Fertility Outcome After Renal Transplantation: A Single-Center Experience

BackgroundWomen suffering from kidney disease are more prone to fertility problems, due to uremia. Fortunately, their fertility rate increases dramatically after renal transplantation. This study analyzes the predictors/risk factors of successful pregnancy with live birth outcome while presenting an overview of the 7-year experience of a single center.MethodsThis retrospective cohort study includes 239 women of reproductive age (18–40 years) who underwent renal transplantation in a tertiary Turkish clinic between October 1, 2011, and August 24, 2017. The subjects were invited to take part in a survey questioning their obstetric characteristics and they were assessed in 2 groups: fertile and infertile. Multivariable linear regression analysis was conducted to determine the predictors of a successful pregnancy.ResultsThirty-five 35 patients wished to become pregnant: 12 got pregnant spontaneously, while 21 failed to become pregnant (spontaneously). The mean age of the patients at the survey was 34 ± 7. Regular menstrual cycles after renal transplantation, tacrolimus-mycophenolate mofetil maintenance protocol, and age at transplantation were found to be predictors of spontaneous pregnancy. The duration of peritoneal dialysis was significantly longer in the infertile group (48 vs 12 months).ConclusionEnd-stage renal disease's negative impacts, including menstrual abnormality and fertility problems, can be overcome by successful kidney transplantation with appropriate immunosuppression. Minimizing the duration of peritoneal dialysis, particularly in patients who desire future fertility, may be accepted as a logical management strategy.     

In Vitro-In Vivo Extrapolation and Hepatic Clearance-Dependent Underprediction

Accurately predicting the hepatic clearance of compounds using in vitro to in vivo extrapolation (IVIVE) is crucial within the pharmaceutical industry. However, several groups have recently highlighted the serious error in the process. Although empirical or regression-based scaling factors may be used to mitigate the common underprediction, they provide unsatisfying solutions because the reasoning behind the underlying error has yet to be determined. One previously noted trend was intrinsic clearance-dependent underprediction, highlighting the limitations of current in vitro systems. When applying these generated in vitro intrinsic clearance values during drug development and making first-in-human dose predictions for new chemical entities though, hepatic clearance is the parameter that must be estimated using a model of hepatic disposition, such as the well-stirred model. Here, we examine error across hepatic clearance ranges and find a similar hepatic clearance-dependent trend, with high clearance compounds not predicted to be so, demonstrating another gap in the field.