A population pharmacokinetic model to predict the individual starting dose of tacrolimus in adult renal transplant recipients

AIMS The aims of this study were to describe the pharmacokinetics of tacrolimus immediately after kidney transplantation, and to develop a clinical tool for selecting the best starting dose for each patient. METHODS Data on tacrolimus exposure were collected for the first 3 months following renal transplantation. A population pharmacokinetic analysis was conducted using nonlinear mixed-effects modelling. Demographic, clinical and genetic parameters were evaluated as covariates. RESULTS A total of 4527 tacrolimus blood samples collected from 337 kidney transplant recipients were available. Data were best described using a two-compartment model. The mean absorption rate was 3.6 h1 , clearance was 23.0 l h–1 (39% interindividual variability, IIV), central volume of distribution was 692 l (49% IIV) and the peripheral volume of distribution 5340 l (53% IIV). Interoccasion variability was added to clearance (14%). Higher body surface area (BSA), lower serum creatinine, younger age, higher albumin and lower haematocrit levels were identified as covariates enhancing tacrolimus clearance. Cytochrome P450 (CYP) 3A5 expressers had a significantly higher tacrolimus clearance (160%), whereas CYP3A4*22 carriers had a significantly lower clearance (80%). From these significant covariates, age, BSA, CYP3A4 and CYP3A5 genotype were incorporated in a second model to individualize the tacrolimus starting dose: Both models were successfully internally and externally validated. A clinical trial was simulated to demonstrate the added value of the starting dose model. CONCLUSIONS For a good prediction of tacrolimus pharmacokinetics, age, BSA, CYP3A4 and CYP3A5 genotype are important covariates. These covariates explained 30% of the variability in CL/F. The model proved effective in calculating the optimal tacrolimus dose based on these parameters and can be used to individualize the tacrolimus dose in the early period after transplantation

Genetic variation in the ABCC2 gene is associated with dose decreases or switches to other cholesterol-lowering drugs during simvastatin and atorvastatin therapy.

Several statins are substrates for the multidrug resistance-associated protein 2 transporter, encoded by the ABCC2 gene. We analyzed in the Rotterdam Study whether the common polymorphisms -24C>T, 1249G>A and 3972C>T in the ABCC2 gene were associated with a dose decrease or switch to another cholesterol-lowering drug in simvastatin and atorvastatin users. These events could indicate an adverse effect or a too strong reduction in cholesterol level. We identified 1014 simvastatin and atorvastatin users during the period 1 January 1991 to 1 January 2010. Associations between genetic variation and the risk of these events were analyzed using Cox proportional hazards modelling. The ABCC2 -24C>T genotype (HR 1.32 95% CI 1.04-1.69) and the H12 haplotype versus the H2 haplotype (HR 1.49; 95% CI 1.06-2.09) were associated with these events in simvastatin users. A similar but not significant association was found in atorvastatin users. To conclude, genetic variation in the ABCC2 gene is associated with these events in simvastatin users

Novel CYP3A4 intron 6 single nucleotide polymorphism is associated with simvastatin-mediated cholesterol reduction in The Rotterdam Study.

CYP3A4 is involved in the oxidative metabolism of many drugs and xenobiotics including the HMG-CoA reductase inhibitor simvastatin. The objective of this study was to investigate whether a new CYP3A4 functional single nucleotide polymorphism (SNP) in intron 6 (CYP3A4*22) modifies the effect of simvastatin on total cholesterol (TOTc) or LDL cholesterol (LDLc) reduction in a population-based cohort study

Therapeutic Drug Monitoring and Dosage Adjustments of Immunosuppressive Drugs When Combined with Nirmatrelvir/Ritonavir in Patients with COVID-19

Abstract:Nirmatrelvir/ritonavir (Paxlovid) consists of a peptidomimetic inhibitor (nirmatrelvir) of the SARS-CoV-2 main protease and a pharmacokinetic enhancer (ritonavir). It is approved for the treatment of mild-to-moderate COVID-19. This combination of nirmatrelvir and ritonavir can mediate significant and complex drug-drug interactions (DDIs), primarily due to the ritonavir component. Indeed, ritonavir inhibits the metabolism of nirmatrelvir through cytochrome P450 3A (CYP3A) leading to higher plasma concentrations and a longer half-life of nirmatrelvir. Coadministration of nirmatrelvir/ritonavir with immunosuppressive drugs (ISDs) is particularly challenging given the major involvement of CYP3A in the metabolism of most of these drugs and their narrow therapeutic ranges. Exposure of ISDs will be drastically increased through the potent ritonavir-mediated inhibition of CYP3A, resulting in an increased risk of adverse drug reactions. Although a decrease in the dosage of ISDs can prevent toxicity, an inappropriate dosage regimen may also result in insufficient exposure and a risk of rejection. Here, we provide some general recommendations for therapeutic drug monitoring of ISDs and dosing recommendations when coadministered with nirmatrelvir/ritonavir. Particularly, tacrolimus should be discontinued, or patients should be given a microdose on day 1, whereas cyclosporine dosage should be reduced to 20% of the initial dosage during the antiviral treatment. Dosages of mammalian target of rapamycin inhibitors (m-TORis) should also be adjusted while dosages of mycophenolic acid and corticosteroids are expected to be less impacted