With the advent of novel vesicular drug delivery systems especially bilosomes, for large molecular weight proteins and peptides, their oral administration seems a viable approach. These nano-vesicles have shown promising results for the effective delivery of insulin and other therapeutics, perhaps due to their structural composition. The present review has elaborated the biopharmaceutical challenges for the oral delivery of therapeutic proteins and peptides as well as presented a novel approach to deliver the essential macromolecules through oral route as bilosomes. The extensive search has been presented related to the formulation, evaluation and in vivo performance of bilosomes. Some of the crucial findings related to bilosomes have corroborated them superior to other colloidal carriers. The successful drug delivery through bilosomes requires significant justifications related to their interaction with the biological membranes. The other aspects such as absolute absorption, safety and toxicity of bilosome drug delivery should also be equally considered.
Chemotherapeutic delivery by oral route in cancer patients has the potential to create "hospitalization free chemotherapy" which is a vision of oncologists, formulation scientists and patients. Such a therapeutic approach will improve patients' compliance, ease the burden of the patients' caregivers and significantly reduce the cost of treatment. In current clinical practice, chemotherapy carried out by intravenous injection or infusion leads to undesired side-effects such as plasma concentrations crossing the maximum safe concentration, rapid body clearance and lower bioavailability. Despite the presence of challenges such as poor aqueous solubility and stability of drugs and the presence of biological barriers like multidrug efflux transporter in the GI tract, oral cancer chemotherapy has the potential to surmount those obstacles. Lipid nanoparticles (LNPs) such as solid lipid nanoparticle, nanostructured lipid carriers, nano lipid-drug conjugates, mixed micelles, liposomes and nanoemulsions have shown some promising results for use in oral anticancer drug delivery through nanotechnological approach. LNPs demonstrate enhanced oral bioavailability owing to their ability to inhibit first pass metabolism via lymphatic absorption by chylomicron-linked and/or M-cell uptake. LNPs reduce the inter- and intrasubject pharmacokinetics variability of administrated drugs. Moreover, certain classes of phospholipids and surfactants used in the formulations of LNPs can suppress the P-glycoprotein efflux system. Here, we shall be discussing the biopharmaceutical challenges in oral cancer chemotherapy and how the LNPs may provide solutions to such challenges. The effect of GI tract environment on LNPs and pharmacokinetics shall also be discussed.
A good data visualization is not only a distortion-free graphical representation of data but also a way to reveal underlying statistical properties of the data. Despite its common use across various stages of data analysis, selecting a good visualization often is a manual process involving many iterations. Recently there has been interest in reducing this effort by developing models that can recommend visualizations, but they are of limited use since they require large training samples (data and visualization pairs) and focus primarily on the design aspects rather than on assessing the effectiveness of the selected visualization.In this paper, we present VizAI, a generative-discriminative framework that first generates various statistical properties of the data from a number of alternative visualizations of the data. It is linked to a discriminative model that selects the visualization that best matches the true statistics of the data being visualized. VizAI can easily be trained with minimal supervision and adapts to settings with varying degrees of supervision easily. Using crowdsourced judgements and a large repository of publicly available visualizations, we demonstrate that VizAI outperforms the state of the art methods that learn to recommend visualizations.
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