MULTISPECIES EVALUATION OF ANTIRETROVIRAL DISPOSITION IN A PUTATIVE TISSUE RESERVOIR OF HIV: IMPLICATIONS FOR ERADICATION

Ongoing HIV replication within gut lymphoid tissues may contribute to the persistence of HIV despite treatment with antiretrovirals (ARVs). ARVs may have reduced exposure in certain tissue areas, but current methods for assessing ARV tissue concentrations cannot test this hypothesis. The goal of this project was to characterize how ARVs distribute within gut tissue, and determine whether or not they concentrate in areas of local HIV gene expression. Drug transporter expression and localization were also evaluated in these tissues to determine what factors influence ARV distribution. Using mass spectrometry imaging (MSI), the ileum and rectum of humanized mice (n=49), non-human primates (NHP, n=12) and humans (n=5) were evaluated for ARV distribution. The co-localization of ARV distribution with CD3+ T cells, drug efflux transporters, and HIV RNA expression was assessed. ARV correlation with CD3+ T cells ranged from -0.09 to 0.32 and was not significantly different between species. HIV RNA was not co-localized with ARV exposure in any species (r range -0.09-0.2). ARV-transporter co-localization was highest for MDR1 in all species, and not significantly different between the ileum and rectum. MSI provided previously unobtainable distributional data, showing ARV localization to specific tissue sites and no co-localization with HIV gene expression. Drug transporters affect ARV tissue disposition and can be exploited to maximize ARV exposure, but quantitative measures of drug transporter protein expression across preclinical species are not available. Gene and protein expression of ARV efflux and uptake transporters were evaluated using qPCR, Western blot, and LC-MS proteomics. Gene and protein expression were generally consistent between infected and uninfected animals and between ileum and rectum. There was poor correlation between methods, and no single method significantly predicted tissue ARV concentrations in a stepwise regression model. We also show that the contribution of human transporter isoforms in humanized mice can significantly affect interspecies comparisons. Human protein expression data was most consistent with humanized mice (1-9 fold different) over NHPs (1-21 fold different). By completing these experiments in two animal species and in humans, we can better understand how HIV persists in tissues and inform the development of targeted therapies for HIV eradication.Doctor of Philosoph

Antiretroviral Pharmacology in Mucosal Tissues

Strategies to prevent HIV infection using pre-exposure prophylaxis (PrEP) are required to curtail the HIV pandemic. The mucosal tissues of the genital and rectal tracts play a critical role in HIV acquisition, but antiretroviral (ARV) disposition and correlates of efficacy within these tissues are not well understood. Pre-clinical and clinical strategies to describe ARV pharmacokinetic-pharmacodynamic relationships (PK/PD) within mucosal tissues are currently being investigated. In this review, we summarize the physiochemical and biologic factors influencing ARV tissue exposure. Further, we discuss the necessary steps to generate relevant PK/PD data and the challenges associated with this process. Finally, we suggest how pre-clinical and clinical data might be practically translated into optimal PrEP dosing strategies for clinical trials testing using mathematical modeling and simulation

Factors influencing the sound preference in urban open spaces

In this paper, based on a large scale survey in Europe and China as well as corresponding laboratory studies, the influencing factors on the sound preference evaluation, considering social, demographical, physical, behavioural and psychological facets, have been systematically examined based on statistical analyses for each of the 19 case study sites. Various sound types have been considered, including natural, human, mechanical and instrumental sounds. In terms of social/demographical factors, the results suggest that age and education level are two factors which universally influence the sound preference significantly, although the influence may vary with different types of urban open spaces and sounds. With increasing age or education level, people tend to prefer natural sounds and are more annoyed by mechanical sounds in general. It has also been found that gender, occupation and residence status generally would not influence the sound preference evaluation significantly, although gender has a rather strong influence for certain sound types such as bird sounds, especially at certain case study sites. In terms of physical factors (season, time of day), behavioural factors (frequency of coming to the site, reason for coming to the site), and psychological factors (site preference), generally speaking, their influence on the sound preference evaluation is insignificant, except for limited case study sites and certain sound types. The influence of home sound environment, in terms of sounds heard at home, on the sound preference has been found to be generally insignificant, except for certain sounds. It is noted that there are some correlations between social/demographical factors and the studied physical/behavioural/psychological factors, which should be taken into account when considering the influence of individual factors on sound preference. (C) 2010 Elsevier Ltd. All rights reserved

Morphologies of High Redshift, Dust Obscured Galaxies from Keck Laser Guide Star Adaptive Optics

Spitzer MIPS images in the Bootes field of the NOAO Deep Wide-Field Survey have revealed a class of extremely dust obscured galaxy (DOG) at z~2. The DOGs are defined by very red optical to mid-IR (observed-frame) colors, R - [24 um] > 14 mag, i.e. f_v (24 um) / f_v (R) > 1000. They are Ultra-Luminous Infrared Galaxies with L_8-1000 um > 10^12 -10^14 L_sun, but typically have very faint optical (rest-frame UV) fluxes. We imaged three DOGs with the Keck Laser Guide Star Adaptive Optics (LGSAO) system, obtaining ~0.06'' resolution in the K'-band. One system was dominated by a point source, while the other two were clearly resolved. Of the resolved sources, one can be modeled as a exponential disk system. The other is consistent with a de Vaucouleurs profile typical of elliptical galaxies. The non-parametric measures of their concentration and asymmetry, show the DOGs to be both compact and smooth. The AO images rule out double nuclei with separations of greater than 0.1'' (< 1 kpc at z=2), making it unlikely that ongoing major mergers (mass ratios of 1/3 and greater) are triggering the high IR luminosities. By contrast, high resolution images of z~2 SCUBA sources tend to show multiple components and a higher degree of asymmetry. We compare near-IR morphologies of the DOGs with a set of z=1 luminous infrared galaxies (LIRGs; L_IR ~ 10^11 L_sun) imaged with Keck LGSAO by the Center for Adaptive Optics Treasury Survey. The DOGs in our sample have significantly smaller effective radii, ~1/4 the size of the z=1 LIRGs, and tend towards higher concentrations. The small sizes and high concentrations may help explain the globally obscured rest-frame blue-to-UV emission of the DOGs.Comment: 9 pages, 7 figures, 2 tables, accepted for publication in the Astronomical Journa

Process evaluation of appreciative inquiry to translate pain management evidence into pediatric nursing practice

Background Appreciative inquiry (AI) is an innovative knowledge translation (KT) intervention that is compatible with the Promoting Action on Research in Health Services (PARiHS) framework. This study explored the innovative use of AI as a theoretically based KT intervention applied to a clinical issue in an inpatient pediatric care setting. The implementation of AI was explored in terms of its acceptability, fidelity, and feasibility as a KT intervention in pain management. Methods A mixed-methods case study design was used. The case was a surgical unit in a pediatric academic-affiliated hospital. The sample consisted of nurses in leadership positions and staff nurses interested in the study. Data on the AI intervention implementation were collected by digitally recording the AI sessions, maintaining logs, and conducting individual semistructured interviews. Data were analysed using qualitative and quantitative content analyses and descriptive statistics. Findings were triangulated in the discussion. Results Three nurse leaders and nine staff members participated in the study. Participants were generally satisfied with the intervention, which consisted of four 3-hour, interactive AI sessions delivered over two weeks to promote change based on positive examples of pain management in the unit and staff implementation of an action plan. The AI sessions were delivered with high fidelity and 11 of 12 participants attended all four sessions, where they developed an action plan to enhance evidence-based pain assessment documentation. Participants labeled AI a 'refreshing approach to change' because it was positive, democratic, and built on existing practices. Several barriers affected their implementation of the action plan, including a context of change overload, logistics, busyness, and a lack of organised follow-up. Conclusions Results of this case study supported the acceptability, fidelity, and feasibility of AI as a KT intervention in pain management. The AI intervention requires minor refinements (e.g., incorporating continued follow-up meetings) to enhance its clinical utility and sustainability. The implementation process and effectiveness of the modified AI intervention require evaluation in a larger multisite study

Quantitative mass spectrometry imaging of emtricitabine in cervical tissue model using infrared matrix-assisted laser desorption electrospray ionization

A quantitative mass spectrometry imaging (QMSI) technique using infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) is demonstrated for the antiretroviral (ARV) drug emtricitabine in incubated human cervical tissue. Method development of the QMSI technique leads to a gain in sensitivity and removal of interferences for several ARV drugs. Analyte response was significantly improved by a detailed evaluation of several cationization agents. Increased sensitivity and removal of an isobaric interference was demonstrated with sodium chloride in the electrospray solvent. Voxel-to-voxel variability was improved for the MSI experiments by normalizing analyte abundance to a uniformly applied compound with similar characteristics to the drug of interest. Finally, emtricitabine was quantified in tissue with a calibration curve generated from the stable isotope-labeled analog of emtricitabine followed by cross-validation using liquid chromatography tandem mass spectrometry (LC-MS/MS). The quantitative IR-MALDESI analysis proved to be reproducible with an emtricitabine concentration of 17.2±1.8 μg/gtissue. This amount corresponds to the detection of 7 fmol/voxel in the IR-MALDESI QMSI experiment. Adjacent tissue slices were analyzed using LC-MS/MS which resulted in an emtricitabine concentration of 28.4±2.8 μg/gtissue

Short Communication: Cheminformatics Analysis to Identify Predictors of Antiviral Drug Penetration into the Female Genital Tract

The exposure of oral antiretroviral (ARV) drugs in the female genital tract (FGT) is variable and almost unpredictable. Identifying an efficient method to find compounds with high tissue penetration would streamline the development of regimens for both HIV preexposure prophylaxis and viral reservoir targeting. Here we describe the cheminformatics investigation of diverse drugs with known FGT penetration using cluster analysis and quantitative structure–activity relationships (QSAR) modeling. A literature search over the 1950–2012 period identified 58 compounds (including 21 ARVs and representing 13 drug classes) associated with their actual concentration data for cervical or vaginal tissue, or cervicovaginal fluid. Cluster analysis revealed significant trends in the penetrative ability for certain chemotypes. QSAR models to predict genital tract concentrations normalized to blood plasma concentrations were developed with two machine learning techniques utilizing drugs' molecular descriptors and pharmacokinetic parameters as inputs. The QSAR model with the highest predictive accuracy had R2test=0.47. High volume of distribution, high MRP1 substrate probability, and low MRP4 substrate probability were associated with FGT concentrations ≥1.5-fold plasma concentrations. However, due to the limited FGT data available, prediction performances of all models were low. Despite this limitation, we were able to support our findings by correctly predicting the penetration class of rilpivirine and dolutegravir. With more data to enrich the models, we believe these methods could potentially enhance the current approach of clinical testing

Mapping Antiretroviral Drugs in Tissue by IR-MALDESI MSI Coupled to the Q Exactive and Comparison with LC-MS/MS SRM Assay

This work describes the coupling of the IR-MALDESI imaging source with the Q Exactive mass spectrometer. IR-MALDESI MSI was used to elucidate the spatial distribution of several HIV drugs in cervical tissues that had been incubated in either a low or high concentration. Serial sections to those analyzed by IR-MALDESI MSI were homogenized and analyzed by LC-MS/MS to quantify the amount of each drug present in the tissue. By comparing the two techniques, an agreement between the average intensities from the imaging experiment with the absolute quantities for each drug was observed. This correlation between these two techniques serves as a prerequisite to quantitative IR-MALDESI MSI. In addition, a targeted MS2 imaging experiment was also conducted to demonstrate the capabilities of the Q Exactive and to highlight the added selectivity that can be obtained with SRM or MRM imaging experiments