Crown Ether Nanovesicles (Crownsomes) Repositioned Phenytoin for Healing of Corneal Ulcers

Breaking Barriers in Eye Treatment: Polymeric Nano-Based Drug-Delivery System for Anterior Segment Diseases and Glaucoma

The eye has anatomical structures that function as robust static and dynamic barriers, limiting the penetration, residence time, and bioavailability of medications administered topically. The development of polymeric nano-based drug-delivery systems (DDS) could be the solution to these challenges: it can pass through ocular barriers, offering higher bioavailability of administered drugs to targeted tissues that are otherwise inaccessible; it can stay in ocular tissues for longer periods of time, requiring fewer drug administrations; and it can be made up of polymers that are biodegradable and nano-sized, minimizing the undesirable effects of the administered molecules. Therefore, therapeutic innovations in polymeric nano-based DDS have been widely explored for ophthalmic drug-delivery applications. In this review, we will give a comprehensive overview of polymeric nano-based drug-delivery systems (DDS) used in the treatment of ocular diseases. We will then examine the current therapeutic challenges of various ocular diseases and analyze how different types of biopolymers can potentially enhance our therapeutic options. A literature review of the preclinical and clinical studies published between 2017 and 2022 was conducted. Thanks to the advances in polymer science, the ocular DDS has rapidly evolved, showing great promise to help clinicians better manage patients.

Supramolecular Biopharmaceutical Carriers Based on Host-Guest Interactions

Traditional drugs have the disadvantages of poor permeability and low solubility, which makes the utilization of pesticides lower and brings many side effects. With the continuous development of supramolecular chemistry in recent years, it has also played an irreplaceable role in the field of pharmaceutical science. Supramolecular macrocycles, such as crown ethers, cyclodextrins, calixarenes, pillararenes and cucurbiturils, are potentially good candidates for drug carriers due to their biocompatibility, hydrophobic cavity and ease of derivatization. The encapsulation of drugs based on host-guest interaction has the advantage of being adjustable and reversible as well as improving the low availability of drugs. Here, the recent advances in methods and strategies for drug encapsulation and release based on supramolecular macrocycles with host-guest interactions have been systematically summarized, laying a bright foundation for the development of novel nanopesticide preparations in the future and pointing out future directions of novel biopesticide research.

Photodynamic therapy fortified with topical oleyl alcohol-based transethosomal 8-methoxypsoralen for ameliorating vitiligo: Optimization and clinical study

Vitiligo is a common autoimmune skin disorder that is characterized by patchy depigmentation of the skin due to melanocytes and melanin loss. Herein, photodynamic therapy mediated 8-methoxypsoralen (8-MOP), has been used fortified with topical oleyl alcohol-based transethosomes; to overcome the poor solubility and adverse effects associated with 8-MOP oral delivery. A 2³ factorial design was used to study the formulation variables. In vitro and ex-vivo characterization besides a clinical study were conducted to assess therapeutic efficacy of the formulation. Results revealed that transethosomes were superior to transfersomes regarding drug protection from degradation. The optimized transethosomal formulation, composed of 50 mg oleyl alcohol, 10 mg Tween 80® and 20% v/v ethanol, exhibited high entrapment efficiency (83.87 ± 4.1%) and drug loading (105.0 ± 0.2%). Moreover, it showed small vesicular size (265.0 ± 2.9 nm) and PDI (0.19). The formulation depicted core and shell structure, high deformability index (12.45 ± 0.7 mL/s) and high ex-vivo skin permeation. The topical application of the developed 8-MOP transethosomal gel enhanced the effect of NB UVB radiation in the treatment of vitiligo patients and exhibited no side effects. Hence, it can be used as a future strategy for delivering 8-MOP without the need of systemic application.

Delineating a potent antiviral activity of Cuphea ignea extract loaded nano-formulation against SARS-CoV-2: In silico and in vitro studies

The outbreak of coronavirus disease-2019, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a worldwide emerging crisis. Polyphenols are a class of herbal metabolites with a broad-spectrum antiviral activity. However, most polyphenols encounter limited efficacy due to their poor solubility and degradation in neutral and basic environments. Thus, the effectiveness of their pharmaceutical application is critically dependent on the delivery systems to overcome the aforementioned drawbacks. Herein, Polyphenols-rich Cuphea ignea extract was prepared and its constituents were identified and quantified. Molecular docking was conducted for 15 compounds in the extract against SARS-CoV-2 main protease, among which rutin, myricetin-3-O-rhamnoside and rosmarinic acid depicted the most promising antiviral activity. Further, a self-nanoemulsifying formulation, composed of 10% oleic acid, 40% tween 20 and propylene glycol 50%, was prepared to improve the solubility of the extract components and enable its concurrent delivery permitting combined potency. Upon dilution with aqueous phases, the formulation rapidly Formsnanoemulsion of good stability and excellent dissolution profile in acidic pH when compared to the crude extract. It inhibited SARS-CoV-2 completely in vitro at a concentration as low as 5.87 μg/mL presenting a promising antiviral remedy for SARS-CoV-2, which may be attributed to the possible synergism between the extract components.

A Machine Learning-Based Biological Drug-Target Interaction Prediction Method for a Tripartite Heterogeneous Network

Drug repositioning is the identification of interactions between drugs and target proteins in pharmaceutical sciences. Traditional large-scale validation through chemical experiments is time-consuming and expensive, while drug repositioning can drastically decrease the cost and duration taken by traditional drug development. With the rapid advancement of high-throughput technologies and the explosion of various biological and medical data, computational drug repositioning methods have been used to systematically identify potential drug-target interactions. Some of them are based on a particular class of machine learning algorithms called kernel methods. In this paper, we propose a new machine learning prediction method combining multiple kernels into a tripartite heterogeneous drug-target-disease interaction spaces in order to integrate multiple sources of biological information simultaneously. This novel network algorithm extends the traditional drug-target interaction bipartite graph to the third disease layer. Meanwhile, Gaussian kernel functions on heterogeneous networks and the regularized least square method of the Kronecker product are used to predict new drug-target interactions. The values of AUPR (area under the precision-recall curve) and AUC (the area under the receiver operating characteristic curve) of the proposed algorithm are significantly improved. Especially, the AUC values are improved to 0.99, 0.99, 0.97, and 0.96 on four benchmark data sets. These experimental results substantiate that the network topology can be used for predicting drug-target interactions.