Studying the stability of polymer nanoparticles by size exclusion chromatography of radioactive polymers

The properties of nanomedicines will influence how they can deliver drugs to patients reproducibly and effectively. For conventional pharmaceutical products, Chemistry, Manufacturing and Control (CMC) documents require monitoring stability and storage conditions. For nanomedicines, studying these important considerations is hindered by a lack of appropriate methods. In this paper, we show how combining radiolabelling with size exclusion chromatography, using a method called SERP (for Size Exclusion of Radioactive Polymers), can inform on the in vitro degradation of polymer nanoparticles. Using nanoparticles composed of biodegradable poly(lactic acid) (PLA) and poly(lactic-co-glycolic acid) (PLGA), we show that SERP is more sensitive than dynamic light scattering (DLS) and nanoparticle tracking analysis (NTA) to detect degradation. We also demonstrate that the properties of the polymer composition and the nature of the aqueous buffer affect nanoparticle degradation. Importantly, we show that minute changes in stability that cannot be detected by DLS and NTA impact the pharmacokinetic of nanoparticles injected in vivo. We believe that SERP might prove a valuable method to document and understand the pharmaceutical quality of polymer nanoparticles.

Encapsulation of conjugated linoleic acid and ruminant trans fatty acids to study the prevention of metabolic syndrome-a review

Studies have reported the potential benefits of consuming conjugated linoleic acid (CLA) and ruminant trans fatty acids (R-TFAs) in reducing the risk factors of metabolic syndrome (MetS). In addition, encapsulation of CLA and R-TFAs may improve their oral delivery and further decrease the risk factors of MetS. The objectives of this review were (1) to discuss the advantages of encapsulation; (2) to compare the materials and techniques used for encapsulating CLA and R-TFAs; and (3) to review the effects of encapsulated vs non-encapsulated CLA and R-TFAs on MetS risk factors. Examination of papers citing micro- and nano-encapsulation methods used in food sciences, as well as the effects of encapsulated vs non-encapsulated CLA and R-TFAs, was conducted using the PubMed database. A total of 84 papers were examined; of these, 18 studies were selected that contained information on the effects of encapsulated CLA and R-TFAs. The 18 studies that described encapsulation of CLA or R-TFAs indicated that micro- or nano-encapsulation processes stabilized CLA and prevented oxidation. CLA was mainly encapsulated using carbohydrates or proteins. So far, oil-in-water emulsification followed by spray-drying were the frequently used techniques for encapsulation of CLA. Further, 4 studies investigated the effects of encapsulated CLA on MetS risk factors compared with non-encapsulated CLA. A limited number of studies investigated the encapsulation of R-TFAs. The effects of encapsulated CLA or R-TFAs on the risk factors for MetS remain understudied; thus, additional studies comparing the effects of encapsulated and non-encapsulated CLA or R-TFAs are needed.

Design of experiment and machine learning inform on the 3D printing of hydrogels for biomedical applications

In the biomedical field, 3D printing has the potential to deliver on some of the promises of personalized therapy, notably by enabling point-of-care fabrication of medical devices, dosage forms and bioimplants. To achieve this full potential, a better understanding of the 3D printing processes is necessary, and non-destructive characterization methods must be developed. This study proposes methodologies to optimize the 3D printing parameters for soft material extrusion. We hypothesize that combining image processing with design of experiment (DoE) analyses and machine learning could help obtaining useful information from a quality-by-design perspective. Herein, we investigated the impact of three critical process parameters (printing speed, printing pressure and infill percentage) on three critical quality attributes (gel weight, total surface area and heterogeneity) monitored with a non-destructive methodology. DoE and machine learning were combined to obtain information on the process. This work paves the way for a rational approach to optimize 3D printing parameters in the biomedical field.