The use of polysaccharides as building blocks in the development of drugs and contrast agents delivery systems is rapidly growing. This can be attributed to the outstanding virtues of polysaccharides such as biocompatibility, biodegradability, upgradability, multiple reacting groups and low cost. The focus of this thesis was to develop and characterize novel starch based hydrogels and nanoparticles for delivery of drugs and imaging agents. To this end, two different systems were developed. The first system includes polymer and nanoparticles prepared by graft polymerization of polymethacrylic acid and polysorbate 80 onto starch. This starch based platform nanotechnology was developed using the design principles based on the pathophysiology of breast cancer, with applications in both medical imaging and breast cancer chemotherapy. The nanoparticles exhibited a high degree of doxorubicin loading as well as sustained pH dependent release of the drug. The drug loaded nanoparticles were significantly more effective against multidrug resistant human breast cancer cells compared to free doxorubicin. Systemic administration of the starch based nanoparticles co-loaded with doxorubicin and a near infrared fluorescent probe allowed for non-invasive real time monitoring of the nanoparticles biodistribution, tumor accumulation, and clearance. Systemic administration of the clinically relevant doses of the drug loaded particles to a mouse model of breast cancer significantly enhanced therapeutic efficacy while minimizing side effects compared to free doxorubicin. A novel, starch based magnetic resonance imaging (MRI) contrast agent with good in vitro and in vivo tolerability was formulated which exhibited superior signal enhancement in tumor and vasculature. The second system is a co-polymeric hydrogel of starch and xanthan gum with adjustable swelling and permeation properties. The hydrogels exhibited excellent film forming capability, and appeared to be particularly useful in controlled delivery applications of larger molecular size compounds. The starch based hydrogels, polymers and nanoparticles developed in this work have shown great potentials for controlled drug delivery and biomedical imaging applications.

Neurotrophin-3 (NT-3) and anti-NogoA have shown promise in regenerative strategies after spinal cord injury; however, conventional methods for localized release to the injured spinal cord are either prone to infection or not suitable for sustained release. To address these issues, we have designed a composite drug delivery system that is comprised of poly(lactic-co-glycolic acid) (PLGA) nanoparticles dispersed in an injectable hydrogel of hyaluronan and methyl cellulose (HAMC). Achieving sustained and bioactive protein release from PLGA particles is a known challenge; consequently, we studied the effects of processing parameters and excipient selection on protein release, stability, and bioactivity. We found that embedding PLGA nanoparticles in HAMC results in more linear drug release due to the formation of a diffusion-limiting layer of methyl cellulose on the particle surface. Co-encapsulated MgCO3 was able to significantly improve NT-3 bioactivity, while trehalose + hyaluronan was able to improve anti-NogoA bioactivity and release.

Neurotrophin-3 (NT-3) and anti-NogoA have shown promise in regenerative strategies after spinal cord injury; however, conventional methods for localized release to the injured spinal cord are either prone to infection or not suitable for sustained release. To address these issues, we have designed a composite drug delivery system that is comprised of poly(lactic-co-glycolic acid) (PLGA) nanoparticles dispersed in an injectable hydrogel of hyaluronan and methyl cellulose (HAMC). Achieving sustained and bioactive protein release from PLGA particles is a known challenge; consequently, we studied the effects of processing parameters and excipient selection on protein release, stability, and bioactivity. We found that embedding PLGA nanoparticles in HAMC results in more linear drug release due to the formation of a diffusion-limiting layer of methyl cellulose on the particle surface. Co-encapsulated MgCO3 was able to significantly improve NT-3 bioactivity, while trehalose + hyaluronan was able to improve anti-NogoA bioactivity and release.

Polymer matrix composites are widely used as structural components in the aerospace industry and wind turbine industry etc. to take advantage of their unique mechanical properties and weight saving ability. Although there have been considerable developments in analyzing delamination growth and effects of voids on certain mechanical properties of composites, none of the present literatures investigates the effects of voids on delamination growth under compression. In this research, a parametric study is performed to investigate the effects of voids on delamination growth in composite laminates under compression. In composite structures, delamination would be created by eccentricities in structural load path, structural discontinuities, and during manufacturing and maintenance processes. Also, the service damage such as the impact of foreign objects may also result in delamination. In the Finite Element model developed, a through-width surface delamination is assumed, and void is placed in critical locations ahead of crack tip. Strain Energy Release Rate (SERR) is calculated by the Virtual Crack Closure Technique (VCCT) in order to study the delamination growth. It is found that the delamination front experiences a mixed-mode delamination behavior when local out-of-plane buckling occurs. During the loading, Mode II SERR increases monotonically while Mode I SERR increases first and then decreases as the delamination front starts to close. Meanwhile, Mode II SERR is found to be much larger than the Mode I component. The presence of void does not significantly alter the transverse displacement of the delaminated part. However, the presence of void increases the Mode II SERR, as well as the total SERR, and this increase depends on the size and location of void. For Mode I SERR, the effect of void is not that prominent.

Thalassemia, a genetic blood disorder, occurs as a result of deformations of hemoglobin structures. Patients with thalassemia develop iron overload from chronic blood transfusions and require regular iron chelation to prevent potentially fatal iron-related complications. Deferiprone is a commercially available drug used as iron chelator for the treatment of thalassemia but the very long-term effectiveness is not clearly known yet. Therefore, some studies were carried out to find effective alternative drugs or delivery methods for treatment of thalassemia. Controlled delivery, which offers safer, more convenient, and more effective means of administering actives, seems promising with this respect. Chitosan, a natural biopolymer produced from deacetylation of chitin, has a variety of promising pharmaceutical uses and is presently considered as a novel carrier material in drug delivery systems. In this study, chitosan microspheres having different degree of deacetylation (DDA) and containing Deferiprone were prepared by oil/water emulsion method and by crosslinking with gluteraldehyde. Particle size, SEM, and in vitro drug release analysis were performed. The average sizes of the prepared microspheres increased with increasing degree of deacetylation of chitosan and with decreasing crosslinking degree. In vitro drug release studies showed that, the release rate of Deferiprone increased as the crosslinking degree increased, contrary to the expectations. This is explained by the crystalline structure of lightly crosslinked chitosan which have ordered and dense structure causing slower release rate for Deferiprone compare to highly crosslinked structures. In the second stage of the study, chitosan films hardened with gluteraldehyde were prepared by film casting method. IR, DSC and mechanical analysis were performed. For the films with various crosslinking degrees, it was found that UTS values differed from 50.6 MPa to 102.7 MPa, mean elastic modulus values differed from 3328.7 MPa to 3790.1 MPa and SAB values differed from 2.06% to 4.29%.

In product pricing and reserving, actuaries are often required to make predictions of future death rates. In the past, this has been performed by using deterministic improvement scales that give only a single mortality trajectory. However, there is enormous likelihood that future death rates will turn out to be different from the projected ones, and so a better assessment of longevity risk would be one that consists of both a mean estimate and a measure of uncertainty. Such assessment can be performed using a stochastic mortality model, which is the core of this thesis. The Lee-Carter model is one of the most popular stochastic mortality models. While it does an excellent job in mean forecasting, it has been criticized for providing overly narrow prediction intervals that may have underestimated uncertainty. This thesis mitigates this problem by relaxing the assumption on the distribution of death counts. We found that the generalization from Poisson to negative binomial is equivalent to allowing gamma heterogeneity within each age-period cells. The proposed extension gives not only a better fit, but also a more conservative prediction interval that may reflect better the uncertainty entailed. The proposed extension is then applied to the construction of mortality improvement scales for Canadian insured lives. Given that the insured lives data series are too short for a direct Lee-Carter projection, we build an extra relational model that could borrow strengths from the Canadian population data, which covers a far longer period. The resultant scales consist of explicit measures of uncertainty. The prediction of the tail of a survival distribution requires a special treatment due to the lack of high quality old-age mortality data. We utilize the asymptotic results in modern extreme value theory to extrapolate death probabilities to the advanced ages, and to statistically determine the age at which the life table should be closed. Such technique is further integrated with the Lee-Carter model to produce a stochastic analysis of old-age mortality, and a prediction of the highest attained age for various cohorts. The mortality models we considered are further applied to the valuation of mortality-related financial products. In particular we investigate the no-negative-equity-guarantee that is offered in most fixed-repayment lifetime mortgages in Britain. The valuation of such guarantee requires a simultaneous consideration of both longevity and house price inflation risk. We found that house price returns can be well described by an ARMA-EGARCH time-series process. Under an ARMA-EGARCH process, however, the Black-Scholes formula no longer applies. We derive our own pricing formula based on the conditional Esscher transformation. Finally, we propose some possible hedging and capital reserving strategies for managing the risks associated with the guarantee.

In product pricing and reserving, actuaries are often required to make predictions of future death rates. In the past, this has been performed by using deterministic improvement scales that give only a single mortality trajectory. However, there is enormous likelihood that future death rates will turn out to be different from the projected ones, and so a better assessment of longevity risk would be one that consists of both a mean estimate and a measure of uncertainty. Such assessment can be performed using a stochastic mortality model, which is the core of this thesis. The Lee-Carter model is one of the most popular stochastic mortality models. While it does an excellent job in mean forecasting, it has been criticized for providing overly narrow prediction intervals that may have underestimated uncertainty. This thesis mitigates this problem by relaxing the assumption on the distribution of death counts. We found that the generalization from Poisson to negative binomial is equivalent to allowing gamma heterogeneity within each age-period cells. The proposed extension gives not only a better fit, but also a more conservative prediction interval that may reflect better the uncertainty entailed. The proposed extension is then applied to the construction of mortality improvement scales for Canadian insured lives. Given that the insured lives data series are too short for a direct Lee-Carter projection, we build an extra relational model that could borrow strengths from the Canadian population data, which covers a far longer period. The resultant scales consist of explicit measures of uncertainty. The prediction of the tail of a survival distribution requires a special treatment due to the lack of high quality old-age mortality data. We utilize the asymptotic results in modern extreme value theory to extrapolate death probabilities to the advanced ages, and to statistically determine the age at which the life table should be closed. Such technique is further integrated with the Lee-Carter model to produce a stochastic analysis of old-age mortality, and a prediction of the highest attained age for various cohorts. The mortality models we considered are further applied to the valuation of mortality-related financial products. In particular we investigate the no-negative-equity-guarantee that is offered in most fixed-repayment lifetime mortgages in Britain. The valuation of such guarantee requires a simultaneous consideration of both longevity and house price inflation risk. We found that house price returns can be well described by an ARMA-EGARCH time-series process. Under an ARMA-EGARCH process, however, the Black-Scholes formula no longer applies. We derive our own pricing formula based on the conditional Esscher transformation. Finally, we propose some possible hedging and capital reserving strategies for managing the risks associated with the guarantee.

Thesis (M.S.)--Rutgers University, 2009. / "Graduate Program in Food Science." Includes bibliographical references (p. 186-195).

Thesis (Ph. D.)--Rutgers University, 2008. / "Graduate Program in Food Science." Includes bibliographical references (p. 184-190).

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