Development of a targeted and controlled nanoparticle delivery system for FoxO1 inhibitors

Background: Poly (lactic-co-glycolic acid) (PLGA) and polyethylene glycol (PEG) are polymers approved by the United States’ Food and Drug Administration. Drugs for various medical treatments have been encapsulated in PLGA-PEG nanoparticles for targeted delivery and reduction of unwanted side effects. Methods: A flow synthesis method for PLGA-PEG nanoparticles containing FoxO1 inhibitors and adipose vasculature targeting agents was developed. A set of nanoparticles including PLGA and PLGA-PEG-P3 unloaded and drug loaded were generated. The particles were characterized by DLS, fluorescence spectroscopy, TEM, and dialysis. Endotoxin levels were measured using the LAL chromogenic assay. Our approach was compared to over 270 research articles using information extraction tools. Results: Nanoparticle hydrodynamic diameters ranged from 142.4 ±0.4 d.nm to 208.7 ±3.6 d.nm while the polydispersity index was less than 0.500 for all samples (0.057 ±0.021 to 0.369 ±0.038). Zeta potentials were all negative ranging from -4.33 mV to -13.4 mV. Stability testing confirmed that size remained unchanged for up to 4 weeks. For AS1842856, loading was 0.5 mg drug/mL solution and encapsulation efficiency was ~100%. Dialysis indicated burst release of drug in the first 4 hours. Conclusion: PLGA encapsulation of AS1842856 was successful but unsuccessful for the two more hydrophilic drugs. Alternative syntheses such as water/oil/water emulsion or liposomal encapsulation are being considered. Analysis of data from published papers on PLGA nanoparticles indicated that our results were consistent with identified process-structure relationships and few groups reported endotoxin levels even though in vivo testing was performed.https://scholarscompass.vcu.edu/gradposters/1071/thumbnail.jp

Attentive Aspect Modeling for Review-aware Recommendation

In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user's reviews and a product's reviews indicate indirect connections between the user and product. However, these aspect-based methods suffer from two problems. First, the common aspects are usually very sparse, which is caused by the sparsity of user-product interactions and the diversity of individual users' vocabularies. Second, a user's interests on aspects could be different with respect to different products, which are usually assumed to be static in existing methods. In this paper, we propose an Attentive Aspect-based Recommendation Model (AARM) to tackle these challenges. For the first problem, to enrich the aspect connections between user and product, besides common aspects, AARM also models the interactions between synonymous and similar aspects. For the second problem, a neural attention network which simultaneously considers user, product and aspect information is constructed to capture a user's attention towards aspects when examining different products. Extensive quantitative and qualitative experiments show that AARM can effectively alleviate the two aforementioned problems and significantly outperforms several state-of-the-art recommendation methods on top-N recommendation task.Comment: Camera-ready manuscript for TOI