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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.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.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.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.

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

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.

La prise d’un traitement immunosuppresseur a vie est obligatoire apres une transplantation. L’immunosuppression permet de prevenir le rejet du greffon par l’organisme receveur en limitant la reponse immunitaire acquise. Pourvoyeurs de nombreux effets indesirables, les immunosuppresseurs ont une marge therapeutique etroite et doivent faire l’objet d’un suivi particulier.

Le pharmacien et l’equipe officinale sont en premiere ligne pour accompagner les personnes transplantees dans la gestion des contraintes imposees par leur traitement. Meme s’ils representent souvent une faible proportion de la clientele officinale, ces patients requierent une attention particuliere. Les conseils a apporter sont nombreux, que ce soit en rapport avec le traitement et l’organisation au quotidien ou dans des contextes plus particuliers, comme les vacances.

La prise en charge d’un patient transplante requiert une surveillance clinique et biologique parfois complexe. L’adaptation de la posologie des immunosuppresseurs est frequente, en particulier en debut de greffe. Elle s’appuie notamment sur le suivi therapeutique pharmacologique, necessaire pour apprecier la variabilite pharmacocinetique des immunosuppresseurs, mais qui peut s’averer contraignant pour le patient.

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. 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. 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. 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.

National audience; Recently, the International Association of Therapeutic Drug Monitoring (IATDMCT), that is the learning society for biological pharmacology and toxicology, issued recommendations on the therapeutic drug monitoring (TDM) of tacrolimus. This is the second consensus after the one issued in 2009. In this document, the role of tacrolimus TDM for the four principal transplanted organs is discussed. The analytical aspects, pharmacogenetics, TDM alternative approaches and the positioning of biomarkers are also presented. Stronger recommendations are about trough concentration targets in kidney and liver transplantation and for other indication of tacrolimus use. For the first time, an area under the curve of tacrolimus concentrations target is recommended for recipients management. Eventually, another set of recommendations are proposed for pharmacodynamic biomarkers used in patients’ follow-up.

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.

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 (βGG 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.

International audience; 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.

La transplantation d’organes consiste a remplacer un organe “malade” par un organe “sain”, appele “greffon”, provenant d’un donneur vivant ou decede. Le nombre de patients sur liste d’attente de greffe est en constante augmentation. La qualite de l’accompagnement du patient transplante est un enjeu medical et societal majeur.

Aims Tacrolimus is a narrow therapeutic range drug that requires fine dose adjustment, for which pharmacokinetic (PK) models have been amply proposed in renal, but not in liver, transplant recipients. This study aimed to build population PK models and Bayesian estimators (BEs) in adult de novo liver transplant patients receiving either the immediate-release (Prograf, twice daily, TD) or prolonged-release (Advagraf, once daily, OD) forms to help PK-guided dose individualization. Methods In total, 160 tacrolimus concentration-time profiles (1654 samples) were collected from 80 patients, at day 7 (D7) and week 6 (W6) post-transplant. Four population PK models were developed using in-parallel parametric and nonparametric approaches for each formulation and period post-transplant. The best limited sampling strategies for estimating the area-under-the-curve (AUC) were selected by comparing predicted values to an independent dataset. Finally, the doses required to reach AUC targets were estimated using each BE and compared to the doses obtained using the trapezoidal AUC. Results Tacrolimus PK was best described using a 1-compartmental model with first-order elimination and 2 γ-distributions to describe the absorption. In the validation datasets, Bayesian AUC estimates yielded mean bias/root mean squared prediction error of −5.06%/13.43% (OD D7), 2.25%/8.51% (OD W6), −2.36%/7.27% (TD D7) and 0.87%/9.07% (TD W6) for the in-parallel parametric approach; and 8.95%/17.84% (OD D7), −0.11%/10.13% (OD W6), 3.57%/18.40% (TD D7) and 4.48%/12.59% (TD W6) for the nonparametric approach. Conclusion The BEs and limited sampling strategies proposed here are able to predict accurately and precisely tacrolimus AUC in liver patients using only 3 plasma concentrations. The dosing methods are available on our ImmunoSuppressive Bayesian dose Adjustment website (www.pharmaco.chu-limoges.fr).

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 therapeutic drug monitoring (TDM), the dose was reduced by 8 times the original dose. This case report highlights the importance of TDM for cefepime, especially in patients presenting altered renal functions, as typical recommendations are estimated for standard patients.

Patients’ experiences of immunosuppressive treatment, including their perception of adverse effects, influences their quality of life and may be a trigger for poor adherence. The Medication Experience Scale for Immunosuppressants (MESI) questionnaire was designed in German to evaluate patients’ experiences of immunosuppressive drugs. The objective of this study was to validate the MESI in French. The MESI was transculturally adapted and validated in French-speaking transplant recipients according to international guidelines. Statistical analysis was performed using R version 3.2.3, and the psychometric properties of the MESI were determined using the psy R package. Validation was performed in 164 kidney, liver, lung, and heart transplant recipients (aged 55.4 ± 13.1 years, 62.8% males, years since transplantation 7.5 ± 7.9). The MESI displayed good psychometric characteristics (intra-class correlation coefficient 0.854, Cronbach’s α 0.79 (95% confidence interval 0.74–0.83, Kappa coefficient 0.30–0.76). There was an association between the MESI score, family status, and the transplanted organ (p

International audience; BACKGROUND AND OBJECTIVES:A new once-daily formulation of tacrolimus (Envarsus®) has recently been developed, with alleged different pharmacokinetics from previous tacrolimus formulations. The objectives of this study were to develop population pharmacokinetic models and Bayesian estimators based on limited sampling strategies for Envarsus® in kidney and liver transplant recipients.MATERIALS AND METHODS:Full tacrolimus concentration-time profiles (13 samples) were drawn from 57 liver (113 profiles) and 49 kidney (97 profiles) graft recipients transplanted for at least 6 months and switched from Prograf® to Envarsus®. The two databases were split into a development (75%) and a validation (25%) dataset. Pharmacokinetic models characterised by a single compartment with first-order elimination and absorption in two phases described by a sum of two gamma distributions were developed using non-parametric (Pmetrics) and parametric (ITSIM) approaches in parallel. The best limited sampling strategy for each patient group was determined using the multiple model optimal algorithm. The performance of the models and derived Bayesian estimators was evaluated in the validation set.RESULTS:The best limited sampling strategy was 0, 8 and 12 h post-dose, leading to a relative bias ± standard deviation (root-mean-square error) between observed and modelled inter-dose area under the curve in the validation dataset of: 0.32 ± 6.86% (6.87%) for ITSIM and 3.4 ± 13.4% (13.2%) for Pmetrics in kidney transplantation; and 0.89 ± 7.32% (7.38%) for ITSIM and -2.62 ± 8.65% (8.89%) for Pmetrics in liver transplantation.CONCLUSION:Population pharmacokinetic models and Bayesian estimators for Envarsus® in kidney and liver transplantation were developed and are now available online for area under the curve-based tacrolimus dose adjustment.

International audience; Objective: Adherence is a dynamic phenomenon and a critical determinant of transplant patients outcome. The objective of this longitudinal study was to explore adherence in kidney transplant patients followed-up for up to three years after transplantation.Methods: Adherence was repeatedly estimated using the Morisky-Green-Levine 4-Item Medication Adherence Scale, in two successive cohorts of 345 (EPIGREN) and 367 (EPHEGREN) kidney transplant recipients. Mixed effect modeling with latent processes and latent classes was used to describe adherence time-profiles.Results: Two latent classes were identified. The adherent class represented 85% of the patients. Patients of the poorer-adherence class displayed a lower adherence at one month (p50 years, fewer depression episodes (5% vs. 13%, p = 0.001) and a better mental health component of quality of life (MCS-SF36 47 ± 11 vs. 41 ± 13, p = 0.015). Survival without acute rejection episodes was longer in the adherent class (p = 0.004).Conclusions: The risk of poor adherence in renal transplant patients can be detected as early as one month post-transplantation, using appropriate and easy tools adapted to routine monitoring.Practice implications: An early focus on vulnerable patients should allow putting into place actions in order to reduce the risk of poor outcome related to poor adherence.

Background: Scientific data on the psychopharmacological effects of new psychoactive substances (NPSs) are scarce. Web fora contain a wealth of information posted by users as trip reports (TRs), but the reliability of the reports remains questionable because of the nature of the used molecule and the potential for dose inaccuracies. We focused on the TRs of designer benzodiazepine (DBZD) users since their psychopharmacological effects are similar to prescription benzodiazepines (BZDs). Moreover, the impact of functional groups on the BZD rings with regards to the potency has been fairly/quite studied, allowing structural analysis. Methods: DBZDs offering more than 15 TRs with at least two accounts on experienced effects were included. Data were analyzed with the empirical phenomenological psychological method. Reported effects were analyzed and the pharmacological potencies of DBZDs were compared by calculating a ‘potency score’. Results: In total, 197 TRs for clonazolam, deschloroetizolam, diclazepam, etizolam, flubromazepam, flubromazolam, meclonazepam, metizolam, nifoxipam and pyrazolam were analyzed. Effects similar to prescription BZDs were reported for all the selected DBZDs. Pyrazolam was reported to be the most anxiolytic DBZD, flubromazolam the most hypnotic, etizolam the most euphoric and flubromazolam and clonazolam as the most amnesic DBZDs. Diclazepam and pyrazolam were not reported to induce euphoria. Flubromazepam, flubromazolam, clonazolam and meclonazepam were the most potent and deschloroetizolam, nifoxipam, metizolam and pyrazolam the least potent. The chemical structure of the different DBZDs and the functional groups on the BZD rings confirmed this ranking, except for nifoxipam. Conclusions: When information on NPSs obtained from Internet fora are abundant, it could be considered as an appreciable data source.

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.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.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.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.

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 (C

Aim: To investigate the potential influence of variants in genes involved in the calcineurin pathway on the efficacy and toxicity of calcineurin inhibitors in renal transplantation. Materials & methods: Twenty-three polymorphisms in thirteen genes were tested in 381 renal transplant recipients receiving ciclosporin (n = 221) or tacrolimus (n = 160) and mycophenolate mofetil. Data were collected prospectively over the first year post-transplantation. Results: Multivariate survival analyses revealed no genetic associations with biopsy proven acute graft rejection and serious infections. Donor–recipient Cytomegalovirus mismatch was the only variable associated with serious infection. Conclusion: This large exploratory study casts doubts on the potential interest of genetic biomarkers related to CNI pharmacodynamics but associations with other phenotypes in transplantation deserve further studies.

International audience; 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.

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.

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.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.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 (P0.0001).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.

Mycophenolic acid (MPA) area under the curve (AUC) has been associated with graft outcome. The aims of our study were: (1) to develop pharmacokinetic tools to optimize MPA inter-dose AUC estimation in heart transplant patients; and (2) to investigate the relationships between acute allograft rejection and MPA AUC, trough level (C0) or mycophenolate mofetil (MMF) dose. Two independent modeling approaches (parametric and non parametric) were used to fit 56 rich MPA pharmacokinetic (PK) profiles collected from 40 adult heart transplant recipients enrolled in the PIGREC study, receiving MMF and a calcineurin inhibitor (CNI), in the first year post-transplantation. In addition, associations between drug exposure (MPA C0, AUC and MMF dose) and acute rejection or MMF adverse events were investigated using time-dependent Cox models with stratification on the type of calcineurin inhibitor. Exposure threshold values were investigated using ROC curve analysis. The 2 models developed fit adequately the data and the use of their combination yielded 100% consistency with the measured AUC in terms of strategy of dose adjustment (maintain, increase or decrease). MPA measured AUC adjusted on CNI exposure was significantly associated with rejection (per unit increase: HR [95% CI] = 0.97 [0.95–0.99], p = 0.0122), while no effect was shown for adverse events attributable to MMF. An AUC threshold of 50 mg × h/L was proposed (sensitivity = 77%, specificity = 25%) beyond which the risk of rejection was significantly increased (low vs. high: HR = 3.48 [1.21–10.0], p = 0.0204). The tools developed have already been made available to the heart transplant community on our ISBA website ( https://pharmaco.chu-limoges.fr ).

To the best of our knowledge, no case has been published in the literature that reports an overdose of tiapride, either alone or in combination with other drugs. We report a self-poisoning case in an 18-year-old girl, with approximately 10 times the usual daily dose (ie, 2.5 g). Although the blood concentration was 20/30-fold higher than usually observed after therapeutic drug intakes (17,300 mcg/L), the patient remained almost asymptomatic.

Introduction Therapeutic drug monitoring (TDM) of everolimus is not performed in oncology and no trough level (C 0 ) target has been yet defined. The aim of this study was to determine everolimus C 0 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. C 0 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). C 0 everolimus higher than 26.3 ng/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 C 0 lower than 11.9 ng/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 C 0 threshold proposed for toxicity (26.3 ng/mL) and for progression (11.9 ng/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.

Every transplant patient will, at least occasionally, miss immunosuppressive drug doses or take them outside the prescribed times. This study aims at quantifying the impact of poor execution on tacrolimus exposure in renal transplant patients. Validated pharmacokinetic tools applied in clinical setting were used to simulate the steady-state pharmacokinetic profiles of the drug when given as the immediate-release formulation to renal transplant patients, being CYP3A5 expressors or not, and who have reached either a standard or a minimized exposure. Situations of interruption due to a missed or delayed dose were simulated and the impact on drug exposure was explored. In case of a missed dose, it was observed that: (i) a single forgotten dose can greatly impact exposure: up to 49% decrease for tacrolimus trough concentration and 70% for AUC0-12 h in patients with the highest clearance values; (ii) patients with a minimized exposure are the most affected by a missed dose; and (iii) a dose of 1.5 times the usual dose may be recommended after a total dose oversight. Considering that intra-patient exposure variability is a predictive factor of poor graft outcome, these modeling results may serve as recommendations for patients, both preventively and in response to their questions.

The aim of this study was to develop routinely applicable limited sampling strategies for assessing cyclosporin (CsA) AUC(0-12 h), and possibly other exposure indices such as AUC(0-4 h) and C(max), in heart transplant patients over the first year post-transplantation.First, the individual pharmacokinetics (PKs) of 14 adult heart-transplant patients receiving Neoral were assessed at three post-transplantation periods, at the end of the first week (W1), the third month (M3) and the first year (Y1). To fit blood concentrations, a PK model specially developed for oral CsA was applied. Second, two statistical methods were compared for AUC(0-12 h) estimation using a limited sampling strategy (maximum of three blood samples): multiple regression analysis (MR) and Bayesian estimation (BE).No significant difference was observed between the individual PK parameters at M3 and Y1, so population modelling was performed taking as a whole the concentration data collected at M3 and Y1. On the contrary, a significant difference ( P0.05) was found for the C2/dose ratio between W1 and M3 and between W1 and Y1 (mean+/-SD =5.47+/-2.33; 7.78+/-1.05; 6.98+/-2.17 ml(-1 )for W1, M3 and Y1, respectively). Also, C(max)/dose and A were found significantly lower at W1 than at M3 ( P0.01 and P0.005, respectively), while lambda(1) was significantly higher at W1 than at both M3 and Y1 ( P0.01). Using three sampling times (t0 h, t1 h and t3 h), BE allowed an accurate prediction of AUC(0-12 h) (mean bias =3.06+/-12.16%; +1.50+/-1.61%; and -0.20+/-11.42% at W1, M3 and Y1, respectively), AUC(0-4 h )and C(max). MR led to satisfactory estimation of AUC(0-12 h) using only two blood samples collected 2 h and 6 h post-dose (R=0.956-0.993; bias =-5.22 to +4.41; precision =6.38 to 9.90%), but this method is unable to estimate any other exposure index and requires strict respect of sampling times, contrary to BE.Neoral monitoring based on full or abbreviated AUC is possible using BE or MR in heart transplant patients over the first year post-transplantation. BE provides a good description of the individual PK profiles and thus might be useful not only in case of potential discrepancies between C2 and clinical findings, but also for clinical trials aimed at finding optimum PK monitoring in heart recipients.

Introduction Le mycophenolate mofetil est un immunosuppresseur couramment utilise pour le traitement de la pemphigoide cicatricielle (PC). Actuellement, le suivi therapeutique pharmacologique (STP) n’est pas realise pour l’acide mycophenolique (AMP), la forme active alors que tous les arguments pour le STP sont reunis : forte variabilite inter-individuelle, mauvaise relation dose/exposition et bonne association exposition/effet (demontree en transplantation renale avec un fort niveau de preuve). L’objectif de ce travail etait d’etudier retrospectivement la relation exposition/reponse basee sur la mesure de l’aire sous la courbe (ASC) chez des patients atteints de PC. Materiel et methodes Huit patients traites pour PC pour lesquels 5 avaient au moins 2 mesures d’ASC (n = 15 mesures) ont ete inclus dans cette analyse. L’ASC quotidienne etait estimee par methode bayesienne a partir du site internet ABIS® ( www.pharmaco.chu-limoges.fr ). La variabilite inter- et intra-individuelle a ete evaluee par Anova. L’exposition etait comparee entre les groupes de reponse (categorises en amelioration, stabilisation ou non-reponse) par test de Kruskal-Wallis. Une courbe ROC etait construite afin de determiner un seuil d’exposition associe a l’amelioration. Resultats Parmi les patients, il y avait 5 femmes et 3 hommes d’âge moyen 79 ans. Un patient etait non repondeur (n = 2 mesures), un patient stable (n = 3 mesures) et 6 patients en amelioration (n = 10 mesures). Une difference significative d’exposition entre les patients etait observee alors que l’exposition intra-individuelle etait faible (p = 0,033). Une difference significative d’expositions etait observee entre les groupes de reponse (moyenne ASC0-24h amelioration = 98 mg/h/L, stable = 74,5 mg/h/L et non-reponse = 69 mg/h/L ; p Kruskal-Wallis = 0,0498). L’analyse ROC montrait qu’un seuil d’ASC0-24h de 88 mg*h/L etait associe a une sensibilite de 80 % et une specificite de 100 % (AUC ROC = 0,9, p = 0,0001) pour l’amelioration (tous les mesures superieures a ce seuil sont en amelioration, 80 % des mesures inferieures a ce seuil ne sont pas en amelioration). Discussion L’adaptation posologique de l’AMP a ainsi permis une amelioration des scores de gravite de PC chez 6/8 patients et une amelioration des scores d’inflammation oculaire chez 5 patients. Ces resultats montrent que toutes les conditions sont reunies pour effectuer le STP de l’AMP en routine. Conclusion Les donnees de cette etude seront a confirmer dans une etude prospective (PHRC PEMPA debut 2017).

International audience; BACKGROUND: Therapeutic drug monitoring of tacrolimus is a major support to patient management and could help improve the outcome of lung transplant recipients, by minimizing the risk of rejections and infections. However, despite the wide use of tacrolimus as part of maintenance immunosuppressive regimens after lung transplantation, little is known about its pharmacokinetics in this population. Better knowledge of the pharmacokinetics of tacrolimus in lung transplant recipients, and the development of tools dedicated to its therapeutic drug monitoring, could thus help improve their outcome. OBJECTIVES: The aims of this study were (i) to characterize the population pharmacokinetics of tacrolimus in lung transplant recipients, including the influence of biological and pharmacogenetic covariates; and (ii) to develop a Bayesian estimator of the tacrolimus area under the blood concentration-time curve from time zero to 12 hours (AUC(12)) for its therapeutic drug monitoring in lung transplant recipients. METHODS: A population pharmacokinetic model was developed by nonlinear mixed-effects modelling using NONMEM® version VI, from 182 tacrolimus full concentration-time profiles collected in 78 lung transplant recipients within the first year post-transplantation. Patient genotypes for the cytochrome P450 3A5 (CYP3A5) A6986G single nucleotide polymorphism (SNP) were characterized by TaqMan allelic discrimination. Patients were divided into an index dataset (n = 125 profiles) and a validation dataset (n = 57 profiles). A Bayesian estimator was derived from the final model using the index dataset, in order to determine the tacrolimus AUC(12) on the basis of a limited number of samples. The predictive performance of the Bayesian estimator was evaluated in the validation dataset by comparing the estimated AUC(12) with the trapezoidal AUC(12). RESULTS: Tacrolimus pharmacokinetics were described using a two-compartment model with Erlang absorption and first-order elimination. The model included cystic fibrosis (CF) and CYP3A5 polymorphism as covariates. The relative bioavailability in patients with CF was approximately 60% of the relative bioavailability observed in patients without CF, and the transfer rate constant between the transit compartments was 2-fold smaller in patients with CF than in those without CF (3.32 vs 7.06 h-1). The apparent clearance was 40% faster in CYP3A5 expressers than in non-expressers (24.5 vs 17.5 L/h). Good predictive performance was obtained with the Bayesian estimator developed using the final model and concentrations measured at 40 minutes and at 2 and 4 hours post-dose, as shown by the mean bias (1.1%, 95% CI -1.4, 3.7) and imprecision (9.8%) between the estimated and the trapezoidal AUC(12). The bias was >20% in 1.8% of patients. CONCLUSION: Population pharmacokinetic analysis showed that lung transplant patients with CF displayed lower bioavailability and a smaller transfer rate constant between transit compartments than those without CF, while the apparent clearance was faster in CYP3A5 expressers than in non-expressers. The Bayesian estimator developed in this study provides an accurate prediction of tacrolimus exposure in lung transplant patients, with and without CF, throughout the first year post-transplantation. This tool may allow routine tacrolimus dose individualization and may be used to conduct clinical trials on therapeutic drug monitoring of tacrolimus after lung transplantation.

International audience; BACKGROUND AND OBJECTIVES: The immunosuppressive drug mycophenolate mofetil is used to prevent rejection after organ transplantation. In kidney transplant recipients, it has been demonstrated that adjustment of the mycophenolate mofetil dose on the basis of the area under the concentration-time curve (AUC) of mycophenolic acid (MPA), the active moiety of mycophenolate mofetil, improves the clinical outcome. Because of the high risks of rejections and infections in lung transplant recipients, therapeutic drug monitoring of the MPA AUC might be even more useful in these patients. The aims of this study were to characterize the pharmacokinetics of MPA in lung and kidney transplant recipients, describe the differences between the two populations and develop a Bayesian estimator of the MPA AUC in lung transplant recipients. METHODS: In total, 460 MPA concentration-time profiles from 41 lung transplant recipients and 116 kidney transplant recipients were included. Nonlinear mixed-effects modelling was used to develop a population pharmacokinetic model. Patients were divided into an index dataset and a validation dataset. The pharmacokinetic model derived from the index dataset was used to develop a Bayesian estimator, which was validated using the 35 lung transplant recipients' profiles from the validation dataset. RESULTS: MPA pharmacokinetics were described using a two-compartment model with lag time, first-order absorption and first-order elimination. The influence of ciclosporin co-treatment and the changes over time post-transplantation were included in the model. Lung transplant recipients had, on average, a 53% slower absorption rate and 50% faster MPA apparent oral clearance than kidney transplant recipients (p

International audience; Although immunosuppressive treatments and therapeutic drug monitoring (TDM) have significantly contributed to the increased success of thoracic transplantation, there is currently no consensus on the best immunosuppressive strategies. Maintenance therapy typically consists of a triple-drug regimen including corticosteroids, a calcineurin inhibitor (ciclosporin or tacrolimus) and either a purine synthesis antagonist (mycophenolate mofetil or azathioprine) or a mammalian target of rapamycin inhibitor (sirolimus or everolimus). The incidence of acute and chronic rejection and of mortality after thoracic transplantation is still high compared with other types of solid organ transplantation. The high allogenicity and immunogenicity of the lungs justify the use of higher doses of immunosuppressants, putting lung transplant recipients at a higher risk of drug-induced toxicities. All immunosuppressants are characterized by large intra- and interindividual variability of their pharmacokinetics and by a narrow therapeutic index. It is essential to know their pharmacokinetic properties and to use them for treatment individualization through TDM in order to improve the treatment outcome. Unlike the kidneys and the liver, the heart and the lungs are not directly involved in drug metabolism and elimination, which may be the cause of pharmacokinetic differences between patients from all of these transplant groups. TDM is mandatory for most immunosuppressants and has become an integral part of immunosuppressive drug therapy. It is usually based on trough concentration (C(0)) monitoring, but other TDM tools include the area under the concentration-time curve (AUC) over the (12-hour) dosage interval or the AUC over the first 4 hours post-dose, as well as other single concentration-time points such as the concentration at 2 hours. Given the peculiarities of thoracic transplantation, a review of the pharmacokinetics and TDM of the main immunosuppressants used in thoracic transplantation is presented in this article. Even more so than in other solid organ transplant populations, their pharmacokinetics are characterized by wide intra- and interindividual variability in thoracic transplant recipients. The pharmacokinetics of ciclosporin in heart and lung transplant recipients have been explored in a number of studies, but less is known about the pharmacokinetics of mycophenolate mofetil and tacrolimus in these populations, and there are hardly any studies on the pharmacokinetics of sirolimus and everolimus. Given the increased use of these molecules in thoracic transplant recipients, their pharmacokinetics deserve to be explored in depth. There are very few data, some of which are conflicting, on the practices and outcomes of TDM of immunosuppressants after thoracic transplantation. The development of sophisticated TDM tools dedicated to thoracic transplantation are awaited in order to accurately evaluate the patients' exposure to drugs in general and, in particular, to immunosuppressants. Finally, large cohort TDM studies need to be conducted in thoracic transplant patients in order to identify the most predictive exposure indices and their target values, and to validate the clinical usefulness of improved TDM in these conditions. In part I of the article, we review the pharmacokinetics and TDM of calcineurin inhibitors. In part II, we will review the pharmacokinetics and TDM of mycophenolate and mammalian target of rapamycin inhibitors, and provide an overall discussion along with perspectives.

Therapeutic drug monitoring (TDM) of mycophenolate mofetil (MMF) is recommended to guide immunosuppression. High-performance liquid chromatography with ultraviolet (HPLC-UV) or the enzyme-multiplied immunoassay technique (EMIT), used to measure mycophenolic acid (MPA) were compared in an exclusive paediatric renal transplant population. Twenty patients were included as part of the pharmacokinetics study of MMF, and 88 additional samples were drawn for TDM. Agreement between HPLC-UV and EMIT was assessed by the Bland-Altman method. With the two methods, pre-dose concentrations were not normally distributed. After logarithmic transformation, their mean was 0.79 +/- 1.16 microg ml(-1) and their mean difference was 0.34 +/- 0.16 microg ml(-1) [95% confidence interval (95%CI 0.30-0.38 microg ml(-1), with antilogarithmic values of these limits of 1.34-1.46 microg ml(-1)). Area under the curve (AUC)(HPLC) and AUC(EMIT) were normally distributed. Their mean was 52.42 +/- 25.91 mg x h/l and their mean difference was 15.22 +/- 8 mg x h/l (95%CI 11.99-18.45 mg x h/l), the Bland-Altman plot showing a bias proportional to the mean. Our data showed the absence of agreement between the HPLC and EMIT methods, with an average positive bias of 15% with the EMIT. Further studies are required to determine which method is best appropriate for TDM of MMF in children.

To demonstrate that injecting drug use is a major risk factor of community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) infection and injecting drug users may be a reservoir of CA-MRSA infection in our community, we conducted a matched case-control study. Cases were CA-MRSA-infected patients at University of California, Davis, Medical Center, Sacramento, CA, from December 1, 2003, to May 31, 2004. Two control groups were community-associated methicillin-susceptible S. aureus (CA-MSSA)-infected patients and a randomly selected uninfected patient group in the same hospital. Controls were matched to cases by age and isolate culture date. One hundred twenty-seven CA-MSSA patients and 381 randomly selected uninfected controls were selected to match the 127 CA-MRSA cases. The adjusted odds ratio of injecting drug use compared with the CA-MSSA group was 2.11 (95% confidence interval [CI], 1.1-4.3) and 4.09 (95% CI, 2.2-7.5) compared with the uninfected group. We suggest that injecting drug use is a significant risk factor for CA-MRSA infection, which could contribute to the increasing prevalence of CA-MRSA in an urban community.

Methicillin-resistant Staphylococcus aureus (MRSA) has long been a common pathogen in healthcare facilities, but in the past decade, it has emerged as a problematic pathogen in the community setting as well. A retrospective case series study of patients from whom MRSA was isolated from December 1, 2003, through May 31, 2004, was conducted at the University of California, Davis, Medical Center. Patient data were collected from electronic medical records and traditional chart reviews to determine whether MRSA acquisition was likely to have been in the community or in the hospital. Antimicrobial susceptibility testing and pulsed-field gel electrophoresis (PFGE) were performed for all confirmed isolates. Skin and soft tissue were the most common infection sites for all MRSA patients. Among the 283 MRSA infections, 127 (44.9%) were defined as community-associated (CA)-MRSA. Ninety-six percent of the CA-MRSA isolates were susceptible to clindamycin. Double-disk diffusion tests were performed to examine inducible clindamycin resistance by erythromycin induction on both CA and hospital-associated (HA) clindamycin-susceptible and erythromycin-resistant isolates. Ten percent (17 of 183) were positive. Most CA-MRSA isolates were identified by PFGE as a unique strain, genotype USA300, which was not genetically related to the predominant genotype, USA100, in the HA-MRSA isolates. Injecting drug users accounted for 49% of CA-MRSA infections but only 19% of the HA-MRSA infections (odds ratio, 4.2; 95% confidence interval, 2.4 to 7.4). Our study shows that a single clone of CA-MRSA accounts for the majority of infections. This strain originated in the community and is not related to MRSA strains from healthcare settings. Injecting drug users could be a major reservoir for CA-MRSA transmission.

The aims of the current study were (1) to study Neoral pharmacokinetics (PK) in stable lung recipients with or without cystic fibrosis (CF), (2) to compare Neoral PK between these two groups, and (3) to design Bayesian estimators for PK forecasting and dose adjustment in these patients using a limited number of blood samples. The individual PK of 19 adult lung transplant recipients, 9 subjects with CF and 10 subjects without CF, were retrospectively studied. Three profiles obtained within 5 days were available for each patient. A PK model combining a gamma distribution to describe the absorption profile and a two-compartment model were applied. Different exposure indices were estimated using nonlinear regression and Bayesian estimation. The PK model developed reliably described the individual PK of Neoral in lung transplant patients with and without CF, and the values of the first and second half-lives were different in these two populations (lambda(1) = 4.14 +/- 3.01 vs. 2.16 +/- 1.75 h(-1); P < 0.01; lambda(2) = 0.36 +/- 0.11 vs. 0.49 +/- 0.12 h(-1); P < 0.01), while the mean absorption time and standard deviation of absorption time tended to be less in patients with cystic fibrosis (P < 0.1). Also, the patients with CF required higher doses than those without CF to achieve similar drug exposure. Consequently, population modeling was performed in CF and non-CF patients separately. Bayesian estimation allowed accurate prediction of AUC(0-12), AUC(0-4), C(max), and T(max) using three blood samples collected at T0h, T1h, and T3h in both groups. This study demonstrated the applicability and good performance of the PK model previously developed for oral cyclosporin and of the MAP Bayesian estimation of cyclosporin systemic exposure in CF and non-CF patients. Moreover, it is the first to propose a monitoring tool specifically designed for cyclosporin monitoring in patients with CF.