Active immunological participation and metabolic shutdown of kidney structural cells during kidney transplant rejection.

Contrary to immune cells, the response of the kidney structural cells in rejection is less established. We performed single-cell RNA sequencing on 18 kidney transplant biopsies from 14 recipients. Single-cell RNA sequencing identified cells from the major compartments of the kidney, next to infiltrated immune cells. Endothelial cells from the glomerulus, peritubular capillaries and vasa recta showed upregulation of class I and II HLA genes, adhesion molecules and cytokines and chemokines, suggesting an active participation in the alloimmune process, with compartment-specific differences. Epithelial cells including proximal tubular, loop of Henle and collecting duct cells, also showed increased expression of immune genes. Strikingly, in proximal tubule cells a strong downregulation of energy metabolism upon inflammation was observed. There was a large overlap between the cell-specific expression changes upon alloimmune inflammation and those observed in two large micro-array biopsy cohorts. In conclusion, the kidney structural cells, being the main target of the alloimmune process, appear to actively contribute herein, enhancing the damaging effects of the infiltrating immune cells. In epithelial cells, a profound shutdown of metabolism was seen upon inflammation, which associated with poor kidney function. These observations highlight the critical role of the graft in triggering and sustaining rejection after transplantation.

Application of machine-learning models to predict the ganciclovir and valganciclovir exposure in children using a limited sampling strategy.

Intravenous ganciclovir and oral valganciclovir display significant variability in ganciclovir pharmacokinetics, particularly in children. Therapeutic drug monitoring currently relies on the area under the concentration-time (AUC). Machine-learning (ML) algorithms represent an interesting alternative to Maximum-a-Posteriori Bayesian-estimators for AUC estimation. The goal of our study was to develop and validate an ML-based limited sampling strategy (LSS) approach to determine ganciclovir AUC0-24 after administration of either intravenous ganciclovir or oral valganciclovir in children. Pharmacokinetic parameters from four published population pharmacokinetic models, in addition to the World Health Organization growth curve for children, were used in the mrgsolve R package to simulate 10,800 pharmacokinetic profiles of children. Different ML algorithms were trained to predict AUC0-24 based on different combinations of two or three samples. Performances were evaluated in a simulated test set and in an external data set of real patients. The best estimation performances in the test set were obtained with the Xgboost algorithm using a 2 and 6 hours post dose LSS for oral valganciclovir (relative mean prediction error [rMPE] = 0.4% and relative root mean square error [rRMSE] = 5.7%) and 0 and 2 hours post dose LSS for intravenous ganciclovir (rMPE = 0.9% and rRMSE = 12.4%). In the external data set, the performance based on these two sample LSS was acceptable: rMPE = 0.2% and rRMSE = 16.5% for valganciclovir and rMPE = -9.7% and rRMSE = 17.2% for intravenous ganciclovir. The Xgboost algorithm developed resulted in a clinically relevant individual estimation using only two blood samples. This will improve the implementation of AUC-targeted ganciclovir therapeutic drug monitoring in children.

A machine learning approach to predict daptomycin exposure from two concentrations based on Monte Carlo simulations.

Daptomycin is a concentration-dependent lipopeptide antibiotic for which exposure/effect relationships have been shown. Machine learning (ML) algorithms, developed to predict the individual exposure to drugs, have shown very good performances in comparison to maximum a posteriori Bayesian estimation (MAP-BE). The aim of this work was to predict the area under the blood concentration curve (AUC) of daptomycin from two samples and a few covariates using XGBoost ML algorithm trained on Monte Carlo simulations. Five thousand one hundred fifty patients were simulated from two literature population pharmacokinetics models. Data from the first model were split into a training set (75%) and a testing set (25%). Four ML algorithms were built to learn AUC based on daptomycin blood concentration samples at pre-dose and 1 h post-dose. The XGBoost model (best ML algorithm) with the lowest root mean square error (RMSE) in a 10-fold cross-validation experiment was evaluated in both the test set and the simulations from the second population pharmacokinetic model (validation). The ML model based on the two concentrations, the differences between these concentrations, and five other covariates (sex, weight, daptomycin dose, creatinine clearance, and body temperature) yielded very good AUC estimation in the test (relative bias/RMSE = 0.43/7.69%) and validation sets (relative bias/RMSE = 4.61/6.63%). The XGBoost ML model developed allowed accurate estimation of daptomycin AUC using C0, C1h, and a few covariates and could be used for exposure estimation and dose adjustment. This ML approach can facilitate the conduct of future therapeutic drug monitoring (TDM) studies.

Absolute Quantification of Human Serum Albumin Isoforms by Internal Calibration Based on a Top-Down LC-MS Approach.

Well-characterized biomarkers using reliable quantitative methods are essential for the management of various pathologies such as diabetes, kidney, and liver diseases. Human serum albumin (HSA) isoforms are gaining interest as biomarkers of advanced liver pathologies. In view of the structural alterations observed for HSA, insights into its isoforms are required to establish them as reliable biomarkers. Therefore, a robust absolute quantification method seems necessary. In this study, we developed and validated a far more advanced top-down liquid chromatography-mass spectrometry (LC-MS) method for the absolute quantification of HSA isoforms, using myoglobin (Mb) as an internal standard for quantification and for mass recalibration. Two different quantification approaches were investigated based on peak integration from the deconvoluted spectrum and extracted ion chromatogram (XIC). The protein mixture human serum albumin/myoglobin eluted in well-shaped separated peaks. Mb allowed a systematic mass recalibration for every sample, resulting in extremely low mass deviations compared to conventional deconvolution-based methods. In total, eight HSA isoforms of interest were quantified. Specific-isoform calibration curves showing good linearity were obtained by using the deconvoluted peaks. Noticeably, the HSA ionization behavior appeared to be isoform-dependent, suggesting that the use of an enriched isoform solution as a calibration standard for absolute quantification studies of HSA isoforms is necessary. Good repeatability, reproducibility, and accuracy were observed, with better sensitivity for samples with low albumin concentrations compared to routine biochemical assays. With a relatively simple workflow, the application of this method for absolute quantification shows great potential, especially for HSA isoform studies in a clinical context, where a high-throughput method and sensitivity are needed.

Revisiting the nature and pharmacodynamics of tacrolimus metabolites.

The toxicity of tacrolimus metabolites and their potential pharmacodynamic (PD) interactions with tacrolimus might respectively explain the surprising combination of higher toxicity and lower efficacy of tacrolimus despite normal blood concentrations, described in extensive metabolizers. To evaluate such interactions, we produced tacrolimus metabolites in vitro and characterized them by high resolution mass spectrometry (HRMS, for all) and nuclear magnetic resonance (NMR, for the most abundant, M-I). We quantified tacrolimus metabolites and checked their structure in patient whole blood and peripheral blood mononuclear cells (PBMC). We explored the interactions of M-I with tacrolimus in silico, in vitro and ex vivo. In vitro metabolization produced isoforms of tacrolimus and of its metabolites M-I and M-III, whose HRMS fragmentation suggested an open-ring structure. M-I and M-III open-ring isomers were also observed in patient blood. By contrast, NMR could not detect these open-ring forms. Transplant patients expressing CYP3A5 exhibited higher M-I/TAC ratios in blood and PBMC than non-expressers. Molecular Dynamics simulations showed that: all possible tacrolimus metabolites and isomers bind FKPB12; and the hypothetical open-ring structures induce looser binding between FKBP12 and calcineurins, leading to lower CN inhibition. In vitro, tacrolimus bound FKPB12 with more affinity than purified M-I, and the pool of tacrolimus metabolites and purified M-I had only weak inhibitory activity on IL2 secretion and not at all on NFAT nuclear translocation. M-I showed no competitive effect with tacrolimus on either test. Finally, M-I or the metabolite pool did not significantly interact with tacrolimus MLR suppression, thus eliminating a pharmacodynamic interaction.

Getting Tacrolimus Dosing Right.

ABSTRACT: Tacrolimus (TAC) dosing is typically guided by the trough concentration (C0). Yet, significant relationships between TAC C0 and clinical outcomes have seldom been reported or only with adverse events. Large retrospective studies found a moderate correlation between TAC C0 and the area under the curve (AUC), where, for any given C0 value, the AUC varied 3- to 4-fold between patients (and vice versa). However, no randomized controlled trial evaluating the dose adjustment based on TAC AUC has been conducted yet. A few observational studies have shown that the AUC is associated with efficacy and, to a lesser extent, adverse effects. Other studies showed the feasibility of reaching predefined target ranges and reducing underexposure and overexposure. TAC AUC0-12 h is now most often assessed using Bayesian estimation, but machine learning is a promising approach. Microsampling devices are well accepted by patients and represent a valuable alternative to venous blood sample collection during hospital visits, especially when a limited sampling strategy is required. As AUC monitoring cannot be proposed very frequently, C0 monitoring has to be used in the interim, which has led to fluctuating doses in patients with an AUC/C0 ratio far from the population mean, because of different dose recommendations between the 2 biomarkers. We proposed estimating the individual AUC/C0 ratio and derived individual C0 targets to be used in between or as a replacement for AUC monitoring. Existing technology and evidence are now sufficient to propose AUC monitoring interspersed with individualized-C0 monitoring for all patients with kidney transplants while collecting real-world data to strengthen the evidence.

Population Pharmacokinetics and Limited Sampling Strategy of Mycophenolate Mofetil for Indian Patients With Lupus Nephritis.

BACKGROUND: Mycophenolic acid is widely used to treat lupus nephritis (LN). However, it exhibits complex pharmacokinetics with large interindividual variability. This study aimed to develop a population pharmacokinetic (popPK) model and a 3-sample limited sampling strategy (LSS) to optimize therapeutic drug monitoring in Indian patients with LN. METHODS: Five blood samples from each LN patient treated with mycophenolic acid were collected at steady-state predose and 1, 2, 4, and 6 hours postdose. Demographic parameters were tested as covariates to explain interindividual variability. PopPK analysis was performed using Monolix and the stochastic approximation expectation-maximization algorithm. An LSS was derived from 500 simulated pharmacokinetic (PK) profiles using maximum a posteriori Bayesian estimation to estimate individual PK parameters and area under the curve (AUC). The LSS-calculated AUC was compared with the AUC calculated using the trapezoidal rule and all the simulated samples. RESULTS: A total of 51 patients were included in this study. Based on the 245 mycophenolic acid concentrations, a 1-compartmental model with double absorption using gamma distributions best fitted the data. None of the covariates improved the model significantly. The model was internally validated using diagnostic plots, prediction-corrected visual predictive checks, and bootstrapping. The best LSS included samples at 1, 2, and 4 hours postdose and exhibited good performances in an external dataset (root mean squared error, 21.9%; mean bias, -4.20%). CONCLUSIONS: The popPK model developed in this study adequately estimated the PK of mycophenolic acid in adult Indian patients with LN. This simple LSS can optimize TDM based on the AUC in routine practice.

In Silico Pharmacokinetics Evaluation of Forgiveness for Doravirine and Rilpivirine.

BACKGROUND: This study aimed to evaluate the concentrations of rilpivirine (RLP) and doravirine (DOR) after 3 days-off using simulations from population pharmacokinetics models. METHODS: The authors conducted a series of 500 sets of 10,000 Monte Carlo simulations to examine the steady-state conditions for 2 common dosage levels: 25 mg/d for RLP and 100 mg/d for DOR. These simulations were conducted under 2 scenarios: 1 without drug cessation and another after a 3-day break. The validity of the implementation was established through a comparison of median trough concentrations (C24h) with previously reported data. Subsequently, the proportion of simulated patients with C24h and C72h after 3 days-off (C72h/3do) that exceeded the inhibitory concentration 50 (IC50), 5.2 mcg/L for DOR and 20.5 mcg/L for RLP respectively, was calculated. The inhibitory quotient (IQ) was also computed, which was 6 times IC50 for DOR and 4.5 times IC50 for RLP. Finally, nomograms were constructed to estimate the probability of having C72h/3do > IC50 or > IQ for different ranges of C24h. RESULTS: Simulated C24h median ± SD for RLP were 61.8 ± 0.4 mcg/L and for DOR 397 ± 0 mcg/L. For RLP, 99.3 ± 0.1% exceeded IC50 at C24h, 16.4 ± 0.4% at C72h/3do, and none surpassed the IQ threshold. In contrast, DOR had 100% ± 0% above IC50 at C24h, 93.6 ± 0.2% at C72h/3do, and 58.6 ± 0.5% exceeded the IQ. CONCLUSIONS: These findings suggest that treatment with DOR may offer a more forgiving therapeutic profile than RLP, given the larger proportion of patients achieving effective drug exposure with DOR. However, it is important to acknowledge a significant limitation of this study, namely, the assumption that drug concentration is a perfect surrogate for drug effectiveness.

Everolimus Personalized Therapy: Second Consensus Report by the International Association of Therapeutic Drug Monitoring and Clinical Toxicology.

ABSTRACT: The Immunosuppressive Drugs Scientific Committee of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology established the second consensus report to guide Therapeutic Drug Monitoring (TDM) of everolimus (EVR) and its optimal use in clinical practice 7 years after the first version was published in 2016. This version provides information focused on new developments that have arisen in the last 7 years. For the general aspects of the pharmacology and TDM of EVR that have retained their relevance, readers can refer to the 2016 document. This edition includes new evidence from the literature, focusing on the topics updated during the last 7 years, including indirect pharmacological effects of EVR on the mammalian target of rapamycin complex 2 with the major mechanism of direct inhibition of the mammalian target of rapamycin complex 1. In addition, various concepts and technical options to monitor EVR concentrations, improve analytical performance, and increase the number of options available for immunochemical analytical methods have been included. Only limited new pharmacogenetic information regarding EVR has emerged; however, pharmacometrics and model-informed precision dosing have been constructed using physiological parameters as covariates, including pharmacogenetic information. In clinical settings, EVR is combined with a decreased dose of calcineurin inhibitors, such as tacrolimus and cyclosporine, instead of mycophenolic acid. The literature and recommendations for specific organ transplantations, such as that of the kidneys, liver, heart, and lungs, as well as for oncology and pediatrics have been updated. EVR TDM for pancreatic and islet transplantation has been added to this edition. The pharmacodynamic monitoring of EVR in organ transplantation has also been updated. These updates and additions, along with the previous version of this consensus document, will be helpful to clinicians and researchers treating patients receiving EVR.

Activities of clinical pharmacologists across Europe: A survey by the European association of clinical pharmacology and therapeutics.

PURPOSE: In order to explore clinical pharmacology and therapeutics (CPT) teaching and practices across continental Europe, the European Association of Clinical Pharmacology and Therapeutics (EACPT) made a survey in 2022 amongst its 27 affiliated societies. METHODS: The survey was made available online to EACPT representatives, and 47 filled-in questionnaires were received from 25 countries (one to five per country), representing all geographic areas of Europe. RESULTS: Clinical pharmacologists (CPs) spend 25%, 30%, 15%, and 25% of their time in teaching, hospital activities, committees, and research, respectively, with large variations across and within countries. CPT courses are given at Schools of Medicine in all the countries except one, mostly organized and taught by medical doctors (MDs). In Central, Western, and Southern Europe, the teachers may have medicine or pharmacy training. Therapeutic drug monitoring and pharmacovigilance were the hospital activities most frequently reported, and clinical/forensic toxicology, rounds of visits, and pharmacogenetics the least. Two-thirds of the panel think CPs should be MDs. However, the transversal nature of CPT was underlined, with patients/diseases and drugs as gravity centres, thus calling for the complementary skills of MDs and PharmDs. Besides, most respondents reported that clinical pharmacists in their country are involved in rounds of visits, pharmacovigilance, TDM, and/or pharmacogenetic testing and that collaborations with them would be beneficial. CONCLUSION: CPT comes with a plurality of backgrounds and activities, all required to embrace the different pathologies and the whole lifecycle of medicinal products, but all of them being rarely performed in any given country. The willingness to use common CPT teaching material and prescribing exams at the European level is a good sign of increasing harmonisation of our discipline Europewide.

Limited sampling strategy and population pharmacokinetic model of mycophenolic acid in pediatric patients with systemic lupus erythematosus: application of a double gamma absorption model with SAEM algorithm.

INTRODUCTION: Mycophenolic acid (MPA), the active metabolite of mycophenolate mofetil (MMF), is widely used in the treatment of systemic lupus erythematosus (SLE). It has been shown that its therapeutic drug monitoring based on the area under the curve (AUC) improves treatment efficacy. MPA exhibits a complex bimodal absorption, and a double gamma distribution model has been already proposed in the past to accurately describe this phenomenon. These previous population pharmacokinetics models (POPPK) have been developed using iterative two stage Bayesian (IT2B) or non-parametric adaptive grid (NPAG) methods. However, non-linear mixed effect (NLME) approaches based on stochastic approximation expectation-maximization (SAEM) algorithms have never been published so far for this particular model. The objectives of this study were (i) to implement the double absorption gamma model in Monolix, (ii) to compare different absorption models to describe the pharmacokinetics of MMF, and (iii) to develop a limited sampling strategy (LSS) to estimate AUC in pediatric SLE patients. MATERIAL AND METHODS: A data splitting of full pharmacokinetic profiles sampled in 67 children extracted either from the expert system ISBA (n = 34) or the hospital Saint Louis (n = 33) was performed into train (75%) and test (25%) sets. A POPPK was developed for MPA in the train set using a NLME and the SAEM algorithm and different absorption models were implemented and compared (first order, transit, or simple and double gamma). The best limited sampling strategy was then determined in the test set using a maximum-a-posteriori Bayesian method to estimate individual PK parameters and AUC based on three blood samples compared to the reference AUC calculated using the trapezoidal rule applied on all samples and performances were assessed in the test set. RESULTS: Mean patient age and dose was 13 years old (5-18) and 18.1 mg/kg (7.9-47.6), respectively. MPA concentrations (764) from 107 occasions were included in the analysis. A double gamma absorption with a first-order elimination from the central compartment best fitted the data. The optimal LSS with samples at 30 min, 2 h, and 3 h post-dose exhibited good performances in the test set (mean bias - 0.32% and RMSE 21.0%). CONCLUSION: The POPPK developed in this study adequately estimated the MPA AUC in pediatric patients with SLE based on three samples. The double absorption gamma model developed with the SAEM algorithm showed very accurate fit and reduced computation time.

Impact of hypoxia and reoxygenation on the extra/intracellular metabolome and on transporter expression in a human kidney proximal tubular cell line.

INTRODUCTION: Ischemia-reperfusion injury (IRI) induces several perturbations that alter immediate kidney graft function after transplantation and may affect long-term graft outcomes. Given the IRI-dependent metabolic disturbances previously reported, we hypothesized that proximal transporters handling endo/exogenous substrates may be victims of such lesions. OBJECTIVES: This study aimed to determine the impact of hypoxia/reoxygenation on the human proximal transport system through two semi-targeted omics analyses. METHODS: Human proximal tubular cells were cultured in hypoxia (6 or 24 h), each followed by 2, 24 or 48-h reoxygenation. We investigated the transcriptomic modulation of transporters. Using semi-targeted LC-MS/MS profiling, we characterized the extra/intracellular metabolome. Statistical modelling was used to identify significant metabolic variations. RESULTS: The expression profile of transporters was impacted during hypoxia (y + LAT1 and OCTN2), reoxygenation (MRP2, PEPT1/2, rBAT, and OATP4C1), or in both conditions (P-gp and GLUT1). The P-gp and GLUT1 transcripts increased (FC (fold change) = 2.93 and 4.11, respectively) after 2-h reoxygenation preceded by 24-h hypoxia. We observed a downregulation (FC = 0.42) of y+LAT1 after 24-h hypoxia, and of PEPT2 after 24-h hypoxia followed by 2-h reoxygenation (FC = 0.40). Metabolomics showed that hypoxia altered the energetic pathways. However, intracellular metabolic homeostasis and cellular exchanges were promptly restored after reoxygenation. CONCLUSION: This study provides insight into the transcriptomic response of the tubular transporters to hypoxia/reoxygenation. No correlation was found between the expression of transporters and the metabolic variations observed. Given the complexity of studying the global tubular transport systems, we propose that further studies focus on targeted transporters.

A multi-omics investigation of tacrolimus off-target effects on a proximal tubule cell-line.

INTRODUCTION: Tacrolimus, an immunosuppressive drug prescribed to a majority of organ transplant recipients is nephrotoxic, through still unclear mechanisms. This study on a lineage of proximal tubular cells using a multi-omics approach aims to detect off-target pathways modulated by tacrolimus that can explain its nephrotoxicity. METHODS: LLC-PK1 cells were exposed to 5 µM of tacrolimus for 24 h in order to saturate its therapeutic target FKBP12 and other high-affine FKBPs and favour its binding to less affine targets. Intracellular proteins and metabolites, and extracellular metabolites were extracted and analysed by LC-MS/MS. The transcriptional expression of the dysregulated proteins PCK-1, as well as of the other gluconeogenesis-limiting enzymes FBP1 and FBP2, was measured using RT-qPCR. Cell viability with this concentration of tacrolimus was further checked until 72 h. RESULTS: In our cell model of acute exposure to a high concentration of tacrolimus, different metabolic pathways were impacted including those of arginine (e.g., citrulline, ornithine) (p < 0.0001), amino acids (e.g., valine, isoleucine, aspartic acid) (p < 0.0001) and pyrimidine (p < 0.01). In addition, it induced oxidative stress (p < 0.01) as shown by a decrease in total cell glutathione quantity. It impacted cell energy through an increase in Krebs cycle intermediates (e.g., citrate, aconitate, fumarate) (p < 0.01) and down-regulation of PCK-1 (p < 0.05) and FPB1 (p < 0.01), which are key enzymes in gluconeogenesis and acid-base balance control. DISCUSSION: The variations found using a multi-omics pharmacological approach clearly point towards a dysregulation of energy production and decreased gluconeogenesis, a hallmark of chronic kidney disease which may also be an important toxicity pathway of tacrolimus.

Tacrolimus population pharmacokinetics in adult heart transplant patients.

INTRODUCTION: Tacrolimus is an immunosuppressant largely used in heart transplantation. However, the calculation of its exposure based on the area under the curve (AUC) requires the use of a population pharmacokinetic (PK) model. The aims of this work were (i) to develop a population PK model for tacrolimus in heart transplant patients, (ii) to derive a maximum a posteriori Bayesian estimator (MAP-BE) based on a limited sampling strategy (LSS) and (iii) to estimate probabilities of target attainment (PTAs) for AUC and trough concentration (C0). MATERIAL AND METHODS: Forty-seven PK profiles (546 concentrations) of 18 heart transplant patients of the Pharmacocinétique des Immunosuppresseurs chez les patients GREffés Cardiaques study receiving tacrolimus (Prograf®) were included. The database was split into a development (80%) and a validation (20%) set. PK parameters were estimated in MONOLIX® and based on this model a Bayesian estimator using an LSS was built. Simulations were performed to calculate the PTA for AUC and C0. RESULTS: The best model to describe the tacrolimus PK was a two-compartment model with a transit absorption and a linear elimination. Only the CYP3A5 covariate was kept in the final model. The derived MAP-BE based on the LSS (0-1-2 h postdose) yielded an AUC bias ± SD = 2.7 ± 10.2% and an imprecision of 9.9% in comparison to the reference AUC calculated using the trapezoidal rule. PTAs based on AUC or C0 allowed new recommendations to be proposed for starting doses (0.11 mg·kg-1 ·12 h-1 for the CYP3A5 nonexpressor and 0.22 mg·kg1 ·12 h-1 for the CYP3A5 expressor). CONCLUSION: The MAP-BE developed should facilitate estimation of tacrolimus AUC in heart transplant patients.

A Hybrid Algorithm Combining Population Pharmacokinetic and Machine Learning for Isavuconazole Exposure Prediction.

OBJECTIVES: Maximum a posteriori Bayesian estimation (MAP-BE) based on a limited sampling strategy and a population pharmacokinetic (POPPK) model is used to estimate individual pharmacokinetic parameters. Recently, we proposed a methodology that combined population pharmacokinetic and machine learning (ML) to decrease the bias and imprecision in individual iohexol clearance prediction. The aim of this study was to confirm the previous results by developing a hybrid algorithm combining POPPK, MAP-BE and ML that accurately predicts isavuconazole clearance. METHODS: A total of 1727 isavuconazole rich PK profiles were simulated using a POPPK model from the literature, and MAP-BE was used to estimate the clearance based on: (i) the full PK profiles (refCL); and (ii) C24h only (C24h-CL). Xgboost was trained to correct the error between refCL and C24h-CL in the training dataset (75%). C24h-CL as well as ML-corrected C24h-CL were evaluated in a testing dataset (25%) and then in a set of PK profiles simulated using another published POPPK model. RESULTS: A strong decrease in mean predictive error (MPE%), imprecision (RMSE%) and the number of profiles outside ± 20% MPE% (n-out20%) was observed with the hybrid algorithm (decreased in MPE% by 95.8% and 85.6%; RMSE% by 69.5% and 69.0%; n-out20% by 97.4% and 100% in the training and testing sets, respectively. In the external validation set, the hybrid algorithm decreased MPE% by 96%, RMSE% by 68% and n-out20% by 100%. CONCLUSION: The hybrid model proposed significantly improved isavuconazole AUC estimation over MAP-BE based on the sole C24h and may improve dose adjustment.

Longitudinal Exposure to Tacrolimus and New-Onset Diabetes Mellitus in Renal Transplant Patients.

PURPOSE: Tacrolimus is an immunosuppressant widely used in transplantations requiring mandatory concentration-controlled dosing to prevent acute rejection or adverse effects, including new-onset diabetes mellitus (NODM). However, no relationship between NODM and tacrolimus exposure has been established. This study aimed to evaluate the relationship between cumulative tacrolimus exposure and NODM occurrence. METHODS: A total of 452 kidney transplant patients were included in this study. Sixteen patients developed NODM during the first 3 months after transplant. We considered all tacrolimus concentration (C0) values collected until the diagnosis of NODM in these patients and until 3 months after transplant in the others. New tacrolimus cumulative exposure metrics were derived from the time profile of the tacrolimus morning predose concentration, C0: the percentage of C0 values > cutoff, the average of C0 values above the cutoff, and the percentage of the area under C0 versus time curve, AUCC0, above the cutoff. The cutoff chosen was 15 ng/mL, corresponding to the higher end of the therapeutic range for the early post-transplant period. The influence of these metrics on NODM and other clinical and biological characteristics was investigated using the Cox models. RESULTS: The percentage of C0 > 15 mcg/L was statistically different between patients with and without NODM (P = 0.01). Only these tacrolimus C0-derived metrics were significantly associated with an increased risk of NODM [HR: 1.73 (1.43-2.10, P < 0.001)]. CONCLUSION: This study shows that tacrolimus concentrations >15 mcg/L affect the incidence of NODM.

Mycophenolate Mofetil Dose Adjustment in Pediatric Kidney Transplant Recipients.

BACKGROUND: The Immunosuppressant Bayesian Dose Adjustment web site aids clinicians and pharmacologists involved in the care of transplant recipients; it proposes dose adjustments based on the estimated area under the concentration-time curve (AUCs). Three concentrations (T 20 min , T 1 h , and T 3 h ) are sufficient to estimate mycophenolic acid (MPA) AUC 0-12 h in pediatric kidney transplant recipients. This study investigates mycophenolate mofetil (MMF) doses and MPA AUC values in pediatric kidney transplant recipients, and target exposure attainment when the proposed doses were followed, through a large-scale analysis of the data set collated since the inception of the Immunosuppressant Bayesian Dose Adjustment web site. METHODS: In this study, 4051 MMF dose adjustment requests, corresponding to 1051 patients aged 0-18 years, were retrospectively analyzed. AUC calculations were performed in the back office of the Immunosuppressant Bayesian Dose Adjustment using published Bayesian and population pharmacokinetic models. RESULTS: The first AUC request was posted >12 months posttransplantation for 41% of patients. Overall, only 50% had the first MPA AUC 0-12 h within the recommended 30-60 mg.h/L range. When the proposed dose was not followed, the proportion of patients with an AUC in the therapeutic range for MMF with cyclosporine or tacrolimus at the subsequent request was lower (40% and 45%, respectively) than when it was followed (58% and 60%, respectively): P = 0.08 and 0.006, respectively. Furthermore, 3 months posttransplantation, the dispersion of AUC values was often lower at the second visit when the proposed doses were followed, namely, P = 0.03, 0.003, and 0.07 in the 4 months-1 year, and beyond 1 year with <6-month or >6-month periods between both visits, respectively. CONCLUSIONS: Owing to extreme interindividual variability in MPA exposure, MMF dose adjustment is necessary; it is efficient at reducing such variability when based on MPA AUC.

Tacrolimus Exposure Before and After a Switch From Twice-Daily Immediate-Release to Once-Daily Prolonged Release Tacrolimus: The ENVARSWITCH Study.

LCP-tacrolimus displays enhanced oral bioavailability compared to immediate-release (IR-) tacrolimus. The ENVARSWITCH study aimed to compare tacrolimus AUC0-24 h in stable kidney (KTR) and liver transplant recipients (LTR) on IR-tacrolimus converted to LCP-tacrolimus, in order to re-evaluate the 1:0.7 dose ratio recommended in the context of a switch and the efficiency of the subsequent dose adjustment. Tacrolimus AUC0-24 h was obtained by Bayesian estimation based on three concentrations measured in dried blood spots before (V2), after the switch (V3), and after LCP-tacrolimus dose adjustment intended to reach the pre-switch AUC0-24 h (V4). AUC0-24 h estimates and distributions were compared using the bioequivalence rule for narrow therapeutic range drugs (Westlake 90% CI within 0.90-1.11). Fifty-three KTR and 48 LTR completed the study with no major deviation. AUC0-24 h bioequivalence was met in the entire population and in KTR between V2 and V4 and between V2 and V3. In LTR, the Westlake 90% CI was close to the acceptance limits between V2 and V4 (90% CI = [0.96-1.14]) and between V2 and V3 (90% CI = [0.96-1.15]). The 1:0.7 dose ratio is convenient for KTR but may be adjusted individually for LTR. The combination of DBS and Bayesian estimation for tacrolimus dose adjustment may help with reaching appropriate exposure to tacrolimus rapidly after a switch.

Within-subject pharmacokinetic variability has a strong influence on individual exposure ratios in bioequivalence studies, hence on drug formulation interchangeability.

INTRODUCTION: Bioequivalence between a reference and a generic drug is based on the hypothesis that a ± 20% change in blood exposure (or ± 10% for drugs with narrow therapeutic index, NTI) following the generic/reference switch will not have any therapeutic consequences. However, the individual exposure ratio between generic and reference can be higher than 1.20 (or 1.10). This study aims to analyse the different parameters influencing the individual exposure ratio, hence the conditions for reference/generic interchangeability. METHODS: Bioequivalence studies with a double cross-over design for a virtual drug were simulated using 100 random sets of 12, 24, 48 or 100 pairs of areas under the curve (AUC), varying the generic/reference AUC geometric mean ratios between 0.80 and 1.25 and the within-subject exposure variance of the reference and the generic formulations. RESULTS: The proportion of subjects with an exposure generic/reference ratio outside the ± 10% or ± 20% acceptance intervals increases when (1) the reference within-subject variance increases; (2) the ratio of the generic within-subject variance on the reference within-subject variance increases; and (3) the generic/reference mean AUC ratio diverges from 1.0. When only considering replicated administrations of the reference, the individual exposure ratio increases with the within-subject variance, yielding values outside the usually accepted individual exposure ratio range of 0.5 to 2 for drugs with narrow therapeutic index as soon as the within-subject variance standard deviation is ≥ 0.25 (equivalent to within-patient CV% > 25%). CONCLUSIONS: Interchangeability between reference and generic formulations, especially for drugs with narrow therapeutic index can only be assumed if, the within-subject variance of generic is less or equal to the within-subject variance of reference or, if this is not the case, if the distribution of the generic/generic individual exposure ratios is included within the therapeutic margins of the reference drug.

Application of machine learning to predict tacrolimus exposure in liver and kidney transplant patients given the MeltDose formulation.

PURPOSE: Machine Learning (ML) algorithms represent an interesting alternative to maximum a posteriori Bayesian estimators (MAP-BE) for tacrolimus AUC estimation, but it is not known if training an ML model using a lower number of full pharmacokinetic (PK) profiles (= "true" reference AUC) provides better performances than using a larger dataset of less accurate AUC estimates. The objectives of this study were: to develop and benchmark ML algorithms trained using full PK profiles to estimate MeltDose®-tacrolimus individual AUCs using 2 or 3 blood concentrations; and to compare their performance to MAP-BE. METHODS: Data from liver (n = 113) and kidney (n = 97) transplant recipients involved in MeltDose-tacrolimus PK studies were used for the training and evaluation of ML algorithms. "True" AUC0-24 h was calculated for each patient using the trapezoidal rule on the full PK profile. ML algorithms were trained to estimate tacrolimus true AUC using 2 or 3 blood concentrations. Performances were evaluated in 2 external sets of 16 (renal) and 48 (liver) transplant patients. RESULTS: Best estimation performances were obtained with the MARS algorithm and the following limited sampling strategies (LSS): predose (0), 8, and 12 h post-dose (rMPE = - 1.28%, rRMSE = 7.57%), or 0 and 12 h (rMPE = - 1.9%, rRMSE = 10.06%). In the external dataset, the performances of the final ML algorithms based on two samples in kidney (rMPE = - 3.1%, rRMSE = 11.1%) or liver transplant recipients (rMPE = - 3.4%, rRMSE = 9.86%) were as good as or better than those of MAP-BEs based on three time points. CONCLUSION: The MARS ML models developed using "true" MeltDose®-tacrolimus AUCs yielded accurate individual estimations using only two blood concentrations.