RIG-I, MDA5 and TLR3 Synergistically Play an Important Role in Restriction of Dengue Virus Infection

Dengue virus (DV) infection is one of the most common mosquito-borne viral diseases in the world. The innate immune system is important for the early detection of virus and for mounting a cascade of defense measures which include the production of type 1 interferon (IFN). Hence, a thorough understanding of the innate immune response during DV infection would be essential for our understanding of the DV pathogenesis. A recent application of the microarray to dengue virus type 1 (DV1) infected lung carcinoma cells revealed the increased expression of both extracellular and cytoplasmic pattern recognition receptors; retinoic acid inducible gene-I (RIG-I), melanoma differentiation associated gene-5 (MDA-5) and Toll-like receptor-3 (TLR3). These intracellular RNA sensors were previously reported to sense DV infection in different cells. In this study, we show that they are collectively involved in initiating an effective IFN production against DV. Cells silenced for these genes were highly susceptible to DV infection. RIG-I and MDA5 knockdown HUH-7 cells and TLR3 knockout macrophages were highly susceptible to DV infection. When cells were silenced for only RIG-I and MDA5 (but not TLR3), substantial production of IFN-β was observed upon virus infection and vice versa. High susceptibility to virus infection led to ER-stress induced apoptosis in HUH-7 cells. Collectively, our studies demonstrate that the intracellular RNA virus sensors (RIG-I, MDA5 and TLR3) are activated upon DV infection and are essential for host defense against the virus

Understanding renal posttransplantation anemia in the pediatric population

Advances in renal transplantation management have proven to be beneficial in improving graft and patient survival. One of the properties of a well-functioning renal allograft is the secretion of adequate amounts of the hormone erythropoietin to stimulate erythropoiesis. Posttransplantation anemia (PTA) may occur at any point in time following transplantation, and the cause is multifactoral. Much of our understanding of PTA is based on studies of adult transplant recipients. The limited number of studies that have been reported on pediatric renal transplant patients appear to indicate that PTA is prevalent in this patient population. Erythropoietin deficiency or resistance is commonly associated with iron deficiency. An understanding of the risk factors, pathophysiology and management of PTA in the pediatric renal transplant population may provide guidelines for clinicians and researchers in the pursuit of larger prospective randomized control studies aimed at improving our limited knowledge of PTA. Recognition of PTA through regular screening and evaluation of the multiple factors that may contribute to its development are recommended after transplantation

External Evaluation of Risperidone Population Pharmacokinetic Models Using Opportunistic Pediatric Data

Risperidone is approved to treat schizophrenia in adolescents and autistic disorder and bipolar mania in children and adolescents. It is also used off-label in younger children for various psychiatric disorders. Several population pharmacokinetic models of risperidone and 9-OH-risperidone have been published. The objectives of this study were to assess whether opportunistically collected pediatric data can be used to evaluate risperidone population pharmacokinetic models externally and to identify a robust model for precision dosing in children. A total of 103 concentrations of risperidone and 112 concentrations of 9-OH-risperidone, collected from 62 pediatric patients (0.16–16.8 years of age), were used in the present study. The predictive performance of five published population pharmacokinetic models (four joint parent-metabolite models and one parent only) was assessed for accuracy and precision of the predictions using statistical criteria, goodness of fit plots, prediction-corrected visual predictive checks (pcVPCs), and normalized prediction distribution errors (NPDEs). The tested models produced similarly precise predictions (Root Mean Square Error [RMSE]) ranging from 0.021 to 0.027 nmol/ml for risperidone and 0.053–0.065 nmol/ml for 9-OH-risperidone). However, one of the models (a one-compartment mixture model with clearance estimated for three subpopulations) developed with a rich dataset presented fewer biases (Mean Percent Error [MPE, %] of 1.0% vs. 101.4, 146.9, 260.4, and 292.4%) for risperidone. In contrast, a model developed with fewer data and a more similar population to the one used for the external evaluation presented fewer biases for 9-OH-risperidone (MPE: 17% vs. 69.9, 47.8, and 82.9%). None of the models evaluated seemed to be generalizable to the population used in this analysis. All the models had a modest predictive performance, potentially suggesting that sources of inter-individual variability were not entirely captured and that opportunistic data from a highly heterogeneous population are likely not the most appropriate data to evaluate risperidone models externally.</jats:p

DataSheet1_External Evaluation of Risperidone Population Pharmacokinetic Models Using Opportunistic Pediatric Data.docx

Risperidone is approved to treat schizophrenia in adolescents and autistic disorder and bipolar mania in children and adolescents. It is also used off-label in younger children for various psychiatric disorders. Several population pharmacokinetic models of risperidone and 9-OH-risperidone have been published. The objectives of this study were to assess whether opportunistically collected pediatric data can be used to evaluate risperidone population pharmacokinetic models externally and to identify a robust model for precision dosing in children. A total of 103 concentrations of risperidone and 112 concentrations of 9-OH-risperidone, collected from 62 pediatric patients (0.16–16.8 years of age), were used in the present study. The predictive performance of five published population pharmacokinetic models (four joint parent-metabolite models and one parent only) was assessed for accuracy and precision of the predictions using statistical criteria, goodness of fit plots, prediction-corrected visual predictive checks (pcVPCs), and normalized prediction distribution errors (NPDEs). The tested models produced similarly precise predictions (Root Mean Square Error [RMSE]) ranging from 0.021 to 0.027 nmol/ml for risperidone and 0.053–0.065 nmol/ml for 9-OH-risperidone). However, one of the models (a one-compartment mixture model with clearance estimated for three subpopulations) developed with a rich dataset presented fewer biases (Mean Percent Error [MPE, %] of 1.0% vs. 101.4, 146.9, 260.4, and 292.4%) for risperidone. In contrast, a model developed with fewer data and a more similar population to the one used for the external evaluation presented fewer biases for 9-OH-risperidone (MPE: 17% vs. 69.9, 47.8, and 82.9%). None of the models evaluated seemed to be generalizable to the population used in this analysis. All the models had a modest predictive performance, potentially suggesting that sources of inter-individual variability were not entirely captured and that opportunistic data from a highly heterogeneous population are likely not the most appropriate data to evaluate risperidone models externally.</p