The influence of the time-of-day administration of the drug on the pharmacokinetics of sunitinib in rabbits

OBJECTIVES: At present it is known that the adjustment of the anticancer therapy to the circadian rhythms in tissues reduces the toxicity of the treatment. Chronotherapy also increases the efficacy of the anticancer treatment, which has been proved for many drugs. Sunitinib is a tyrosine kinase inhibitor, which is broadly used for the treatment of numerous cancers. The aim of the study was a comparison of the concentrations and pharmacokinetics of sunitinib after a single administration to rabbits at 08:00 (control group) and 20:00. Additionally, the effect of sunitinib on glucose levels was investigated. MATERIALS AND METHODS: The research was carried out on two groups of rabbits: I08:00, a group with the drug administered at 08:00 (n=8) and II20:00, a group with the drug administered at 20:00 (n=8). The rabbits were treated with sunitinib at an oral dose of 25 mg. Plasma concentrations of sunitinib and its metabolite (SU12662) were measured with a validated HPLC method with UV detection. RESULTS: The comparison of the sunitinib Cmax and AUC0-t in the group with sunitinib administered at 20:00 with the control group gave the ratios of 2.20 (90% confidence interval (CI) (2.17; 2.22) and 1.64 (1.61; 1.68), respectively. Statistically significant differences between the groups under analysis were revealed for Cmax (p \u3c 0.0001), AUC0-t (p = 0.0079), AUC0-∞ (p = 0.0149), and tmax (p = 0.0085). The mean glycemia drop was higher in group I08:00. than in group II20:00 (22.7% vs. 14.3%; p = 0.0622). The glycemia values returned to the initial values in 24 h after the administration of the drug in both groups. CONCLUSIONS: The research proved a significant influence of the time-of-day administration on the pharmacokinetics of sunitinib

Estimation of genetic variation in residual variance in female and male broiler chickens

In breeding programs, robustness of animals and uniformity of end product can be improved by exploiting genetic variation in residual variance. Residual variance can be defined as environmental variance after accounting for all identifiable effects. The aims of this study were to estimate genetic variance in residual variance of body weight, and to estimate genetic correlations between body weight itself and its residual variance and between female and male residual variance for broilers. The data sets comprised 26 972 female and 24 407 male body weight records. Variance components were estimated with ASREML. Estimates of the heritability of residual variance were in the range 0.029 (s.e.50.003) to 0.047 (s.e.50.004). The genetic coefficients of variation were high, between 0.35 and 0.57. Heritabilities were higher in females than in males. Accounting for heterogeneous residual variance increased the heritabilities for body weight as well. Genetic correlations between body weight and its residual variance were 20.41 (s.e.50.032) and 20.45 (s.e.50.040), respectively, in females and males. The genetic correlation between female and male residual variance was 0.11 (s.e.50.089), indicating that female and male residual variance are different traits. Results indicate good opportunities to simultaneously increase the mean and improve uniformity of body weight of broilers by selection

Persistence of accuracy of genomic estimated breeding values over generations in layer chickens

<p>Abstract</p> <p>Background</p> <p>The predictive ability of genomic estimated breeding values (GEBV) originates both from associations between high-density markers and QTL (Quantitative Trait Loci) and from pedigree information. Thus, GEBV are expected to provide more persistent accuracy over successive generations than breeding values estimated using pedigree-based methods. The objective of this study was to evaluate the accuracy of GEBV in a closed population of layer chickens and to quantify their persistence over five successive generations using marker or pedigree information.</p> <p>Methods</p> <p>The training data consisted of 16 traits and 777 genotyped animals from two generations of a brown-egg layer breeding line, 295 of which had individual phenotype records, while others had phenotypes on 2,738 non-genotyped relatives, or similar data accumulated over up to five generations. Validation data included phenotyped and genotyped birds from five subsequent generations (on average 306 birds/generation). Birds were genotyped for 23,356 segregating SNP. Animal models using genomic or pedigree relationship matrices and Bayesian model averaging methods were used for training analyses. Accuracy was evaluated as the correlation between EBV and phenotype in validation divided by the square root of trait heritability.</p> <p>Results</p> <p>Pedigree relationships in outbred populations are reduced by 50% at each meiosis, therefore accuracy is expected to decrease by the square root of 0.5 every generation, as observed for pedigree-based EBV (Estimated Breeding Values). In contrast the GEBV accuracy was more persistent, although the drop in accuracy was substantial in the first generation. Traits that were considered to be influenced by fewer QTL and to have a higher heritability maintained a higher GEBV accuracy over generations. In conclusion, GEBV capture information beyond pedigree relationships, but retraining every generation is recommended for genomic selection in closed breeding populations.</p

Comparison of analyses of the QTLMAS XIV common dataset. II: QTL analysis

Background - A quantitative and a binary trait for the 14th QTLMAS 2010 workshop were simulated under a model which combined additive inheritance, epistasis and imprinting. This paper aimed to compare results submitted by the participants of the workshop.Methods - The results were compared according to three criteria: the success rate (ratio of mapped QTL to the total number of simulated QTL), and the error rate (ratio of false positives to the number of reported positions), and mean distance between a true mapped QTL and the nearest submitted position. Results - Seven groups submitted results for the quantitative trait and five for the binary trait. Among the 37 simulated QTL 17 remained undetected. Success rate ranged from 0.05 to 0.43, error rate was between 0.00 and 0.92, and the mean distance ranged from 0.26 to 0.77 Mb. Conclusions - Our comparison shows that differences among methods used by the participants increases with the complexity of genetic architecture. It was particularly visible for the quantitative trait which was determined partly by non-additive QTL. Furthermore, an imprinted QTL with a large effect may remain undetected if the applied model tests only for Mendelian genes

Applications of Genomic Selection in Poultry

Here we describe the application of genomic selection in both layer and broiler breeder populations. A brown egg layer line was partitioned into two sub-lines, one used for genomic selection and the other as a pedigree selected control. Generation interval in the genomic sub-line was halved and the sub-line size was reduced compared to the traditionally-selected control. The genomic sub-line outperformed pedigree-selected contemporaries in 12 of 16 traits evaluated, and genomic estimated breeding values were more accurate and persistent than pedigree-based estimates. Genome wide association studies for all available traits identified several regions associated with economically important traits. Similar improvements in prediction accuracy were observed in broilers. Estimation of the Mendelian sampling term for full sibs without own phenotypic information contributed to this gain. The development of robust imputation methods enabled the implementation of genomic selection into the routine evaluations to accelerate genetic progress