Adaptative Strategy of Immunosuppressive Drugs Dosage Adjustments When Combined With Nirmatrelvir/Ritonavir in Solid Organ Transplant Recipients With COVID-19.

Nirmatrelvir/ritonavir is a promising option for preventing severe COVID-19 in solid organ transplant recipients with SARS-CoV-2 infection. However, concerns have arisen regarding potential drug interactions with calcineurin inhibitors (CNI). This two-phase multicentre retrospective study, involving 113 patients on tacrolimus and 13 on cyclosporine A, aimed to assess the feasibility and outcomes of recommendations issued by The French societies of transplantation (SFT) and pharmacology (SFPT) for CNI management in this context. The study first evaluated adherence to recommendations, CNI exposure, and clinical outcomes. Notably, 96.5% of patients on tacrolimus adhered to the recommendations, maintaining stable tacrolimus trough concentrations (C0) during nirmatrelvir/ritonavir treatment. After reintroduction, most patients experienced increased C0, with 42.9% surpassing 15 ng/mL, including three patients exceeding 40 ng/mL. Similar trends were observed in cyclosporine A patients, with no COVID-19-related hospitalizations. Moreover, data from 22 patients were used to refine the reintroduction strategy. Modelling analyses suggested reintroducing tacrolimus at 50% of the initial dose on day 8, and then at 100% from day 9 as the optimal approach. In conclusion, the current strategy effectively maintains consistent tacrolimus exposure during nirmatrelvir/ritonavir treatment, and a stepwise reintroduction of tacrolimus may be better suited to the low CYP3A recovery.

Better Rejection-Free Survival at Three Years in Kidney Transplant Recipients With Model-Informed Precision Dosing of Mycophenolate Mofetil.

The clinical impact of individual dose adjustment of mycophenolate mofetil is still debated, due to conflicting results from randomized clinical trials. This retrospective study aimed to compare 3-year rejection-free survival and adverse effects between adult kidney transplant recipients (KTRs) with or without mycophenolate mofetil model-informed precision dosing (MIPD). MIPD is defined here as mycophenolic acid area under the curve (AUC0-12h ) estimation using a limited sampling strategy, pharmacokinetic models and Bayesian estimators; dose recommendation to reach AUC0-12h = 45 mg.h/L; using a widely used online expert system. The study, nested in two multicenter prospective cohort studies, focused on patients who received a mycophenolate drug and were followed up for 1-3 years. Mycophenolate mofetil MIPD was prescribed as per local practice, on a regular basis, when deemed necessary, or not at all. The MIPD group included 341 KTRs and the control group 392. At 3 years, rejection-free survival was respectively 91.2% and 80.6% (P < 0.001) and the cumulative incidence of rejection 5.08% vs. 12.7% per patient × year (hazard ratio = 0.49 (0.34, 0.71), P < 0.001), corresponding to a 2.5-fold reduction. Significant association with rejection-free survival was confirmed in patients at low or high risk of rejection (P = 0.017 and 0.013) and in patients on tacrolimus, but not on cyclosporine (P < 0.001 and 0.205). The mycophenolate mofetil MIPD group had significantly more adverse effects, but most occurred before the first AUC0-12h , suggesting some may be the reason why MIPD was ordered.

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.

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.

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.

New perspectives for the therapeutic drug monitoring of tacrolimus: Quantification in volumetric DBS based on an automated extraction and LC-MS/MS analysis.

Volumetric microsampling devices have been developed for home-based capillary blood sampling and are now increasingly proposed for the therapeutic drug monitoring (TDM) of immunosuppressive drugs. Our objective was to validate a LC-MS/MS method for tacrolimus quantification based on both a manual and an automated extraction of dried blood spots (DBS) collected with a volumetric microsampling device. DBS collection was performed by placing a drop of whole blood (WB) pre-spiked with tacrolimus onto a sealing film and placing the hemaPEN® device (Trajan Scientific and Medical, Melbourne, Australia) into the drop according to the device specifications. Tacrolimus was quantified using a fully automatic preparation module connected to a LCMS system (CLAM-3020® and LCMS-8060®, Shimadzu, Marne-la-Vallée, France). The method was validated analytically and clinically in accordance with the EMA and IATDMCT guidelines. The method was linear from 1 to 100 µg/L. Within- and between-run accuracy and precision fulfilled the validation criteria (biases and imprecision <15% or 20% for the lower limit of quantification). No hematocrit effect, matrix effect or carry-over was observed. No selectivity issue was identified and dilution integrity was confirmed. Tacrolimus in DBS was stable for 14 days at room temperature and +4°C, and for 72h at +60°C. There was a good correlation between tacrolimus concentrations measured in WB and in DBS of 20 kidney and liver transplant recipients (r=0.93 and 0.87, for manual and automated extraction respectively). A method for tacrolimus measurement in DBS collected with volumetric micro-sampling device, based on a fully automated process from pre-treatment to LC-MS/MS analysis was developed and validated according to analytical and clinical criteria. This performing sampling and analytical procedure opens the perspective of an easier, faster and more efficient TDM of tacrolimus for patients, clinicians and laboratories.

Does the Tacrolimus Trough Level Adequately Predict Drug Exposure in Patients Requiring a High Tacrolimus Dose?

Tacrolimus (Tac) has a narrow therapeutic range. Dosing is generally targeted at Tac trough levels (C 0), notwithstanding conflicting reports on the correlation between Tac C 0 and systemic exposure measured by the area-under-the-concentration-over-time curve (AUC). The Tac dose required to meet the target C 0 varies highly among patients. We hypothesized that patients requiring a relatively high Tac dose for a certain C 0 may show a higher AUC. Methods: We retrospectively analyzed data from 53 patients in which a 24-h Tac AUC24 estimation was performed at our center. Patients were divided into those taking a low (≤0.15 mg/kg) or high (>0.15 mg/kg) once-daily Tac dose. Multiple linear regression models were used to investigate if the association between C 0 and AUC24 changes according to dose level. Results: Despite the large difference in mean Tac dose between the low- and high-dose group (7 versus 17 mg/d), C 0 levels were similar. However, the mean AUC24 was substantially higher in the high-dose group (320 ± 96 h·µg/L versus 255 ± 81 h·µg/L, P < 0.001). This difference remained significant after adjusting for age and race. For a same C 0, every 0.01 mg/kg increase in Tac dose resulted in an AUC24 increase of 3.59 h·µg/L. Conclusions: This study challenges the general belief that C 0 levels are sufficiently reliable to estimate systemic drug exposure. We demonstrated that patients requiring a relatively high Tac dose to attain therapeutic C 0 levels have higher drug exposure and could therefore potentially be overdosed.

Doravirine Exposure Decreased by Dialysis in a HIV Patient: A Grand Round.

BACKGROUND: The authors report the case of a 66-year-old male patient who was hemodialyzed 3 times per week for chronic renal failure and treated with 100 mg of doravirine once daily in combination with dolutegravir for HIV-1. No dose adjustment is required for doravirine in cases of severe renal injury, but the effect of dialysis on its exposure is poorly understood. METHODS RESULTS: Two series of 2 samples were drawn before and after 4-hour hemodialysis and showed an average doravirine concentration decrease of 48.1 ± 6.7%. The effects of hemodialysis were important, contrary to what was expected and has been previously reported. In addition, intraindividual variability was low. Nevertheless, because the concentrations reported were largely above the inhibitory concentration 50 (IC 50 ), no dose adjustment was required. CONCLUSIONS: The decrease in doravirine concentration due to hemodialysis observed in this case report was quite significant. Therefore, therapeutic drug monitoring might be recommended in certain patients undergoing doravirine treatment also on hemodialysis.

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.

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.

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.

Can the Area Under the Curve/Trough Level Ratio Be Used to Optimize Tacrolimus Individual Dose Adjustment?

BACKGROUND: The aim of this work was to evaluate, in a large data set of renal transplant recipients, the intraindividual variability of the area under the curve (AUC)/predose concentration (C0) ratio in comparison with that of AUC, C0, AUC/dose, and C0/dose. METHODS: Patients with at least 2 tacrolimus AUC estimation requests were extracted from the Immunosuppressant Bayesian dose Adjustment website, and relative variations between 2 consecutive visits for the different metrics were calculated and compared. RESULTS: Data from 1325 patients on tacrolimus (3827 measured C0 and estimated AUC) showed that the lowest mean relative variation between 2 consecutives visits was for the AUC/C0 ratio (95% confidence interval [CI] relative fold change = -43% to 44% for AUC/C0; 95% CI, -77% to 72% for AUC; 95% CI, -82% to 98% for AUC/dose; 95% CI, -81% to 80% for C0 and 95% CI, -94% to 117% for C0/dose. The correlation between 2 consecutive requests, whether close or far apart, was also best for the AUC/C0 ratio ( r = 0.33 and r = 0.34, respectively) in comparison with C0 ( r = 0.21 and r = 0.22, respectively) and AUC ( r = 0.19 and 0.28, respectively). Regression analysis between AUC0-24 and C0 showed that for some patients, the usual C0 targets translated into some very unusual AUC values. As the AUC/C0 ratio is quite stable during large periods, individualized C0 targets can be derived from the AUC targets, and an algorithm that estimates the individualized C0 was developed for situations in which prior AUC estimates are available or not. CONCLUSIONS: In this study, we confirmed in a large data set that the AUC/C0 ratio yields low intraindividual variability, whereas C0 shows the largest, and we propose to calculate individualized C0 targets based on this ratio.

Iohexol plasma and urinary concentrations in cirrhotic patients: A pilot study.

BACKGROUND: Renal failure is an independent prognostic factor for survival in patients with cirrhosis. Equations to calculate serum creatinine significantly overestimate the glomerular filtration rate (GFR). Plasma clearance of direct biomarkers has been used to improve the accuracy of evaluations of GFR in this population, but no study has simultaneously measured plasma and urinary clearance, which is the gold standard. AIM: To study calculated plasma and urinary concentrations of iohexol, based on the kinetics of samples collected over 24 h from cirrhotic patients with three different grades of ascites. METHODS: One dose of iohexol (5 mL) was injected intravenously and plasma concentrations were measured 11 times over 24 h in nine cirrhotic patients. The urinary concentration of iohexol was also measured, in urine collected at 4, 8, 12 and 24 h. RESULTS: The plasma and urinary curves of iohexol were similar; however, incomplete urinary excretion was detected at 24 h. Within the estimated GFR limits of our population (> 30 and < 120 mL/min/1.73 m²), the median measured GFR (mGFR) was 63.7 mL/min/1.73 m² (range: 41.3-111.3 mL/min/1.73 m²), which was an accurate reflection of the actual GFR. Creatinine-based formulas for estimating GFR showed significant bias and imprecision, while the Brochner-Mortensen (BM) equation accurately estimated the mGFR (r = 0.93). CONCLUSION: Plasma clearance of iohexol seems useful for determining GFR regardless of the ascites grade. We will secondly devise a pharmacokinetics model requiring fewer samples andvalidate the BM equation.

Management of drug-drug interactions with nirmatrelvir/ritonavir in patients treated for Covid-19: Guidelines from the French Society of Pharmacology and Therapeutics (SFPT).

OBJECTIVES: Nirmatrelvir in association with ritonavir (PAXLOVID™, Pfizer) is an antiviral agent targeting the 3-chymotrypsin-like cysteine protease enzyme (3C-like protease or Mpro) which is a key enzyme of the viral cycle of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This combination with a well-known pharmacokinetic enhancer leads to a high risk for drug-drug interactions in a polymedicated elected population for treatment. The aim of this work was to provide recommendations on behalf of the national French society of pharmacology (French Society of Pharmacology and Therapeutics; SFPT), by suggesting optimal and pragmatic therapeutic strategies if nirmatrelvir/ritonavir is to be given together with drugs commonly used, in order to ensure secured physicians' prescription. METHODS: Six clinical pharmacologists search the scientific literature to provide a first draft of recommendations. Thereafter, twelve other clinical pharmacologists verified the recommendations and proposed modifications. The final draft was then validated by all 18 participants. RESULTS: Five distinct recommendations were issued: i) contra-indications, ii) "PAXLOVID™ not recommended with the comedication", iii) "PAXLOVID™ possible whether the comedication is discontinued", iv) "PAXLOVID™ possible only after an expert advice" and v) "PAXLOVID™ possible without modification of the associated treatment". The final document comprises recommendations for 171 drugs/therapeutic classes aiming to secure prescription. In complex situations, clinicians are advised to contact their pharmacology department to obtain specific recommendations on the management of drug-drug interactions with nirmatrelvir/ritonavir. CONCLUSION: These recommendations intend to be a help for clinicians willing to prescribe nirmatrelvir/ritonavir and to prevent drug-drug interactions leading to adverse drug reactions or loss of efficacy. They constitute a guideline for primary care situations. Of course, some complex situations may require expert advices and here, again, clinical pharmacologists are at the forefront in providing therapeutic advice.

Therapeutic education as a tool to improve patient-reported and clinical outcomes after renal transplantation: results of the EPHEGREN multicenter retrospective cohort study.

Patients are not always aware of the inconveniences associated with renal transplantation, which they compare with a « rebirth ¼, and from which they expect complete recovery. Therapeutic education is proposed to prepare patients for their life after transplantation. This study evaluated the impact of pretransplant therapeutic education on patient-reported outcomes and rejection-free survival over the first year. We collected data from 383 renal transplant patients followed-up in seven centers. Patients who benefited from therapeutic education before transplantation (N = 182) were compared with patients who did not (N = 139) for quality-of-life, adherence and adverse events using the Pearson's chi-square test, one-way ANOVA or t-test. The association between therapeutic education and time to acute rejection was investigated using Cox models. The patients who benefited from therapeutic education reported adverse events less frequently (e.g., tremor: 9% vs. 32.4%, P = 0.01) and better quality-of-life (MCS-QOL: 50.7 ± 8.1 vs. 47.7 ± 9.5, P = 0.02; PCS-QOL: 49.1 ± 7.1 vs. 46.0 ± 9.2, P = 0.013). No difference was found on adherence. Rejection-free survival was slightly better in the therapeutic education group (HR = 0.44, 95% CI = [0.19-1.01]). This multicenter retrospective cohort study suggests that integrating therapeutic education to care pathways entails clinical benefit, in terms of quality-of-life, self-reported adverse events and rejection-free survival. Randomized clinical trials are necessary to confirm this.

Towards therapeutic drug monitoring of mycophenolic acid in mucous membrane pemphigoid: A retrospective single-centre study.

BACKGROUND: Mycophenolate mofetil (MMF) is theoretically a treatment of choice for mucous membrane pemphigoid (MMP), due to its good long-term tolerance and efficacy especially in elderly patients. However, no therapeutic monitoring is currently performed despite its large inter-individual variability. OBJECTIVES: The aim of this study was to investigate the exposure/effect relationship based on the area under the curve (AUC) or trough level of mycophenolic acid in MMP patients. METHODS: Thirteen patients (n = 29 AUC measurements performed between February 2013 and November 2016) treated for MMP at Limoges University Hospital were evaluated using the Mucous Membrane Pemphigoid Disease Area Index score, and patients were classified as improvement (>50% decrease vs. baseline) vs. stabilisation (<50%) or non-response (no improvement). AUC was estimated using a population pharmacokinetic model and Bayesian estimation. The association between exposure parameters, demographic variables and response group was investigated using time-dependent Cox models, and an AUC threshold for 'improvement' was also investigated. RESULTS: An improvement was observed in approximately 70% of the patients. Only the MPA AUC0-24 h was retained in the multivariate analysis with a decreased risk of stabilisation/non-response per 10 mg*h/L increase, (HR = 0.64, 95% CI = [0.43-0.94], P = 0.0038). That led to an AUC0-24 h threshold of 89 mg*h/L associated with excellent performances (AUC ROC = 0.828, Sen = 75%, Spe = 100%, P = 0.0001). DISCUSSION/CONCLUSION: An association between MPA exposure and disease was observed. Therapeutic drug monitoring can be proposed with an AUC0-24 h threshold of 89 mg*h/L. It might improve the long-term response of patients to this drug with better tolerance than rituximab or cyclophosphamide.

A Population Pharmacokinetic Modeling Approach to Determine the Efficacy of Intravenous Amikacin in Children with Cystic Fibrosis.

BACKGROUND: In children with cystic fibrosis (CF), the currently recommended amikacin dose ranges between 30 and 35 mg/kg/d; however, data supporting this dosing efficacy are lacking. In this article, the objectives were to develop a nonparametric pharmacokinetic population model for amikacin in children with CF and investigate the efficacy and toxicity at different dose rates for distinct minimum inhibitory concentration (MIC) clinical breakpoints using Monte Carlo simulations. METHODS: Data from 94 children with CF (613 serum concentrations) from the Bordeaux University Hospital's CF-centre were analyzed. After determination of nonparametric pharmacokinetic population model parameters and associated influent covariates in Pmetrics, 1000 Monte Carlo simulations were performed for 7 different dose rates between 30 and 60 mg/kg/d, to predict the probability of obtaining peak serum amikacin ≥10 × MIC and trough level ≤2.5 mg/L, for MIC values between 1 and 16 mg/L. RESULTS: The median (min-max) age and weight were 10 (0.3-17) years and 29 (6-71) kg, respectively, with only 2 children younger than 1 year of age. Body weight and creatinine clearance significantly impacted the amikacin volume of distribution and clearance. The mean relative bias/root mean squared error between observed and individual predicted concentrations was -0.68%/8.1%. Monte Carlo simulations showed that for sensitive bacteria with MICs ≤ 4, 30 mg/kg/d was most appropriate for a 100% success rate; for bacteria with MICs ≥ 8 [eg, Pseudomonas aeruginosa (MICamikacin = 8)], a dose of at least 40 mg/kg/d allowed a high success probability (90%), with a trough level below 2.5 mg/L. CONCLUSIONS: For intermediate pathogens, a dose of at least 40 mg/kg/d can improve efficacy, with an acceptable calculated residual trough level in cases of normal or hyperfiltration. Because amikacin undergoes renal clearance, which is immature until 1 year of age, dosing recommendations for this age group may be markedly high, warranting cautious interpretation.

Clinical Pharmacokinetics and Bayesian Estimators for the Individual Dose Adjustment of a Generic Formulation of Tacrolimus in Adult Kidney Transplant Recipients.

BACKGROUND: Tacrolimus has a narrow therapeutic range and requires dose adjustment, usually based on the trough blood concentration but preferably on the area under the concentration-time curve over 12 h post-dose (AUC0-12h). The single-arm, multicentre, clinical study IMPAKT aimed: (i) to develop, in de novo kidney transplant recipients, pharmacokinetic models and maximum a-posteriori Bayesian estimators for a generic, immediate-release, oral formulation of tacrolimus to estimate tacrolimus AUC0-12h at different post-transplant periods using a limited sampling strategy, and considering the CYP3A5*3 polymorphism as a covariate and (ii) to compare the performance of these Bayesian estimators to those previously developed for the original formulation. METHODS: Thirty patients were enrolled and 29 provided nine blood samples over 9 h at day 7 and months 1 and 3 post-transplant. Tacrolimus blood profiles measured with liquid chromatography-tandem mass spectrometry were modelled using one-compartment, double gamma absorption, linear elimination models developed in-house. Different limited sampling strategies of three time-points within 4 h post-dose were tested for the maximum a-posteriori Bayesian estimator of tacrolimus AUC0-12h. The models and estimators were validated internally and their performance compared to that of models previously developed for the original formulation. RESULTS: The concentration-time curves, AUC0-12h/dose and trough blood concentration/dose exhibited wide inter-individual variability. The covariate-free pharmacokinetic models developed for the three post-transplant periods closely fitted the individual profiles. Maximum a-posteriori Bayesian estimators based on three different limited sampling strategies and no covariate yielded accurate AUC0-12h estimates, including for the five cytochrome P450 3A5 expressers and for the four patients without corticosteroids. The 0-1 h-3 h strategy finally chosen had very low bias (- 4.0 to - 2.5%) and imprecision (root mean square error 5.5-9.2%). The maximum a-posteriori Bayesian estimators previously developed for the reference product fitted the generic profiles with similar performance. CONCLUSIONS: We developed original pharmacokinetic models and accurate maximum a-posteriori Bayesian estimators to estimate patient exposure and adjust the dose of generic tacrolimus, and confirmed that the robust tools previously developed for the original formulation can be applied to this generic.