Molecular-Level Dysregulation of Insulin Pathways and Inflammatory Processes in Peripheral Blood Mononuclear Cells by Circadian Misalignment.

Circadian misalignment due to night work has been associated with an elevated risk for chronic diseases. We investigated the effects of circadian misalignment using shotgun protein profiling of peripheral blood mononuclear cells taken from healthy humans during a constant routine protocol, which was conducted immediately after participants had been subjected to a 3-day simulated night shift schedule or a 3-day simulated day shift schedule. By comparing proteomic profiles between the simulated shift conditions, we identified proteins and pathways that are associated with the effects of circadian misalignment and observed that insulin regulation pathways and inflammation-related proteins displayed markedly different temporal patterns after simulated night shift. Further, by integrating the proteomic profiles with previously assessed metabolomic profiles in a network-based approach, we found key associations between circadian dysregulation of protein-level pathways and metabolites of interest in the context of chronic metabolic diseases. Endogenous circadian rhythms in circulating glucose and insulin differed between the simulated shift conditions. Overall, our results suggest that circadian misalignment is associated with a tug of war between central clock mechanisms controlling insulin secretion and peripheral clock mechanisms regulating insulin sensitivity, which may lead to adverse long-term outcomes such as diabetes and obesity. Our study provides a molecular-level mechanism linking circadian misalignment and adverse long-term health consequences of night work.

leapR: An R Package for Multiomic Pathway Analysis.

A generalized goal of many high-throughput data studies is to identify functional mechanisms that underlie observed biological phenomena, whether they be disease outcomes or metabolic output. Increasingly, studies that rely on multiple sources of high-throughput data (genomic, transcriptomic, proteomic, metabolomic) are faced with a challenge of summarizing the data to generate testable hypotheses. However, this requires a time-consuming process to evaluate numerous statistical methods across numerous data sources. Here, we introduce the leapR package, a framework to rapidly assess biological pathway activity using diverse statistical tests and data sources, allowing facile integration of multisource data. The leapR package with a user manual and example workflow is available for download from GitHub (https://github.com/biodataganache/leapR).

Night shift schedule causes circadian dysregulation of DNA repair genes and elevated DNA damage in humans.

Circadian disruption has been identified as a risk factor for health disorders such as obesity, cardiovascular disease, and cancer. Although epidemiological studies suggest an increased risk of various cancers associated with circadian misalignment due to night shift work, the underlying mechanisms have yet to be elucidated. We sought to investigate the potential mechanistic role that circadian disruption of cancer hallmark pathway genes may play in the increased cancer risk in shift workers. In a controlled laboratory study, we investigated the circadian transcriptome of cancer hallmark pathway genes and associated biological pathways in circulating leukocytes obtained from healthy young adults during a 24-hour constant routine protocol following 3 days of simulated day shift or night shift. The simulated night shift schedule significantly altered the normal circadian rhythmicity of genes involved in cancer hallmark pathways. A DNA repair pathway showed significant enrichment of rhythmic genes following the simulated day shift schedule, but not following the simulated night shift schedule. In functional assessments, we demonstrated that there was an increased sensitivity to both endogenous and exogenous sources of DNA damage after exposure to simulated night shift. Our results suggest that circadian dysregulation of DNA repair may increase DNA damage and potentiate elevated cancer risk in night shift workers.

Risk assessment of predicted serum concentrations of bisphenol A in children and adults following treatment with dental composite restoratives, dental sealants, or orthodontic adhesives using physiologically based pharmacokinetic modeling.

Bisphenol A (BPA) is a chemical used to manufacture bisphenol A glycidyl methacrylate (BisGMA). BisGMA has been used for decades in dental composite restoratives, sealants, and adhesives. Based on published studies, exposure to low concentrations of BPA are possible from dental and orthodontic devices. The serum BPA concentrations arising from such devices and oral doses were predicted using a PBPK model in children and adult females based on 1) published extraction data for cured and uncured 3M ESPE Filtek Supreme Ultra Flowable, 3M ESPE Filtek Bulk Fill Restorative, and 3M ESPE Clinpro Sealant and 2) published 20% ethanol/water and water rinsate data following orthodontic application with 3M ESPE Transbond MIP Primer and 3M ESPE Transbond XT Adhesive. Predicted oral exposure to BPA arising from these dental and orthodontic devices is low (median <10 ng/treatment) and predicted serum BPA concentrations were also low (<10-4 nM). Even the maximum predicted exposure in this study (533.2 ng/treatment) yields a margin of exposure of 7.5 relative to the EFSA t-TDI (4 µg/kg-day) and is only 2.8% of the daily BPA exposure for the US population in a 58-kg woman (15,660 ng/day). Therefore, the exposure to BPA arising from the 3M ESPE dental and orthodontic devices evaluated in this study is negligible relative to daily BPA exposure in the general population and these potential BPA sources do not constitute a risk to patients.

ISiCLE: A Quantum Chemistry Pipeline for Establishing in Silico Collision Cross Section Libraries.

High-throughput, comprehensive, and confident identifications of metabolites and other chemicals in biological and environmental samples will revolutionize our understanding of the role these chemically diverse molecules play in biological systems. Despite recent technological advances, metabolomics studies still result in the detection of a disproportionate number of features that cannot be confidently assigned to a chemical structure. This inadequacy is driven by the single most significant limitation in metabolomics, the reliance on reference libraries constructed by analysis of authentic reference materials with limited commercial availability. To this end, we have developed the in silico chemical library engine (ISiCLE), a high-performance computing-friendly cheminformatics workflow for generating libraries of chemical properties. In the instantiation described here, we predict probable three-dimensional molecular conformers (i.e., conformational isomers) using chemical identifiers as input, from which collision cross sections (CCS) are derived. The approach employs first-principles simulation, distinguished by the use of molecular dynamics, quantum chemistry, and ion mobility calculations, to generate structures and chemical property libraries, all without training data. Importantly, optimization of ISiCLE included a refactoring of the popular MOBCAL code for trajectory-based mobility calculations, improving its computational efficiency by over 2 orders of magnitude. Calculated CCS values were validated against 1983 experimentally measured CCS values and compared to previously reported CCS calculation approaches. Average calculated CCS error for the validation set is 3.2% using standard parameters, outperforming other density functional theory (DFT)-based methods and machine learning methods (e.g., MetCCS). An online database is introduced for sharing both calculated and experimental CCS values ( metabolomics.pnnl.gov ), initially including a CCS library with over 1 million entries. Finally, three successful applications of molecule characterization using calculated CCS are described, including providing evidence for the presence of an environmental degradation product, the separation of molecular isomers, and an initial characterization of complex blinded mixtures of exposure chemicals. This work represents a method to address the limitations of small molecule identification and offers an alternative to generating chemical identification libraries experimentally by analyzing authentic reference materials. All code is available at github.com/pnnl .

Evaluation of In Silico Multifeature Libraries for Providing Evidence for the Presence of Small Molecules in Synthetic Blinded Samples.

The current gold standard for unambiguous molecular identification in metabolomics analysis is comparing two or more orthogonal properties from the analysis of authentic reference materials (standards) to experimental data acquired in the same laboratory with the same analytical methods. This represents a significant limitation for comprehensive chemical identification of small molecules in complex samples. The process is time consuming and costly, and the majority of molecules are not yet represented by standards. Thus, there is a need to assemble evidence for the presence of small molecules in complex samples through the use of libraries containing calculated chemical properties. To address this need, we developed a Multi-Attribute Matching Engine (MAME) and a library derived in part from our in silico chemical library engine (ISiCLE). Here, we describe an initial evaluation of these methods in a blinded analysis of synthetic chemical mixtures as part of the U.S. Environmental Protection Agency's (EPA) Non-Targeted Analysis Collaborative Trial (ENTACT, Phase 1). For molecules in all mixtures, the initial blinded false negative rate (FNR), false discovery rate (FDR), and accuracy were 57%, 77%, and 91%, respectively. For high evidence scores, the FDR was 35%. After unblinding of the sample compositions, we optimized the scoring parameters to better exploit the available evidence and increased the accuracy for molecules suspected as present. The final FNR, FDR, and accuracy were 67%, 53%, and 96%, respectively. For high evidence scores, the FDR was 10%. This study demonstrates that multiattribute matching methods in conjunction with in silico libraries may one day enable reduced reliance on experimentally derived libraries for building evidence for the presence of molecules in complex samples.

PIXiE: an algorithm for automated ion mobility arrival time extraction and collision cross section calculation using global data association.

MOTIVATION: Drift tube ion mobility spectrometry coupled with mass spectrometry (DTIMS-MS) is increasingly implemented in high throughput omics workflows, and new informatics approaches are necessary for processing the associated data. To automatically extract arrival times for molecules measured by DTIMS at multiple electric fields and compute their associated collisional cross sections (CCS), we created the PNNL Ion Mobility Cross Section Extractor (PIXiE). The primary application presented for this algorithm is the extraction of data that can then be used to create a reference library of experimental CCS values for use in high throughput omics analyses. RESULTS: We demonstrate the utility of this approach by automatically extracting arrival times and calculating the associated CCSs for a set of endogenous metabolites and xenobiotics. The PIXiE-generated CCS values were within error of those calculated using commercially available instrument vendor software. AVAILABILITY AND IMPLEMENTATION: PIXiE is an open-source tool, freely available on Github. The documentation, source code of the software, and a GUI can be found at https://github.com/PNNL-Comp-Mass-Spec/PIXiE and the source code of the backend workflow library used by PIXiE can be found at https://github.com/PNNL-Comp-Mass-Spec/IMS-Informed-Library . CONTACT: erin.baker@pnnl.gov or thomas.metz@pnnl.gov. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

All that is silver is not toxic: silver ion and particle kinetics reveals the role of silver ion aging and dosimetry on the toxicity of silver nanoparticles.

BACKGROUND: When suspended in cell culture medium, nano-objects composed of soluble metals such as silver can dissolve resulting in ion formation, altered particle properties (e.g. mass, morphology, etc.), and modulated cellular dose. Cultured cells are exposed not just to nanoparticles but to a complex, dynamic mixture of altered nanoparticles, unbound ions, and ion-ligand complexes. Here, three different cell types (RAW 264.7 macrophages and bone marrow derived macrophages from wild-type C57BL/6 J mice and Scavenger Receptor A deficient (SR-A(-/-)) mice) were exposed to 20 and 110 nm silver nanoparticles, and RAW 264.7 cells were exposed to freshly mixed silver ions, aged silver ions (ions incubated in cell culture medium), and ions formed from nanoparticle dissolution. The In Vitro Sedimentation, Diffusion, Dissolution, and Dosimetry Model (ISD3) was used to predict dose metrics for each exposure scenario. RESULTS: Silver nanoparticles, freshly mixed ions, and ions from nanoparticle dissolution were toxic, while aged ions were not toxic. Macrophages from SR-A(-/-) mice did not take up 20 nm silver nanoparticles as well as wild-types but demonstrated no differences in silver levels after exposure to 110 nm nanoparticles. Dose response modeling with ISD3 predicted dose metrics suggest that amount of ions in cells and area under the curve (AUC) of ion amount in cells are the most predictive of cell viability after nanoparticle and combined nanoparticle/dissolution-formed-ions exposures, respectively. CONCLUSIONS: Results of this study suggest that the unbound silver cation is the ultimate toxicant, and ions formed extracellularly drive toxicity after exposure to nanoparticles. Applying computational modeling (ISD3) to better understand dose metrics for soluble nanoparticles allows for better interpretation of in vitro hazard assessments.

ISD3: a particokinetic model for predicting the combined effects of particle sedimentation, diffusion and dissolution on cellular dosimetry for in vitro systems.

BACKGROUND: The development of particokinetic models describing the delivery of insoluble or poorly soluble nanoparticles to cells in liquid cell culture systems has improved the basis for dose-response analysis, hazard ranking from high-throughput systems, and now allows for translation of exposures across in vitro and in vivo test systems. Complimentary particokinetic models that address processes controlling delivery of both particles and released ions to cells, and the influence of particle size changes from dissolution on particle delivery for cell-culture systems would help advance our understanding of the role of particles and ion dosimetry on cellular toxicology. We developed ISD3, an extension of our previously published model for insoluble particles, by deriving a specific formulation of the Population Balance Equation for soluble particles. RESULTS: ISD3 describes the time, concentration and particle size dependent dissolution of particles, their delivery to cells, and the delivery and uptake of ions to cells in in vitro liquid test systems. We applied the model to calculate the particle and ion dosimetry of nanosilver and silver ions in vitro after calibration of two empirical models, one for particle dissolution and one for ion uptake. Total media ion concentration, particle concentration and total cell-associated silver time-courses were well described by the model, across 2 concentrations of 20 and 110 nm particles. ISD3 was calibrated to dissolution data for 20 nm particles as a function of serum protein concentration, but successfully described the media and cell dosimetry time-course for both particles at all concentrations and time points. We also report the finding that protein content in media affects the initial rate of dissolution and the resulting near-steady state ion concentration in solution for the systems we have studied. CONCLUSIONS: By combining experiments and modeling, we were able to quantify the influence of proteins on silver particle solubility, determine the relative amounts of silver ions and particles in exposed cells, and demonstrate the influence of particle size changes resulting from dissolution on particle delivery to cells in culture. ISD3 is modular and can be adapted to new applications by replacing descriptions of dissolution, sedimentation and boundary conditions with those appropriate for particles other than silver.

Exposure assessment of process-related contaminants in food by biomarker monitoring.

Exposure assessment is a fundamental part of the risk assessment paradigm, but can often present a number of challenges and uncertainties. This is especially the case for process contaminants formed during the processing, e.g. heating of food, since they are in part highly reactive and/or volatile, thus making exposure assessment by analysing contents in food unreliable. New approaches are therefore required to accurately assess consumer exposure and thus better inform the risk assessment. Such novel approaches may include the use of biomarkers, physiologically based kinetic (PBK) modelling-facilitated reverse dosimetry, and/or duplicate diet studies. This review focuses on the state of the art with respect to the use of biomarkers of exposure for the process contaminants acrylamide, 3-MCPD esters, glycidyl esters, furan and acrolein. From the overview presented, it becomes clear that the field of assessing human exposure to process-related contaminants in food by biomarker monitoring is promising and strongly developing. The current state of the art as well as the existing data gaps and challenges for the future were defined. They include (1) using PBK modelling and duplicate diet studies to establish, preferably in humans, correlations between external exposure and biomarkers; (2) elucidation of the possible endogenous formation of the process-related contaminants and the resulting biomarker levels; (3) the influence of inter-individual variations and how to include that in the biomarker-based exposure predictions; (4) the correction for confounding factors; (5) the value of the different biomarkers in relation to exposure scenario's and risk assessment, and (6) the possibilities of novel methodologies. In spite of these challenges it can be concluded that biomarker-based exposure assessment provides a unique opportunity to more accurately assess consumer exposure to process-related contaminants in food and thus to better inform risk assessment.

Transgenerational inheritance of neurobehavioral and physiological deficits from developmental exposure to benzo[a]pyrene in zebrafish.

Benzo[a]pyrene (B[a]P) is a well-known genotoxic polycylic aromatic compound whose toxicity is dependent on signaling via the aryl hydrocarbon receptor (AHR). It is unclear to what extent detrimental effects of B[a]P exposures might impact future generations and whether transgenerational effects might be AHR-dependent. This study examined the effects of developmental B[a]P exposure on 3 generations of zebrafish. Zebrafish embryos were exposed from 6 to 120h post fertilization (hpf) to 5 and 10µM B[a]P and raised in chemical-free water until adulthood (F0). Two generations were raised from F0 fish to evaluate transgenerational inheritance. Morphological, physiological and neurobehavioral parameters were measured at two life stages. Juveniles of the F0 and F2 exhibited hyper locomotor activity, decreased heartbeat and mitochondrial function. B[a]P exposure during development resulted in decreased global DNA methylation levels and generally reduced expression of DNA methyltransferases in wild type zebrafish, with the latter effect largely reversed in an AHR2-null background. Adults from the F0 B[a]P exposed lineage displayed social anxiety-like behavior. Adults in the F2 transgeneration manifested gender-specific increased body mass index (BMI), increased oxygen consumption and hyper-avoidance behavior. Exposure to benzo[a]pyrene during development resulted in transgenerational inheritance of neurobehavioral and physiological deficiencies. Indirect evidence suggested the potential for an AHR2-dependent epigenetic route.

Implications of Bioremediation of Polycyclic Aromatic Hydrocarbon-Contaminated Soils for Human Health and Cancer Risk.

Bioremediation uses soil microorganisms to degrade polycyclic aromatic hydrocarbons (PAHs) into less toxic compounds and can be performed in situ, without the need for expensive infrastructure or amendments. This review provides insights into the cancer risks associated with PAH-contaminated soils and places bioremediation outcomes in a context relevant to human health. We evaluated which bioremediation strategies were most effective for degrading PAHs and estimated the cancer risks associated with PAH-contaminated soils. Cancer risk was statistically reduced in 89% of treated soils following bioremediation, with a mean degradation of 44% across the B2 group PAHs. However, all 180 treated soils had postbioremediation cancer risk values that exceeded the U.S. Environmental Protection Agency (USEPA) health-based acceptable risk level (by at least a factor of 2), with 32% of treated soils exceeding recommended levels by greater than 2 orders of magnitude. Composting treatments were most effective at biodegrading PAHs in soils (70% average reduction compared with 28-53% for the other treatment types), which was likely due to the combined influence of the rich source of nutrients and microflora introduced with organic compost amendments. Ultimately, bioremediation strategies, in the studies reviewed, were unable to successfully remove carcinogenic PAHs from contaminated soils to concentrations below the target cancer risk levels recommended by the USEPA.

Completing the Link between Exposure Science and Toxicology for Improved Environmental Health Decision Making: The Aggregate Exposure Pathway Framework.

Driven by major scientific advances in analytical methods, biomonitoring, computation, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the "systems approaches" used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) concept in the toxicological sciences. Aggregate exposure pathways offer an intuitive framework to organize exposure data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathways and adverse outcome pathways, completing the source to outcome continuum for more meaningful integration of exposure assessment and hazard identification. Together, the two frameworks form and inform a decision-making framework with the flexibility for risk-based, hazard-based, or exposure-based decision making.

Development of a physiologically based pharmacokinetic model for assessment of human exposure to bisphenol A.

A previously developed physiologically based pharmacokinetic (PBPK) model for bisphenol A (BPA) in adult rhesus monkeys was modified to characterize the pharmacokinetics of BPA and its phase II conjugates in adult humans following oral ingestion. Coupled with in vitro studies on BPA metabolism in the liver and the small intestine, the PBPK model was parameterized using oral pharmacokinetic data with deuterated-BPA (d6-BPA) delivered in cookies to adult humans after overnight fasting. The availability of the serum concentration time course of unconjugated d6-BPA offered direct empirical evidence for the calibration of BPA model parameters. The recalibrated PBPK adult human model for BPA was then evaluated against published human pharmacokinetic studies with BPA. A hypothesis of decreased oral uptake was needed to account for the reduced peak levels observed in adult humans, where d6-BPA was delivered in soup and food was provided prior to BPA ingestion, suggesting the potential impact of dosing vehicles and/or fasting on BPA disposition. With the incorporation of Monte Carlo analysis, the recalibrated adult human model was used to address the inter-individual variability in the internal dose metrics of BPA for the U.S. general population. Model-predicted peak BPA serum levels were in the range of pM, with 95% of human variability falling within an order of magnitude. This recalibrated PBPK model for BPA in adult humans provides a scientific basis for assessing human exposure to BPA that can serve to minimize uncertainties incurred during extrapolations across doses and species.

24-hour human urine and serum profiles of bisphenol A: Evidence against sublingual absorption following ingestion in soup.

Extensive first-pass metabolism of ingested bisphenol A (BPA) in the gastro-intestinal tract and liver restricts blood concentrations of bioactive BPA to <1% of total BPA in humans and non-human primates. Absorption of ingested BPA through non-metabolizing tissues of the oral cavity, recently demonstrated in dogs, could lead to the higher serum BPA concentrations reported in some human biomonitoring studies. We hypothesized that the extensive interaction with the oral mucosa by a liquid matrix, like soup, relative to solid food or capsules, might enhance absorption through non-metabolizing oral cavity tissues in humans, producing higher bioavailability and higher serum BPA concentrations. Concurrent serum and urine concentrations of d6-BPA, and its glucuronide and sulfate conjugates, were measured over a 24hour period in 10 adult male volunteers following ingestion of 30µg d6-BPA/kg body weight in soup. Absorption of d6-BPA was rapid (t1/2=0.45h) and elimination of the administered dose was complete 24h post-ingestion, evidence against any tissue depot for BPA. The maximum serum d6-BPA concentration was 0.43nM at 1.6h after administration and represented <0.3% of total d6-BPA. Pharmacokinetic parameters, pharmacokinetic model simulations, and the significantly faster appearance half-life of d6-BPA-glucuronide compared to d6-BPA (0.29h vs 0.45h) were evidence against meaningful absorption of BPA in humans through any non-metabolizing tissue (<1%). This study confirms that typical exposure to BPA in food produces picomolar to subpicomolar serum BPA concentrations in humans, not nM concentrations reported in some biomonitoring studies.

An integrated approach for the in vitro dosimetry of engineered nanomaterials.

BACKGROUND: There is a great need for screening tools capable of rapidly assessing nanomaterial toxicity. One impediment to the development of reliable in vitro screening methods is the need for accurate measures of cellular dose. We present here a methodology that enables accurate determination of delivered to cell dose metrics. This methodology includes (1) standardization of engineered nanomaterial (ENM) suspension preparation; (2) measurement of ENM characteristics controlling delivery to cells in culture; and (3) calculation of delivered dose as a function of exposure time using the ISDD model. The approach is validated against experimentally measured doses, and simplified analytical expressions for the delivered dose (Relevant In Vitro Dose (RID)f function) are derived for 20 ENMs. These functions can be used by nanotoxicologists to accurately calculate the total mass (RIDM), surface area (RIDSA), or particle number (RIDN) delivered to cells as a function of exposure time. RESULTS: The proposed methodology was used to derive the effective density, agglomerate diameter and RID functions for 17 industrially-relevant metal and metal oxide ENMs, two carbonaceous nanoparticles, and non-agglomerating gold nanospheres, for two well plate configurations (96 and 384 well plates). For agglomerating ENMs, the measured effective density was on average 60% below the material density. We report great variability in delivered dose metrics, with some materials depositing within 24 hours while others require over 100 hours for delivery to cells. A neutron-activated tracer particle system was employed to validate the proposed in vitro dosimetry methodology for a number of ENMs (measured delivered to cell dose within 9% of estimated). CONCLUSIONS: Our findings confirm and extend experimental and computational evidence that agglomerate characteristics affect the dose delivered to cells. Therefore measurement of these characteristics is critical for effective use of in vitro systems for nanotoxicology. The mixed experimental/computational approach to cellular dosimetry proposed and validated here can be used by nanotoxicologists to accurately calculate the delivered to cell dose metrics for various ENMs and in vitro conditions as a function of exposure time. The RID functions and characterization data for widely used ENMs presented here can together be used by experimentalists to design and interpret toxicity studies.

Comparative iron oxide nanoparticle cellular dosimetry and response in mice by the inhalation and liquid cell culture exposure routes.

BACKGROUND: Toxicity testing the rapidly growing number of nanomaterials requires large scale use of in vitro systems under the presumption that these systems are sufficiently predictive or descriptive of responses in in vivo systems for effective use in hazard ranking. We hypothesized that improved relationships between in vitro and in vivo models of experimental toxicology for nanomaterials would result from placing response data in vitro and in vivo on the same dose scale, the amount of material associated with cells. METHODS: Balb/c mice were exposed nose-only to an aerosol (68.6 nm CMD, 19.9 mg/m(3), 4 hours) generated from of 12.8 nm superparamagnetic iron oxide particles (SPIO). Target cell doses were calculated, histological evaluations conducted, and biomarkers of response were identified by global transcriptomics. Representative murine epithelial and macrophage cell types were exposed in vitro to the same material in liquid suspension for four hours and levels of nanoparticle regulated cytokine transcripts identified in vivo were quantified as a function of measured nanoparticle cellular dose. RESULTS: Target tissue doses of 0.009-0.4 µg SPIO/cm(2) in lung led to an inflammatory response in the alveolar region characterized by interstitial inflammation and macrophage infiltration. In vitro, higher target tissue doses of ~1.2-4 µg SPIO/ cm(2) of cells were required to induce transcriptional regulation of markers of inflammation, CXCL2 & CCL3, in C10 lung epithelial cells. Estimated in vivo macrophage SPIO nanoparticle doses ranged from 1-100 pg/cell, and induction of inflammatory markers was observed in vitro in macrophages at doses of 8-35 pg/cell. CONCLUSIONS: Application of target tissue dosimetry revealed good correspondence between target cell doses triggering inflammatory processes in vitro and in vivo in the alveolar macrophage population, but not in the epithelial cells of the alveolar region. These findings demonstrate the potential for target tissue dosimetry to enable the more quantitative comparison of in vitro and in vivo systems and advance their use for hazard assessment and extrapolation to humans. The mildly inflammogentic cellular doses experienced by mice were similar to those calculated for humans exposed to the same material at the existing permissible exposure limit of 10 mg/m(3) iron oxide (as Fe).

Physiologically-based pharmacokinetic model for Fentanyl in support of the development of Provisional Advisory Levels.

Provisional Advisory Levels (PALs) are tiered exposure limits for toxic chemicals in air and drinking water that are developed to assist in emergency responses. Physiologically-based pharmacokinetic (PBPK) modeling can support this process by enabling extrapolations across doses, and exposure routes, thereby addressing gaps in the available toxicity data. Here, we describe the development of a PBPK model for Fentanyl - a synthetic opioid used clinically for pain management - to support the establishment of PALs. Starting from an existing model for intravenous Fentanyl, we first optimized distribution and clearance parameters using several additional IV datasets. We then calibrated the model using pharmacokinetic data for various formulations, and determined the absorbed fraction, F, and time taken for the absorbed amount to reach 90% of its final value, t90. For aerosolized pulmonary Fentanyl, F=1 and t90<1 min indicating complete and rapid absorption. The F value ranged from 0.35 to 0.74 for oral and various transmucosal routes. Oral Fentanyl was absorbed the slowest (t90~300 min); the absorption of intranasal Fentanyl was relatively rapid (t90~20-40 min); and the various oral transmucosal routes had intermediate absorption rates (t90~160-300 min). Based on these results, for inhalation exposures, we assumed that all of the Fentanyl inhaled from the air during each breath directly, and instantaneously enters the arterial circulation. We present model predictions of Fentanyl blood concentrations in oral and inhalation scenarios relevant for PAL development, and provide an analytical expression that can be used to extrapolate between oral and inhalation routes for the derivation of PALs.

A multi-route model of nicotine-cotinine pharmacokinetics, pharmacodynamics and brain nicotinic acetylcholine receptor binding in humans.

The pharmacokinetics of nicotine, the pharmacologically active alkaloid in tobacco responsible for addiction, are well characterized in humans. We developed a physiologically based pharmacokinetic/pharmacodynamic model of nicotine pharmacokinetics, brain dosimetry and brain nicotinic acetylcholine receptor (nAChRs) occupancy. A Bayesian framework was applied to optimize model parameters against multiple human data sets. The resulting model was consistent with both calibration and test data sets, but in general underestimated variability. A pharmacodynamic model relating nicotine levels to increases in heart rate as a proxy for the pharmacological effects of nicotine accurately described the nicotine related changes in heart rate and the development and decay of tolerance to nicotine. The PBPK model was utilized to quantitatively capture the combined impact of variation in physiological and metabolic parameters, nicotine availability and smoking compensation on the change in number of cigarettes smoked and toxicant exposure in a population of 10,000 people presented with a reduced toxicant (50%), reduced nicotine (50%) cigarette Across the population, toxicant exposure is reduced in some but not all smokers. Reductions are not in proportion to reductions in toxicant yields, largely due to partial compensation in response to reduced nicotine yields. This framework can be used as a key element of a dosimetry-driven risk assessment strategy for cigarette smoke constituents.

Elucidating regulatory processes of intense physical activity by multi-omics analysis.

BACKGROUND: Physiological and biochemical processes across tissues of the body are regulated in response to the high demands of intense physical activity in several occupations, such as firefighting, law enforcement, military, and sports. A better understanding of such processes can ultimately help improve human performance and prevent illnesses in the work environment. METHODS: To study regulatory processes in intense physical activity simulating real-life conditions, we performed a multi-omics analysis of three biofluids (blood plasma, urine, and saliva) collected from 11 wildland firefighters before and after a 45 min, intense exercise regimen. Omics profiles post- versus pre-exercise were compared by Student's t-test followed by pathway analysis and comparison between the different omics modalities. RESULTS: Our multi-omics analysis identified and quantified 3835 proteins, 730 lipids and 182 metabolites combining the 3 different types of samples. The blood plasma analysis revealed signatures of tissue damage and acute repair response accompanied by enhanced carbon metabolism to meet energy demands. The urine analysis showed a strong, concomitant regulation of 6 out of 8 identified proteins from the renin-angiotensin system supporting increased excretion of catabolites, reabsorption of nutrients and maintenance of fluid balance. In saliva, we observed a decrease in 3 pro-inflammatory cytokines and an increase in 8 antimicrobial peptides. A systematic literature review identified 6 papers that support an altered susceptibility to respiratory infection. CONCLUSION: This study shows simultaneous regulatory signatures in biofluids indicative of homeostatic maintenance during intense physical activity with possible effects on increased infection susceptibility, suggesting that caution against respiratory diseases could benefit workers on highly physical demanding jobs.