Deceased Kidney Donor Biomarkers: Relationship between Delayed Kidney Function and Graft Function Three Years after Transplantation.

With an increasing number of marginal donors, additional methods for the evaluation of cadaveric kidney quality are required. This study aimed to evaluate pretransplant deceased donor serum (s) and urine (u) biomarkers, including neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), interleukin-18, and C-X-C motif chemokine 10 (CXCL10) for predicting early and late graft function. In total, 43 deceased kidney donors and 76 corresponding recipients were enrolled. Delayed graft function (DGF) occurred in 27.6% of cases. sIL-18, sKIM-1, uNGAL, and uKIM-1 were predictors of DGF. A model incorporating sIL-18, uKIM-1, and clinical factors was developed to predict DGF (AUROC 0.863). Univariate analysis showed a negative association between uKIM and graft eGFR at 6, 12, 24, and 36 months, but this was not confirmed in the multivariate analysis. In conclusion, we report a superior performance of donor biomarkers for predicting DGF and later graft function over serum creatinine. Higher levels of donor sIL-18 and uKIM in conjunction with expanded-criteria donors and longer cold ischemia times predicted DGF. With no renal tubular damage in zero-time donor biopsies, higher pretransplant urine and serum NGAL levels were associated with better allograft function one year after transplantation, and sNGAL with graft function three years after transplantation.

A systematic review of the development and application of home cage monitoring in laboratory mice and rats.

BACKGROUND: Traditionally, in biomedical animal research, laboratory rodents are individually examined in test apparatuses outside of their home cages at selected time points. However, the outcome of such tests can be influenced by various factors and valuable information may be missed when the animals are only monitored for short periods. These issues can be overcome by longitudinally monitoring mice and rats in their home cages. To shed light on the development of home cage monitoring (HCM) and the current state-of-the-art, a systematic review was carried out on 521 publications retrieved through PubMed and Web of Science. RESULTS: Both the absolute (~ × 26) and relative (~ × 7) number of HCM-related publications increased from 1974 to 2020. There was a clear bias towards males and individually housed animals, but during the past decade (2011-2020), an increasing number of studies used both sexes and group housing. In most studies, animals were kept for short (up to 4 weeks) time periods in the HCM systems; intermediate time periods (4-12 weeks) increased in frequency in the years between 2011 and 2020. Before the 2000s, HCM techniques were predominantly applied for less than 12 h, while 24-h measurements have been more frequent since the 2000s. The systematic review demonstrated that manual monitoring is decreasing in relation to automatic techniques but still relevant. Until (and including) the 1990s, most techniques were applied manually but have been progressively replaced by automation since the 2000s. Independent of the year of publication, the main behavioral parameters measured were locomotor activity, feeding, and social behaviors; the main physiological parameters were heart rate and electrocardiography. External appearance-related parameters were rarely examined in the home cages. Due to technological progress and application of artificial intelligence, more refined and detailed behavioral parameters have been investigated in the home cage more recently. CONCLUSIONS: Over the period covered in this study, techniques for HCM of mice and rats have improved considerably. This development is ongoing and further progress as well as validation of HCM systems will extend the applications to allow for continuous, longitudinal, non-invasive monitoring of an increasing range of parameters in group-housed small rodents in their home cages.

The Glomerular Filtration Rate Estimators in the Pharmacokinetic Modelling in Acute Kidney Injury: An Observational Study.

The glomerular filtration rate (GFR), according to which the drug dose for patients with chronic kidney disease (CKD) is adjusted, is computed with estimators (eGFR) that are developed specifically for CKD. These particular types of estimators are also used in population pharmacokinetic (pop PK) modelling in drug development. Similar approaches without scientific validation have been proposed for patients with acute kidney injury (AKI), yet it is uncertain which specific eGFR should be used for drug dosing or in pop PK models in patients with AKI. In our study, we included 34 patients with AKI and vancomycin (VCM) treatment, and we built both individual PK and pop PK (non-linear mixed-effects, one-compartment) models to see which eGFR estimator is the best covariate. In these models different eGFRs (Cockcroft-Gault, MDRD, CKD-EPI 2009, Jelliffe and Jelliffe, Chen et al., and Yashiro et al. 2013) were used. We included six additional patients to validate the final pop PK model. All eGFRs underrate the true renal clearance in the AKI, so we created pop PK models for VCM dosing in AKI with all eGFRs, to discover that the most accurate model was the one with the Cockcroft-Gault estimator. Since the eGFRs underestimate the true renal clearance in AKI, they are inaccurate for clinical drug dosing decisions, with the exception of the Cockcroft-Gault one, which is appropriate for the pop PK models intended for drug development purposes in AKI.

Analyses of AUC(0-12) and C0 Compliances within Therapeutic Ranges in Kidney Recipients Receiving Cyclosporine or Tacrolimus.

The AUC (area under the concentration time curve) is considered the pharmacokinetic exposure parameter best associated with clinical effects. Unfortunately, no prospective studies of clinical outcomes have been conducted in adult transplant recipients to investigate properly the potential benefits of AUC(0-12) monitoring compared to the C0-guided therapy. The aim of the present study was to compare two methods, C0 (through level) and AUC(0-12) (area under the concentration time curve), for assessing cyclosporine and tacrolimus concentrations. The study included 340 kidney recipients. The AUC(0-12) was estimated using a Bayesian estimator and a three-point limited sampling strategy. Therapeutic drug monitoring of tacrolimus performed by using AUC(0-12) and C0 showed that tacrolimus in most cases is overdosed when considering C0, while determination of the AUC(0-12) showed that tacrolimus is effectively dosed for 27.8-40.0% of patients receiving only tacrolimus and for 25.0-31.9% of patients receiving tacrolimus with MMF (mycophenolate mofetil). In the 1-5 years post-transplantation group, 10% higher CsA (cyclosporine) dose was observed, which was proportionate with a 10% higher AUC(0-12) exposure value. This indicates good compatibility of the dosage and the AUC(0-12) method. The Bland-Altman plot demonstrated that C0 and AUC(0-12) might be interchangeable methods, while the ROC (receiver operating characteristic) curve analysis of the C0/AUC(0-12) ratio in the tacrolimus-receiving patient group demonstrated reliable performance to predict IFTA (interstitial fibrosis and tubular atrophy) after kidney transplantation, with an ROC curve of 0.660 (95% confidence interval (CI): 0.576-0.736), p < 0.01. Moreover, AUC(0-12) and C0 of tacrolimus depend on concomitant medication and adjustment of the therapeutic range for AUC(0-12) might influence the results.

Pharmacokinetic evaluation of MFF in combinations with tacrolimus and cyclosporine. Findings of C0 and AUC.

We hypothesized that area under the concentration time curve (AUC(0-12)) is more accurate pharmacokinetic predictor vs trough level of mycophenolic acid (C0).Study was performed at the University Hospital of Limoges (France) and included 238 renal recipients aged 22 to 82 years. Risk of nephropathy was evaluated by analyzing data of protocol biopsies according to the Banff 97 classification.Assessment of immunosuppressants' exposures was based on the calculation of the mean of AUC(0-12). The AUC(0-12) was estimated using a Bayesian estimator and a 3-point limited sampling strategy. Cyclosporine and tacrolimus analyses were performed using liquid chromatography-mass spectrometry method. The measurement of total mycophenolic acid was performed using a validated high-performance liquid chromatography method with ultraviolet detection. IBM SPSS 20.0 was used for statistical analysis.The most accurate dosing of mycophenolate mofetil (MMF) was observed in patients receiving MMF with tacrolimus, 70.6% of patients' AUC(0-12) exposures were within the therapeutic range. The highest rates of low dosing were observed in patients receiving MMF with cyclosporine, 30.9% of patients had AUC(0-12) exposures below the therapeutic range. The assessment of AUC(0-12) revealed 38% of chronic nephropathy cases, while C0 enables to identify only 20% of chronic nephropathy cases.Probability test results showed that more likely AUC(0-12) and C0 will be maintained within the therapeutic width if patients receive MMF with tacrolimus vs MMF with cyclosporine: 0.6320 vs 0.6410 for AUC(0-12) determination and 0.8415 vs 0.4827 for C0 determination.Combination of MMF with tacrolimus is dosed more precisely vs dosing of MMF with cyclosporine. 72 (70.6%) patients AUC(0-12) and 79 (77.5%) patients C0 out of 102 patients were within the therapeutic range. The AUC(0-12) monitoring of mycophenolic acid in patients receiving MMF with tacrolimus or in patients receiving MMF with cyclosporine enabled to identify more overdosing and possible risky cases.Study results show that standard MMF dosing without monitoring and with mycophenolic acid level within the therapeutic width is possible and demonstrates less risky cases in patients receiving MMF with tacrolimus, while patients receiving MMF with cyclosporine should be intensively monitored to achieve the highest safety. However, AUC(0-12) monitoring is advised showing better compliance vs C0 monitoring.