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During ischemia and reperfusion injury (IRI), mitochondria may release mitochondrial DNA (mtDNA). mtDNA can serve as a propagator of further injury but in specific settings has anti-inflammatory capacities as well. Therefore, the aim of this study was to study the perioperative dynamics of plasma mtDNA during living donor kidney transplantation (LDKT) and its potential as a marker of graft outcome. Fifty-six donor–recipient couples from the Volatile Anesthetic Protection of Renal Transplants-1 (VAPOR-1) trial were included. Systemic venous, systemic arterial, and renal venous samples were taken at multiple timepoints during and after LDKT. Levels of mtDNA genes changed over time and between vascular compartments. Several donor, recipient, and transplantation-related variables significantly explained the course of mtDNA genes over time. mtDNA genes predicted 1-month and 24-month estimated glomerular filtration rate (eGFR) and acute rejection episodes in the two-year follow-up period. To conclude, mtDNA is released in plasma during the process of LDKT, either from the kidney or from the whole body in response to transplantation. While circulating mtDNA levels positively and negatively predict post-transplantation outcomes, the exact mechanisms and difference between mtDNA genes are not yet understood and need further exploration. [ABSTRACT FROM AUTHOR]

Aim: To evaluate whether atrasentan plasma exposure explains between‐patient variability in urinary albumin‐to‐creatinine ratio (UACR) response, a surrogate for kidney protection, and B‐type natriuretic peptide (BNP) response, a surrogate for fluid expansion. Methods: Type 2 diabetic patients with chronic kidney disease (n = 4775) received 0.75 mg atrasentan for 6 weeks in the active run‐in period. Individual area under the concentration‐time‐curve (AUC) was estimated using a population pharmacokinetic model. The association between atrasentan AUC, other clinical characteristics, and UACR and BNP response, was estimated using linear regression. Results: The median atrasentan AUC was 43.8 ng.h/mL with a large variation among patients (2.5th‐97.5th percentiles [P]: 12.6 to 197.5 ng.h/mL). Median UACR change at the end of enrichment was −36.0% and median BNP change was 8.7%, which also varied among patients (UACR, 2.5th‐97.5th P: −76.2% to 44.5%; BNP, 2.5th‐97.5th P: −71.5% to 300.0%). In the multivariable analysis, higher atrasentan AUC was associated with greater UACR reduction (4.88% per doubling in ng.h/mL [95% confidence interval {CI}: 6.21% to 3.52%], P <.01) and greater BNP increase (3.08% per doubling in ng.h/mL [95% CI: 1.12% to 4.11%], P <.01) independent of estimated glomerular filtration rate, haemoglobin or BNP. Caucasian patients compared with black patients had greater UACR reduction (7.06% [95% CI: 1.38% to 13.07%]) and also greater BNP increase (8.75% [95% CI: 1.65% to 15.35%]). UACR response was not associated with BNP response (r = 0.06). Conclusion: Atrasentan plasma exposure varied among individual patients and partially explained between‐patient variability in efficacy and safety response. [ABSTRACT FROM AUTHOR]

Aims: The AleCardio trial aimed to characterize the efficacy and safety of peroxisome proliferator‐activated receptor‐αγ agonist aleglitazar in patients with type 2 diabetes mellitus and acute coronary syndrome. The trial terminated early because of futility and safety signals. We evaluated whether the safety signals could be attributed to increased exposure to aleglitazar. Materials and Methods: The AleCardio trial enrolled 7226 patients to receive aleglitazar 150 μg or matching placebo on top of standard care. A population pharmacokinetic analysis was conducted in a pharmacokinetic substudy to identify covariates that explained interindividual variability in exposure. Subsequently, the effect of these covariates on surrogate and clinical outcomes was assessed in the full patient population. Results: Concomitant administration of clopidogrel was identified as a covariate that influenced the apparent clearance of aleglitazar. Patients using clopidogrel had a mean predicted area under the plasma‐concentration‐time curve (AUC0–24) of 174.7 ng h/mL (SD: ±112.9 ng h/mL) versus 142.2 ng h/mL (SD: ±92.6 ng h/mL) in patients without clopidogrel. The effect of aleglitazar compared with placebo on HbA1c, haemoglobin, serum creatinine and adiponectin was modified by concomitant clopidogrel use (P for interaction 0.007, 0.002, <0.001 and < 0.001, respectively). Conclusions: Concomitant use of clopidogrel was identified as a covariate that explained interindividual variability in exposure to aleglitazar. Patients using clopidogrel showed an additional lowering of HbA1c, at the expense of an additional decrease in haemoglobin, and an increase in serum creatinine and adiponectin. Clopidogrel is a moderate inhibitor of CYP2C8. Because aleglitazar is metabolized by CYP2C8, a pharmacokinetic interaction could explain differences in exposure and response to aleglitazar. [ABSTRACT FROM AUTHOR]

Aims Methods Results Conclusion Use of infliximab (IFX) has improved outcomes in children with inflammatory bowel disease (IBD). However, a proportion of patients does not respond to IFX or loses response over time. Population pharmacokinetic (PopPK) modelling is a promising approach for IFX dose optimization, but with the increasing number of PopPK models in literature, model evaluation is essential. The aims of this study are: (i) to validate the predictive performance of existing IFX PopPK models using a cohort of children with IBD; and (ii) to perform a Bayesian estimation of the most suitable model to predict the next IFX concentrations.PubMed was searched for IFX PopPK models in children. Selected models were rebuilt and analysed using R. Model performance was assessed through goodness‐of‐fit‐plots, residuals against time, prediction error and prediction‐corrected visual predictive checks. The validation cohort consisted of 73 children with IBD who were treated with IFX in our centre between 2017 and 2023 (340 IFX measurements).We identified 9 PopPK models. Model bias for individual predicted values ranged from −9.29% to 8.01% compared to bias for population predicted values. The model by Vande Casteele et al. demonstrated superior performance (individual predicted bias 2.13, population predicted bias −6.11); upon Bayesian estimation, it predicted induction trough levels with median error of 12.95% but had a median error of −69% predicting maintenance concentrations.The model by Vande Casteele et al. displayed superior performance in initial evaluations but had a high error in estimating next IFX levels and can only be used in practice to predict induction levels. [ABSTRACT FROM AUTHOR]