Background: Social media has become a new source for obtaining real-world data on adverse drug reactions. Many studies have investigated the use of social media to detect early signals of adverse drug reactions. However, the trustworthiness of signals derived from social media is questionable. To confirm this, a confirmatory study with a positive control (eg, new black box warnings, labeling changes, or withdrawals) is required. Objective: This study aimed to evaluate the use of social media in detecting new black box warnings, labeling changes, or withdrawals in advance. Methods: This scoping review adhered to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist. A researcher searched PubMed and EMBASE in January 2021. Original studies analyzing black box warnings, labeling changes, or withdrawals from social media were selected, and the results of the studies were summarized. Results: A total of 14 studies were included in this scoping review. Most studies (8/14, 57.1%%) collected data from a single source, and 10 (71.4%) used specialized health care social networks and forums. The analytical methods used in these studies varied considerably. Three studies (21.4%) manually annotated posts, while 5 (35.7%) adopted machine learning algorithms. Nine studies (64.2%) concluded that social media could detect signals 3 months to 9 years before action from regulatory authorities. Most of these studies (8/9, 88.9%) were conducted on specialized health care social networks and forums. On the contrary, 5 (35.7%) studies yielded modest or negative results. Of these, 2 (40%) used generic social networking sites, 2 (40%) used specialized health care networks and forums, and 1 (20%) used both generic social networking sites and specialized health care social networks and forums. The most recently published study recommends not using social media for pharmacovigilance. Several challenges remain in using social media for pharmacovigilance regarding coverage, data quality, and analytic processing. Conclusions: Social media, along with conventional pharmacovigilance measures, can be used to detect signals associated with new black box warnings, labeling changes, or withdrawals. Several challenges remain; however, social media will be useful for signal detection of frequently mentioned drugs in specialized health care social networks and forums. Further studies are required to advance natural language processing and mine real-world data on social media.

PEGylated Eudragit L100 (ELP)-containing proliponiosomes (PLNs) were developed for improved oral delivery of celecoxib (CXB). The successful introduction of PEG 2000 or 5000 to Eudragit L100 (EL) was confirmed via proton nuclear magnetic resonance analysis of which calculated molar substitution ratio of PEG to EL was 36.0 or 36.7, respectively. CXB, ELP, phospholipid, and non-ionic surfactants were dissolved in dimethyl sulfoxide and lyophilized to produce CXB-loaded PLNs (CXB@PLNs). The physical state of CXB@PLNs was evaluated using differential scanning calorimetry and powder X-ray diffractometry, which revealed that crystalline CXB was transformed into amorphous form after the fabrication procedure. The reconstitution of CXB@PLNs in aqueous media generated CXB-loaded liponiosomes with nano-sized mean diameters and spherical morphology. CXB@PLNs displayed enhanced dissolution rate and permeability compared to CXB suspension. In vivo pharmacokinetic studies performed on rats demonstrated the improved oral bioavailability of CXB@PLNs compared to that of CXB suspension. No serious systemic toxicity was observed in the blood biochemistry tests performed on rats. These results suggest that the developed PLNs could be promising oral delivery systems for improving the bioavailability of poorly water-soluble drugs, such as CXB.

Aminoacyl-tRNA synthetase complex-interacting multifunctional protein 1 (AIMP1)-derived peptide (AdP) has been developed as a cosmeceutical ingredient for skin anti-aging given its fibroblast-activating (FA) and melanocyte-inhibiting (MI) functions. However, a suitable strategy for the topical delivery of AdP was required due to its low-permeable properties. In this study, FA and MI domains of AdP (FA-AdP and MI-AdP, respectively) were determined by functional domain mapping, where the activities of several fragments of AdP on fibroblast and melanocyte were tested, and a hydrosol-based topical delivery system for these AdP fragments was prepared. The excipient composition of the hydrosol was optimized to maximize the viscosity and drying rate by using Box-Behnken design. The artificial skin deposition of FA-AdP-loaded hydrosol was evaluated using Keshary-Chien diffusion cells equipped with Strat-M membrane (STM). The quantification of the fluorescent dye-tagged FA-AdP in STM was carried out by near-infrared fluorescence imaging. The optimized hydrosol showed 127-fold higher peptide deposition in STM than free FA-AdP (p < 0.05). This work suggests that FA- and MI-AdP are active-domains for anti-wrinkle and whitening activities, respectively, and the hydrosol could be used as a promising cosmetic formulation for the delivery of AdPs to the skin.

BACKGROUND Social media has become a new source for obtaining real-world data on adverse drug reactions. Many studies have investigated the use of social media to detect early signals of adverse drug reactions. However, the trustworthiness of signals derived from social media is questionable. To confirm this, a confirmatory study with a positive control (eg, new black box warnings, labeling changes, or withdrawals) is required. OBJECTIVE This study aimed to evaluate the use of social media in detecting new black box warnings, labeling changes, or withdrawals in advance. METHODS This scoping review adhered to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist. A researcher searched PubMed and EMBASE in January 2021. Original studies analyzing black box warnings, labeling changes, or withdrawals from social media were selected, and the results of the studies were summarized. RESULTS A total of 14 studies were included in this scoping review. Most studies (8/14, 57.1%%) collected data from a single source, and 10 (71.4%) used specialized health care social networks and forums. The analytical methods used in these studies varied considerably. Three studies (21.4%) manually annotated posts, while 5 (35.7%) adopted machine learning algorithms. Nine studies (64.2%) concluded that social media could detect signals 3 months to 9 years before action from regulatory authorities. Most of these studies (8/9, 88.9%) were conducted on specialized health care social networks and forums. On the contrary, 5 (35.7%) studies yielded modest or negative results. Of these, 2 (40%) used generic social networking sites, 2 (40%) used specialized health care networks and forums, and 1 (20%) used both generic social networking sites and specialized health care social networks and forums. The most recently published study recommends not using social media for pharmacovigilance. Several challenges remain in using social media for pharmacovigilance regarding coverage, data quality, and analytic processing. CONCLUSIONS Social media, along with conventional pharmacovigilance measures, can be used to detect signals associated with new black box warnings, labeling changes, or withdrawals. Several challenges remain; however, social media will be useful for signal detection of frequently mentioned drugs in specialized health care social networks and forums. Further studies are required to advance natural language processing and mine real-world data on social media.

The blood–brain barrier (BBB) excludes most therapeutic agents from the central nervous system and remains a major constraint in the treatment of glioblastoma, Alzheimer’s disease, and other neurological disorders. Receptor-mediated transcytosis (RMT) offers a non-invasive route in which ligand-functionalized carriers engage receptors on brain endothelial cells and undergo vesicular transport across the BBB. This review examines transferrin (Tf), apolipoprotein E (ApoE), and receptor-binding peptides as the principal ligand classes used to engineer BBB-penetrating nanoplatforms, including polymeric nanoparticles, liposomes, micelles, lipid nanoparticles, and biomimetic carriers. Unlike reviews organized primarily by carrier type, administration route, or disease indication, we use a ligand-centered framework to connect receptor selection with affinity and avidity, surface density and orientation, intracellular trafficking, and carrier architecture. Particular attention is given to the progression from full-length natural proteins to compact peptides and stability-engineered ligands, including retro-inverso, stapled, and stimulus-cleavable designs. We also summarize representative within-study improvements in brain delivery and therapeutic outcome while emphasizing that effect sizes depend on the BBB model, disease model, dosing regimen, comparator, and analytical endpoint. Finally, we discuss species specificity, receptor-mediated safety liabilities, conjugation reproducibility, scale-up, and human-relevant BBB evaluation. These considerations position ligand engineering and pharmaceutical manufacturability as interdependent requirements for translating RMT nanoplatforms into clinically viable CNS drug-delivery systems.