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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.

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.

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.

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; 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.