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.

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.

Synergizing pharmacometrics and pharmacovigilance for medication error management: the case of secukinumab.

PURPOSE: To demonstrate the effective integration of pharmacometrics and pharmacovigilance in managing medication errors, highlighted by a case involving secukinumab in a patient with hidradenitis suppurativa. METHODS: We present the case of a 41-year-old male with progressive hidradenitis suppurativa, unresponsive to multiple antibiotic regimens and infliximab treatment. Due to a medication error, the patient received 300 mg of secukinumab daily for 4 days instead of weekly, totaling 1200 mg. The regional pharmacovigilance center assessed potential toxicity, and a pharmacometric analysis using a population pharmacokinetic model was performed to inform dosing adjustments. RESULTS: Clinical data indicated that the received doses were within a non-toxic range. No adverse effects were observed. Pharmacometric simulations revealed a risk of underexposure due to the dosing error. Based on these simulations, it was recommended to restart monthly secukinumab injections on day 35 after the initial dose. Measured plasma concentrations before re-administration confirmed the model's accuracy. CONCLUSION: This case highlights the crucial collaboration between clinical services, pharmacovigilance, and pharmacometrics in managing medication errors. Such interdisciplinary efforts ensure therapeutic efficacy and patient safety by maintaining appropriate drug exposure levels.

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.

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.

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.

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.

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.

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.

Tacrolimus Bayesian Dose Adjustment in Pediatric Renal Transplant Recipients.

BACKGROUND: Immunosuppressant Bayesian Dose Adjustment (ISBA) is an online expert system that estimates the area under the curve (AUC) of immunosuppressive drugs through pharmacokinetic modelling and Bayesian estimation to propose dose adjustments to reach predefined exposure targets. The ISBA database was retrospectively analyzed to describe tacrolimus pharmacokinetics and exposure, evaluate the efficiency of ISBA dose recommendations, and propose tacrolimus AUC0-12h target ranges for pediatric renal allograft recipients treated with immediate release tacrolimus. METHODS: The database included 1935 tacrolimus dose adjustment requests from 419 patients <19 years old who were treated with immediate-release tacrolimus and followed in 21 French hospitals. The tacrolimus exposure evolution with patient age and posttransplantation time, the correlation between trough tacrolimus concentration (C0) and AUC0-12h at different periods posttransplantation, and the efficiency of dose recommendations to avoid underexposure and overexposure and to decrease between-patient AUC variability were investigated. RESULTS: Tacrolimus AUC showed large between-patient variability (CV% = 40%) but moderate within-patient variability (median = 24.3% over a 3-month period). Dose-standardized exposure but not the AUC/C0 ratio significantly decreased with time posttransplantation and patient age. We derived AUC0-12h ranges from the consensual C0 ranges using linear regression equations. When the ISBA recommended dose was applied, the AUC distribution was narrower and a significantly higher proportion was within the targets (P < 0.0001). CONCLUSIONS: ISBA efficiently reduced tacrolimus underexposure and overexposure. The AUC0-12h target ranges for pediatric patients derived from the database were similar to those previously reported for adults. Estimating the AUC/C0 ratio could help determine personalized C0 targets.

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.

Effect of genetic polymorphisms in CYP3A4, CYP3A5, and m-TOR on everolimus blood exposure and clinical outcomes in cancer patients.

Genetic variations in CYP3A4, CYP3A5, and m-TOR could contribute to interpatient variability regarding m-TOR inhibitors pharmacokinetics or cellular effects. The purpose of this study was to evaluate the influence of selected candidate variations in these genes on everolimus pharmacokinetics, efficacy, and toxicity in cancer patients. Thirty-four patients receiving everolimus for breast (n = 22) or renal (n = 10) cancers, or neuroendocrine tumors of pancreatic origin (n = 2) were included in the study. Six variants in genes related to everolimus pharmacokinetics (CYP3A4*22 and CYP3A5*3) or pharmacodynamics (m-TOR rs2295079, rs2295080, rs2024627 and rs1057079) were genotyped. Associations with trough concentrations (C0), dose reductions, or treatment interruptions due to toxicity and progression-free survival were investigated using generalized estimating equations and Cox models. CYP3A5 nonexpressers had significantly higher C0 as compared with expressers (ßGG vs AG = + 6.32 ± 2.22 ng/mL, p = 0.004). m-TOR rs2024627 was significantly associated with an increased risk of cancer progression studied alone or as part of an haplotype (T vs C: HR = 2.60, 95% CI [1.16-5.80], p = 0.020; CTCG vs other haplotypes HR = 2.29, 95% CI [1.06-4.95], p = 0.035, respectively). This study showed that CYP3A5 expression impacts everolimus pharmacokinetics in cancer patients and identified a genetic variation in m-TOR associated with the risk of cancer progression.

Population pharmacokinetics and Bayesian estimators for intravenous mycophenolate mofetil in haematopoietic stem cell transplant patients.

AIMS: Intravenous mycophenolate mofetil (IV MMF), a prodrug of mycophenolic acid (MPA), is used during nonmyeloablative and reduced-intensity conditioning haematopoetic stem cell transplantation (HCT) to improve engraftment and reduce graft-versus-host disease. The aims of this study were to develop population pharmacokinetic models and Bayesian estimators based on limited sampling strategies to allow for individual dose adjustment of intravenous mycophenolate mofetil administered by infusion in haematopoietic stem cell transplant patients. METHODS: Sixty-three MPA concentration-time profiles (median [min-max] = 6 [4-7] samples) were collected from 34 HCT recipients transplanted for 14 (1-45) days and administered IV MMF every 8 hours, concomitantly with cyclosporine. The database was split into development (75%) and validation (25%) datasets. Pharmacokinetic models characterized by a single compartment with first-order elimination, combined with two gamma distributions to describe the transformation of MMF into mycophenolic acid, were developed using in parallel nonparametric (Pmetrics) and parametric (ITSIM) approaches. The performances of the models and the derived Bayesian estimators were evaluated in the validation set. RESULTS: The best limited sampling strategy led to a bias (min, max), root mean square error between observed and modeled interdose areas under the curve in the validation dataset of -11.72% (-31.08%, 5.00%), 14.9% for ITSIM and -2.21% (-23.40%, 30.01%), 12.4% for Pmetrics with three samples collected at 0.33, 2 and 3 hours post dosing. CONCLUSION: Population pharmacokinetic models and Bayesian estimators for IV MMF in HCT have been developed and are now available online (https://pharmaco.chu-limoges.fr) for individual dose adjustment based on the interdose area under the curve.

Neurotoxicity Despite a Renal Function-Adjusted Cefepime Regimen: A Case Study.

An 83-year-old man, presenting decreased renal function (estimated glomerular filtration rate 21 mL/min/1.73 m), was treated for a bone and joint infection (on a trans-metatarsal right foot amputation) caused by Klebsiella Pneumonia sensitive to cefepime. The starting dose (1 g bid) was based on recommendations for patients presenting severe infections. One week after treatment initiation, the patient developed neurotoxicity, exhibiting extremely high plasma cefepime concentrations. Based on TDM, the dose was reduced by 8 times the original dose. This case report highlights the importance of therapeutic drug monitoring for cefepime, especially in patients presenting altered renal functions, as typical recommendations are estimated for standard patients.

Population pharmacokinetics of gentamicin in haemodialysis patients: modelling, simulations and recommendations.

PURPOSE: The usual recommended dose for gentamicin is 3 to 7 mg/kg/day for patients with a normal renal function while 1.7 mg/kg/day is recommended for patients undergoing chronic haemodialysis. The objectives of this study were to develop a population pharmacokinetics model (POPPK) for gentamicin, designed for patients undergoing dialysis, and to investigate the best dosing scheme for different MIC clinical breakpoints using Monte Carlo simulations. METHODS: In this monocentric prospective interventional open clinical study, 23 patients (141 gentamicin samples) were included. The covariates investigated were weight, creatinine, dialysis (yes/no), dialysis flow and dialysis duration. The POPPK model was developed in Pmetrics and 1000 time-concentration profiles were simulated for 9 doses between 2 and 10 mg/kg/day, with an inter-dose period of 24, 48 or 96 h to predict the probability of having both a serum peak > 8*MIC and a trough < 1 mg/L for MIC values between 0.25 and 4 mg/L. RESULTS: A two-compartment model including the dialysis on the elimination constant and bodyweight on the volume of distribution best described the data. A 30-min gentamicin infusion of 2 mg/kg/day (for MIC = 1 mg/L) or 8 mg/kg/day (for MIC = 4 mg/L) just before dialysis eliminated by two dialysis sessions before the next administration (dose interval of at least 96 h) led to a peak > 8*MIC for > 90% of the simulations and a trough concentration < 1 mg/L at 96 h for 92% and 34% respectively. CONCLUSION: The gentamicin dose generally used to treat infections in dialysis patients is insufficient and might be increased to 3-8 mg/kg/day just before dialysis, taking into account the type of infection.

Self-poisoning with 60 tablets of Apixaban, a pharmacokinetics case report.

A 67-year-old man was admitted to the emergency department about 5 h after deliberate self-poisoning with 300 mg of Apixaban. The clinical examination did not show any organ dysfunctions or haemorrhagic signs, and the patient's life was not in danger. The first analysis, upon admission, showed a concentration of 2655 µg l-1 of Apixaban. The Cmax was observed 17 h after the intake (3654 µg l-1 ), about four times the classical Tmax value (median [range]: 4 h [2-4]). The Apixaban was then eliminated following a first order elimination with a calculated half-life of 10.8 h. The anti-Xa activity seems to be linearly related to concentration up to 4000 µg l-1 . This report suggests that the use of activated charcoal should be effective up to 17 h after a massive intake.

Therapeutic Drug Monitoring of Tacrolimus-Personalized Therapy: Second Consensus Report.

Ten years ago, a consensus report on the optimization of tacrolimus was published in this journal. In 2017, the Immunosuppressive Drugs Scientific Committee of the International Association of Therapeutic Drug Monitoring and Clinical Toxicity (IATDMCT) decided to issue an updated consensus report considering the most relevant advances in tacrolimus pharmacokinetics (PK), pharmacogenetics (PG), pharmacodynamics, and immunologic biomarkers, with the aim to provide analytical and drug-exposure recommendations to assist TDM professionals and clinicians to individualize tacrolimus TDM and treatment. The consensus is based on in-depth literature searches regarding each topic that is addressed in this document. Thirty-seven international experts in the field of TDM of tacrolimus as well as its PG and biomarkers contributed to the drafting of sections most relevant for their expertise. Whenever applicable, the quality of evidence and the strength of recommendations were graded according to a published grading guide. After iterated editing, the final version of the complete document was approved by all authors. For each category of solid organ and stem cell transplantation, the current state of PK monitoring is discussed and the specific targets of tacrolimus trough concentrations (predose sample C0) are presented for subgroups of patients along with the grading of these recommendations. In addition, tacrolimus area under the concentration-time curve determination is proposed as the best TDM option early after transplantation, at the time of immunosuppression minimization, for special populations, and specific clinical situations. For indications other than transplantation, the potentially effective tacrolimus concentrations in systemic treatment are discussed without formal grading. The importance of consistency, calibration, proficiency testing, and the requirement for standardization and need for traceability and reference materials is highlighted. The status for alternative approaches for tacrolimus TDM is presented including dried blood spots, volumetric absorptive microsampling, and the development of intracellular measurements of tacrolimus. The association between CYP3A5 genotype and tacrolimus dose requirement is consistent (Grading A I). So far, pharmacodynamic and immunologic biomarkers have not entered routine monitoring, but determination of residual nuclear factor of activated T cells-regulated gene expression supports the identification of renal transplant recipients at risk of rejection, infections, and malignancy (B II). In addition, monitoring intracellular T-cell IFN-g production can help to identify kidney and liver transplant recipients at high risk of acute rejection (B II) and select good candidates for immunosuppression minimization (B II). Although cell-free DNA seems a promising biomarker of acute donor injury and to assess the minimally effective C0 of tacrolimus, multicenter prospective interventional studies are required to better evaluate its clinical utility in solid organ transplantation. Population PK models including CYP3A5 and CYP3A4 genotypes will be considered to guide initial tacrolimus dosing. Future studies should investigate the clinical benefit of time-to-event models to better evaluate biomarkers as predictive of personal response, the risk of rejection, and graft outcome. The Expert Committee concludes that considerable advances in the different fields of tacrolimus monitoring have been achieved during this last decade. Continued efforts should focus on the opportunities to implement in clinical routine the combination of new standardized PK approaches with PG, and valid biomarkers to further personalize tacrolimus therapy and to improve long-term outcomes for treated patients.

Pharmacokinetic Therapeutic Drug Monitoring of Advagraf in More Than 500 Adult Renal Transplant Patients, Using an Expert System Online.

BACKGROUND: Immunosuppressant Bayesian dose adjustment (ISBA) is an online expert system, routinely used by approximately 140 transplantation centers in the world for the dose adjustment of immunosuppressive drugs in transplant patients. This system determines the area under the curve (AUC) of the drug by pharmacokinetic modeling and Bayesian estimation. The purpose of this study was to analyze tacrolimus exposure after administration of its modified-release formulation (Advagraf) in kidney allograft recipients, to optimize its therapeutic drug monitoring. METHODS: This is a retrospective study of exposure indices measured locally [trough tacrolimus concentration (C0), C0/dose] or estimated through ISBA (AUC, AUC/dose, AUC/C0), of their evolution over posttransplantation time, and of the correlations between them. RESULTS: A total of 922 requests posted by 28 different centers for routine Advagraf adjustment in 530 different patients treated with Advagraf were studied. The exposure to, and dose requirement of, tacrolimus significantly increased across the first posttransplant months before reaching steady state. The AUC:C0 ratio (on which C0 monitoring is implicitly based) was stable across the different posttransplant periods, although with high interindividual variability. C0-AUC correlation was stronger in the late than in the early posttransplant period (r = 0.75 versus 0.63; P = 0.0075). Using the regression equations obtained, AUC ranges corresponding to different applicable C0 target ranges were calculated to guide dose adjustment. When one of the doses recommended was administered, the following AUC was significantly more often in the predicted target ranges (P < 0.0001). CONCLUSIONS: This study improves our knowledge of Advagraf pharmacokinetic variability and relations between exposure indices and the scientific background of the expert service provided through the ISBA Web site.

Towards therapeutic drug monitoring of everolimus in cancer? Results of an exploratory study of exposure-effect relationship.

INTRODUCTION: Therapeutic drug monitoring (TDM) of everolimus is not performed in oncology and no trough level (C0) target has been yet defined. The aim of this study was to determine everolimus C0 target for toxicity and efficacy. MATERIALS AND METHODS: Clinical, biological and radiologic data from 54 patients were collected. Toxicity event was defined by termination, temporary interruption and/or dose reduction of everolimus while efficacy was defined as progression-free survival. C0 values were dichotomized by ROC curve analysis and the association between exposure and outcome was determined using Cox models for repeated events (toxicity) or Cox model censured at the first event (progression free survival). RESULTS: Among the 42 patients (77.8%) with breast cancer, 10 (18.5%) kidney cancer and 2 (3.7%) neuroendocrine cancer, adverse events were reported in 75.9% of the patients (everolimus termination in 25.9% patients). C0 everolimus higher than 26.3ng/mL (Sen=0.38,Spe=0.88) were associated with a 4-fold increased risk of toxicity (HR=4.12, IC95%=[1.48-11.5], p=0.0067) whereas C0 lower than 11.9ng/mL were associated with a 3-fold increased risk of progression (HR=3.2, IC95%=[1.33-7.81],p=0.001). DISCUSSION: Further studies are required to evaluate the everolimus C0 threshold proposed for toxicity (26.3ng/mL) and for progression (11.9ng/mL) especially with a large number of patients and more homogeneous types of cancer. However, these results are in favour of TDM for everolimus in oncology.