10 research outputs found
Optimising clonidine dosage for sedation in mechanically ventilated children: a pharmacokinetic simulation study
BACKGROUND: Clonidine is in widespread off-label use as a sedative in mechanically ventilated children, despite limited evidence of efficacy. A variety of dosage regimens have been utilised in clinical practice and in research studies. Within these studies, clonidine has inconsistently shown useful sedation properties. One of the reasons attributed to the inconsistent signs of efficacy is suboptimal clonidine dosing. AIM: This study aims to propose a target plasma concentration and simulate clonidine pharmacokinetics (PK) in a cohort of mechanically ventilated children to evaluate the adequacy of clonidine dosage regimens used in clinical practice and research studies. METHOD: A literature search was undertaken to identify a clonidine PKPD model, from which a target concentration for sedation was defined. Using a previously published PK model the projected plasma concentrations of 692 mechanically ventilated children (demographics taken from a recent study) were generated. Doses from recently published clinical studies were investigated. Adequacy of each regimen to attain therapeutic clonidine plasma concentrations was assessed. RESULTS: A target plasma concentration of above 2 μg/L was proposed. Nine dosage regimens (four intravenous boluses, four intravenous infusions and one nasogastric route boluses) were evaluated ranging from 1μg/kg 8 hourly intravenous boluses to a regimen up to 3μg/kg/hr continuous intravenous infusion. Regimens with a loading dose of 2μg/kg followed by variable continuous infusion of up to 2μg/kg/hr titrated according to sedation score appear most suitable. CONCLUSIONS: The variety of dosage regimens in previous studies of clonidine along with difficulties in the conduct of interventional studies may have contributed to the lack of efficacy data to support its use. Simulations of clonidine plasma concentrations based on known population pharmacokinetic parameters suggest a loading dose followed by higher than current practice maintenance dose infusion is required to achieve adequate steady-state concentrations early in treatment. Further PKPD studies will aid in the determination of the optimal clonidine dosage regimen. This article is protected by copyright. All rights reserved
First steps to understand heat tolerance of temperate maize at adult stage: identification of QTL across multiple environments with connected segregating populations
Combined linkage and linkage disequilibrium QTL mapping in multiple families of maize (Zea mays L.) line crosses highlights complementarities between models based on parental haplotype and single locus polymorphism
International audienceAdvancements in genotyping are rapidly decreasing marker costs and increasing marker density. This opens new possibilities for mapping quantitative trait loci (QTL), in particular by combining linkage disequilibrium information and linkage analysis (LDLA). In this study, we compared different approaches to detect QTL for four traits of agronomical importance in two large multi-parental datasets of maize (Zea mays L.) of 895 and 928 testcross progenies composed of 7 and 21 biparental families, respectively, and genotyped with 491 markers. We compared to traditional linkage-based methods two LDLA models relying on the dense genotyping of parental lines with 17,728 SNP: one based on a clustering approach of parental line segments into ancestral alleles and one based on single marker information. The two LDLA models generally identified more QTL (60 and 52 QTL in total) than classical linkage models (49 and 44 QTL in total). However, they performed inconsistently over datasets and traits suggesting that a compromise must be found between the reduction of allele number for increasing statistical power and the adequacy of the model to potentially complex allelic variation. For some QTL, the model exclusively based on linkage analysis, which assumed that each parental line carried a different QTL allele, was able to capture remaining variation not explained by LDLA models. These complementarities between models clearly suggest that the different QTL mapping approaches must be considered to capture the different levels of allelic variation at QTL involved in complex traits