NANOPARTICLE AGGLOMERATES FOR PULMONARY DRUG DELIVERY

The Pulmonary route has been traditionally used to treat diseases of the respiratory tract. However, important research within the last two decades have shown that in addition to treating local diseases, a wide range of systemic diseases can be treated by delivering drugs to the lungs. The recent FDA approval to market Exubera, an inhalable form of insulin developed by Pfizer, to treat Diabetes, may just be the stepping stone that the pharmaceutical industry needs to market other drugs to treat systemic diseases via the lungs. However, this technology still needs repeated drug doses to control glucose levels, as the inhaled drug is cleared rapidly. Technologies have been developed where inhaled particles are capable of controlled release of drug from the lungs. An important feature of these technologies is the large geometric size of the particles that makes it difficult for the lung macrophages to clear these particles, which results in longer residence times for the particles in the lungs. Owing to the porosity, these particles have lower densities making them deliverable to the deep lungs. However, no modulation of drug release can be achieved with these technologies when more drug release may be required. This additional requirement can only be assuaged by additional dosing of the drug formulation, which can have undesirable effects due to excess loading of excipients in the lungs. In an attempt to bring about modulation of release from long residence time particles, a novel concept was developed in our laboratory that has been termed as the Agglomerated Vesicle Technology (AVT). Liposomes with encapsulated drug were agglomerated using well known cross linking chemistries to form agglomerates in the micron sized range. The large particles exhibited aerodynamic sizes in the respirable size range with minimal damage to the particles upon nebulization. By breaking the cross links between the liposomes with a cleaving agent, it was anticipated that triggered release of drug from the AVT particles could be achieved. In vivo studies done in healthy rabbits showed that post-administration modulation of drug release is possible from the AVT particles after the introduction of the cleaving agent. This study has important implications for the future development of this technology, where the AVT particles can be made “sensitive” to the product of disease. It is envisaged that a single dose of AVT containing the appropriate drug when administered to the lungs would maintain drug levels at a controlled rate over an extended period of time. When the need for more drug arises, the product of the disease would trigger the AVT particles to release more drug as needed to control the condition, thus eliminating the need for repeated drug doses and improved compliance amongst patients.

Gold nanoparticle aerosols for rodent inhalation and translocation studies

The intensive use of nano-sized particles in many different applications necessitates studies on their risk assessment as there are still open questions on their safe handling and utilization. For reliable risk assessment, the interaction of nanoparticles (NP) with biological systems after various routes of exposure needs to be investigated using well-characterized NP. We report here on the generation of gold-NP (Au-NP) aerosols for inhalation studies with the spark ignition technique, and their characterization in terms of chemical composition, physical structure, morphology, and specific surface area, and on interaction with lung tissues and lung cells after 1 h inhalation by mice. The originally generated agglomerated Au-NP were converted into compact spherical Au-NP by thermal annealing at 600 °C, providing particles of similar mass, but different size and specific surface area. Since there are currently no translocation data available on inhaled Au-NP in the 10–50 nm diameter range, the emphasis was to generate NP as small as 20 nm for inhalation in rodents. For anticipated in vivo systemic translocation and dosimetry analyses, radiolabeled Au-NP were created by proton irradiating the gold electrodes of the spark generator, thus forming gamma ray emitting 195Au with 186 days half-life, allowing long-term biokinetic studies. The dissolution rate of 195Au from the NP was below detection limits. The highly concentrated, polydisperse Au-NP aerosol (1–2 × 107 NP/cm3) proved to be constant over several hours in terms of its count median mobility diameter, its geometric standard deviation and number concentration. After collection on filters particles can be re-suspended and used for instillation or ingestion studies.

Quantification of gold nanoparticle cell uptake under controlled biological conditions and adequate resolution

Aim: We examined cellular uptake mechanisms of fluorescently labeled polymer-coated gold nanoparticles (NPs) under different biological conditions by two quantitative, microscopic approaches. Materials & methods: Uptake mechanisms were evaluated using endocytotic inhibitors that were tested for specificity and cytotoxicity. Cellular uptake of gold NPs was analyzed either by laser scanning microscopy or transmission electron microscopy, and quantified by means of stereology using cells from the same experiment. Results: Optimal inhibitor conditions were only achieved with chlorpromazine (clathrin-mediated endocytosis) and methyl-β-cyclodextrin (caveolin-mediated endocytosis). A significant methyl-β-cyclodextrin-mediated inhibition (63-69%) and chlorpromazine-mediated increase (43-98%) of intracellular NPs was demonstrated with both imaging techniques, suggesting a predominant uptake via caveolin-medicated endocytois. Transmission electron microscopy imaging revealed more than 95% of NPs localized in intracellular vesicles and approximately 150-times more NP events/cell were detected than by laser scanning microscopy. Conclusion: We emphasize the importance of studying NP-cell interactions under controlled experimental conditions and at adequate microscopic resolution in combination with stereology. Original submitted 10 July 2012; Revised submitted 23 January 2013.

Evaluation of the accuracy of multilevel analysis parameter estimates for intra-cluster correlation in correlated binary data

Many studies in biostatistics deal with binary data. Some of these studies involve correlated observations, which can complicate the analysis of the resulting data. Studies of this kind typically arise when a high degree of commonality exists between test subjects. If there exists a natural hierarchy in the data, multilevel analysis is an appropriate tool for the analysis. Two examples are the measurements on identical twins, or the study of symmetrical organs or appendages such as in the case of ophthalmic studies. Although this type of matching appears ideal for the purposes of comparison, analysis of the resulting data while ignoring the effect of intra-cluster correlation has been shown to produce biased results.^ This paper will explore the use of multilevel modeling of simulated binary data with predetermined levels of correlation. Data will be generated using the Beta-Binomial method with varying degrees of correlation between the lower level observations. The data will be analyzed using the multilevel software package MlwiN (Woodhouse, et al, 1995). Comparisons between the specified intra-cluster correlation of these data and the estimated correlations, using multilevel analysis, will be used to examine the accuracy of this technique in analyzing this type of data. ^

An in vitro toxicity evaluation of gold-, PLLA- and PCL-coated silica nanoparticles in neuronal cells for nanoparticle-assisted laser-tissue soldering.

The uptake of silica (Si) and gold (Au) nanoparticles (NPs) engineered for laser-tissue soldering in the brain was investigated using microglial cells and undifferentiated and differentiated SH-SY5Y cells. It is not known what effects NPs elicit once entering the brain. Cellular uptake, cytotoxicity, apoptosis, and the potential induction of oxidative stress by means of depletion of glutathione levels were determined after NP exposure at concentrations of 10(3) and 10(9)NPs/ml. Au-, silica poly (ε-caprolactone) (Si-PCL-) and silica poly-L-lactide (Si-PLLA)-NPs were taken up by all cells investigated. Aggregates and single NPs were found in membrane-surrounded vacuoles and the cytoplasm, but not in the nucleus. Both NP concentrations investigated did not result in cytotoxicity or apoptosis, but reduced glutathione (GSH) levels predominantly at 6 and 24h, but not after 12 h of NP exposure in the microglial cells. NP exposure-induced GSH depletion was concentration-dependent in both cell lines. Si-PCL-NPs induced the strongest effect of GSH depletion followed by Si-PLLA-NPs and Au-NPs. NP size seems to be an important characteristic for this effect. Overall, Au-NPs are most promising for laser-assisted vascular soldering in the brain. Further studies are necessary to further evaluate possible effects of these NPs in neuronal cells.

Sparse computation for large-scale binary classification

Well-known data mining algorithms rely on inputs in the form of pairwise similarities between objects. For large datasets it is computationally impossible to perform all pairwise comparisons. We therefore propose a novel approach that uses approximate Principal Component Analysis to efficiently identify groups of similar objects. The effectiveness of the approach is demonstrated in the context of binary classification using the supervised normalized cut as a classifier. For large datasets from the UCI repository, the approach significantly improves run times with minimal loss in accuracy.

Index Tracking Using Data-Mining Techniques and Mixed-Binary Linear Programming

Index tracking has become one of the most common strategies in asset management. The index-tracking problem consists of constructing a portfolio that replicates the future performance of an index by including only a subset of the index constituents in the portfolio. Finding the most representative subset is challenging when the number of stocks in the index is large. We introduce a new three-stage approach that at first identifies promising subsets by employing data-mining techniques, then determines the stock weights in the subsets using mixed-binary linear programming, and finally evaluates the subsets based on cross validation. The best subset is returned as the tracking portfolio. Our approach outperforms state-of-the-art methods in terms of out-of-sample performance and running times.