Evaluation of Everolimus Pharmacokinetic Monitoring Based on Trough Concentration and Area Under the Blood Concentration Time Curve in Kidney Transplantation

Evaluation of Everolimus Pharmacokinetic Monitoring Based on Trough Concentration and Area Under the Blood Concentration Time Curve in Kidney Transplantation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluation of Everolimus Pharmacokinetic Monitoring Based on Trough Concentration and Area Under the Blood Concentration Time Curve in Kidney Transplantation Shota Fukae, Yoichi Kakuta, Soichi Matsumura, Ryo Tanaka, Masataka Kawamura, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7986395/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Jan, 2026 Read the published version in BMC Nephrology → Version 1 posted 12 You are reading this latest preprint version Abstract Backgrounds Everolimus (EVR) is an mTOR inhibitor used in kidney transplantation to minimize calcineurin inhibitor exposure. Although therapeutic drug monitoring (TDM) based on trough concentrations (C0) is standard, the relationship between time-point EVR concentrations, systemic exposure, and metabolic complications of tacrolimus (TAC)-based therapy remains unclear. This study aimed to identify the optimal sampling point that reflects systemic exposure and to evaluate its association with adverse effects. Methods We analyzed 827 pharmacokinetic assessments of 168 kidney transplant recipients who received TAC-based immunosuppression. EVR concentrations were measured at 0 (C0), 1, 2, 3, and 4 hours post-dose, and the area under the concentration–time curve from 0 to 4 hours (AUC₀–₄) was calculated. Correlations between each time point and the AUC₀–₄ were evaluated, and logistic regression adjusted for TAC trough concentration was used to assess the associations between EVR exposure and adverse events, including proteinuria and de novo hyperlipidemia (HL). Results The median participant age was 50 years, and 39.3% of participants were female. Among all time points, C2 showed the strongest correlation with the AUC₀–₄ (r = 0.944, p < 0.001), whereas C0 showed only a moderate association (r = 0.524, p < 0.001). Time-course analysis revealed that peak the concentration typically occurred 2 hours post-dose, although the profiles varied according to the post-transplant duration. EVR AUC₀–₄ was independently and positively associated with the development of de novo HL (adjusted OR 1.02, p = 0.03), whereas higher TAC trough concentrations exhibited a protective effect (adjusted OR 0.84, p = 0.01), suggesting that TAC co-administration may modulate EVR-induced lipid dysregulation. No significant relationship was observed between EVR exposure and proteinuria. Receiver operating characteristic analysis yielded an AUC of 0.603 with an optimal cutoff of 39.9 ng·h/mL for predicting HL. Conclusions C2 most accurately reflected systemic EVR exposure under tacrolimus-based regimens. Higher EVR exposure, as represented by AUC₀–₄ or C2, was associated with an increased risk of de novo hyperlipidemia, whereas higher TAC levels mitigated this effect. These findings support the clinical utility of C2- or AUC-based TDM for individualized EVR dosing to balance the efficacy and safety in kidney transplant recipients. Clinical trial number Not applicable. Area under the concentration-time curve (AUC) Everolimus Kidney transplantation Pharmacokinetics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Everolimus (EVR) is a proliferation-signal inhibitor developed as an immunosuppressive therapy following solid-organ transplantation. Among kidney transplant recipients (KTRs), calcineurin inhibitor (CNI)-associated nephrotoxicity remains a major barrier to long-term graft survival. EVR has emerged as a potential agent to mitigate this complication by minimizing or withdrawing CNI exposure. Additionally, EVR is known to exert a broad range of pharmacological effects, including anti-fibrotic, anti-angiogenic, antiviral, and antitumor activities, making it a uniquely multifunctional agent within immunosuppressive regimens[ 1 – 5 ]. However, the clinical applicability of EVR is limited by its narrow therapeutic window and frequent adverse effects such as proteinuria, hyperlipidemia, oral mucositis, and thrombocytopenia[ 6 ]. Therapeutic drug monitoring (TDM) has been implemented to optimize efficacy while minimizing toxicity, with a recommended target trough concentration of 3.0–8.0 ng/mL[ 7 , 8 ]. Previous studies on cyclosporine-based regimens have demonstrated correlations between EVR trough concentrations and the area under the concentration–time curve (AUC), supporting the clinical use of TDM[ 8 – 11 ]. However, pharmacokinetic data on tacrolimus (TAC) -based therapy, which is currently the standard of care, remains limited. Furthermore, few studies have systematically compared multiple post-dose sampling points or examined the relationship between EVR exposure and adverse events in real-world KTRs. Given the increasing use of TAC-based regimens, a comprehensive understanding of EVR pharmacokinetics and its exposure–response relationship is essential for refining dosing strategies and improving patient outcomes. Accordingly, this study aimed to characterize the post-dose pharmacokinetic profile of EVR, evaluate the correlations between trough and other time-point concentrations and the AUC, and assess the association between EVR exposure and adverse events, particularly proteinuria and de novo hyperlipidemia, in KTRs receiving TAC-based immunosuppression. 2. Methods 2.1. Study population Patients who had undergone kidney transplantation at the University of Osaka between September 1989 and May 2023 were screened for eligibility. Excluded from the study were patients who did not receive EVR as part of their immunosuppressive regimens and among the EVR-treated patients, those who did not undergo pharmacokinetic assessment based on the area under the curve (AUC) from 0 to 4 hours after oral administration. Thus, 168 patients were included in the final analysis. All the patients were managed by a kidney transplantation team at the University of Osaka, Japan. A total of 827 pharmacokinetic assessments of EVR were performed. All participants provided written informed consent, and the study protocol was approved by the Osaka University Hospital Institutional Review Board (approval number 21374). 2.2. Immunosuppression protocol All patients underwent induction immunosuppression with basiliximab. Maintenance immunosuppression consisted of TAC, mycophenolic acid, and corticosteroids, which were tapered over three weeks. EVR was administered as de novo therapy beginning in April 2015 or as an add-on to existing maintenance immunosuppressive regimens in earlier cases. The target trough level for EVR was 3.0–8.0 ng/mL. For each pharmacokinetic assessment, blood samples for both EVR and TAC were obtained simultaneously under stable dosing conditions. To ensure pharmacokinetic consistency, all analyses in this study were restricted to KTRs receiving TAC-based regimens, and patients treated with cyclosporine were excluded. 2.3. Quantification of everolimus EVR blood concentrations were measured using the latex turbidimetric immunoassay method with the Nanopia® TDM Everolimus assay (Sekisui Medical Co., Ltd., Japan). Blood samples were collected during routine outpatient follow-up visits, during the perioperative period of kidney transplantation, or during hospitalization for protocol or indicated biopsies of the allograft kidneys. Sampling time points included immediately before EVR administration (C0; trough level) and at 1, 2, 3, and 4 hours after dosing (C1 to C4). The AUC₀–₄ was calculated using the trapezoidal method based on EVR concentrations measured at C0 to C4. 2.4. Evaluation Correlations between EVR concentrations at each time point (C0–C4) and AUC₀–₄ were analyzed. In addition, the relationship between the EVR and TAC trough concentrations was evaluated. The potential association between EVR exposure and adverse effects such as proteinuria and hyperlipidemia (HL) were also investigated. De novo HL was defined as the initiation of lipid-lowering therapy or a new diagnosis of HL after the introduction of EVR in patients who did not been receive lipid-lowering agents at the time of kidney transplantation. 2.5. Statistical analysis Continuous variables are expressed as median (interquartile range, [IQR]), and categorical variables as counts and percentages. Correlations between EVR concentrations (C0–C4) and AUC₀–₄ were assessed using Spearman’s rank correlation coefficients. Comparisons of continuous variables between the two groups were performed using the Mann–Whitney U test or Student’s t-test, as appropriate. Logistic regression analysis was performed, and multicollinearity was evaluated using the variance inflation factor (VIF); all variables had a VIF < 2. Receiver operating characteristic (ROC) curve analysis was performed, and the optimal cutoff value was determined using Youden’s index. All analyses were performed using the R software (version 4.3.2; R Foundation for Statistical Computing, Vienna, Austria) and JMP Pro (version 17.0; SAS Institute Inc., Cary, NC, USA). P -value < 0.05 was considered statistically significant. 3. Results 3.1. Characteristics of participants The baseline characteristics of the 168 TAC-treated KTRs are summarized in Table 1. A total of 827 pharmacokinetic samples were included in the final analysis. The median age was 50 years (IQR, 39.5–60), and 39.3% of the participants were female. Living-donor transplantation accounted for 94.6% of the cases. The median oral dose of EVR was 2.0 mg/day (IQR, 1.5–2.5), and the median duration of EVR therapy was 2 months (IQR, 0–14). All pharmacokinetic assessments were performed in patients receiving TAC-based immunosuppressive therapy. The most common primary renal disease was IgA nephropathy (21.4%), followed by diabetic nephropathy (17.3%) and chronic glomerulonephritis (16.1%). 3.2. Graft and patient survival and infectious outcomes after kidney transplantation During the follow-up period, the overall survival rate of the study population was 96.4% at both 5 and 10 years. Death-censored graft survival rates were 93.0% and 91.7% at 5 and 10 years, respectively (Figure 1A, 1B). The cumulative incidence of viral infections at 5 years was 9.2% for cytomegalovirus (CMV), 2.2% for BK virus (BKV) nephropathy, and 49.6% for SARS-CoV-2. Although the incidences of CMV and BKV infections remained unchanged after 10 years, the incidence of SARS-CoV-2 infection increased to 75.3% (Figure 1C). 3.3. Correlation between EVR Trough Concentrations and AUC Analysis of the correlation between EVR trough concentrations (C0) and AUC₀–₄ across all data points demonstrated a moderate positive correlation (r = 0.524, p < 0.001) (Figure 2A). In contrast, among the non-trough concentrations, C2 showed a remarkably strong correlation with AUC₀–₄, indicating an exceptionally high association (r = 0.944, p < 0.001) (Figure 2B). Furthermore, the time-course evaluation of EVR blood concentrations (C0–C4) revealed a peak concentration at C2, with a median value of 6.7 ng/mL (Supplemental Figure S1). 3.4. Comparison of EVR pharmacokinetics Stratified by 3-Month Post-Transplant Period We stratified patients according to EVR treatment duration (<3 months vs. ≥3 months) and performed subgroup analyses. Among the 827 samples analyzed, 420 were obtained from patients receiving EVR for <3 months and 407 from those treated for ≥3 months. The correlation between EVR C0 and AUC₀–₄ was comparable between groups, with correlation coefficients of r = 0.662 and r = 0.587, respectively (both p < 0.001) (Figure 3A, 3C). In contrast, the concentration–time profiles differed between the groups. In the <3-month group, the highest median concentrations were observed at C3 and C4 (both 5.8 ng/mL), with median values of 3.6, 4.3, and 5.4 ng/mL at C0, C1, and C2, respectively. In the ≥3-month group, the peak concentration occurred at C1 (median 11.2 ng/mL), followed by 8.6, 6.8, 5.7, and 3.3 ng/mL at C2, C3, C4, and C0, respectively (Figure 3B, 3D). When stratified by TAC trough levels, patients treated with EVR for ≥3 months had significantly higher TAC troughs than those treated for <3 months (median 5.8 vs. 4.2 ng/mL, p 5 ng/mL showed significantly higher 1 hour post-dose EVR concentrations than those with TAC ≤5 ng/mL (median 8.8 vs. 5.4 ng/mL, p < 0.05, t-test) (Supplemental Figure S2B). 3.5. Relationship between EVR exposure and urinary protein levels The relationship between urinary protein levels, a known adverse effect of EVR, and AUC₀–₄ was investigated in patients with available urine protein/creatinine ratio data. No significant correlation was observed between the EVR AUC₀–₄ and urinary protein/creatinine ratio in the overall cohort or in the subgroup of patients treated for more than three months (both p > 0.05) (Figure 4). Similarly, no correlation was found in the 0.05, Supplemental Figure S3). 3.6. EVR exposure and de novo hyperlipidemia The association between EVR exposure and the development of de novo HL was evaluated in patients without preexisting HL. In univariate logistic regression analysis, both EVR AUC₀–₄ and TAC trough concentrations were significantly associated with de novo HL (Table 2). In multivariable analysis including EVR AUC₀–₄, EVR trough concentration, and TAC trough concentration, both EVR AUC₀–₄ (OR 1.02, 95% CI 1.00–1.04, p = 0.03) and TAC trough concentration (OR 0.84, 95% CI 0.74–0.96, p = 0.01) remained independent factors. Recipients who developed de novo HL had significantly higher EVR AUC₀–₄ values compared with those who did not (p < 0.05, Figure 5B). The EVR trough concentration (C0) showed no significant association (p = 0.51, Figure 5A). ROC analysis determined an optimal cutoff value of 39.9 ng·h/mL for EVR AUC₀–₄ (Figure 5C). These findings suggest that systemic EVR exposure, rather than the trough concentration, contributes to the risk of hyperlipidemia. 4. Discussion In the present study, we comprehensively evaluated the pharmacokinetics and clinical relevance of EVR in KTRs receiving tacrolimus-based immunosuppressive therapy. We demonstrated that C2 showed the strongest correlation with AUC₀–₄, indicating that C2 is a more accurate surrogate marker for systemic exposure than the conventional trough concentration (C0). The timing of the peak concentration also varied according to the post-transplantation period, suggesting that EVR pharmacokinetics dynamically change with time after transplantation. These findings provide practical insights for optimizing TDM in tacrolimus-based regimens. Previous studies, including those by Kovarik and Budde, have reported correlations between EVR trough concentrations and AUC in kidney transplantation under cyclosporine-based regimens[9, 11]. However, the pharmacokinetic profiles of tacrolimus-based therapy have not been systematically characterized. Unlike prior model-based or cyclosporine-focused studies, our real-world dataset of 827 pharmacokinetic assessments from 168 tacrolimus-treated recipients directly compared multiple sampling points (C0–C4) with AUC₀–₄, therapy confirming the superiority of C2 as a practical surrogate of systemic exposure. Serial measurements are essential for precise immunosuppressive management, because inter-individual variability in drug metabolism often causes fluctuations in EVR levels[12]. Our findings reaffirm that although C0 moderately correlates with AUC₀–₄, C2 most accurately reflects systemic exposure and may serve as an optimal limited-sampling point for TDM in clinical practice. Mechanistically, EVR exerts its immunosuppressive effects by binding to FKBP12[13], a mechanism similar to that of TAC[14]. Previous studies have reported that EVR and TAC exhibit antagonistic pharmacokinetic interactions[15] and that a lower dose-to-concentration ratio of TAC is associated with decreased clearance and elevated blood concentrations of EVR[16]. These findings are consistent with our current results, suggesting that reduced TAC exposure may enhance the pharmacological activity of EVR. HL is a common complication in KTRs that contributes to cardiovascular disease and significantly affects patient outcomes[17]. EVR modulates the expression of enzymes involved in lipid metabolism, such as lipoprotein lipase, thereby impairing the clearance of plasma lipids by adipose tissue, resulting in hypercholesterolemia[18, 19]. A meta-analysis indicated that therapy with mammalian target of rapamycin inhibitors (mTORi) combined with mycophenolic acid doubled the risk of hyperlipidemia compared to CNI-based regimens[20]. Furthermore, lipid-lowering agents are prescribed approximately 60% more frequently to patients receiving mTORi-based regimens than in those receiving other therapies[21]. Optimizing the EVR dose while considering immunological risks is a rational therapeutic strategy for managing hyperlipidemia in KTRs. In our cohort, both EVR AUC₀–₄ and TAC trough concentrations were independently associated with the development of de novo HL, whereas EVR trough concentrations (C0) were not. These results suggest that systemic exposure, rather than a single time-point levels, contributes to lipid dysregulation, and that TAC co-administration may modulate this effect by influencing EVR pharmacokinetics. This protective association with higher TAC levels may be partly explained by pharmacokinetic interactions through shared CYP3A metabolism or by balanced immunosuppressive effects that indirectly stabilize lipid homeostasis. The association between EVR exposure and proteinuria was insignificant, possibly reflecting the multifactorial nature of proteinuria after kidney transplantation, which involves both immunological and nonimmunological mechanisms. Although our study did not identify a direct exposure–response relationship for proteinuria, the findings reinforce the clinical importance of individualized dosing strategies that consider both efficacy and adverse events. This study had several limitations. This was a single-center retrospective analysis conducted in a predominantly Japanese cohort, which may have limited the generalizability of our findings. Direct measurement of AUC₀–₂₄ was not feasible, although previous studies have demonstrated a strong correlation between AUC₀–₄ and AUC₀–₂₄. Additionally, we did not assess the intracellular pharmacodynamics or FKBP12-binding kinetics, which could further elucidate the mechanistic basis of TAC–EVR interactions. 5. Conclusion Under tacrolimus-based immunosuppression, C2 most accurately reflects systemic EVR exposure and may serve as a reliable single sampling point for TDM. Higher EVR AUC₀–₄ was associated with hyperlipidemia, whereas higher TAC trough levels were protective, suggesting an exposure-dependent and interaction-modulated risk. These findings highlight the potential of C2- or AUC-based TDM as a practical and individualized approach for optimizing EVR therapy in kidney transplant recipients. Abbreviations AUC; area under the concentration–time curve CNI; calcineurin inhibitor EVR; everolimus HL; hyperlipidemia IQR; interquartile range KTR; kidney transplant recipient mTORi; mammalian target of rapamycin inhibitors ROC; receiver operating characteristic TAC; tacrolimus TDM; therapeutic drug monitoring VIF; variance inflation factor Declarations Ethics approval and consent to participate: Approval was obtained from the Institutional Review Board (Osaka University Hospital Institutional Review Board, protocol number: 21374) before initiating the study, and all patients provided written informed consent. The study was conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication: Written informed consent for publication was obtained from all the participants. Availability of data and materials: The datasets used and analyzed during this study are available from the corresponding author upon reasonable request. Conflict of interest : The authors declare no conflicts of interest. F unding : The authors have received no specific funding for this study. Authors Contributions: Conceptualization, SF and YK; Investigation, SF; Formal analysis, YK; Data curation, SM; Project administration, YK, SM, RT, MK, and SN; Visualization, SF; Writing—original draft, SF; Writing—review and editing, YK and NN; Supervision, NN. Acknowledgements : We thank Shiro Takahara (Kansai Medical Hospital) and Nobumasa Fujimoto (Takatsuki General Hospital) for their technical support. References Guba M, von Breitenbuch P, Steinbauer M, Koehl G, Flegel S, Hornung M, et al. Rapamycin inhibits primary and metastatic tumor growth by antiangiogenesis: involvement of vascular endothelial growth factor. Nat Med. 2002;8:128–35. https://doi.org/10.1038/nm0202-128. Contreras AG, Dormond O, Edelbauer M, Calzadilla K, Hoerning A, Pal S, et al. mTOR—Understanding the Clinical Effects. 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Am J Transplant. 2008;8:1384–92. https://doi.org/10.1111/j.1600-6143.2008.02272.x. Tables Table 1: Characteristics of participants and pharmacokinetic samples Participants (n = 168) Age 50 (39.5 - 60) Sex, n (%) Female Male 66 (39.3) 102 (60.7) Living/Deceased, n(%) 159 (94.6%) / 9 (5.4%) Primary disease, n (%) IgA nephropathy Diabetic nephropathy Chronic glomerulonephritis Polycystic kidney disease Renal sclerosis RPGN (excluding IgA nephropathy) Lupus nephritis FSGS Renal Dysplasia/Hypoplasia Interstitial nephritis Fanconi syndrome Others † Unknown 36 (21.4) 29 (17.3) 27 (16.1) 16 (9.5) 9 (5.4) 6 (3.6) 5 (3.0) 4 (2.4) 4 (2.4) 3 (1.8) 2 (1.2) 6 (3.6) 20 (11.9) Pharmacokinetic samples (n = 827) EVR oral dosage (mg/day) 2.0 (1.5 – 2.5) Duration of taking EVR (Month) 2 (0 - 14) TAC trough (ng/ml) 5.0 (3.8 - 6.8) Data are presented as median (interquartile range) or number (%). RPGN, rapidly progressive glomerulonephritis; FSGS, Focal segmental glomerulosclerosis † One case each of: Reflux nephropathy, sponge kidney, gouty kidney, Alport syndrome, nephronophthisis, and aristolochic acid nephropathy Table 2 : Logistic regression analyses identifying factors associated with de novo HL . Variable Univariate regression Multivariate analysis OR (95% CI) p-value OR (95% CI) p-value Age 1.00 (0.98-1.02) 0.99 Female 1.15 (0.76-1.76) 0.51 TAC C0 0.828 (0.73-0.93) 0.01 0.84 (0.74-0.96) 0.01 EVR C0 0.966 (0.84-1.10) 0.62 0.94 (0.78-1.13) 0.51 EVR AUC ₀ – ₄ 1.01 (1.00-1.03) 0.03 1.02 (1.00-1.04) 0.03 Urinary protein 0.828 (0.509-1.19) 0.38 OR, odds ratio; CI, confidence interval; EVR, everolimus; TAC, tacrolimus. Additional Declarations No competing interests reported. Supplementary Files Supplementalmaterials.pdf Supplemental materials Supplemental Figure S1: Distribution of everolimus (EVR) blood concentrations at each sampling time point (C0–C4) Violin plots showing the distribution of EVR concentrations at the trough (C0) and 1-, 2-, 3-, and 4- hours post-dose (C1–C4). Horizontal bars indicate median values, and boxes represent interquartile ranges. Whiskers denote the 10th–90th percentiles. The figure shows that the EVR concentrations peaked at C2, with a gradual decline thereafter. The median concentrations at each time point were as follows: C0: 3.5 ng/mL, C1: 6.5 ng/mL, C2: 6.7 ng/mL, C3: 6.3 ng/mL, and C4: 5.7 ng/mL. Supplemental Figure S2: Association between TAC trough levels and EVR concentrations. (A) Violin plots comparing TAC trough levels between patients treated with EVR for <3 months (<3 M, red) and ≥3 months (≥3 M, green). Patients receiving EVR for ≥3 months exhibited significantly higher TAC trough concentrations than those treated for <3 months (median 5.8 vs. 4.2 ng/mL, p 5 ng/mL, red; TAC ≤ 5 ng/mL, blue). Patients with higher TAC trough levels demonstrated significantly elevated EVR concentrations at 1-hour post-dose (C1) compared with those with lower TAC levels (mean 8.8 vs. 5.4 ng/mL, p < 0.05, t -test). The error bars represent the standard error (SE). * p < 0.05; ** p < 0.01. Supplemental Figure S3: Association between EVR AUC₀–₄ and urinary protein receiving EVR for less than 3 months Scatter plot illustrating the correlation between AUC₀–₄ and the urine protein-to-creatinine ratio in patients receiving EVR for less than 3 months. r , Spearman’s correlation coefficient with the corresponding p value. Supplemental Table S1: Characteristics of de novo hyperlipemia data, median (IQR). IQR, interquartile range. The two groups were compared using Student’s t-test. Cite Share Download PDF Status: Published Journal Publication published 19 Jan, 2026 Read the published version in BMC Nephrology → Version 1 posted Editorial decision: Revision requested 23 Dec, 2025 Reviews received at journal 22 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviews received at journal 03 Dec, 2025 Reviews received at journal 17 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviewers invited by journal 05 Nov, 2025 Editor invited by journal 03 Nov, 2025 Editor assigned by journal 31 Oct, 2025 Submission checks completed at journal 31 Oct, 2025 First submitted to journal 30 Oct, 2025 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. 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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-7986395","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545617526,"identity":"2a60e4ba-6fb6-494e-9314-3fcfa29cc88d","order_by":0,"name":"Shota Fukae","email":"","orcid":"","institution":"The University of Osaka Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shota","middleName":"","lastName":"Fukae","suffix":""},{"id":545617527,"identity":"aa3dc494-9cc4-4003-9772-b8f8db77ee24","order_by":1,"name":"Yoichi Kakuta","email":"data:image/png;base64,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","orcid":"","institution":"The University of Osaka Graduate School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yoichi","middleName":"","lastName":"Kakuta","suffix":""},{"id":545617528,"identity":"5f8c2572-f40e-422d-831a-bf28d7a4e215","order_by":2,"name":"Soichi Matsumura","email":"","orcid":"","institution":"The University of Osaka Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Soichi","middleName":"","lastName":"Matsumura","suffix":""},{"id":545617529,"identity":"b896d505-d3db-4f46-aea0-82539628defb","order_by":3,"name":"Ryo Tanaka","email":"","orcid":"","institution":"The University of Osaka Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ryo","middleName":"","lastName":"Tanaka","suffix":""},{"id":545617530,"identity":"ed8edb80-0c76-41ca-816e-385fb8447dd9","order_by":4,"name":"Masataka Kawamura","email":"","orcid":"","institution":"The University of Osaka Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Masataka","middleName":"","lastName":"Kawamura","suffix":""},{"id":545617531,"identity":"8ccbfdb5-2da0-4f9a-9828-b3a65cc95001","order_by":5,"name":"Shigeaki Nakazawa","email":"","orcid":"","institution":"The University of Osaka Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shigeaki","middleName":"","lastName":"Nakazawa","suffix":""},{"id":545617532,"identity":"2ea9333d-2726-408f-be31-4e62bd310ef8","order_by":6,"name":"Norio Nonomura","email":"","orcid":"","institution":"The University of Osaka Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Norio","middleName":"","lastName":"Nonomura","suffix":""}],"badges":[],"createdAt":"2025-10-30 07:38:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7986395/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7986395/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12882-026-04754-y","type":"published","date":"2026-01-19T15:57:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96250741,"identity":"a29aafee-e633-4cfc-9d71-d2318a6fc864","added_by":"auto","created_at":"2025-11-19 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07:41:04","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89180,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7986395/v1/07e50171d8d4fb91311b0fb4.html"},{"id":96180391,"identity":"57ed90db-d81a-4dd6-96c6-33008400d4d4","added_by":"auto","created_at":"2025-11-18 12:27:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65817,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraft survival and cumulative incidence of viral infections in all patients\u003cbr\u003e\n \u003c/strong\u003e(A) Kaplan–Meier curve showing graft survival probability in all patients. The 5- and 10-year graft survival rates were 93.0% and 91.7%, respectively. (B) Kaplan–Meier curve showing overall patient survival probability in all patients. The 5- and 10-year overall survival rates were both 96.4%. (C) Cumulative incidence of CMV infection (blue), BKV nephropathy (red), and SARS-CoV-2 infection (green) over time.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7986395/v1/934a139f5ebf0eab3082c60b.png"},{"id":96180394,"identity":"7a806f98-4ba7-46ae-acc7-907f1051198f","added_by":"auto","created_at":"2025-11-18 12:27:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":91432,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComprehensive evaluation of everolimus concentrations and their relationship with AUC₀–₄ at each point\u003cbr\u003e\n \u003c/strong\u003e(A) Correlation between EVR trough concentration (C0) and the area under the concentration–time curve from 0 to 4 hours (AUC₀–₄).\u003cbr\u003e\n(B) Correlations between AUC₀–₄ and EVR concentrations measured at 1 hour (C1), 2 hours (C2), 3 hours (C3), and 4 hours (C4) after administration, respectively.\u003cbr\u003e\nEach dot represents an individual pharmacokinetic measurement (n = 838).\u003cbr\u003e\nDashed lines indicate the linear regression fit.\u003cbr\u003e\nSpearman’s correlation coefficients (r) and regression equations are shown in each panel.\u003cbr\u003e\nAmong all time points, C2 exhibited the strongest correlation with AUC₀–₄ (r = 0.944, p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7986395/v1/1c89c725ab2064e14a136724.png"},{"id":96180393,"identity":"288928bf-b7a6-4095-bcc2-934ff74a3474","added_by":"auto","created_at":"2025-11-18 12:27:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":87033,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between AUC₀–₄ and everolimus concentrations according to duration of administration (\u0026lt;3 months vs. ≥3 months)\u003cbr\u003e\n \u003c/strong\u003eCorrelation between AUC₀–₄ and EVR blood concentrations at each time point, stratified by duration of administration (\u0026lt;3 months vs. ≥3 months). (A, C) Scatter plots illustrating the correlation between EVR trough concentration (C0) and AUC₀–₄.\u003cbr\u003e\nPanel A: patients receiving EVR for \u0026lt;3 months; Panel C: ≥3 months. Spearman’s correlation coefficient (r) and corresponding p-values are shown.\u003cbr\u003e\n(B, D) Beeswarm plots showing EVR blood concentrations at each time point (C0 to C4). Horizontal bars indicate median values, with interquartile ranges and extremes.\u003cbr\u003e\nPanel B (\u0026lt;3 months): median concentrations were C0: 3.6 ng/mL, C1: 4.3 ng/mL, C2: 5.4 ng/mL, C3: 5.8 ng/mL, and C4: 5.8 ng/mL.\u003cbr\u003e\nPanel D (≥3 months): median concentrations were C0: 3.3 ng/mL, C1: 11.2 ng/mL, C2: 8.6 ng/mL, C3: 6.8 ng/mL, and C4: 5.7 ng/mL.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7986395/v1/793f14147693914eca723815.png"},{"id":96180392,"identity":"1d88f0a2-d9e0-4f81-8055-7ec3688110ec","added_by":"auto","created_at":"2025-11-18 12:27:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":46358,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between EVR AUC₀–₄ and urinary protein excretion Scatter plot illustrating the correlation between the area under the concentration-time–time curve from 0 to 4 hours (AUC₀–₄) and urine protein/creatinine ratio. r, Spearman’s correlation coefficient with corresponding p value. (A) All samples, (B) EVR administration ≥ 3 months\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7986395/v1/a47dea0a120a8fc18796c30c.png"},{"id":96180396,"identity":"07c5d332-ce1c-487d-9fb8-ac279ce196b7","added_by":"auto","created_at":"2025-11-18 12:27:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":105887,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of EVR exposure and predictive ability based on the presence of de novo HL\u003cbr\u003e\n (A) Bee swarm plot showing the distribution of EVR trough concentrations (ng/mL) according to the presence of de novo hyperlipidemia (HL). No significant difference was observed between groups (unpaired t-test, n.s.). \u003cem\u003en.s.\u003c/em\u003e, not significant.\u003cbr\u003e\n(B) Bee swarm plot showing the distribution of EVR AUC₀–₄ (ng·h/mL). The de novo HL(+) group showed significantly higher values (unpaired t-test, *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05).\u003cbr\u003e\n(C) The ROC curve shows the predictive performance of EVR AUC₀–₄ for the presence of de novo HL, with an area under the curve (AUC) of 0.603.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7986395/v1/86ffcdce7ab63ed7177a149c.png"},{"id":101151916,"identity":"b780e486-b50a-4bf2-8fb0-af44005d7dc1","added_by":"auto","created_at":"2026-01-26 16:08:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1477814,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7986395/v1/6571c570-3239-4d1f-b853-aa3c991147d2.pdf"},{"id":96250808,"identity":"c2b1b713-9ea5-4509-8895-9f38e07195e6","added_by":"auto","created_at":"2025-11-19 07:39:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":396138,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSupplemental Figure S1: \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eDistribution of everolimus (EVR) blood concentrations at each sampling time point (C0–C4)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eViolin plots showing the distribution of EVR concentrations at the trough (C0) and 1-, 2-, 3-, and 4- hours post-dose (C1–C4).\u003cbr\u003e\nHorizontal bars indicate median values, and boxes represent interquartile ranges.\u003cbr\u003e\nWhiskers denote the 10th–90th percentiles.\u003cbr\u003e\nThe figure shows that the EVR concentrations peaked at C2, with a gradual decline thereafter. The median concentrations at each time point were as follows: C0: 3.5 ng/mL, C1: 6.5 ng/mL, C2: 6.7 ng/mL, C3: 6.3 ng/mL, and C4: 5.7 ng/mL.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSupplemental Figure S2: \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eAssociation between TAC trough levels and EVR concentrations.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Violin plots comparing TAC trough levels between patients treated with EVR for \u0026lt;3 months (\u0026lt;3 M, red) and ≥3 months (≥3 M, green). Patients receiving EVR for ≥3 months exhibited significantly higher TAC trough concentrations than those treated for \u0026lt;3 months (median 5.8 vs. 4.2 ng/mL, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003et\u003c/em\u003e-test).\u003cbr\u003e\n(B) Line plots showing time-course EVR concentrations stratified by TAC trough cutoff (TAC \u0026gt; 5 ng/mL, red; TAC ≤ 5 ng/mL, blue). Patients with higher TAC trough levels demonstrated significantly elevated EVR concentrations at 1-hour post-dose (C1) compared with those with lower TAC levels (mean 8.8 vs. 5.4 ng/mL, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003et\u003c/em\u003e-test).\u003cbr\u003e\n The error bars represent the standard error (SE). * p \u0026lt; 0.05; ** p \u0026lt; 0.01.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSupplemental Figure S3: \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eAssociation between EVR AUC₀–₄ and urinary protein receiving EVR for less than 3 months\u003c/strong\u003e\u003cbr\u003e\n Scatter plot illustrating the correlation between AUC₀–₄ and the urine protein-to-creatinine ratio in patients receiving EVR for less than 3 months. \u003cem\u003er\u003c/em\u003e, Spearman’s correlation coefficient with the corresponding \u003cem\u003ep\u003c/em\u003evalue.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSupplemental Table S1:\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eCharacteristics of de novo hyperlipemia\u003c/strong\u003e\u003cbr\u003e\n data, median (IQR). IQR, interquartile range. The two groups were compared using Student’s t-test.\u003c/p\u003e","description":"","filename":"Supplementalmaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7986395/v1/bfd160f8b6f4d7c408f8da0e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of Everolimus Pharmacokinetic Monitoring Based on Trough Concentration and Area Under the Blood Concentration Time Curve in Kidney Transplantation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEverolimus (EVR) is a proliferation-signal inhibitor developed as an immunosuppressive therapy following solid-organ transplantation. Among kidney transplant recipients (KTRs), calcineurin inhibitor (CNI)-associated nephrotoxicity remains a major barrier to long-term graft survival. EVR has emerged as a potential agent to mitigate this complication by minimizing or withdrawing CNI exposure. Additionally, EVR is known to exert a broad range of pharmacological effects, including anti-fibrotic, anti-angiogenic, antiviral, and antitumor activities, making it a uniquely multifunctional agent within immunosuppressive regimens[\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the clinical applicability of EVR is limited by its narrow therapeutic window and frequent adverse effects such as proteinuria, hyperlipidemia, oral mucositis, and thrombocytopenia[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therapeutic drug monitoring (TDM) has been implemented to optimize efficacy while minimizing toxicity, with a recommended target trough concentration of 3.0\u0026ndash;8.0 ng/mL[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious studies on cyclosporine-based regimens have demonstrated correlations between EVR trough concentrations and the area under the concentration\u0026ndash;time curve (AUC), supporting the clinical use of TDM[\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, pharmacokinetic data on tacrolimus (TAC) -based therapy, which is currently the standard of care, remains limited. Furthermore, few studies have systematically compared multiple post-dose sampling points or examined the relationship between EVR exposure and adverse events in real-world KTRs.\u003c/p\u003e\u003cp\u003eGiven the increasing use of TAC-based regimens, a comprehensive understanding of EVR pharmacokinetics and its exposure\u0026ndash;response relationship is essential for refining dosing strategies and improving patient outcomes. Accordingly, this study aimed to characterize the post-dose pharmacokinetic profile of EVR, evaluate the correlations between trough and other time-point concentrations and the AUC, and assess the association between EVR exposure and adverse events, particularly proteinuria and de novo hyperlipidemia, in KTRs receiving TAC-based immunosuppression.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study population\u003c/h2\u003e\u003cp\u003ePatients who had undergone kidney transplantation at the University of Osaka between September 1989 and May 2023 were screened for eligibility. Excluded from the study were patients who did not receive EVR as part of their immunosuppressive regimens and among the EVR-treated patients, those who did not undergo pharmacokinetic assessment based on the area under the curve (AUC) from 0 to 4 hours after oral administration. Thus, 168 patients were included in the final analysis. All the patients were managed by a kidney transplantation team at the University of Osaka, Japan. A total of 827 pharmacokinetic assessments of EVR were performed.\u003c/p\u003e\u003cp\u003eAll participants provided written informed consent, and the study protocol was approved by the Osaka University Hospital Institutional Review Board (approval number 21374).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Immunosuppression protocol\u003c/h2\u003e\u003cp\u003eAll patients underwent induction immunosuppression with basiliximab. Maintenance immunosuppression consisted of TAC, mycophenolic acid, and corticosteroids, which were tapered over three weeks. EVR was administered as de novo therapy beginning in April 2015 or as an add-on to existing maintenance immunosuppressive regimens in earlier cases. The target trough level for EVR was 3.0\u0026ndash;8.0 ng/mL. For each pharmacokinetic assessment, blood samples for both EVR and TAC were obtained simultaneously under stable dosing conditions. To ensure pharmacokinetic consistency, all analyses in this study were restricted to KTRs receiving TAC-based regimens, and patients treated with cyclosporine were excluded.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Quantification of everolimus\u003c/h2\u003e\u003cp\u003eEVR blood concentrations were measured using the latex turbidimetric immunoassay method with the Nanopia\u0026reg; TDM Everolimus assay (Sekisui Medical Co., Ltd., Japan). Blood samples were collected during routine outpatient follow-up visits, during the perioperative period of kidney transplantation, or during hospitalization for protocol or indicated biopsies of the allograft kidneys. Sampling time points included immediately before EVR administration (C0; trough level) and at 1, 2, 3, and 4 hours after dosing (C1 to C4). The AUC₀\u0026ndash;₄ was calculated using the trapezoidal method based on EVR concentrations measured at C0 to C4.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Evaluation\u003c/h2\u003e\u003cp\u003eCorrelations between EVR concentrations at each time point (C0\u0026ndash;C4) and AUC₀\u0026ndash;₄ were analyzed. In addition, the relationship between the EVR and TAC trough concentrations was evaluated. The potential association between EVR exposure and adverse effects such as proteinuria and hyperlipidemia (HL) were also investigated. De novo HL was defined as the initiation of lipid-lowering therapy or a new diagnosis of HL after the introduction of EVR in patients who did not been receive lipid-lowering agents at the time of kidney transplantation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables are expressed as median (interquartile range, [IQR]), and categorical variables as counts and percentages. Correlations between EVR concentrations (C0\u0026ndash;C4) and AUC₀\u0026ndash;₄ were assessed using Spearman\u0026rsquo;s rank correlation coefficients.\u003c/p\u003e\u003cp\u003eComparisons of continuous variables between the two groups were performed using the Mann\u0026ndash;Whitney U test or Student\u0026rsquo;s t-test, as appropriate. Logistic regression analysis was performed, and multicollinearity was evaluated using the variance inflation factor (VIF); all variables had a VIF\u0026thinsp;\u0026lt;\u0026thinsp;2. Receiver operating characteristic (ROC) curve analysis was performed, and the optimal cutoff value was determined using Youden\u0026rsquo;s index. All analyses were performed using the R software (version 4.3.2; R Foundation for Statistical Computing, Vienna, Austria) and JMP Pro (version 17.0; SAS Institute Inc., Cary, NC, USA). \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. Characteristics of participants\u003cbr\u003e\u003c/strong\u003eThe baseline characteristics of the 168 TAC-treated KTRs are summarized in Table 1. A total of 827 pharmacokinetic samples were included in the final analysis. The median age was 50 years (IQR, 39.5–60), and 39.3% of the participants were female. Living-donor transplantation accounted for 94.6% of the cases. The median oral dose of EVR was 2.0 mg/day (IQR, 1.5–2.5), and the median duration of EVR therapy was 2 months (IQR, 0–14). All pharmacokinetic assessments were performed in patients receiving TAC-based immunosuppressive therapy. The most common primary renal disease was IgA nephropathy (21.4%), followed by diabetic nephropathy (17.3%) and chronic glomerulonephritis (16.1%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Graft and patient survival and infectious outcomes after kidney transplantation\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;During the follow-up period, the overall survival rate of the study population was 96.4% at both 5 and 10 years. Death-censored graft survival rates were 93.0% and 91.7% at 5 and 10 years, respectively (Figure 1A, 1B). The cumulative incidence of viral infections at 5 years was 9.2% for cytomegalovirus (CMV), 2.2% for BK virus (BKV) nephropathy, and 49.6% for SARS-CoV-2. Although the incidences of CMV and BKV infections remained unchanged after 10 years, the incidence of SARS-CoV-2 infection increased to 75.3% (Figure 1C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Correlation between EVR Trough Concentrations and AUC\u003cbr\u003e\u003c/strong\u003eAnalysis of the correlation between EVR trough concentrations (C0) and AUC₀–₄\u0026nbsp;across all data points demonstrated a moderate positive correlation (r = 0.524, p \u0026lt; 0.001) (Figure 2A). In contrast, among the non-trough concentrations, C2 showed a remarkably strong correlation with AUC₀–₄, indicating an exceptionally high association (r = 0.944, p \u0026lt; 0.001) (Figure 2B). Furthermore, the time-course evaluation of EVR blood concentrations (C0–C4) revealed a peak concentration at C2, with a median value of 6.7 ng/mL (Supplemental Figure S1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. Comparison of EVR pharmacokinetics Stratified by 3-Month Post-Transplant Period\u003cbr\u003e\u003c/strong\u003eWe stratified patients according to EVR treatment duration (\u0026lt;3 months vs. ≥3 months) and performed subgroup analyses. Among the 827 samples analyzed, 420 were obtained from patients receiving EVR for \u0026lt;3 months and 407 from those treated for ≥3 months. The correlation between EVR C0 and AUC₀–₄\u0026nbsp;was comparable between groups, with correlation coefficients of r = 0.662 and r = 0.587, respectively (both p \u0026lt; 0.001) (Figure 3A, 3C).\u003cbr\u003e\u0026nbsp;In contrast, the concentration–time profiles differed between the groups. In the \u0026lt;3-month group, the highest median concentrations were observed at C3 and C4 (both 5.8 ng/mL), with median values of 3.6, 4.3, and 5.4 ng/mL at C0, C1, and C2, respectively. In the ≥3-month group, the peak concentration occurred at C1 (median 11.2 ng/mL), followed by 8.6, 6.8, 5.7, and 3.3 ng/mL at C2, C3, C4, and C0, respectively (Figure 3B, 3D).\u003cbr\u003e\u0026nbsp;When stratified by TAC trough levels, patients treated with EVR for ≥3 months had significantly higher TAC troughs than those treated for \u0026lt;3 months (median 5.8 vs. 4.2 ng/mL, p \u0026lt; 0.05, t-test) (Supplemental Figure S2A). Using a TAC trough cutoff of 5 ng/mL, patients with TAC \u0026gt;5 ng/mL showed significantly higher 1 hour post-dose EVR concentrations than those with TAC ≤5 ng/mL (median 8.8 vs. 5.4 ng/mL, p \u0026lt; 0.05, t-test) (Supplemental Figure S2B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5. Relationship between EVR exposure and urinary protein levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationship between urinary protein levels, a known adverse effect of EVR, and AUC₀–₄\u0026nbsp;was investigated in patients with available urine protein/creatinine ratio data. No significant correlation was observed between the EVR AUC₀–₄\u0026nbsp;and urinary protein/creatinine ratio in the overall cohort or in the subgroup of patients treated for more than three months (both p \u0026gt; 0.05) (Figure 4). Similarly, no correlation was found in the \u0026lt;3-month group (p \u0026gt; 0.05, Supplemental Figure S3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6. EVR exposure and de novo hyperlipidemia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe association between EVR exposure and the development of de novo HL was evaluated in patients without preexisting HL. In univariate logistic regression analysis, both EVR AUC₀–₄ and TAC trough concentrations were significantly associated with de novo HL (Table 2). In multivariable analysis including EVR AUC₀–₄, EVR trough concentration, and TAC trough concentration, both EVR AUC₀–₄ (OR 1.02, 95% CI 1.00–1.04, p = 0.03) and TAC trough concentration (OR 0.84, 95% CI 0.74–0.96, p = 0.01) remained independent factors. Recipients who developed de novo HL had significantly higher EVR AUC₀–₄ values compared with those who did not (p \u0026lt; 0.05, Figure 5B). The EVR trough concentration (C0) showed no significant association (p = 0.51, Figure 5A). ROC analysis determined an optimal cutoff value of 39.9 ng·h/mL for EVR AUC₀–₄ (Figure 5C). These findings suggest that systemic EVR exposure, rather than the trough concentration, contributes to the risk of hyperlipidemia.\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn the present study, we comprehensively evaluated the pharmacokinetics and clinical relevance of EVR in KTRs receiving tacrolimus-based immunosuppressive therapy. We demonstrated that C2 showed the strongest correlation with AUC₀–₄, indicating that C2 is a more accurate surrogate marker for systemic exposure than the conventional trough concentration (C0).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The timing of the peak concentration also varied according to the post-transplantation period, suggesting that EVR pharmacokinetics dynamically change with time after transplantation.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;These findings provide practical insights for optimizing TDM in tacrolimus-based regimens.\u003c/p\u003e\n\u003cp\u003ePrevious studies, including those by Kovarik and Budde, have reported correlations between EVR trough concentrations and AUC in kidney transplantation under cyclosporine-based regimens[9, 11]. However, the pharmacokinetic profiles of tacrolimus-based therapy have not been systematically characterized. Unlike prior model-based or cyclosporine-focused studies, our real-world dataset of 827 pharmacokinetic assessments from 168 tacrolimus-treated recipients directly compared multiple sampling points (C0–C4) with AUC₀–₄, therapy confirming the superiority of C2 as a practical surrogate of systemic exposure.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Serial measurements are essential for precise immunosuppressive management, because \u0026nbsp;inter-individual variability in drug metabolism often causes fluctuations in EVR levels[12]. Our findings reaffirm that although C0 moderately correlates with AUC₀–₄, C2 most accurately reflects systemic exposure and may serve as an optimal limited-sampling point for TDM in clinical practice.\u003c/p\u003e\n\u003cp\u003eMechanistically, EVR exerts its immunosuppressive effects by binding to FKBP12[13],\u0026nbsp;a mechanism similar to that of TAC[14].\u0026nbsp;Previous studies have reported that EVR and TAC exhibit antagonistic pharmacokinetic interactions[15]\u0026nbsp;and that a lower dose-to-concentration ratio of TAC is associated with decreased clearance and elevated blood concentrations of EVR[16]. These findings are consistent with our current results, suggesting that reduced TAC exposure may enhance the pharmacological activity of EVR.\u003c/p\u003e\n\u003cp\u003eHL is a common complication in KTRs that contributes to cardiovascular disease and significantly affects patient outcomes[17]. EVR modulates the expression of enzymes involved in lipid metabolism, such as lipoprotein lipase, thereby impairing the clearance of plasma lipids by adipose tissue, resulting in hypercholesterolemia[18, 19]. A meta-analysis indicated that therapy with mammalian target of rapamycin inhibitors (mTORi) combined with mycophenolic acid doubled the risk of hyperlipidemia compared to CNI-based regimens[20]. Furthermore, lipid-lowering agents are prescribed approximately 60% more frequently to patients receiving mTORi-based regimens than in those receiving other therapies[21]. Optimizing the EVR dose while considering immunological risks is a rational therapeutic strategy for managing hyperlipidemia in KTRs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our cohort, both EVR AUC₀–₄\u0026nbsp;and TAC trough concentrations were independently associated with the development of de novo HL, whereas EVR trough concentrations (C0) were not. These results suggest that systemic exposure, rather than a single time-point levels, contributes to lipid dysregulation, and that TAC co-administration may modulate this effect by influencing EVR pharmacokinetics. This protective association with higher TAC levels may be partly explained by pharmacokinetic interactions through shared CYP3A metabolism or by balanced immunosuppressive effects that indirectly stabilize lipid homeostasis. The association between EVR exposure and proteinuria was insignificant, possibly reflecting the multifactorial nature of proteinuria after kidney transplantation, which involves both immunological and nonimmunological mechanisms. Although our study did not identify a direct exposure–response relationship for proteinuria, the findings reinforce the clinical importance of individualized dosing strategies that consider both efficacy and adverse events.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This study had several limitations. This was a single-center retrospective analysis conducted in a predominantly Japanese cohort, which may have limited the generalizability of our findings. Direct measurement of AUC₀–₂₄ was not feasible, although previous studies have demonstrated a strong correlation between AUC₀–₄ and AUC₀–₂₄. Additionally, we did not assess the intracellular pharmacodynamics or FKBP12-binding kinetics, which could further elucidate the mechanistic basis of TAC–EVR interactions.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eUnder tacrolimus-based immunosuppression, C2 most accurately reflects systemic EVR exposure and may serve as a reliable single sampling point for TDM.\u003c/p\u003e\u003cp\u003eHigher EVR AUC₀\u0026ndash;₄ was associated with hyperlipidemia, whereas higher TAC trough levels were protective, suggesting an exposure-dependent and interaction-modulated risk.\u003c/p\u003e\u003cp\u003eThese findings highlight the potential of C2- or AUC-based TDM as a practical and individualized approach for optimizing EVR therapy in kidney transplant recipients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC; area under the concentration–time curve\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;CNI; calcineurin inhibitor\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;EVR; everolimus\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;HL; hyperlipidemia\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;IQR; interquartile range\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;KTR; kidney transplant recipient\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;mTORi; mammalian target of rapamycin inhibitors\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;ROC; receiver operating characteristic\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;TAC; tacrolimus\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;TDM; therapeutic drug monitoring\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;VIF; variance inflation factor\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003cbr\u003e\u003c/strong\u003eApproval was obtained from the Institutional Review Board (Osaka University Hospital Institutional Review Board, protocol number: 21374) before initiating the study, and all patients provided written informed consent. The study was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003cbr\u003e\u003c/strong\u003eWritten informed consent for publication was obtained from all the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003cbr\u003e\u003c/strong\u003eThe datasets used and analyzed during this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003cbr\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003cstrong\u003eunding\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors have received no specific funding for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions:\u003cbr\u003e\u003c/strong\u003eConceptualization, SF and YK; Investigation, SF; Formal analysis, YK; Data curation, SM; Project administration, YK, SM, RT, MK, and SN; Visualization, SF; Writing—original draft, SF; Writing—review and editing, YK and NN; Supervision, NN.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e:\u003cbr\u003e\u003c/strong\u003eWe thank Shiro Takahara (Kansai Medical Hospital) and Nobumasa Fujimoto (Takatsuki General Hospital) for their technical support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGuba M, von Breitenbuch P, Steinbauer M, Koehl G, Flegel S, Hornung M, et al. Rapamycin inhibits primary and metastatic tumor growth by antiangiogenesis: involvement of vascular endothelial growth factor. Nat Med. 2002;8:128\u0026ndash;35. https://doi.org/10.1038/nm0202-128.\u003c/li\u003e\n\u003cli\u003eContreras AG, Dormond O, Edelbauer M, Calzadilla K, Hoerning A, Pal S, et al. mTOR\u0026mdash;Understanding the Clinical Effects. Transplant Proc. 2008;40:S9\u0026ndash;12. https://doi.org/10.1016/j.transproceed.2008.10.011.\u003c/li\u003e\n\u003cli\u003eEisen H. Long-term cardiovascular risk in transplantation\u0026mdash;insights from the use of everolimus in heart transplantation. Nephrology Dialysis Transplantation. 2006;21 suppl_3:iii9\u0026ndash;13. https://doi.org/10.1093/ndt/gfl295.\u003c/li\u003e\n\u003cli\u003eOrmiston JA, Serruys PW, Regar E, Dudek D, Thuesen L, Webster MW, et al. A bioabsorbable everolimus-eluting coronary stent system for patients with single de-novo coronary artery lesions (ABSORB): a prospective open-label trial. The Lancet. 2008;371:899\u0026ndash;907. https://doi.org/10.1016/S0140-6736(08)60415-8.\u003c/li\u003e\n\u003cli\u003eClippinger AJ, Maguire TG, Alwine JC. The Changing Role of mTOR Kinase in the Maintenance of Protein Synthesis during Human Cytomegalovirus Infection. J Virol. 2011;85:3930\u0026ndash;9. https://doi.org/10.1128/JVI.01913-10.\u003c/li\u003e\n\u003cli\u003eCortazar F, Molnar MZ, Isakova T, Czira ME, Kovesdy CP, Roth D, et al. Clinical Outcomes in Kidney Transplant Recipients Receiving Long-Term Therapy With Inhibitors of the Mammalian Target of Rapamycin. American Journal of Transplantation. 2012;12:379\u0026ndash;87. https://doi.org/10.1111/j.1600-6143.2011.03826.x.\u003c/li\u003e\n\u003cli\u003eKovarik JM, Kaplan B, Tedesco Silva H, Kahan BD, Dantal J, Vitko S, et al. Exposure-response relationships for everolimus in de novo kidney transplantation: defining a therapeutic range. Transplantation. 2002;73:920\u0026ndash;5. https://doi.org/10.1097/00007890-200203270-00016.\u003c/li\u003e\n\u003cli\u003eKovarik JM, Eisen H, Dorent R, Mancini D, Vigano M, Rouilly M, et al. Everolimus in de novo cardiac transplantation: pharmacokinetics, therapeutic range, and influence on cyclosporine exposure. The Journal of Heart and Lung Transplantation. 2003;22:1117\u0026ndash;25. https://doi.org/10.1016/S1053-2498(02)01221-4.\u003c/li\u003e\n\u003cli\u003eKovarik J. Population pharmacokinetics of everolimus in de novo renal transplant patients: Impact of ethnicity and comedications. Clin Pharmacol Ther. 2001;70:247\u0026ndash;54. https://doi.org/10.1067/mcp.2001.118022.\u003c/li\u003e\n\u003cli\u003eKovarik J. Longitudinal assessment of everolimus in de novo renal transplant recipients over the first post-transplant year: Pharmacokinetics, exposure-response relationships, and influence on cyclosporine. Clin Pharmacol Ther. 2001;69:48\u0026ndash;56. https://doi.org/10.1067/mcp.2001.112969.\u003c/li\u003e\n\u003cli\u003eBudde K, Fritsche L, Waiser J, Glander P, Slowinski T, Neumayer H-H, et al. Pharmacokinetics of the immunosuppressant everolimus in maintenance renal transplant patients. Eur J Med Res. 2005;10:169\u0026ndash;74.\u003c/li\u003e\n\u003cli\u003eJackson KD, Achour B, Lee J, Geffert RM, Beers JL, Latham BD. Novel Approaches to Characterize Individual Drug Metabolism and Advance Precision Medicine. Drug Metabolism and Disposition. 2023;51:1238\u0026ndash;53. https://doi.org/10.1124/dmd.122.001066.\u003c/li\u003e\n\u003cli\u003eVan Duyne GD, Standaert RF, Karplus PA, Schreiber SL, Clardy J. Atomic Structures of the Human Immunophilin FKBP-12 Complexes with FK506 and Rapamycin. J Mol Biol. 1993;229:105\u0026ndash;24. https://doi.org/10.1006/jmbi.1993.1012.\u003c/li\u003e\n\u003cli\u003eLiu J, Farmer JD, Lane WS, Friedman J, Weissman I, Schreiber SL. Calcineurin is a common target of cyclophilin-cyclosporin A and FKBP-FK506 complexes. Cell. 1991;66:807\u0026ndash;15. https://doi.org/10.1016/0092-8674(91)90124-H.\u003c/li\u003e\n\u003cli\u003evan Rossum HH, Romijn FPHTM, Smit NPM, de Fijter JW, van Pelt J. Everolimus and sirolimus antagonize tacrolimus based calcineurin inhibition via competition for FK-binding protein 12. Biochem Pharmacol. 2009;77:1206\u0026ndash;12. https://doi.org/10.1016/j.bcp.2008.12.009.\u003c/li\u003e\n\u003cli\u003eItohara K, Yano I, Nakagawa S, Sugimoto M, Hirai M, Yonezawa A, et al. Population pharmacokinetics of everolimus in adult liver transplant patients: Comparison to tacrolimus disposition and extrapolation to pediatrics. Clin Transl Sci. 2022;15:2652\u0026ndash;62. https://doi.org/10.1111/cts.13389.\u003c/li\u003e\n\u003cli\u003ePirsch JD, D\u0026rsquo;Alessandro AM, Sollinger HW, Knechtle SJ, Reed A, Kalayoglu M, et al. Hyperlipidemia and transplantation. Journal of the American Society of Nephrology. 1992;2:S238. https://doi.org/10.1681/ASN.V212s238.\u003c/li\u003e\n\u003cli\u003eHoude VP, Br\u0026ucirc;l\u0026eacute; S, Festuccia WT, Blanchard P-G, Bellmann K, Deshaies Y, et al. Chronic Rapamycin Treatment Causes Glucose Intolerance and Hyperlipidemia by Upregulating Hepatic Gluconeogenesis and Impairing Lipid Deposition in Adipose Tissue. Diabetes. 2010;59:1338\u0026ndash;48. https://doi.org/10.2337/db09-1324.\u003c/li\u003e\n\u003cli\u003eKaplan B, Qazi Y, Wellen JR. Strategies for the management of adverse events associated with mTOR inhibitors. Transplant Rev. 2014;28:126\u0026ndash;33. https://doi.org/10.1016/j.trre.2014.03.002.\u003c/li\u003e\n\u003cli\u003eMurakami N, Riella L V, Funakoshi T. Risk of metabolic complications in kidney transplantation after conversion to mTOR inhibitor: a systematic review and meta-analysis. Am J Transplant. 2014;14:2317\u0026ndash;27. https://doi.org/10.1111/ajt.12852.\u003c/li\u003e\n\u003cli\u003eKasiske BL, de Mattos A, Flechner SM, Gallon L, Meier-Kriesche H-U, Weir MR, et al. Mammalian target of rapamycin inhibitor dyslipidemia in kidney transplant recipients. Am J Transplant. 2008;8:1384\u0026ndash;92. https://doi.org/10.1111/j.1600-6143.2008.02272.x.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 1:\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Characteristics of participants and pharmacokinetic samples\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"501\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 501px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipants (n = 168)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 318px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e50 (39.5 - 60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 318px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Female\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Male\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e66 (39.3)\u003c/p\u003e\n \u003cp\u003e102 (60.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 318px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiving/Deceased, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e159 (94.6%) / 9 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 318px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary disease, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; IgA nephropathy\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; Diabetic nephropathy\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; Chronic glomerulonephritis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; Polycystic kidney disease\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; Renal sclerosis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;RPGN (excluding IgA nephropathy)\u003c/strong\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; Lupus nephritis \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;FSGS\u003c/strong\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; Renal Dysplasia/Hypoplasia\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; Interstitial nephritis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; Fanconi syndrome\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; Others\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; Unknown\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e36 (21.4)\u003c/p\u003e\n \u003cp\u003e29 (17.3)\u003c/p\u003e\n \u003cp\u003e27 (16.1)\u003c/p\u003e\n \u003cp\u003e16 (9.5)\u003c/p\u003e\n \u003cp\u003e9 (5.4)\u003c/p\u003e\n \u003cp\u003e6 (3.6)\u003c/p\u003e\n \u003cp\u003e5 (3.0)\u003c/p\u003e\n \u003cp\u003e4 (2.4)\u003c/p\u003e\n \u003cp\u003e4 (2.4)\u003c/p\u003e\n \u003cp\u003e3 (1.8)\u003c/p\u003e\n \u003cp\u003e2 (1.2)\u003c/p\u003e\n \u003cp\u003e6 (3.6)\u003c/p\u003e\n \u003cp\u003e20 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 318px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePharmacokinetic samples (n = 827)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 318px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEVR oral dosage (mg/day)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e2.0 (1.5 \u0026ndash; 2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 318px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration of taking EVR (Month)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e2 (0 - 14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 318px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTAC trough (ng/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e5.0 (3.8 - 6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as median (interquartile range) or number (%). RPGN, rapidly progressive glomerulonephritis; FSGS, Focal segmental glomerulosclerosis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/strong\u003eOne case each of: Reflux nephropathy, sponge kidney, gouty kidney, Alport syndrome, nephronophthisis, and aristolochic acid nephropathy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e2\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e:\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Logistic regression analyses identifying factors associated with de novo\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eHL\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"756\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate regression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1.00 (0.98-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1.15 (0.76-1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTAC C0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.828 (0.73-0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.84 (0.74-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEVR C0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.966 (0.84-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.94 (0.78-1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEVR AUC\u003c/strong\u003e\u003cstrong\u003e₀\u003c/strong\u003e\u003cstrong\u003e\u0026ndash;\u003c/strong\u003e\u003cstrong\u003e₄\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1.01 (1.00-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1.02 (1.00-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrinary protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.828 (0.509-1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOR, odds ratio; CI, confidence interval; EVR, everolimus; TAC, tacrolimus.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Area under the concentration-time curve (AUC), Everolimus, Kidney transplantation, Pharmacokinetics","lastPublishedDoi":"10.21203/rs.3.rs-7986395/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7986395/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackgrounds\u003c/h2\u003e\u003cp\u003eEverolimus (EVR) is an mTOR inhibitor used in kidney transplantation to minimize calcineurin inhibitor exposure. Although therapeutic drug monitoring (TDM) based on trough concentrations (C0) is standard, the relationship between time-point EVR concentrations, systemic exposure, and metabolic complications of tacrolimus (TAC)-based therapy remains unclear. This study aimed to identify the optimal sampling point that reflects systemic exposure and to evaluate its association with adverse effects.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe analyzed 827 pharmacokinetic assessments of 168 kidney transplant recipients who received TAC-based immunosuppression. EVR concentrations were measured at 0 (C0), 1, 2, 3, and 4 hours post-dose, and the area under the concentration\u0026ndash;time curve from 0 to 4 hours (AUC₀\u0026ndash;₄) was calculated. Correlations between each time point and the AUC₀\u0026ndash;₄ were evaluated, and logistic regression adjusted for TAC trough concentration was used to assess the associations between EVR exposure and adverse events, including proteinuria and de novo hyperlipidemia (HL).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe median participant age was 50 years, and 39.3% of participants were female. Among all time points, C2 showed the strongest correlation with the AUC₀\u0026ndash;₄ (r\u0026thinsp;=\u0026thinsp;0.944, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas C0 showed only a moderate association (r\u0026thinsp;=\u0026thinsp;0.524, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Time-course analysis revealed that peak the concentration typically occurred 2 hours post-dose, although the profiles varied according to the post-transplant duration. EVR AUC₀\u0026ndash;₄ was independently and positively associated with the development of de novo HL (adjusted OR 1.02, p\u0026thinsp;=\u0026thinsp;0.03), whereas higher TAC trough concentrations exhibited a protective effect (adjusted OR 0.84, p\u0026thinsp;=\u0026thinsp;0.01), suggesting that TAC co-administration may modulate EVR-induced lipid dysregulation. No significant relationship was observed between EVR exposure and proteinuria. Receiver operating characteristic analysis yielded an AUC of 0.603 with an optimal cutoff of 39.9 ng\u0026middot;h/mL for predicting HL.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eC2 most accurately reflected systemic EVR exposure under tacrolimus-based regimens. Higher EVR exposure, as represented by AUC₀\u0026ndash;₄ or C2, was associated with an increased risk of de novo hyperlipidemia, whereas higher TAC levels mitigated this effect. These findings support the clinical utility of C2- or AUC-based TDM for individualized EVR dosing to balance the efficacy and safety in kidney transplant recipients.\u003c/p\u003e\u003ch2\u003eClinical trial number\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"Evaluation of Everolimus Pharmacokinetic Monitoring Based on Trough Concentration and Area Under the Blood Concentration Time Curve in Kidney Transplantation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 12:27:00","doi":"10.21203/rs.3.rs-7986395/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-23T08:40:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-22T21:53:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"80794886381456592959141974829084723319","date":"2025-12-08T17:04:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-03T23:13:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-17T14:00:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157827055862064953522008463468686484814","date":"2025-11-13T10:54:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279526492686979641185661953773084099985","date":"2025-11-08T14:38:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-05T18:56:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-03T08:57:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-31T12:50:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-31T12:49:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2025-10-30T07:27:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"59ec52f7-24ca-4de2-a4bb-a513cf47874c","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:04:29+00:00","versionOfRecord":{"articleIdentity":"rs-7986395","link":"https://doi.org/10.1186/s12882-026-04754-y","journal":{"identity":"bmc-nephrology","isVorOnly":false,"title":"BMC Nephrology"},"publishedOn":"2026-01-19 15:57:29","publishedOnDateReadable":"January 19th, 2026"},"versionCreatedAt":"2025-11-18 12:27:00","video":"","vorDoi":"10.1186/s12882-026-04754-y","vorDoiUrl":"https://doi.org/10.1186/s12882-026-04754-y","workflowStages":[]},"version":"v1","identity":"rs-7986395","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7986395","identity":"rs-7986395","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}