Simultaneous analysis of tramadol, O-desmethyltramadol, and N-desmethyltramadol enantiomers in rat plasma by high-performance liquid chromatography-tandem mass spectrometry: application to pharmacokinetics

Tramadol (T) is available as a racemic mixture of (+)‐trans‐T and (−)‐trans‐T. The main metabolic pathways are O‐demethylation and N‐demethylation, producing trans‐O‐desmethyltramadol ( M1 ) and trans‐N‐desmethyltramadol ( M2 ) enantiomers, respectively. The analgesic effect of T is related to the opioid activity of (+)‐trans‐T and (+)‐ M1 and to the monoaminergic action of (+/−)‐trans‐T. This is the first study using tandem mass spectrometry as a detection system for the simultaneous analysis of trans‐T, M1 , and M2 enantiomers. The analytes were resolved on a Chiralpak® AD column using hexane:ethanol (95.5:4.5, v/v) plus 0.1% diethylamine as the mobile phase. The quantitation limits were 0.5 ng/ml for trans‐T and M1 and 0.1 ng/ml for M2 . The method developed and validated here was applied to a pharmacokinetic study in rats. Male Wistar rats (n = 6 at each time point) received a single oral dose of 20 mg/kg racemic trans‐T. Blood samples were collected up to 12 h after drug administration. The kinetic disposition of trans‐T and M2 was enantioselective (AUC_(+)/(−) ratio = 4.16 and 6.36, respectively). The direction and extent of enantioselectivity in the pharmacokinetics of trans‐T and M2 in rats were comparable to data previously reported for healthy volunteers, suggesting that rats are a suitable model for enantioselective studies of trans‐T pharmacokinetics. Chirality, 2011. © 2010 Wiley‐Liss, Inc.     

Genetic Structure of the Population of Alternaria solani in Brazil

Understanding the genetic structure of the population of Alternaria solani (AS) is an important component of epidemiological studies of early blight, a severe disease that affects potato (Po) and tomato (To) worldwide. Up to 150 isolates obtained from both hosts were analysed with RAPD and AFLP markers to estimate the amount and distribution of genetic variability of AS in Brazil. Using RAPD, gene diversity (h = 0.20) and scaled indices of diversity of Shannon (H′ = 0.66) and Stoddart and Taylor’s (G = 0.31) for the Po population were higher than those of the To (h = 0.07, H′ = 0.34, G = 0.17). For AFLP, the statistics for the Po (h = 0.17, H′ = 0.86, G = 0.49) and To (h = 0.17, H′ = 0.85, G = 0.36) populations were similar. For each RAPD and AFLP locus, the allele frequency for the overall population ranged from 0.006 to 0.988, and 0.007 to 0.993, respectively. Genetic differentiation was high (G_ST = 0.41 and θ = 0.59) and moderately high (G_ST = 0.23 and θ = 0.37) when estimated with RAPD and AFLP, respectively. Based on cluster analyses, there was strong evidence of association of pathogen haplotypes with host species. The null hypothesis of random association of alleles was rejected in the analysis of both RAPD (I_A = 13.1, P < 0.001) and AFLP (I_A = 2.2, P < 0.001) markers. The average number of migrants was estimated to be around one and two individuals per generation, using RAPD and AFLP, respectively. There was no correlation between genetic distance and geographical origin of AS haplotypes for RAPD (r = ?0.07, P = 0.84) and AFLP (r = ?0.03, P = 0.70). The AS population is clonal with high genetic variability, and there is genetic differentiation between the populations that affect To and Po.     

Variability of a diterpene with potential anti-HIV activity isolated from the Brazilian brown alga <Emphasis Type="Italic">Dictyota menstrualis</Emphasis>

This study evaluated the variability in the production of an HIV-1 antiviral diterpene in individuals of Dictyota menstrualis throughout reproductive stages. The brown alga Dictyota has an isomorphic biphasic life cycle. The quantification of the active principle by GC–FID indicated a greater production of the diterpene in the female gametophytic phase (42.11 ppm). However, these individuals had the highest variation between individuals (standard deviation of 68.20 ppm in the range from 0.39 to 227 ppm). Sporophytic individuals showed less variability. This variability in the production of the antiviral diterpene is important to the development of future antiviral drugs.     

Testing the suitability of insect orders as indicators for olive farming systems

• 1 A previous study suggested the use of certain insects groups as indicators for detecting organic olive farming in Southern Spain. To validate the use of these groups, insects were collected from olive orchards in Cordoba and Granada, comprising two Andalusian provinces with different surrounding landscapes.
• 2 Canopies were sampled using the branch‐beating technique during pre‐blooming and post‐blooming periods over 3 years in Granada (1999, 2000 and 2003) and 1 year in Cordoba (2003).
• 3 Using a nonparametric linear discriminant analysis method, based on the k‐nearest neighbour algorithm, two discriminant functions were constructed. A first discriminant model took into account interannual variability in Granada Province and the second model focused on environmental heterogeneity between the two provinces. Cross‐validation techniques, such as leave‐one‐out and split‐sample, were applied to the associated discriminant functions for each model to check their performance.
• 4 Even though differences existed with respect to the insect composition of the regions, the second model correctly classified 78.1% of the sampled blocks under the non‐organic and organic farming systems at the same time as taking into account two orders: Coleoptera and Hemiptera [excluding Euphyllura olivina olivina (Psyllidae) and the Heteroptera suborder]. The results suggest that the relative abundance of these groups in the post‐blooming period could constitute a potential bio‐indicator of organic olive farming system.

     

Beyond missing heritability: prediction of complex traits

Despite rapid advances in genomic technology, our ability to account for phenotypic variation using genetic information remains limited for many traits. This has unfortunately resulted in limited application of genetic data towards preventive and personalized medicine, one of the primary impetuses of genome-wide association studies. Recently, a large proportion of the "missing heritability" for human height was statistically explained by modeling thousands of single nucleotide polymorphisms concurrently. However, it is currently unclear how gains in explained genetic variance will translate to the prediction of yet-to-be observed phenotypes. Using data from the Framingham Heart Study, we explore the genomic prediction of human height in training and validation samples while varying the statistical approach used, the number of SNPs included in the model, the validation scheme, and the number of subjects used to train the model. In our training datasets, we are able to explain a large proportion of the variation in height (h(2) up to 0.83, R(2) up to 0.96). However, the proportion of variance accounted for in validation samples is much smaller (ranging from 0.15 to 0.36 depending on the degree of familial information used in the training dataset). While such R(2) values vastly exceed what has been previously reported using a reduced number of pre-selected markers (<0.10), given the heritability of the trait (～ 0.80), substantial room for improvement remains.