Long-Acting Injectable Aqueous Suspensions-Summary From an AAPS Workshop.

Through many years of clinical application of long-acting injectables, there is clear proof that this type of formulation does not just provide the patient with convenience, but more importantly a more effective treatment of the medication provided. The formulation approach therefore contains huge untapped potential to improve the quality of life of many patients with a variety of different diseases. This review provides a summary of some of the central talks provided at the workshop with focus on aqueous suspensions and their use as a long-acting injectable. Elements as formulation, manufacturing, in vitro dissolution methods, in vitro and in vivo correlation, in silico modelling provide an insight into some of the current understandings, learnings, and not least gaps in the field.

Predicting Sagittal Plane Lifting Postures From Image Bounding Box Dimensions.

OBJECTIVE: A method for automatically classifying lifting postures from simple features in video recordings was developed and tested. We explored if an "elastic" rectangular bounding box, drawn tightly around the subject, can be used for classifying standing, stooping, and squatting at the lift origin and destination. BACKGROUND: Current marker-less video tracking methods depend on a priori skeletal human models, which are prone to error from poor illumination, obstructions, and difficulty placing cameras in the field. Robust computer vision algorithms based on spatiotemporal features were previously applied for evaluating repetitive motion tasks, exertion frequency, and duty cycle. METHODS: Mannequin poses were systematically generated using the Michigan 3DSSPP software for a wide range of hand locations and lifting postures. The stature-normalized height and width of a bounding box were measured in the sagittal plane and when rotated horizontally by 30°. After randomly ordering the data, a classification and regression tree algorithm was trained to classify the lifting postures. RESULTS: The resulting tree had four levels and four splits, misclassifying 0.36% training-set cases. The algorithm was tested using 30 video clips of industrial lifting tasks, misclassifying 3.33% test-set cases. The sensitivity and specificity, respectively, were 100.0% and 100.0% for squatting, 90.0% and 100.0% for stooping, and 100.0% and 95.0% for standing. CONCLUSIONS: The tree classification algorithm is capable of classifying lifting postures based only on dimensions of bounding boxes. APPLICATIONS: It is anticipated that this practical algorithm can be implemented on handheld devices such as a smartphone, making it readily accessible to practitioners.

To Target or Not to Target: Active vs. Passive Tumor Homing of Filamentous Nanoparticles Based on Potato virus X.

Nanoparticles are promising platforms for the diagnosis and treatment of cancer. Diverse classes and shapes of materials have been investigated to establish design principles that achieve the effective partitioning of medical cargos between tumors and healthy tissues. Molecular targeting strategies combined with specific nanoparticle shapes confer tissue-specificity on the carriers, allowing the cell-specific delivery of cargos. We recently developed a filamentous platform technology in which the plant virus Potato virus X (PVX) was used as a scaffold. These particles are flexible 515 × 13 nm filaments that encourage passive tumor homing. Here we sought to advance the PVX platform by including a molecular targeting strategy based on cyclic RGD peptides, which specifically bind to integrins upregulated on tumor cells, neovasculature, and metastatic sites. Although the RGD-targeted filaments outperformed the PEGylated stealth filaments in vitro, enhanced tumor cell targeting did not translate into improved tumor homing in vivo in mouse tumor models. The RGD-PVX and PEG-PVX filaments showed contrasting biodistribution profiles. Both formulations were cleared by the liver and spleen, but only the stealth filaments accumulated in tumors, whereas the RGD-targeted filaments were sequestered in the lungs. These results provide insight into the design principles for virus-based nanoparticles that promote the delivery of medical cargos to the appropriate cell types.

The Impact of Aspect Ratio on the Biodistribution and Tumor Homing of Rigid Soft-Matter Nanorods.

The size and shape of nanocarriers can affect their fate in vivo, but little is known about the effect of nanocarrier aspect ratio on biodistribution in the setting of cancer imaging and drug delivery. The production of nanoscale anisotropic materials is a technical challenge. A unique biotemplating approach based on of rod-shaped nucleoprotein nanoparticles with predetermined aspect ratios (AR 3.5, 7, and 16.5) is used. These rigid, soft-matter nanoassemblies are derived from tobacco mosaic virus (TMV) components. The role of nanoparticle aspect ratio is investigated, while keeping the surface chemistries constant, using either PEGylated stealth nanoparticles or receptor-targeted RGD-displaying formulations. Aspect ratio has a profound impact on the behavior of the nanoparticles in vivo and in vitro. PEGylated nanorods with the lowest aspect ratio (AR 3.5) achieve the most efficient passive tumor-homing behavior because they can diffuse most easily, whereas RGD-labeled particles with a medium aspect ratio (AR 7) are more efficient at tumor targeting because this requires a balance between infusibility and ligand-receptor interactions. The in vivo behavior of nanoparticles can therefore be tailored to control biodistribution, longevity, and tumor penetration by modulating a single parameter: the aspect ratio of the nanocarrier.