Leveraging Lymphatic System Targeting in Systemic Lupus Erythematosus for Improved Clinical Outcomes.

The role of advanced drug delivery strategies in drug repositioning and minimizing drug attrition rates, when applied early in drug discovery, is poised to increase the translational impact of various therapeutic strategies in disease prevention and treatment. In this context, drug delivery to the lymphatic system is gaining prominence not only to improve the systemic bioavailability of various pharmaceutical drugs but also to target certain specific diseases associated with the lymphatic system. Although the role of the lymphatic system in lupus is known, very little is done to target drugs to yield improved clinical benefits. In this review, we discuss recent advances in drug delivery strategies to treat lupus, the various routes of drug administration leading to improved lymph node bioavailability, and the available technologies applied in other areas that can be adapted to lupus treatment. Moreover, this review also presents some recent findings that demonstrate the promise of lymphatic targeting in a preclinical setting, offering renewed hope for certain pharmaceutical drugs that are limited by efficacy in their conventional dosage forms. These findings underscore the potential and feasibility of such lymphatic drug-targeting approaches to enhance therapeutic efficacy in lupus and minimize off-target effects of the pharmaceutical drugs. SIGNIFICANCE STATEMENT: The World Health Organization estimates that there are currently 5 million humans living with some form of lupus. With limited success in lupus drug discovery, turning to effective delivery strategies with existing drug molecules, as well as those in the early stage of discovery, could lead to better clinical outcomes. After all, effective delivery strategies have been proven to improve treatment outcomes.

Zinc supplement reverses short-term memory deficit in sodium benzoate-induced neurotoxicity in male Wistar rats by enhancing anti-oxidative capacity via Nrf 2 up-regulation.

Sodium benzoate (SB) is a commonly-used food preservative, with a controversial report to its neurological benefit and toxicity. Zinc (Zn) is a trace element that plays a crucial role in memory, inflammation and oxidative stress. This study was to investigate the effect of SB on rat cognition and memory and the possible modulatory effect of Zn supplement. Twenty four male Wistar rats were divided into four groups of six animals each. Animals in groups 1-4 were treated with normal saline 1 ml/kg, SB 200 mg/kg, zinc sulphate 10 ml/kg and SB 200 mg/kg + zinc sulphate 10 ml/kg/day daily respectively for three weeks. After treatment, the animals were subjected to different behavioural tests, and then sacrificed. Their blood samples were collected for catalase(CAT), superoxide dismutase(SOD) and interleukin-1B(IL-1B) assay. Brain samples were also collected for nuclear factor-erythroid-related factor 2(Nrf2), and acetylcholinesterase (AchE) mRNA gene expression. The serum levels of CAT and SOD were (p < 0.0001; p < 0.0001) reduced in the SB only-treated group compared to the other groups. Nrf2 gene expression was totally shut down in the SB only-treated group but, up-regulated in the Zn-treated groups (p < 0.0001). The serum level of IL-1B was higher in the SB only-treated group compared to the other groups. SB-treated group spent longer time in the close arm (p = <0.0001), shorter time in the open arm (p = <0.0001) and had higher anxiety index (p = 0.0045) than the Zn-treated groups. Conclusively, Zinc improves memory deficit, has anxiolytic, anti-oxidant and anti-inflammatory properties.

Effects of Kras activation and Pten deletion alone or in combination on MUC1 biology and epithelial-to-mesenchymal transition in ovarian cancer.

Mucin1 (MUC1) is an epithelial glycoprotein overexpressed in ovarian cancer and actively involved in tumor cell migration and metastasis. Using novel in vitro and in vivo MUC1-expressing conditional (Cre-loxP) ovarian tumor models, we focus here on MUC1 biology and the roles of Kras activation and Pten deletion during cell transformation and epithelial-to-mesenchymal transition (EMT). We generated several novel murine ovarian cancer cell lines derived from the ovarian surface epithelia (OSE) of mice with conditional mutations in Kras, Pten or both. In addition, we also generated several tumor-derived new cell lines that reproduce the original tumor phenotype in vivo and mirror late stage metastatic disease. Our results demonstrate that de novo activation of oncogenic Kras does not trigger increased proliferation, cellular transformation or EMT, and prevents MUC1 upregulation. In contrast, Pten deletion accelerates cell proliferation, triggers cellular transformation in vitro and in vivo, and stimulates MUC1 expression. Ovarian tumor-derived cell lines MKP-Liver and MKP-Lung cells reproduce in vivo EMT and represent the first immune competent mouse model for distant hematogenous spread. Whole genome microarray expression analysis using tumor and OSE-derived cell lines reveal a 121 gene signature associated with EMT and metastasis. When applied to n=542 cases from The Cancer Genome Atlas (TCGA) ovarian cancer dataset, the gene signature identifies a patient subset with decreased survival (P=0.04). Using an extensive collection of novel murine cell lines we have identified distinct roles for Kras and Pten on MUC1 and EMT in vivo and in vitro. The data has implications for future design of combination therapies targeting Kras mutations, Pten deletions and MUC1 vaccines.

3D brain segmentation using active appearance models and local regressors.

We describe an efficient and accurate method for segmenting sets of subcortical structures in 3D MR images of the brain. We first find the approximate position of all the structures using a global Active Appearance Model (AAM). We then refine the shape and position of each structure using a set of individual AAMs trained for each. Finally we produce a detailed segmentation by computing the probability that each voxel belongs to the structure, using regression functions trained for each individual voxel. The models are trained using a large set of labelled images, using a novel variant of 'groupwise' registration to obtain the necessary image correspondences. We evaluate the method on a large dataset, and demonstrate that it achieves results comparable with some of the best published.

Comparison and evaluation of segmentation techniques for subcortical structures in brain MRI.

The automation of segmentation of medical images is an active research area. However, there has been criticism of the standard of evaluation of methods. We have comprehensively evaluated four novel methods of automatically segmenting subcortical structures using volumetric, spatial overlap and distance-based measures. Two of the methods are atlas-based - classifier fusion and labelling (CFL) and expectation-maximisation segmentation using a dynamic brain atlas (EMS), and two model-based - profile active appearance models (PAM) and Bayesian appearance models (BAM). Each method was applied to the segmentation of 18 subcortical structures in 270 subjects from a diverse pool varying in age, disease, sex and image acquisition parameters. Our results showed that all four methods perform on par with recently published methods. CFL performed significantly better than the other three methods according to all three classes of metrics.

Comparing the similarity of statistical shape models using the Bhattacharya metric.

A variety of different methods of finding correspondences across sets of images to build statistical shape models have been proposed, each of which is likely to result in a different model. When dealing with large datasets (particularly in 3D), it is difficult to evaluate the quality of the resulting models. However, if the different methods are successfully modelling the true underlying shape variation, the resulting models should be similar. If two different techniques lead to similar models, it suggests that they are indeed approximating the true shape change. In this paper we explore a method of comparing statistical shape models by evaluating the Bhattacharya overlap between their implied shape distributions. We apply the technique to investigate the similarity of three models of the same 3D dataset constructed using different methods.