3.09\u0026ndash;4.8 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.07 ( 3.92\u0026ndash;4.22 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e402.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.77 ( 3.13 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.91 ( 1.59 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.3955E-89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0083E-85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCABOZANTINIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.45 ( 2.76\u0026ndash;4.3 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.59 ( 5.44\u0026ndash;5.74 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e617.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.38 ( 2.81 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.76 ( 1.43 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.9555E-135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.5495E-132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEMTRICITABINE;TENOFOVIR DISOPROXIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.71 ( 3.77\u0026ndash;5.88 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.41 ( 2.25\u0026ndash;2.57 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e124.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.58 ( 3.8 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.2 ( 1.87 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.30355E-28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.56557E-25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTENOFOVIR DISOPROXIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.21 ( 6.53\u0026ndash;10.33 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.36 ( 2.2\u0026ndash;2.52 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e117.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.82 ( 6.45 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.97 ( 2.63 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.78494E-27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.54571E-24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTISAGENLECLEUCEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.41 ( 9.82\u0026ndash;15.68 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.9 ( 2.74\u0026ndash;3.06 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e185.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.52 ( 9.47 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.53 ( 3.18 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0045E-41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.2064E-38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIOPROMIDE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.42 ( 3.48\u0026ndash;5.63 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.06 ( 3.9\u0026ndash;4.22 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e343.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.32 ( 3.53 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.11 ( 1.76 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.94098E-76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.13512E-73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAXICABTAGENE CILOLEUCEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.35 ( 3.37\u0026ndash;5.62 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.57 ( 2.41\u0026ndash;2.74 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e138.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.25 ( 3.44 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.09 ( 1.72 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.28008E-31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.53737E-28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIBAVIRIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.59 ( 2\u0026ndash;3.34 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.9 ( 4.74\u0026ndash;5.07 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e419.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.56 ( 2.07 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.35 ( 0.98 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.62931E-92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.1578E-89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEFAVIRENZ;EMTRICITABINE;TENOFOVIR DISOPROXIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.88 ( 6.04\u0026ndash;10.29 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.45 ( 4.29\u0026ndash;4.62 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e361.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.53 ( 6.02 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.91 ( 2.52 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.67685E-80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.2199E-77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALEMTUZUMAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.35 ( 3.32\u0026ndash;5.69 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.6 ( 2.42\u0026ndash;2.77 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e122.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.25 ( 3.39 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.09 ( 1.7 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.32761E-28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.99646E-25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGALSIDASE BETA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.04 ( 2.32\u0026ndash;3.99 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.31 ( 3.12\u0026ndash;3.5 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e169.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3 ( 2.39 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.58 ( 1.19 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.68348E-38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.42386E-35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANTITHYMOCYTE IMMUNOGLOBULIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.6 ( 2.75\u0026ndash;4.72 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.98 ( 2.78\u0026ndash;3.19 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e120.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.54 ( 2.82 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.82 ( 1.43 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.69174E-27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.03178E-24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Time-to-onset patterns across implicated drugs\u003c/h2\u003e \u003cp\u003eTime-to-onset distributions for the 19 signaled drugs revealed two distinct temporal patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). One cluster exhibited ultra-acute onset (median\u0026thinsp;\u0026asymp;\u0026thinsp;1 day), exemplified by iopromide and CAR-T products (tisagenlecleucel, axicabtagene ciloleucel), consistent with immediate infusion-related, inflammatory or hemodynamic mechanisms[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A second cluster showed delayed onset (median latency often\u0026thinsp;\u0026gt;\u0026thinsp;100 days), including rofecoxib, tenofovir disoproxil (and tenofovir-containing regimens), several psychotropic agents, olanzapine/ quetiapine, rosiglitazone and digoxin\u0026mdash;patterns compatible with cumulative toxicity, long-term hemodynamic effects, or indirect metabolic pathways[\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese latency patterns provide mechanistic clues that may guide clinical vigilance: agents in the ultra-acute cluster warrant close monitoring around the time of administration, whereas drugs in the delayed cluster may require longer-term surveillance for progressive renovascular compromise.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4. In vitro and short-term in vivo assessment of endothelial cytotoxicity induced by FAERS-identified high-risk drugs\u003c/h2\u003e \u003cp\u003eWe selected imatinib, rofecoxib and tenofovir disoproxil for experimental validation and treated HEK293A (293A) renal epithelial cells and human umbilical vein endothelial cells (HUVEC) with a concentration gradient for 48 h; outcomes included bright-field morphology, CCK-8 viability, FITC/PI apoptosis/necrosis and JC-1 mitochondrial membrane potential assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). All three agents produced dose-dependent cytopathic changes and reductions in metabolic viability in both cell types, but morphological disruption (cell rounding, detachment, monolayer loss) and viability loss were consistently greater in HUVEC than in 293A (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u0026ndash;E). FITC/PI flow cytometry demonstrated drug- and dose-specific increases in early and late apoptotic/necrotic fractions relative to DMSO controls: high-dose imatinib preferentially increased PI-positive late death in HUVEC, rofecoxib showed a mixed early/late apoptotic pattern that varied with dose, and tenofovir disoproxil induced progressive apoptotic enrichment at higher concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). JC-1 ratiometric analysis confirmed dose-dependent mitochondrial depolarization with both imatinib and rofecoxib, the decline in red/green ratio being substantially larger in HUVEC than in 293A (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB\u0026ndash;C), consistent with mitochondrial dysfunction contributing to endothelial injury. To provide preliminary in vivo correlation, C57BL/6 mice received daily intraperitoneal injections of imatinib, rofecoxib or tenofovir disoproxil for one week and kidneys were examined by H\u0026amp;E (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Control kidneys showed intact capillaries and small veins without congestion, perivascular hemorrhage or obvious thrombi (baseline). Imatinib-treated kidneys demonstrated marked congestion of small vessels and capillary clusters, focal capillary dilation and traces of peritubular/interstitial erythrocyte extravasation; no organized fibrin thrombi were identified on H\u0026amp;E, recognizing that H\u0026amp;E has limited sensitivity for early or microthrombi. Rofecoxib treatment produced moderate capillary congestion with scattered focal extravasation. Tenofovir treatment led to perivascular inflammatory cell infiltrates adjacent to small vessels, focal congestion and small hemorrhagic foci, consistent with a perivascular/venular inflammatory response.\u003c/p\u003e \u003cp\u003eThe in vitro and short-term in vivo data provide concordant evidence that these agents can compromise endothelial integrity under defined exposure conditions, supporting biological plausibility for the FAERS renovascular signals while acknowledging that in vitro toxic concentrations exceed typical clinical plasma levels and that immune-mediated or hemodynamic mechanisms may also contribute in patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.5. GEO-based transcriptional evidence linking drug exposures to vascular adhesion and junctional programs\u003c/h2\u003e \u003cp\u003eTo seek independent transcriptomic support for the experimentally observed vascular effects, we analyzed two public GEO datasets: GSE59671 (human aortic smooth muscle cells, rofecoxib 24 h) and GSE137534 (isolated vascular fragments from imatinib-treated mice, 24 h). In GSE59671, rofecoxib exposure was associated with upregulation of individual transcripts (notably PDE4B, CRISPLD2 and NPR3) and with coordinated downregulation of gene sets involved in cell\u0026ndash;cell adhesion and junction organization (e.g., GO:0016338, GO:0045216), as indicated by negative normalized enrichment scores and significant adjusted p/q values(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-E, Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). GSE137534 (vascular fragments after imatinib) showed transcriptional remodeling of pathways related to adenylate cyclase signaling and integrin-mediated adhesion, consistent with altered vascular signaling and junctional regulation. Collectively, the GEO signatures corroborate our experimental observations by linking rofecoxib and imatinib exposure to perturbation of adhesion/junctional and related signaling programs in vascular cells, thereby providing an independent, dataset-level rationale for endothelial dysfunction as a mechanistic contributor to drug-associated renovascular lesions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.6. qPCR validation of GEO-selected candidate genes in 293A and HUVEC cells\u003c/h2\u003e \u003cp\u003eWe selected representative GEO-derived candidates for targeted qPCR validation: PDE4B, CRISPLD2 and NPR3 from the rofecoxib signature, and ADCY family members and KNN4 from the imatinib signature.\u003c/p\u003e \u003cp\u003eRofecoxib exposure reproducibly increased CRISPLD2 expression in both 293A and HUVEC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), in agreement with the GEO observation(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-B). By contrast, PDE4B and NPR3 did not reach statistical significance in our cell models, suggesting that their modulation may be context-dependent or below the detection threshold in these in vitro systems( Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-B). Imatinib elicited cell-type specific transcriptional responses: both ADCY (adenylate cyclase) family transcripts and KNN4 were downregulated in 293A, whereas in HUVEC ADCY expression decreased but KNN4 expression increased (all significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) ( Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC-D). These divergent directions between epithelial and endothelial cells indicate cell-type specific regulation and reinforce the notion that drug-induced transcriptional remodeling of adhesion and signaling programs can differ across vascular versus parenchymal compartments. The qPCR results provide targeted experimental confirmation for selected GEO signals (notably CRISPLD2 and ADCY-related perturbation) while highlighting gene- and cell-specific effects that warrant further mechanistic follow-up.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, FAERS database and multiple signal detection algorithms were used to screen 19 drug signals significantly associated with renal vascular injury, and the occurrence time sequence characteristics of these signals were analyzed. The results showed that these drugs can be divided into two groups: one is the \"hyper-acute\" drugs (e.g., CAR-T cell therapy products, iodinated contrast media), whose adverse event onset latency is very short (median about 1 day); The other group is \"delayed\" drugs (such as non-steroidal anti-inflammatory drug rofecoxib, antiviral drug tenofovir and some psychotropic drugs), showing a long incubation period of several months or even years. Contrast agents are known to rapidly damage renal microcirculation by inducing renal vasoconstriction and red blood cell aggregation, whereas CAR-T therapy is often accompanied by cytokine release syndrome, which leads to systemic inflammation and endothelial activation, thereby inducing renal vascular injury in a very short time[\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Drugs such as rofecoxib and tenofovir often develop vascular lesions after long-term use[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This may be related to their accumulation in the kidney or their mediating chronic immune damage.\u003c/p\u003e \u003cp\u003eIn vitro experiments further revealed the difference in toxicity between the above-mentioned high-risk drugs on vascular endothelial cells (HUVEC) and renal epithelial cells (293A). Under the same exposure concentration and time, the survival rate of HUVEC decreased and the proportion of apoptosis was higher than that of 293A cells, which indicated that endothelial cells were more sensitive to these drugs. Specifically, high concentration of imatinib significantly induced PI-stained cell death in HUVEC; Rofecoxib induced HUVEC apoptosis at the early stage. Tenofovir also induced early apoptosis in huvecs in a dose-dependent manner. Mechanistically, imatinib, as a PDGF/VEGF receptor inhibitor, may disrupt the signaling pathways of endothelial cell proliferation and survival and aggravate apoptosis[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Rofecoxib not only has endothelial toxicity, but also induces vasoconstriction and procoagulability by inhibiting prostaglandin I₂ production[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Tenofovir, on the other hand, is known to accumulate in the kidney, causing mitochondrial dysfunction and oxidative stress, leading to apoptosis[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These results preliminarily demonstrated the biological feasibility of some FAERS signaling drugs to directly damage the endothelium under fully exposed conditions. It should be noted that in vitro toxicity experiments often use drug concentrations that are higher than clinical blood concentrations, so these results mainly address the potential for direct endothelial cell damage at high or cumulative exposures, rather than correspond quantitatively to risk in humans.\u003c/p\u003e \u003cp\u003eShort-term in vivo administration experiments in mice provided pathological support for the in vitro results. Small vessel congestion, capillary clumps and focal erythrocyte exudation were observed in the kidneys of imatinib-treated mice. Moderate capillary congestion with scattered oozing foci was observed in rofecoxib group. Perivascular infiltration of inflammatory cells and focal hemorrhagic foci were observed in the tenofovir group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). These morphological changes suggest that these drugs can cause microvascular structural disruption, local hyperemia, and inflammatory cell exudation at high doses of short-term exposure. Previous studies have shown that long-term rofecoxib treatment can enhance thrombophilia in mice (shortening the occlusion time of the venous model), consistent with our observation of vascular congestion and exudation[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These in vivo results are consistent with the impairment of endothelial barrier function seen in vitro, suggesting that these drugs may promote renal vasculopathy by inducing endothelial cell injury and inflammatory response in vivo.\u003c/p\u003e \u003cp\u003eTranscriptome analysis of GEO database further revealed the disorder of drug regulation on vascular adhesion/connection pathway. In HASMC cells, rofecoxib treatment for 24 hours resulted in significant down-regulation of several cell-cell adhesion and cell-matrix adhesion related genes. In the cerebrovascular segment, imatinib also perturbed integrin-mediated signaling pathways. These results suggest that the junctional structure between vascular cells may be relaxed or dissociated after the treatment of these drugs, which in turn weakens the integrity of the endothelial barrier. Our GEO and cellular results suggest that drug-induced downregulation of connexin may provide a molecular basis for the loss of adhesion and susceptibility to apoptosis observed in endothelial cells in vitro.\u003c/p\u003e \u003cp\u003eqPCR results verified the expression changes of key molecules in the above transcriptomic findings in the cell model. After rofecoxib treatment, CRISPLD2 was significantly up-regulated in 293A and HUVEC. ADCY was significantly downregulated in both cell types after imatinib treatment, whereas KCNQ4 was significantly upregulated in HUVEC. All of these molecules are involved in signaling pathways related to endothelial function. ADCY encodes adenylate cyclase, and its down-regulation may lead to a decrease in cAMP production, thereby inhibiting the cAMP/PKA pathway, which is important for maintaining endothelial integrity and anti-inflammatory response[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. KCNQ4 is a subunit of voltage-gated potassium channel[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Changes in its expression may indirectly affect endothelial tone and signal transduction by regulating membrane potential and intracellular Ca\u0026sup2;⁺ homeostasis. As a secreted protein, CRISPLD2 interacts with inflammatory mediators, and its up-regulation or reflection reflects the feedback regulatory response of cells to drug stress[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Taken together, the dysregulation of these molecules further suggests the disruption of intercellular connectivity and signaling networks, providing a molecular basis for drug-induced endothelial dysfunction.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered with caution. First, FAERS, as a spontaneous adverse event reporting system, has its own problems of reporting bias, incomplete information, and lack of a medication denominator. Secondly, the representativeness of in vitro models is limited. Although HUVEC is a classic endothelial cell model, it cannot reflect the particularity of molecular and microenvironment of renal microvessels, such as glomerular and medullary vessels. 293A cells are embryonical-derived renal epithelial cells, which cannot completely represent the physiological state of mature renal tubules or podocytes. Further validation of the signals and mechanisms in more physiologically relevant systems, such as primary renal microvascular endothelium, co-culture systems, renal organoids, or animal models, is warranted. In addition, the mouse model in this study was only a short-term high-dose exposure and could not fully simulate the chronic cumulative toxicity process. Future studies should consider different doses, different routes of administration, and long-term observation to more comprehensively evaluate the effects of drugs on renal vascular structure and function.\u003c/p\u003e \u003cp\u003eThrough large-scale FAERS signal analysis combined with in vitro/in vivo experimental verification, this study systematically identified and clarified the association and potential mechanism of multiple drugs with renal vascular injury, and revealed the heterogeneity of related drugs in the time sequence of action. The results of cell and animal experiments demonstrated that the selected representative drugs were able to directly damage the vascular endothelium under certain conditions, providing support for the biological feasibility behind FAERS signaling. Despite the limitations, these findings provide a reference for future research on the mechanisms and risk prediction of drug-induced renal vasculopathy.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this integrated pharmacovigilance and experimental study, FAERS signal mining (2004\u0026ndash;2024) identified nineteen drugs disproportionately associated with drug-related renal vascular lesions and revealed a bimodal time-to-onset pattern distinguishing hyperacute (e.g., CAR-T, contrast media) from delayed (e.g., rofecoxib, tenofovir disoproxil) presentations. Targeted in vitro and short-term in vivo experiments demonstrated that imatinib, rofecoxib and tenofovir disoproxil can compromise endothelial viability and induce vascular structural changes under defined exposure conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical statement\u003c/h2\u003e \u003cp\u003e All animal experiments were conducted in strict accordance with the guide for the Care and Use of Laboratory Animals (NIH, USA) and were approved by the Institutional Animal Care and Use Committee of Shanghai Novopathway Biotechnology Co., Ltd. (Approval No.: P0820251216A).\u003c/p\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare that there are no actual or potential conflicts of interest related to this study.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China [Grant Number 8247114926].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH.H. conceptualized the study and developed the methodology; Y.Y. developed the software; H.H., Y.Y., and X.Y. performed validation and conducted formal analysis; J.J. conducted investigation; J.R. curated data; H.H. wrote the original draft; L.Z. reviewed and edited the manuscript; P.Z. prepared visualizations; Z.Z. supervised the research; L.Z. administered the project; and J.J. acquired funding. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors extend their gratitude to the U.S. FDA for providing access to the FAERS database, which made this pharmacovigilance study possible. We thank all the clinicians, researchers, and patients whose contributions to adverse event reporting underpin the data used in this analysis. We also acknowledge the technical support from our institutional core facilities for their assistance with cell culture and flow cytometry experiments.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003ehttps://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCampbell RE, Chen CH, Edelstein CL (2023) Overview of antibiotic-induced nephrotoxicity. 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Accessed 18 Dec 2025\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Drug-induced renal vascular disorders, Pharmacovigilance, FAERS database, Signal detection, Time-to-onset analysis, Adverse drug reactions","lastPublishedDoi":"10.21203/rs.3.rs-8665668/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8665668/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eDrug-related renal vascular lesions are an underrecognized cause of severe nephrotoxicity. We aimed to map drug signals for renal vascular injury in FAERS and to provide experimental evidence for plausible vascular mechanisms.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe extracted 19,231 FAERS reports (Q1 2004\u0026ndash;Q4 2024) and applied four disproportionality algorithms (ROR, PRR, MGPS, BCPNN) plus χ\u0026sup2; screening and multivariable logistic regression to prioritize suspect agents. Time-to-onset and reporting characteristics were described. Three high-risk drugs (imatinib, rofecoxib, tenofovir disoproxil) were tested in vitro on HUVECs and HEK293A cells using CCK-8 viability and FITC/PI apoptosis assays; short-term intraperitoneal dosing in mice provided preliminary histologic correlation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNineteen drugs met signal criteria. Time-to-onset analysis revealed a bimodal distribution: an \u0026ldquo;ultra-acute\u0026rdquo; cluster (median\u0026thinsp;\u0026asymp;\u0026thinsp;1 day; e.g., CAR-T products, contrast agents) and a delayed cluster (months\u0026ndash;years; e.g., rofecoxib, tenofovir). In vitro, all three tested drugs produced concentration- and time-dependent reductions in cell viability and increased apoptotic/necrotic fractions, with endothelial (HUVEC) cells more susceptible than renal epithelial (293A) cells. Short-term murine kidney histology showed small-vessel congestion, focal hemorrhage, and perivascular inflammation after drug exposure.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCombined pharmacovigilance and experimental data identify candidate drugs associated with renal vascular lesions and provide preliminary mechanistic plausibility for direct endothelial injury. These findings support tailored clinical vigilance and prioritization of mechanistic and prospective validation studies.\u003c/p\u003e","manuscriptTitle":"Pharmacovigilance Study of Drug-Related Renal Vascular Lesions Based on FAERS and Experimental Exploration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-03 14:43:11","doi":"10.21203/rs.3.rs-8665668/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c63a6782-597d-400a-83a3-3298dfa609ec","owner":[],"postedDate":"April 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-11T17:54:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-03 14:43:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8665668","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8665668","identity":"rs-8665668","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}