Steroid-resistant nephrotic syndrome (SRNS) causes 15% of chronic kidney disease cases. A mutation in 1 of over 40 monogenic genes can be detected in approximately 30% of individuals with SRNS whose symptoms manifest before 25 years of age. However, in many patients, the genetic etiology remains unknown. Here, we have performed whole exome sequencing to identify recessive causes of SRNS. In 7 families with SRNS and facultative ichthyosis, adrenal insufficiency, immunodeficiency, and neurological defects, we identified 9 different recessive mutations in SGPL1, which encodes sphingosine-1-phosphate (S1P) lyase. All mutations resulted in reduced or absent SGPL1 protein and/or enzyme activity. Overexpression of cDNA representing SGPL1 mutations resulted in subcellular mislocalization of SGPL1. Furthermore, expression of WT human SGPL1 rescued growth of SGPL1-deficient dpl1Δ yeast strains, whereas expression of disease-associated variants did not. Immunofluorescence revealed SGPL1 expression in mouse podocytes and mesangial cells. Knockdown of Sgpl1 in rat mesangial cells inhibited cell migration, which was partially rescued by VPC23109, an S1P receptor antagonist. In Drosophila, Sply mutants, which lack SGPL1, displayed a phenotype reminiscent of nephrotic syndrome in nephrocytes. WT Sply, but not the disease-associated variants, rescued this phenotype. Together, these results indicate that SGPL1 mutations cause a syndromic form of SRNS.

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 h⁻¹, 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: urn:x-wiley:03065251:media:bcp13838:bcp13838-math-0001 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.

Introduction Tacrolimus (Tac) has a narrow therapeutic window and shows large between-patient pharmacokinetic variability. As a result, over-immunosuppression and under-immunosuppression are frequently encountered in daily clinical practice. Unraveling the impact of genetic polymorphisms on Tac pharmacokinetics may help to refine therapy. In this study, the associations of single-nucleotide polymorphisms (SNPs) in drug-metabolizing enzymes (CYP3A) with Tac pharmacokinetics were investigated in renal transplant recipients. Participants and methods In a cohort of 272 kidney transplant recipients, associations between functional genetic variants (CYP3A4*22 and CYP3A5*3) and dose-adjusted predose Tac concentrations (C₀) and daily doses of Tac at days 5–7 and 15 and 1, 3, 6 and 12 months after renal transplantation were evaluated. Patients were genotyped and clustered according to both CYP3A4*22 and CYP3A5*3 allelic status: poor (PM) (CYP3A4*22 carriers with CYP3A5*3/*3), intermediate (IM) (CYP3A4*1/*1 with CYP3A5*3/*3 or CYP3A4*22 carriers with CYP3A5*1 carriers) and extensive CYP3A-metabolizers (EM) (CYP3A4*1/*1 and CYP3A5*1 carriers). Results EM had an 88% lower dose-adjusted C₀ compared with IM. PM had a 26% higher dose-adjusted C₀ compared with IM. The percentage of patients with supratherapeutic Tac exposure (C₀>15 ng/ml) was significantly higher in PM (43.5%) compared with EM (0%) at days 5–7 after transplantation (P=0.01). About 30% of EM had subtherapeutic exposure (C₀<5 ng/ml) at days 5–7 after transplantation (P=0.001). Conclusion The combined CYP3A4 and CYP3A5 genotype of renal transplant recipients has a major influence on the Tac dose required to reach the target exposure.

Treatment of solid-organ transplant (SOT) patients with ganciclovir (GCV)-valganciclovir (VGCV) according to the manufacturer's recommendations may result in over- or underexposure. Bayesian prediction based on a population pharmacokinetics model may optimize GCV-VGCV dosing, achieving the area under the curve (AUC) therapeutic target. We conducted a two-arm, randomized, open-label, 40% superiority trial in adult SOT patients receiving GCV-VGCV as prophylaxis or treatment of cytomegalovirus infection. Group A was treated according to the manufacturer's recommendations. For group B, the dosing was adjusted based on target exposures using a Bayesian prediction model (NONMEM). Fifty-three patients were recruited (27 in group A and 26 in group B). About 88.6% of patients in group B and 22.2% in group A reached target AUC, achieving the 40% superiority margin ( P < 0.001; 95% confidence interval [CI] difference, 47 to 86%). The time to reach target AUC was significantly longer in group A than in group B (55.9 ± 8.2 versus 15.8 ± 2.3 days, P < 0.001). A shorter time to viral clearance was observed in group B than in group A (12.5 versus 17.6 days; P = 0.125). The incidences of relapse (group A, 66.67%, and group B, 9.01%) and late-onset infection (group A, 36.7%, and group B, 7.7%) were higher in group A. Neutropenia and anemia were related to GCV overexposure. GCV-VCGV dose adjustment based on a population pharmacokinetics Bayesian prediction model optimizes GCV-VGCV exposure. (This study has been registered at ClinicalTrials.gov under registration no. NCT01446445.)