Human in vivo Pharmacokinetics of [¹⁴C]Dibenzo[def,p]chrysene by Accelerator Mass Spectrometry Following Oral Micro-Dosing

Dibenzo(def,p)chrysene (DBC), (also known as dibenzo[a,l]pyrene), is a high molecular weight polycyclic aromatic hydrocarbon (PAH) found in the environment, including food, produced by the incomplete combustion of hydrocarbons. DBC, classified by IARC as a 2A probable human carcinogen, has a relative potency factor (RPF) in animal cancer models 30-fold higher than benzo[a]pyrene. No data are available describing disposition of high molecular weight (>4 rings) PAHs in humans to compare to animal studies. Pharmacokinetics of DBC was determined in 3 female and 6 male human volunteers following oral micro-dosing (29 ng, 5 nCi) of [14C]-DBC. This study was made possible with highly sensitive accelerator mass spectrometry (AMS), capable of detecting [14C]-DBC equivalents in plasma and urine following a dose considered of de minimus risk to human health. Plasma and urine were collected over 72 h. The plasma Cmax was 68.8 ± 44.3 fg*mL-1 with a Tmax of 2.25 ± 1.04 h. Elimination occurred in two distinct phases; a rapid (α)-phase, with a T1/2 of 5.8 ± 3.4 h and apparent elimination rate constant (Kel) of 0.17 ± 0.12 fg*h-1 followed by a slower (β)-phase, with a T1/2 of 41.3 ± 29.8 h and apparent Kel of 0.03 ± 0.02 fg*h-1. In spite of the high degree of hydrophobicity (log Kow of 7.4), DBC was eliminated rapidly in humans, as are most PAHs in animals, compared to other hydrophobic persistent organic pollutants such as, DDT, PCBs and TCDD. Preliminary examination utilizing a new UHPLC-AMS interface, suggests the presence of polar metabolites in plasma as early as 45 min following dosing. This is the first in vivo dataset describing pharmacokinetics in humans of a high molecular weight PAH and should be a valuable addition to risk assessment paradigms

Preliminary physiologically based pharmacokinetic models for benzo[a]pyrene and dibenzo[def,p]chrysene in rodents

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental contaminants generated as byproducts of natural and anthropogenic combustion processes. Despite significant public health concern, physiologically based pharmacokinetic (PBPK) modeling efforts for PAHs have so far been limited to naphthalene, plus simpler PK models for pyrene, nitropyrene, and benzo[a]pyrene (B[a]P). The dearth of published models is due in part to the high lipophilicity, low volatility, and myriad metabolic pathways for PAHs, all of which present analytical and experimental challenges. Our research efforts have focused upon experimental approaches and initial development of PBPK models for the prototypic PAH, B[a]P, and the more potent, albeit less studied transplacental carcinogen, dibenzo[def,p]chrysene (DBC). For both compounds, model compartments included arterial and venous blood, flow limited lung, liver, richly perfused and poorly perfused tissues, diffusion limited fat, and a two compartment theoretical gut (for oral exposures). Hepatic and pulmonary metabolism was described for both compounds, as were fractional binding in blood and fecal clearance. Partition coefficients for parent PAH along with their diol and tetraol metabolites were estimated using published algorithms and verified experimentally for the hydroxylated metabolites. The preliminary PBPK models were able to describe many, but not all, of the available data sets, comprising multiple routes of exposure (oral, intravenous) and nominal doses spanning several orders of magnitude. Supported by Award Number P42 ES016465 from the National Institute of Environmental Health Sciences. Published by Elsevier Inc.Keywords: Metabolic activation, Gastrointestinal absorption, Cytochrome P450, Mouse skin, Benzo[a]Pyrene, Rat mammary gland, Ultimate carcinogens, Aromatic hydrocarbons, Tumor initiating activity, Partition coefficient

A Genetic Epidemiological Mega Analysis of Smoking Initiation in Adolescents

Introduction. Previous studies in adolescents were not adequately powered to accurately disentangle genetic and environmental influences on smoking initiation across adolescence. Methods. Mega-analysis of pooled genetically informative data on smoking initiation was performed, with structural equation modeling, to test equality of prevalence and correlations across cultural backgrounds, and to estimate the significance and effect size of genetic and environmental effects according to the classical twin study, in adolescent male and female twins from same-sex and opposite-sex twin pairs (N=19 313 pairs) between age 10 and 19, with 76 358 longitudinal assessments between 1983 and 2007, from 11 population-based twin samples from the US, Europe and Australia. Results. Although prevalences differed between samples, twin correlations did not, suggesting similar etiology of smoking initiation across developed countries. The estimate of additive genetic contributions to liability of smoking initiation increased from approximately 15% to 45% from age 13 to 19. Correspondingly, shared environmental factors accounted for a substantial proportion of variance in liability to smoking initiation at age 13 (70%) and gradually less by age 19 (40%). Conclusions. Both additive genetic and shared environmental factors significantly contribute to variance in smoking initiation throughout adolescence. The present study, the largest genetic epidemiological study on smoking initiation to date, found consistent results across 11 studies for the etiology of smoking initiation. Environmental factors, especially those shared by siblings in a family, primarily influence smoking initiation variance in early adolescence, while an increasing role of genetic factors is seen at later ages, which has important implications for prevention strategies. IMPLICATIONS: This is the first study to find evidence of genetic factors in liability to smoking initiation at ages as young as 12. It also shows the strongest evidence to date for decay of effects of the shared environment from early adolescence to young adulthood. We found remarkable consistency of twin correlations across studies reflecting similar etiology of liability to initiate smoking across different cultures and time periods. Thus familial factors strongly contribute to individual differences in who starts to smoke with a gradual increase in the impact of genetic factors and a corresponding decrease in that of the shared environment

General and specific patterns of cortical gene expression as spatial correlates of complex cognitive functioning

Gene expression varies across the brain. This spatial patterning denotes specialised support for particular brain functions. However, the way that a given gene's expression fluctuates across the brain may be governed by general rules. Quantifying patterns of spatial covariation across genes would offer insights into the molecular characteristics of brain areas supporting, for example, complex cognitive functions. Here, we use principal component analysis to separate general and unique gene regulatory associations with cortical substrates of cognition. We find that the region-to-region variation in cortical expression profiles of 8235 genes covaries across two major principal components: gene ontology analysis suggests these dimensions are characterised by downregulation and upregulation of cell-signalling/modification and transcription factors. We validate these patterns out-of-sample and across different data processing choices. Brain regions more strongly implicated in general cognitive functioning (g; 3 cohorts, total meta-analytic N = 39,519) tend to be more balanced between downregulation and upregulation of both major components (indicated by regional component scores). We then identify a further 29 genes as candidate cortical spatial correlates of g, beyond the patterning of the two major components (|β| range = 0.18 to 0.53). Many of these genes have been previously associated with clinical neurodegenerative and psychiatric disorders, or with other health-related phenotypes. The results provide insights into the cortical organisation of gene expression and its association with individual differences in cognitive functioning

General and specific patterns of cortical gene expression as spatial correlates of complex cognitive functioning

Gene expression varies across the brain. This spatial patterning denotes specialised support for particular brain functions. However, the way that a given gene's expression fluctuates across the brain may be governed by general rules. Quantifying patterns of spatial covariation across genes would offer insights into the molecular characteristics of brain areas supporting, for example, complex cognitive functions. Here, we use principal component analysis to separate general and unique gene regulatory associations with cortical substrates of cognition. We find that the region-to-region variation in cortical expression profiles of 8235 genes covaries across two major principal components: gene ontology analysis suggests these dimensions are characterised by downregulation and upregulation of cell-signalling/modification and transcription factors. We validate these patterns out-of-sample and across different data processing choices. Brain regions more strongly implicated in general cognitive functioning (g; 3 cohorts, total meta-analytic N = 39,519) tend to be more balanced between downregulation and upregulation of both major components (indicated by regional component scores). We then identify a further 29 genes as candidate cortical spatial correlates of g, beyond the patterning of the two major components (|β| range = 0.18 to 0.53). Many of these genes have been previously associated with clinical neurodegenerative and psychiatric disorders, or with other health-related phenotypes. The results provide insights into the cortical organisation of gene expression and its association with individual differences in cognitive functioning

General and specific patterns of cortical gene expression as spatial correlates of complex cognitive functioning

We thank the participants of the three cohorts (UKB, Generation Scotland (STRADL) and LBC1936) for their participation and the research teams for their work in collecting, processing and giving access to these data for analysis. We are also thankful to the brain donors to the Allen Human Brain Atlas, BrainSpan Atlas and Human Brain Transcriptome Project, and to the people who collected and processed the data and made it openly available For the purpose of open access, the author has applied a CC-BY public copyright licence to any Author Accepted Manuscript version arising from this submission.Peer reviewe