PLOS ONE RESEARCH ARTICLE Early prognostic performance of miR155-5p monitoring for the risk of rejection: Logistic regression with a population pharmacokinetic approach in adult kidney transplant patients Luis Quintairos ID1,2☯, Helena Colom1☯*, Olga Millán2,3, Virginia Fortuna2, Cristina Espinosa2, Lluis Guirado4, Klemens Budde5, Claudia Sommerer6, Ana Lizana2, Yolanda López-Púa7, Mercè Brunet2,3 a1111111111 1 Faculty of Pharmacy and Food Sciences, Biopharmaceutics and Pharmacokinetics Unit, Department of a1111111111 Pharmacy, Pharmaceutical Technology and Physical Chemistry, University of Barcelona, Barcelona, Spain, a1111111111 2 Pharmacology and Toxicology Laboratory, Biochemistry and Molecular Genetics, Biomedical Diagnostic a1111111111 Center (CDB), Hospital Clinic of Barcelona, University of Barcelona, Barcelona, Spain, 3 Biomedical a1111111111 Research Center in Hepatic and Digestive Diseases (CIBERehd), Instituto de Salud Carlos III, Madrid, Spain, 4 Renal Transplant Unit, Nephrology Department, Fundació Puigvert, Barcelona, Spain, 5 Medizinische Klinik mit Schwerpunkt Nephrologie, Charité Universitätsmedizin Berlin, Berlin, Germany, 6 Department of Nephrology, University of Heidelberg, University Hospital of Heidelberg and Mannheim, Heidelberg, Germany, 7 Quality Unit of CDB, Hospital Clinic of Barcelona, University of Barcelona, Barcelona, Spain OPEN ACCESS ☯ These authors contributed equally to this work. Citation: Quintairos L, Colom H, Millán O, Fortuna * helena.colom@ub.edu V, Espinosa C, Guirado L, et al. (2021) Early prognostic performance of miR155-5p monitoring for the risk of rejection: Logistic regression with a Abstract population pharmacokinetic approach in adult kidney transplant patients. PLoS ONE 16(1): Previous results from our group and others have shown that urinary pellet expression of e0245880. https://doi.org/10.1371/journal. pone.0245880 miR155-5p and urinary CXCL-10 production could play a key role in the prognosis and diag- nosis of acute rejection (AR) in kidney transplantation patients. Here, a logistic regression Editor: Justyna Gołębiewska, Medical University of Gdansk, POLAND model was developed using NONMEM to quantify the relationships of miR155-5p urinary expression, CXCL-10 urinary concentration and tacrolimus and mycophenolic acid (MPA) Received: August 21, 2020 exposure with the probability of AR in adult kidney transplant patients during the early post- Accepted: January 10, 2021 transplant period. Owing to the contribution of therapeutic drug monitoring to achieving tar- Published: January 22, 2021 get exposure, neither tacrolimus nor MPA cumulative exposure was identified as a predictor Copyright: © 2021 Quintairos et al. This is an open of AR in the studied population. Even though CXCL-10 urinary concentration showed a access article distributed under the terms of the trend, its effect on AR was not significant. In contrast, urinary miR155-5p expression was Creative Commons Attribution License, which prognostic of clinical outcome. Monitoring miR155-5p urinary pellet expression together permits unrestricted use, distribution, and reproduction in any medium, provided the original with immunosuppressive drug exposure could be very useful during routine clinical practice author and source are credited. to identify patients with a potential high risk of rejection at the early stages of the post-trans- Data Availability Statement: All relevant data are plant period. This early risk assessment would allow for the optimization of treatment and within the manuscript and its Supporting improved prevention of AR. Information files. Funding: This work was funded by TEVA Pharmaceuticals (EudraCT number: 2013-001817- 33) and mutua Madrileña (AP153362014). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. PLOS ONE | https://doi.org/10.1371/journal.pone.0245880 January 22, 2021 1 / 20 PLOS ONE PKPD model predicting kidney acute rejection Competing interests: This work was funded by Introduction TEVA Pharmaceuticals, a commercial company. This does not alter our adherence to PLOS ONE Despite advances in immunosuppression, allograft rejection and clinical outcomes remain a policies on sharing data and materials. There are challenge in solid organ transplantation. The dosages of immunosuppressive drugs (ISDs) are no patents, marketed products or products in adjusted to achieve target concentrations and to prevent rejection or drug-related adverse development associated with this research. events. The combined pharmacokinetic monitoring of ISDs and immunological biomarkers Fundació Clı́nic per la Recerca Biomèdica (FCRB) may provide patient risk stratification and allow for personalized therapy. The results from (Research Center of Hospital Clı́nic of Barcelona) acted as promoter of the study and was several studies demonstrate the need to identify and validate noninvasive prognostic and diag- responsible for starting and managing of the study, nostic biomarkers for allograft rejection and to monitor the allograft condition at early time but did not provide financial support. points with the aim to achieve personal therapy adjustment before graft injury occurs [1–3]. In a previous study on kidney transplant recipients [4], our group identified miR155-5p and interferon gamma inducible chemokine 10 (CXCL-10) as prognostic and diagnostic bio- markers of rejection based on a receiver operating characteristic (ROC) curve data analysis. These results are in agreement with those reported by Wilflingseder et al. and Soltaninejad et al. in kidney transplant recipients [5,6]. MicroRNAs (miRs) are endogenous noncoding RNA molecules that regulate gene expres- sion [7] at the post-transcriptional level and are involved in regulating several aspects of inflammation [8] as well as affecting the immune response [9]. miR155-5p is a mediator of the inflammatory response and a regulator of IFN-γ production in T-cells and NK-cells. This miR participates in the regulation of adaptative immunity and antibody-related T-cell responses. CXCL-10 is predominantly secreted by leukocytes in transplanted allografts and tubular epithelial cells, and it binds CXC receptor 3 (CXCR3), which is predominantly expressed on T lymphocytes [10]. CXCL-10 has been identified as a long- and short-term graft function [11] and injury [12] biomarker in kidney transplant recipients. In these previous studies, data analyses for drug exposure and biomarkers and their correla- tion with rejection did not consider the most appropriate models, such as logistic regression models [13], by means of the population approach. This study aimed to develop a logistic regression model to investigate whether urinary miR155-5p pellet expression and urinary CXCL-10 concentration may predict the probability of rejection in adult kidney transplant recipients. Moreover, the utility of measuring tacroli- mus and mycophenolic acid (MPA) cumulative exposures to reduce the risk of rejection was also evaluated. Material and methods Ethical conduct of the study The study was approved by the ethics committees of all participating centers (Charité: Ethik- Kommission des Landes Berlin Landesamt für Gesundheit und Soziales Berlin Fehrbelliner Platz 1 10707 Berlin; Heidelberg: Ethik-Kommission der Medizinischen Fakultät Heidelberg. Alte Glockengießerei 11/1 D-69115 Heidelberg;Puigvert: Comité Ético de Investigación Clı́n- ica (CEIC) Fundación Puigvert). All patients provided their written informed consent prior to their inclusion in the study. In all centers, the medical costs were covered according to national health insurance regulations. No cash payments were provided to donors or donors’ families. Population In a European multicenter prospective observational study coordinated and performed by our group (EudraCT number: 2013-001817-33), 80 de novo adult kidney recipients from deceased or living donor patients were recruited between 2014 and 2015 by three European centers: Charité Universitäts Medizin, Berlin, (Germany), Universitätsklinikum, Heidelberg, PLOS ONE | https://doi.org/10.1371/journal.pone.0245880 January 22, 2021 2 / 20 PLOS ONE PKPD model predicting kidney acute rejection (Germany) and Fundació Puigvert, Barcelona, (Spain) [4]. Living donors (relatives or well- known donors) had undergone a strict evaluation both of the medical and psychological status according to the guidelines and by the ethical board of each center. The cause of death of deceased donors did not involve kidney injury but traumatic brain injury, hemorrhagic stroke, ischemic stroke, cerebral anoxia or non-heartbeating, or controlled asystolia; these donors were included through Eurotransplant regular allocation process. Data from 58 out of 80 patients were included in the pharmacokinetic sub-study. Some of the planned samples to be collected according to the protocol could not be obtained for differ- ent reasons, all of them related to unavoidable difficulties frequently arising in the clinical practice such as clotted blood samples, hemolysis occurrence, no attendance of patient to the visit, difficulties in the blood extraction or sample loss during the analytical procedures, among others. This issue led to less available tacrolimus and MPA concentration-time data for the PK/PD analysis. Recipients who were older than 70 years old, hepatitis B or C positive, human immunodeficiency virus (HIV) positive, or combined liver-kidney recipients were excluded. The study was intended to cover the early post-transplantation stages spanning the first 6 months. Patients had a total of five visits: 1st week, 1st month, 2nd month, 3rd month and 6th month after transplantation. The day of the 1st week visit varied from 1 to 11 days post- transplant. The diagnosis of acute rejection (AR) was based on renal function impairment detected by creatinine concentrations and glomerular filtration rate (GFR) by the modification of Diet in Renal Disease (MDRD) method [14]. GFR was stratified based on Kidney Disease Outcomes Quality Initiative guideline for evaluation, classification, and stratification of chronic kidney disease (KDOQI CKD) classification as: chronic dialysis or need for re-trans- plantation as end-stage kidney-failure; GFR<29 as severe decrease in renal function; 300.05). By contrast significant differences were found between the expression of miRNA155 (p<0.001) and CXCL-10 (p = 0.004) in both groups, with higher levels of both biomarkers in patients who presented AR compared with those who did not. Among patients who experienced rejection, only two had tacrolimus trough concentrations below the recommended acceptable range (4 ng/mL– 11 ng/mL) [3] at the earliest post-trans- plant stages (week 1-month 1); for two other patients, not coincident with the previous, MPA trough concentrations were under the recommended therapeutic range (< 1.6 ng/mL) [36]. Of note, MPA concentrations lower than 1.6 ng/mL were also observed at week 1 in 17 patients who did not develop acute rejection. Population pharmacokinetic models Tacrolimus. A total of 1102 concentration time points from 58 patients were included in the analysis. A two-compartment model with lag time, first-order absorption and first-order elimination fit the data best. A log transformation of the data stabilized the model. BSV was only be associated with the whole blood clearance (CL), intercompartmental clearance (Q) and central volume of distribution (VC). Residual error was better described by an additive model on log-transformed data. The final estimated PK parameters and their precisions (RSE), BSV effect on each parameter and η-shrinkage and ε-shrinkage values were acceptable and are listed in Table 4. The observed versus population predicted concentrations and conditional weighted residu- als versus time plots displayed in S1 Fig confirmed that the final model adequately described the study population as a whole, without appreciable bias. The low values of ε-shrinkage (5%) Table 4. Estimated parameters and bootstrap results of the final population PK models. Tacrolimus MPA Parameter Units Value (RSE%) Bootstrap results� median (p2.5th—p97.5th) Value (RSE%) Bootstrap results� median (p2.5th—p97.5th) Disposition Parameters CL L/h/70 kg 16.5 (10) 16.0 (13.4–19.6) 11.8 (5) 11.7 (10.5–12.9) VC L/70 kg 311 (9) 318.4 (258.8–362.5) 106 (22) 103.7 (48.5–162.5) Q L/h/70 kg 20.5 (12) 21.1 (14.9–26.1) 37.1 (9) 39.1 (18.9–55.3) VP L/70kg 56300 (8) 60038.6 (42571.5–70008.1) 800 FIX - Absorption parameters KA h-1 3.08 (39) 3.42 (2.54–3.62) 1.79 (16) 1.76 (1.25–2.33) tLAG h 0.295 (22) 0.308 (0.24–0.35) 0.243 (29) 0.24 (0.18–0.30) Between-subject Value (RSE%) SHR Bootstrap results� median (p2.5th—p97.5th) Value (RSE%) SHR Bootstrap results� median (p2.5th—p97.5th) variability BSVCL % 57.6 (14) 7% 58.6 (40.4–70.8) 34.9 (11) 8% 35.2 (26.4–41.7) BSVQ % 68.9 (13) 11% 68.9 (46.3–85.7) - - - BSVVC % 55.6 (17) 6% 55.5 (32.7–71.5) 133.8 (15) 16% 136.2 (75.5–173.5) BSVVP % - - - 164.6 (14) 22% 159.8 (88.8–215.0) Residual error Proportional % 36.6 (12) 5% 37.5 (33.0–42.2) 55.3 (6) 7% 55.5 (50–60) Abbreviations: CL, whole blood (Tac) or plasma (MPA) clearance; VC and VP, central and peripheral volumes of distribution; Q, intercompartmental clearance between compartments; KA, absorption rate constant; tLAG, lag time; BSV, between-subject variability; RSE, relative standard error; SHR, shrinkage, (-) Not estimated parameter. � 1000 resamplings were performed. https://doi.org/10.1371/journal.pone.0245880.t004 PLOS ONE | https://doi.org/10.1371/journal.pone.0245880 January 22, 2021 9 / 20 PLOS ONE PKPD model predicting kidney acute rejection allowed for the inspection of the observed versus individual predicted concentrations plots with no evidence of misspecification. Model evaluation. The pcVPC indicated that the model acceptably predicted the median trend of the observed tacrolimus concentrations (Fig 1).The 2.5th, 50th and 97.5th percentiles of the observed data lied within the 95% prediction intervals of the corresponding simulated percentiles, although a slight underprediction was observed around the peak concentration. The median and 2.5th and 97.5th percentiles obtained by the bootstrap method displayed in Table 4 indicated that all the estimates of population parameters were within the 95% confi- dence interval (CI), and the maximum relative deviation of the bootstrap median with respect to the population value was 10%. The distribution of the normalized prediction distribution errors (npde) of the observed data overlapped with the distribution of the errors of the simulated data confirming that the model can predict the median concentration in the overall dataset accurately (S2 Fig). The stepwise covariate modeling did not identify any covariate as significant; therefore, the results at this stage were considered the final model. Mycophenolic acid A total of 1071 concentration time points from 58 patients were used to develop a two-com- partment model with time-lagged first-order absorption and first-order elimination. BSV could be associated only with plasma CL, VC and VP. VP was fixed at referenced values taken from the literature (800 L) to adequately estimate the rest of parameters. Residual error was best described by a proportional error model. The estimated final parameters and their precision, BSV effect on each parameter and η-shrinkage and ε-shrink- age values were acceptable and are listed in Table 4. Goodness-of-fit plots did not show any remarkable bias as shown in S3 Fig. The observed versus population predicted concentrations plot suggested that the population prediction described adequately the central tendency of the data. No model misspecification was detected related to the structural part or residual error modeling when exploring the remaining plots. Fig 1. Prediction-corrected visual predictive check for the final tacrolimus population pharmacokinetic model. Blue shaded areas represent the 95% confidence interval of the 97.5th and 2.5th percentiles of the simulated data, and red shaded areas represent the 95% confidence interval of the median of the simulated data. Red dotted lines represent the 97.5th and 2.5th percentiles of the observed data, and the continuous red line is the median of the observed data. https://doi.org/10.1371/journal.pone.0245880.g001 PLOS ONE | https://doi.org/10.1371/journal.pone.0245880 January 22, 2021 10 / 20 PLOS ONE PKPD model predicting kidney acute rejection Fig 2. Prediction-corrected visual predictive check for the final mycophenolic acid population pharmacokinetic model. Blue shaded areas represent the 95% confidence interval of the 97.5th and 2.5th percentiles of the simulated data, and red shaded areas represent the 95% confidence interval of the median of the simulated data. Red dotted lines represent the 97.5th and 2.5th percentiles of the observed data, and the continuous red line is the median of the observed data. Dark blue dots are the observation-time points. https://doi.org/10.1371/journal.pone.0245880.g002 Model evaluation. The pcVPC indicated that the final model had acceptable predictive value (Fig 2) except around the absorption phase. Overall, the 97.5th, 2.5th and 50th percen- tiles of the observed data were within the 95% prediction intervals of the corresponding per- centiles for the simulated data. The median and 2.5th and 97.5th percentiles obtained by the bootstrap method are shown in Table 4. All parameters were within the 95% CI, and the relative deviation of the bootstrap median with respect to the population value was lower than 6%. The distribution of the normalized prediction distribution errors (npde) for the observed data overlapped with the distribution of the errors for the simulated data (S4 Fig). The base model was considered the final model because none of the tested covariates led to a significant reduction in the MOFV value. Logistic regression The logistic regression model was built with a total of 183 observations/time points from 58 patients. The final model consisted of the linear combination of the baseline effect and the effect of miR155-5p urinary pellet expression. None of the other tested explanatory variables (tacrolimus and MPA estimated mean trough concentrations and cumulative exposures [AUC] and CXCL-10 expression) adequately improved the model so they were not retained. The parameter estimates and RSEs, bootstrap results and final model equations are presented in Table 5. Logistic regression model evaluation. The results of the VPC for the miR155-5p-AR logistic regression model (Fig 3) showed that the simulated and the observed median lines were superimposed except at high biomarker concentrations, likely due to the lack of sufficient data. According to the grouped bar plot in Fig 4, the proportions of rejection occurrence for simulated data at all biomarker concentrations were in agreement with those for observed data. PLOS ONE | https://doi.org/10.1371/journal.pone.0245880 January 22, 2021 11 / 20 PLOS ONE PKPD model predicting kidney acute rejection Table 5. Final parameters of the miR155-5p expression -Acute rejection (AR) risk logistic regression model. Parameter Values (RSE%) Bootstrap� mean (P2.5th-P97.5th) ß0 -5.89 (15) -5.94 (-9.88 –-4.80) ß1 3.51 (24) 3.45 (2.34–56.72) LOGIT(Pi) = ß0 + ß1� miR155-5p Abbreviations: ß0, Baseline risk of rejection; ß1, Slope of explanatory variable effect; RSE%, relative standard error expressed as %. � 200 resamplings were performed. https://doi.org/10.1371/journal.pone.0245880.t005 The results of bootstrap analysis (Table 4) confirmed the accuracy of the final parameter estimates and the stability of the model. The population parameters were all inside the 95% bootstrap CI with a bias lower than 12%. Discussion To our knowledge, this study is the first of adult kidney transplant recipients where a popula- tion logistic regression model was developed to identify the potential predictive factors of the risk of AR, using a non-linear mixed effects analysis. Our major aim was to investigate the Fig 3. VPC of miR155-5p expression (ΔCt) -AR logistic regression model. Solid line: Median of observed AR proportion versus. miR155-5p expression. Dotted line: Median of predicted AR proportion versus miR155-5p expression; shaded area: 95% prediction interval of AR versus miR155-5p expression. https://doi.org/10.1371/journal.pone.0245880.g003 PLOS ONE | https://doi.org/10.1371/journal.pone.0245880 January 22, 2021 12 / 20 PLOS ONE PKPD model predicting kidney acute rejection Fig 4. Grouped bar graph of acute rejection proportions versus miR155-5p expression (ΔCt). The green and gray bars are the predicted and observed proportions of rejection occurrence, respectively. https://doi.org/10.1371/journal.pone.0245880.g004 effect of biomarker expression and ISD exposure on AR. Thus, tacrolimus and MPA popula- tion pharmacokinetic models were developed to predict precise exposures. Among the bio- markers evaluated, only urinary miR155-5p expression was predictive of the clinical event. Neither tacrolimus nor MPA individual predicted cumulative exposures (AUC) nor mean observed trough concentrations where identified as predictive factors of AR. This was probably due to the successful implementation of therapeutic drug monitoring overall post-transplant period, even though at the earliest stages (from week 1 to month 1), patients who developed AR showed a tendency toward a lower tacrolimus exposure than those who did not. Similarly occurred with MPA, with comparable exposures between patients experiencing AR and those who did not. These findings were in agreement with those of Sánchez-Fructuoso et Al. [36], who found a significant relationship between lower MPA exposures in week 1 and AR occur- rence during month 1 post-transplant in kidney recipients through a logistic regression analy- sis. However, no statistically significant relationship was found in tacrolimus exposure due to similar exposures between patients with AR and patients without. The low number of rejection events could also have contributed to the lack of statistical significance between ISD and AR occurrence of our study. To our knowledge, this study is the first of adult kidney transplant recipients where a popu- lation logistic regression model was developed to identify the potential predictive factors of the risk of AR, using a non-linear mixed effects analysis. Our major aim was to investigate the effect of biomarker expression and ISD exposure on AR. Thus, tacrolimus and MPA PLOS ONE | https://doi.org/10.1371/journal.pone.0245880 January 22, 2021 13 / 20 PLOS ONE PKPD model predicting kidney acute rejection population pharmacokinetic models were developed to predict precise exposures. Among the biomarkers evaluated, only urinary miR155-5p expression was predictive of the clinical event. Neither tacrolimus nor MPA individual predicted cumulative exposure (AUC) nor mean observed trough concentrations where identified as predictive factors of AR. The successful implementation of therapeutic drug monitoring overall post-transplant period could explain this lack of predictive ability, even though at the earliest post-transplant stages of the study (from week 1 to month 1), patients who developed AR showed a tendency toward a lower tacrolimus exposure than those who did not. Similarly occurred with MPA, with comparable exposures between patients experiencing AR and those who did not. These findings were in agreement with those of Sánchez-Fructuoso et Al. [36], who found a significant relationship between lower MPA exposures in week 1 and AR occurrence during month 1 post-transplant in kidney recipients, through a logistic regression analysis. However, no significant relation- ship was found in the case of tacrolimus due to similar exposures between patients with AR and those without. The low number of rejection events may also explain the lack of signifi- cance between ISD and AR occurrence of our study. As aforementioned, miR155-5p was a unique biomarker that predicted the probability of AR in the target population. This finding is in agreement with the results of a previous study by our group [4], where urinary miR155-5p pellet expression significantly increased before an AR episode, demonstrating that miR155-5p expression could be effective as a prognostic bio- marker. Anglicheau et al. [37] also identified miR155-5p biopsy expression as a diagnostic bio- marker for AR in kidney transplant recipients. The logistic regression model developed in the current study allowed us to confirm these findings and provide a quantitative measure of the effect of changes of miR155-5p on AR risk. Based on this model, an increase of 0.1 units in miR155-5p urinary expression can lead to a 10% increase in AR risk. These results suggest that miR155-5p expression may be considered not only as a diagnostic but also a prognostic biomarker due to the early and progressive increase in its expression before the occurrence of the clinical event. Unlike miR155-5p expression, the significance between urinary concentration of CXCL-10 and the risk of AR could not be proven. Several published studies have addressed the influence of the urinary concentration of CXCL-10 on the risk of kidney clinical outcomes, such as the risk of tubulitis occurrence in adult transplanted kidney patients [38] or the risk of tubulitis and acute T-cell-mediated rejection (TCMR) in pediatric renal transplantation [39]. In all of these reports, a logistic regression analysis was performed that confirmed the statistical signifi- cance of each relationship [39]. These findings suggest that additional studies should be per- formed with CXCL-10 with a higher number of patients to confirm the previous results. All of these results support the necessity of monitoring ISDs simultaneously with biomark- ers in kidney transplant recipients for a rapid prognostic and diagnostic assessment of rejec- tion, graft outcome and early personalized treatment adjustment, as reported by Millan et al. [4]. The implementation of the measurement of urinary miR155-5p expression and CXCL-10 production in routine clinical practice is feasible, considering that the majority of medical cen- ters involved in transplantation use standard methodologies in laboratories. Regarding the fre- quency of monitoring of these biomarkers after kidney transplantation we and others suggest to monitor them at the 1st week, 2nd week and 1st month post-transplantation (when the inci- dence of AR is high) and during the process of identifying good candidate patients for ISD treatment minimization (approximately the 3rd month after transplantation). Obviously, the frequency of monitoring of these biomarkers requires further investigation in the context of multicenter randomized clinical trials. PLOS ONE | https://doi.org/10.1371/journal.pone.0245880 January 22, 2021 14 / 20 PLOS ONE PKPD model predicting kidney acute rejection As outlined above, accurate tacrolimus and MPA individual cumulative exposures to be used in the logistic regression model were obtained from the respective population pharmaco- kinetic models. Although a large number of pharmacokinetic models exist in the literature for tacrolimus and MPA in renal transplant populations, very few of them used SAEM as parame- ter estimation method. In our case, the efficient handle of full omega blocks of this method provided better estimates of parameter standard errors than first-order conditional estimation methods. According to most of the previously reported models developed from more than only trough concentrations, the pharmacokinetic profiles of tacrolimus [40,41] and MPA [26,42] were best described by two-compartment models with first-order absorption. Also in agree- ment with most studies [43], elimination was described by a first-order kinetics. No dose- dependence related with the elimination process or erythrocyte (tacrolimus) or protein bind- ing (MPA) saturation was found in any case. The estimated CL/F value of tacrolimus was in line with those previously reported (CL/ F = 16.5 L/h [70 kg] versus 16.1 L/h [44], 16.5 L/h [45], and 16.3 L/h [40]). Although hemato- crit values were not available, the tacrolimus CL/F was close to that expected for a standard hematocrit level of 45% (16.1 L/h), as reported by Storset et al. [41]. Although previous studies have reported the influence of metabolic mass in terms of BSA [46] or free fat mass [44] on tacrolimus clearance, and other authors have estimated empirical allometry exponents for CL/ F and VC/F [47], we did not find statistical significance of size covariates on disposition parameters, likely due the scarce number of overweight patients. However, the standard approach used by others [48,49] for scaling body weight by using the fixed exponent values of 0.75 and 1 for flow parameters and distribution volumes, respectively, improved our model predictions. One of the limitations of our study was the difficulty encountered to properly describe the absorption process in a more physiological way such as the erlang distribution or transit com- partment models as others [45]. Unlike other studies [44], the classical lag-time modeling did not allow us to adequately capture peak concentrations, affecting the central compartment dis- tribution volume (Vc) estimated value. The VC of tacrolimus was slightly higher than the val- ues obtained by Musuamba et al. (221 L) [50] and Ogasawara et al. (214 L) [51], but larger differences were observed compared with studies that described the absorption process by means of transit compartment models [45,52] or those with rich sampling designs during the absorption phase [48]. Ka values (3.08 h-1) also reflected sensibility to the aforementioned points. We found a higher value than those reported by other authors (1.01 h-1 [44], 0.47 h-1 [45], and 0.45 h-1 [40]) but similar to or lower to others that used erlang distribution or transit compartment models [52], regardless of the classical lag-time or erlang distribution models were applied. Basically, differences relied on the success of modeling the absorption phase that in turn depended on the availability of sufficiently informative data. Higher values of peripheral distribution volume (Vp) were found with respect to those reported in other similar studies. Between-patient variability could not be included in this parameter. Both facts could be attributed to the lack of sufficient data. The Vp prediction is highly dependent on the concentration values associated with the post -distributive phase where the sampling was scarce this probably leading to model overprediction. The estimated CL/F value for MPA (CL = 11.8 L/h) was lower than that reported previously (28.8 L/h [53]; 25 L/h [40]). Of note, these studies were performed at the earliest phase (9–15 days) of the transplantation period, when the presence of poor renal function could have altered the MPA binding to albumin, resulting in higher clearance values. By contrast, our CL/ F value was similar to that calculated from free MPA CLu/F (410 L/h) in the study of Colom PLOS ONE | https://doi.org/10.1371/journal.pone.0245880 January 22, 2021 15 / 20 PLOS ONE PKPD model predicting kidney acute rejection et al. [54], taking into account the mean MPA unbound fraction found in that study (fu = 0.023) (CL/F = fu�CLu/F = 9.43 L). It is noteworthy that most of the data analyzed in the study of Colom et al. were obtained on different occasions during the first year post-transplan- tation, as was almost the case of the present study (6 months). Even though the MPA Ka value (Ka = 1.79 h-1) was close to that of previous studies (Ka = 1.79 h-1 versus 1.22 h-1 [26] and 2.3 h-1 [40]), the lack of data hampered a more physio- logical description of the absorption process, similar to tacrolimus. Our study presents some other intrinsic limitations. First, the small sample size or low number of patients having AR limited the investigation of tacrolimus exposure and of CXCL- 10 as potential predictors of AR. Second, the biomarker analytical determination was con- ducted in the same laboratory (Pharmacology and Toxicology Laboratory of the Hospital Clinic of Barcelona), and an interlaboratory validation to compare results is desirable. Finally, the study was only performed in Caucasian patients, and additional studies in other races are required. Conclusions A logistic regression model that shows the prognostic capability of miR155-5p for the risk of AR has been developed. This model reinforces the promising results of miR155-5p as a prog- nostic biomarker for rejection in adult kidney transplants. Indeed, monitoring of miR155-5p could be very useful during routine clinical practice to identify potential patients at high risk of rejection at the early stages of the post-transplant period. This information would allow for the consequent optimization of their treatment. No significant effect of ISD exposures or CXCL- 10 on AR was evident, likely due to the low sample size. Therefore, additional multicentric ran- domized studies are required to confirm the potential of miR155-5p and to further investigate CXCL-10 as prognostic biomarkers of risk of rejection and as useful tools to complement tacrolimus and MPA monitoring. Supporting information S1 Fig. Tacrolimus goodness of fit plots. Left upper panel: Observed versus population pre- dicted concentrations (ng/mL). Right upper panel: Observed versus individual predicted con- centrations (ng/mL). Left lower panel: Individual weighted residuals versus individual predicted concentrations (ng/mL). Right lower panel: Conditional weighted residuals versus time (h). (PNG) S2 Fig. Tacrolimus NPDE plots. Left upper panel: Quantile-quantile plot of the npde versus the expected standard normal distribution. Right upper panel: Histogram of the npde with the density of the standard normal distribution overlayed. Left lower panel: Scatterplot of the nor- malized prediction distribution errors versus time (h). Right lower panel: Scatterplot of the normalized prediction distribution errors versus predicted concentrations (ng/mL). (PNG) S3 Fig. MPA goodness of fit plots. Left upper panel: Observed versus population predicted MPA concentrations (μg/mL). Right upper panel: Observed versus individual predicted MPA concentrations(μg/mL). Left lower panel: Weighted residuals versus individual predicted MPA concentrations (μg/mL). Right lower panel: Conditional weighted residuals versus time (h). (PNG) S4 Fig. MPA NPDE plots. Left upper panel: Quantile-quantile plot of the npde versus the expected standard normal distribution. Right upper panel: Histogram of the npde with the PLOS ONE | https://doi.org/10.1371/journal.pone.0245880 January 22, 2021 16 / 20 PLOS ONE PKPD model predicting kidney acute rejection density of the standard normal distribution overlayed. Left lower panel: Scatterplot of the nor- malized prediction distribution errors versus time (h). Right lower panel: Scatterplot of the normalized prediction distribution errors versus predicted concentrations (μg/mL). (PNG) S1 Table. Tacrolimus PK model dataset. (CSV) S2 Table. MPA PK model dataset. (CSV) S3 Table. Logistic regression model dataset. (CSV) S1 Appendix. Model codes. (DOCX) Author Contributions Conceptualization: Helena Colom, Mercè Brunet. Formal analysis: Luis Quintairos. Investigation: Olga Millán, Virginia Fortuna, Cristina Espinosa, Ana Lizana. Methodology: Luis Quintairos, Helena Colom. Project administration: Lluis Guirado, Klemens Budde, Claudia Sommerer, Mercè Brunet. Resources: Lluis Guirado, Klemens Budde, Claudia Sommerer, Mercè Brunet. Supervision: Helena Colom, Mercè Brunet. Validation: Yolanda López-Púa. Writing – original draft: Luis Quintairos, Helena Colom. Writing – review & editing: Olga Millán, Mercè Brunet. References 1. Brunet M, Shipkova M, Van Gelder T, Wieland E, Sommerer C, Budde K, et al. Barcelona Consensus on Biomarker-Based Immunosuppressive Drugs Management in Solid Organ Transplantation. Ther Drug Monit. 2016; 38. https://doi.org/10.1097/FTD.0000000000000287 PMID: 26977997 2. Menon MC, Murphy B, Heeger PS. Moving Biomarkers toward Clinical Implementation in Kidney Trans- plantation. J Am Soc Nephrol. 2017; 28: 735–747. https://doi.org/10.1681/ASN.2016080858 PMID: 28062570 3. Brunet M, van Gelder T, Asberg A, Haufroid V, Hesselink DA, Langman L, et al. Therapeutic drug moni- toring of tacrolimus-personalized therapy: Second consensus report. Ther Drug Monit. 2019; 261–307. https://doi.org/10.1097/FTD.0000000000000640 PMID: 31045868 4. Millán O, Budde K, Sommerer C, Aliart I, Rissling O, Bardaji B, et al. Urinary miR-155-5p and CXCL10 as prognostic and predictive biomarkers of rejection, graft outcome and treatment response in kidney transplantation. Br J Clin Pharmacol. 2017; 10. https://doi.org/10.1111/bcp.13399 PMID: 28880456 5. Soltaninejad E, Nicknam MH, Nafar M, Ahmadpoor P, Pourrezagholi F, Sharbafi MH, et al. Differential expression of microRNAs in renal transplant patients with acute T-cell mediated rejection. Transpl Immunol. 2015; 33: 1–6. https://doi.org/10.1016/j.trim.2015.05.002 PMID: 26002284 6. Wilflingseder J, Regele H, Perco P, Kainz A, Soleiman A, Mühlbacher F, et al. MiRNA profiling discrimi- nates types of rejection and injury in human renal allografts. Transplantation. 2013; 95: 835–841. https://doi.org/10.1097/TP.0b013e318280b385 PMID: 23511211 PLOS ONE | https://doi.org/10.1371/journal.pone.0245880 January 22, 2021 17 / 20 PLOS ONE PKPD model predicting kidney acute rejection 7. Yoshino H, Yonemori M, Miyamoto K, Tatarano S, Kofuji S, Nohata N, et al. microRNA-210-3p depletion by CRISPR/Cas9 promoted tumorigenesis through revival of TWIST1 in renal cell carcinoma. Oncotar- get. 2017; 8: 20881–20894. https://doi.org/10.18632/oncotarget.14930 PMID: 28152509 8. Connell RMO, Kahn D, Gibson WSJ, Round JL, Rebecca L, Chaudhuri AA, et al. MicroRNA-155 pro- motes autoimmune inflammation by enhancing inflammatory T cell development Ryan. 2011; 33: 607– 619. https://doi.org/10.1016/j.immuni.2010.09.009.MicroRNA-155 9. Spiegel JC, Lorenzen JM, Thum T. Role of microRNAs in immunity and organ transplantation. Expert Rev Mol Med. 2011; 13: e37. https://doi.org/10.1017/S1462399411002080 PMID: 22152163 10. Kim SC, Page EK, Knechtle SJ. Urine proteomics in kidney transplantation. Transplant Rev. 2014; 28: 15–20. https://doi.org/10.1016/j.trre.2013.10.004 PMID: 24321302 11. Matz M, Beyer J, Wunsch D, Mashreghi MF, Seiler M, Pratschke J, et al. Early post-transplant urinary IP-10 expression after kidney transplantation is predictive of short- and long-term graft function. Kidney Int. 2006; 69: 1683–1690. https://doi.org/10.1038/sj.ki.5000343 PMID: 16572110 12. Hu H, Aizenstein BD, Puchalski A, Burmania JA, Hamawy MM, Knechtle SJ. Elevation of CXCR3-Bind- ing Chemokines in Urine Indicates Acute Renal-Allograft Dysfunction. Am J Transplant. 2004; 4: 432– 437. https://doi.org/10.1111/j.1600-6143.2004.00354.x PMID: 14961998 13. Sperandei S. Understanding logistic regression analysis. Biochem Medica. 2014; 24: 12–18. https://doi. org/10.11613/BM.2014.003 PMID: 24627710 14. Mengel M, Sis B, Haas M, Colvin RB, Halloran PF, Racusen LC, et al. Banff 2011 meeting report: New concepts in antibody-mediated rejection. American Journal of Transplantation. 2012. pp. 563–570. https://doi.org/10.1111/j.1600-6143.2011.03926.x PMID: 22300494 15. Pascual J, Del Castillo D, Cabello M, Pallardó L, Grinyó JM, Fernández AM, et al. Interaction between everolimus and tacrolimus in renal transplant recipients: A pharmacokinetic controlled trial. Transplan- tation. 2010; 89: 994–1000. https://doi.org/10.1097/TP.0b013e3181ccd7f2 PMID: 20335831 16. Rissling O, Bauer S, Shipkova M, Glander P, Mai M, Hambach P, et al. Simultaneous determination of mycophenolate and its metabolite mycophenolate-7-o-glucuronide with an isocratic HPLC-UV-based method in human plasma and stability evaluation. Scand J Clin Lab Invest. 2016; 76: 612–619. https:// doi.org/10.1080/00365513.2016.1230775 PMID: 27676419 17. Grinyó JM, Ekberg H, Mamelok RD, Oppenheimer F, Sánchez-Plumed J, Gentil MA, et al. The pharma- cokinetics of mycophenolate mofetil in renal transplant recipients receiving standard-dose or low-dose cyclosporine, low-dose tacrolimus or low-dose sirolimus: The Symphony pharmacokinetic substudy. Nephrol Dial Transplant. 2009; 24: 2269–2276. https://doi.org/10.1093/ndt/gfp162 PMID: 19357111 18. Certara. Phoenix WinNonlin 6.4. 210 North Tucker Boulevard Suite 350, St. Louis, MO 63101 USA. 19. R Developement Core Team, R: A language and environment for statistical computing. 2018. Available: http://www.r-project.org. 20. Jonsson EN, Karlsson MO. Xpose—An S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM. Comput Methods Programs Biomed. 1998; 58: 51–64. https://doi.org/ 10.1016/S0169-2607(98)00067-4 21. Keizer R. vpc: Create Visual Predictive. 2018. 22. Lindbom L, Ribbing J, Jonsson EN. Perl-speaks-NONMEM (PsN)—A Perl module for NONMEM related programming. Comput Methods Programs Biomed. 2004; 75: 85–94. https://doi.org/10.1016/j. cmpb.2003.11.003 PMID: 15212851 23. Keizer RJ, Schellens JHM, Huitema ADR. Piraña and PCluster: A modeling environment and cluster Pira na infrastructure for NONMEM. Comput Methods Programs Biomed. 2011; 101: 72–79. https://doi. org/10.1016/j.cmpb.2010.04.018 PMID: 20627442 24. Beal S, Sheiner L, Boeckmann A, Bauer R. NONMEM users guide: introduction to NONMEM 7.4.1. Elli- cott City: Icon Development Solutions; 2017. p. 128. https://doi.org/10.1017/CBO9781107415324.004 25. Bauer RJ. NONMEM Tutorial Part II: Estimation Methods and Advanced Examples. CPT Pharmaco- metrics Syst Pharmacol. 2019; 8: 538–556. https://doi.org/10.1002/psp4.12422 PMID: 31044558 26. Colom H, Lloberas N, Andreu F, Caldés A, Torras J, Oppenheimer F, et al. Pharmacokinetic modeling of enterohepatic circulation of mycophenolic acid in renal transplant recipients. Kidney Int. 2014; 85: 1434–1443. https://doi.org/10.1038/ki.2013.517 PMID: 24402086 27. Savic RM, Jonker DM, Kerbusch T, Karlsson MO. Implementation of a transit compartment model for describing drug absorption in pharmacokinetic studies. J Pharmacokinet Pharmacodyn. 2007; 34: 711– 726. https://doi.org/10.1007/s10928-007-9066-0 PMID: 17653836 28. Anderson BJ, Holford NHG. Mechanism-Based Concepts of Size and Maturity in Pharmacokinetics. Annu Rev Pharmacol Toxicol. 2008; 48: 303–332. https://doi.org/10.1146/annurev.pharmtox.48. 113006.094708 PMID: 17914927 PLOS ONE | https://doi.org/10.1371/journal.pone.0245880 January 22, 2021 18 / 20 PLOS ONE PKPD model predicting kidney acute rejection 29. Germovsek E, Barker CIS, Sharland M, Standing JF. Scaling clearance in paediatric pharmacokinetics: All models are wrong, which are useful? Br J Clin Pharmacol. 2017; 83: 777–790. https://doi.org/10. 1111/bcp.13160 PMID: 27767204 30. Karlsson MO, Sheiner LB. The importance of modeling interoccasion variability in population pharmaco- kinetic analyses. J Pharmacokinet Biopharm. 1993; 21: 735–750. https://doi.org/10.1007/BF01113502 PMID: 8138894 31. Dosne AG, Bergstrand M, Karlsson MO. A strategy for residual error modeling incorporating scedasti- city of variance and distribution shape. J Pharmacokinet Pharmacodyn. 2016; 43: 137–151. https://doi. org/10.1007/s10928-015-9460-y PMID: 26679003 32. Wählby U, Jonsson EN, Karlsson MO. Comparison of stepwise covariate model building strategies in population pharmacokinetic-pharmacodynamic analysis. AAPS PharmSci. 2002. https://doi.org/10. 1208/ps040427 PMID: 12645999 33. Hosmer DW Jr., Lemeshow S, Sturdivant RX. Applied Logistic Regression. First edit. Hoboken, NJ, USA: John Wiley & Sons, Inc.; 2013. https://doi.org/10.1002/9781118548387 34. Savic RM, Karlsson MO. Importance of Shrinkage in Empirical Bayes Estimates for Diagnostics: Prob- lems and Solutions. AAPS J. 2009; 11: 558–569. https://doi.org/10.1208/s12248-009-9133-0 PMID: 19649712 35. Bergstrand M, Hooker AC, Wallin JE, Karlsson MO. Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. AAPS J. 2011; 13: 143–151. https://doi.org/10.1208/ s12248-011-9255-z PMID: 21302010 36. Sánchez-Fructuoso AI, de la Higuera MAM, Giorgi M, Ramos F, Garcia Ledesma P, Calvo N, et al. Inadequate Mycophenolic Acid Exposure and Acute Rejection in Kidney Transplantation. Transplant Proc. 2009; 41: 2104–2105. https://doi.org/10.1016/j.transproceed.2009.05.015 PMID: 19715844 37. Anglicheau D, Sharma VK, Ding R, Hummel A, Snopkowski C, Dadhania D, et al. MicroRNA expression profiles predictive of human renal allograft status. Proc Natl Acad Sci. 2009; 106: 5330–5335. https:// doi.org/10.1073/pnas.0813121106 PMID: 19289845 38. Ho J, Rush DN, Karpinski M, Storsley L, Gibson IW, Bestland J, et al. Validation of urinary CXCL10 As a marker of borderline, subclinical, and clinical tubulitis. Transplantation. 2011; 92: 878–882. https://doi. org/10.1097/TP.0b013e31822d4de1 PMID: 21876477 39. Blydt-Hansen TD, Gibson IW, Gao A, Dufault B, Ho J. Elevated urinary CXCL10-to-creatinine ratio is associated with subclinical and clinical rejection in pediatric renal transplantation. Transplantation. 2015; 99: 797–804. https://doi.org/10.1097/TP.0000000000000419 PMID: 25222013 40. Musuamba FT, Mourad M, Haufroid V, Demeyer M, Capron A, Delattre IK, et al. A Simultaneous D- Optimal Designed Study for Population Pharmacokinetic Analyses of Mycophenolic Acid and Tacroli- mus Early After Renal Transplantation. J Clin Pharmacol. 2012; 52: 1833–1843. https://doi.org/10. 1177/0091270011423661 PMID: 22207766 41. Størset E, Holford N, Midtvedt K, Bremer S, Bergan S, Åsberg A. Importance of hematocrit for a tacroli- mus target concentration strategy. Eur J Clin Pharmacol. 2014. https://doi.org/10.1007/s00228-013- 1584-7 PMID: 24071959 42. Winter BCM De, Gelder T Van, Glander P, Cattaneo D, Tedesco-silva H, Neumann I, et al. Population Pharmacokinetics of Mycophenolic Acid A Comparison between Enteric-Coated Mycophenolate Sodium and Mycophenolate Mofetil in Renal Transplant Recipients. 2008; 47: 827–838. https://doi.org/ 10.2165/0003088-200847120-00007 PMID: 19026038 43. Kirubakaran R, Stocker SL, Hennig S, Day RO, Carland JE. Population Pharmacokinetic Models of Tacrolimus in Adult Transplant Recipients: A Systematic Review. Clinical Pharmacokinetics. Adis; 2020. https://doi.org/10.1007/s40262-020-00922-x PMID: 32783100 44. Størset E, Holford N, Hennig S, Bergmann TK, Bergan S, Bremer S, et al. Improved prediction of tacroli- mus concentrations early after kidney transplantation using theory-based pharmacokinetic modelling. Br J Clin Pharmacol. 2014; 78: 509–523. https://doi.org/10.1111/bcp.12361 PMID: 25279405 45. Andreu F, Colom H, Grinyó JM, Torras J, Cruzado JM, Lloberas N. Development of a population PK model of tacrolimus for adaptive dosage control in stable kidney transplant patients. Ther Drug Monit. 2015; 37: 246–255. https://doi.org/10.1097/FTD.0000000000000134 PMID: 25254416 46. Andrews LM, Hesselink DA, van Schaik RHN, van Gelder T, de Fijter JW, Lloberas N, et al. A population pharmacokinetic model to predict the individual starting dose of tacrolimus in adult renal transplant recipients. Br J Clin Pharmacol. 2019; 85: 601–615. https://doi.org/10.1111/bcp.13838 PMID: 30552703 47. Nanga TM, Doan TTP, Marquet P, Musuamba FT. Toward a robust tool for pharmacokinetic-based personalization of treatment with tacrolimus in solid organ transplantation: A model-based meta-analy- sis approach. Br J Clin Pharmacol. 2019; 85: 2793–2823. https://doi.org/10.1111/bcp.14110 PMID: 31471970 PLOS ONE | https://doi.org/10.1371/journal.pone.0245880 January 22, 2021 19 / 20 PLOS ONE PKPD model predicting kidney acute rejection 48. Åsberg A, Midtvedt K, Van Guilder M, Størset E, Bremer S, Bergan S, et al. Inclusion of CYP3A5 geno- typing in a nonparametric population model improves dosing of tacrolimus early after transplantation. Transpl Int. 2013; 26: 1198–1207. https://doi.org/10.1111/tri.12194 PMID: 24118301 49. Bergmann TK, Hennig S, Barraclough KA, Isbel NM, Staatz CE. Population Pharmacokinetics of Tacro- limus in Adult Kidney Transplant Patients: Impact of CYP3A5 Genotype on Starting Dose. 2014. 50. Musuamba FT, Mourad M, Haufroid V, De Meyer M, Capron A, Delattre IK, et al. Statistical tools for dose individualization of mycophenolic acid and tacrolimus co-administered during the first month after renal transplantation. Br J Clin Pharmacol. 2013; 75: 1277–1288. https://doi.org/10.1111/bcp.12007 PMID: 23072565 51. Ogasawara K, Chitnis SD, Gohh RY, Christians U, Akhlaghi F. Multidrug resistance-associated protein 2 (MRP2/ABCC2) haplotypes significantly affect the pharmacokinetics of tacrolimus in kidney transplant recipients. Clin Pharmacokinet. 2013; 52: 751–762. https://doi.org/10.1007/s40262-013-0069-2 PMID: 23633119 52. Woillard JB, De Winter BCM, Kamar N, Marquet P, Rostaing L, Rousseau A. Population pharmacoki- netic model and Bayesian estimator for two tacrolimus formulations—twice daily Prograf® and once daily Advagraf®. Br J Clin Pharmacol. 2011; 71: 391–402. https://doi.org/10.1111/j.1365-2125.2010. 03837.x PMID: 21284698 53. de Winter BCM, Mathot RAA, Sombogaard F, Neumann I, van Hest RM, Doorduijn JK, et al. Differences in clearance of mycophenolic acid among renal transplant recipients, hematopoietic stem cell transplant recipients, and patients with autoimmune disease. Ther Drug Monit. 2010; 32: 606–14. https://doi.org/ 10.1097/FTD.0b013e3181efd715 PMID: 20720518 54. Colom H, Andreu F, van Gelder T, Hesselink DA, de Winter BCM, Bestard O, et al. Prediction of Free from Total Mycophenolic Acid Concentrations in Stable Renal Transplant Patients: A Population-Based Approach. Clin Pharmacokinet. 2017; 1–17. https://doi.org/10.1007/s40262-016-0423-2 PMID: 27314261 PLOS ONE | https://doi.org/10.1371/journal.pone.0245880 January 22, 2021 20 / 20