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Background and Objectives: Protease inhibitors such as darunavir are an important therapeutic option in the anti-human immunodeficiency virus arsenal. Current dosage guidelines recommend using cobicistat- or ritonavir-boosted darunavir 800 mg every 24 h (q24h) in protease inhibitor-naïve patients, or ritonavir-boosted darunavir 600 mg q12h in experienced patients. However, darunavir displays a large, poorly characterized, inter-individual pharmacokinetic variability. The objectives of this study were to investigate the pharmacokinetics of darunavir and to elucidate the sources of its inter-individual variability using population pharmacokinetic modeling. Then, to determine the appropriateness of current treatment guidelines and the feasibility of alternative dosing regimens in a representative cohort of adult patients using simulations., Methods: Sparse pharmacokinetic samples were collected in 127 patients with human immunodeficiency virus type 1 infection, then supplemented with rich sampling data from a subset of 12 individuals. Data were analyzed using the nonlinear mixed-effects modeling software NONMEM. The effect of reduced doses (600 mg q24h and 400 mg q24h) or reduced frequency of administration (800 mg q24h for 5 days followed by 2 days of treatment interruption) was simulated., Results: Our model adequately described the pharmacokinetics of darunavir. Predictors of individual exposure were CYP3A5*3 and SLCO3A1 rs8027174 genotypes, sex, and alpha-1 acid glycoprotein level. No relationship was apparent between darunavir area under the curve and treatment efficacy or safety. For reduced dose regimens, darunavir concentrations remained above the protein binding-corrected EC 50 in the majority of subjects. More stringent pharmacokinetic targets were not reached in a significant proportion of patients., Conclusions: These results add to the growing body of evidence that darunavir-based therapy could be simplified to reduce costs and toxicity, as well as to improve patient compliance. However, the heterogeneity in pharmacokinetic response should be considered when assessing whether individual patients could benefit from a particular regimen, for instance through the use of population pharmacokinetic models., Clinical Trial Registration: ClinicalTrials.gov identifier: NCT03101644, date of registration: 5 April, 2017.

Background: Single nucleotide polymorphisms (SNPs) in the CYP3A5 and CYP3A4 genes have been reported to be an important cause of variability in the pharmacokinetics of tacrolimus in renal transplant patients. The aim of this study was to merge all of the new genetic information available with tacrolimus pharmacokinetics to generate a more robust population model with data from renal transplant recipients., Methods: Tacrolimus exposure data from 304 renal transplant recipients were collected throughout the first year after transplantation and were simultaneously analyzed with a population pharmacokinetic approach using NONMEM ® version 7.2., Results: The tacrolimus whole-blood concentration versus time data were best described by a two-open-compartment model with inter-occasion variability assigned to plasma clearance. The following factors led to the final model, which significantly decreased the minimum objective function value (p < 0.001): a new genotype cluster variable combining the CYP3A5*3 and CYP3A4*22 SNPs defined as extensive, intermediate, and poor metabolizers; the standardization of tacrolimus whole blood concentrations to a hematocrit value of 45%; and age included as patients <63 years versus patients ≥63 years. External validation confirmed the prediction ability of the model with median bias and precision values of 1.17 ng/mL (95% confidence interval [CI] -3.68 to 4.50) and 1.64 ng/mL (95% CI 0.11-5.50), respectively. Simulations showed that, for a given age and hematocrit at the same fixed dose, extensive metabolizers required the highest doses followed by intermediate metabolizers and then poor metabolizers., Conclusions: Tacrolimus disposition in renal transplant recipients was described using a new population pharmacokinetic model that included the CYP3A5*3 and CYP3A4*22 genotype, age, and hematocrit.

The calcineurin inhibitor tacrolimus is the backbone of immunosuppressive drug therapy after solid organ transplantation. Tacrolimus is effective in preventing acute rejection but has considerable toxicity and displays marked inter-individual variability in its pharmacokinetics and pharmacodynamics. The genetic basis of these phenomena is reviewed here. With regard to its pharmacokinetic variability, a single nucleotide polymorphism (SNP) in cytochrome P450 (CYP) 3A5 (6986A>G) has been consistently associated with tacrolimus dose requirement. Patients expressing CYP3A5 (those carrying the A nucleotide, defined as the *1 allele) have a dose requirement that is around 50 % higher than non-expressers (those homozygous for the G nucleotide, defined as the *3 allele). A randomised controlled study in kidney transplant recipients has demonstrated that a CYP3A5 genotype-based approach to tacrolimus dosing leads to more patients reaching the target concentration early after transplantation. However, no improvement of clinical outcomes (rejection incidence, toxicity) was observed, which may have been the result of the design of this particular study. In addition to CYP3A5 genotype, other genetic variants may also contribute to the variability in tacrolimus pharmacokinetics. Among these, the CYP3A4*22 and POR*28 SNPs are the most promising. Individuals carrying the CYP3A4*22 T-variant allele have a lower tacrolimus dose requirement than individuals with the CYP3A4*22 CC genotype and this effect appears to be independent of CYP3A5 genotype status. Individuals carrying the POR*28 T-variant allele have a higher tacrolimus dose requirement than POR*28 CC homozygotes but this association was only found in CYP3A5-expressing individuals. Other, less well-defined SNPs have been inconsistently associated with tacrolimus dose requirement. It is envisaged that in the future, algorithms incorporating clinical, demographic and genetic variables will be developed that will aid clinicians with the determination of the tacrolimus starting dose for an individual transplant recipient. Such an approach may limit early tacrolimus under-exposure and toxicity. With regard to tacrolimus pharmacodynamics, no strong genotype-phenotype relationships have been identified. Certain SNPs associate with rejection risk but these observations await replication. Likewise, the genetic basis of tacrolimus-induced toxicity remains unclarified. SNPs in the genes encoding for the drug transporter ABCB1 and the CYP3A enzymes may relate to chronic nephrotoxicity but findings have been inconsistent. No genetic markers reliably predict new-onset diabetes mellitus after transplantation, hypertension or neurotoxicity. The CYP3A5*1 SNP is currently the most promising biomarker for tailoring tacrolimus treatment. However, before CYP3A5 genotyping is incorporated into the routine clinical care of transplant recipients, prospective clinical trials are needed to determine whether such a strategy improves patient outcomes. The role of pharmacogenetics in tacrolimus pharmacodynamics should be explored further by the study of intra-lymphocyte and tissue tacrolimus concentrations.