Bioavailability Enhancement of Olmesartan Medoxomil Using Hot-Melt Extrusion: In-Silico, In-Vitro, and In-Vivo Evaluation

Accelerated Oral Healing by Angelica gigas Nakai from Hot Melt Extrusion Technology: An In Vitro Study

Background and Objectives: In spite of the oral environment being healing-prone, its dynamic changes may affect wound healing. The purpose of this study was to assess the oral wound healing effect of Angelica gigas Nakai (AG) prepared by hot-melt extrusion. Materials and Methods: Human gingival fibroblast (HGF) cells were treated with AG or AG via hot-melt extrusion (AGH) for 24 h to determine the optimal concentration. For evaluating the anti-inflammatory effect of AG and AGH, a nitric oxide assay was performed under lipopolysaccharide (LPS) stimulation. The wound-healing effects of AG and AGH were evaluated using cell proliferation/migration assays and wound-healing marker expression through qRT-PCR. Results: Both AG and AGH showed no cytotoxicity on HGH cells. Regarding nitric oxide production, AGH significantly decreased LPS-induced nitric oxide production (p < 0.05). AGH showed a significantly positive result in the cell proliferation/cell migration assay compared with that in AG and the control. Regarding wound healing marker expression, AGH showed significantly greater VEGF and COL1α1 expression levels than those in the others (p < 0.05), whereas α-SMA expression was significantly different among the groups. Conclusions: Within the limits of this study, AGH accelerated oral wound healing in vitro.

Molecular modeling in cardiovascular pharmacology: Current state of the art and perspectives

Molecular modeling in pharmacology is a promising emerging tool for exploring drug interactions with cellular components. Recent advances in molecular simulations, big data analysis, and artificial intelligence (AI) have opened new opportunities for rationalizing drug interactions with their pharmacological targets. Despite the obvious utility and increasing impact of computational approaches, their development is not progressing at the same speed in different fields of pharmacology. Here, we review current in silico techniques used in cardiovascular diseases (CVDs), cardiological drug discovery, and assessment of cardiotoxicity. In silico techniques are paving the way to a new era in cardiovascular medicine, but their use somewhat lags behind that in other fields.

How Does the Addition of Kollidon®VA64 Inhibit the Recrystallization and Improve Ezetimibe Dissolution from Amorphous Solid Dispersions?

Amorphization serves as a strategy for the improvement of poor dissolution characteristics of many drug compounds. However, in many formulations the content of polymeric stabilizer is high, which is undesirable from the perspective of future applications. Thus, studying the composition-dependent stability of amorphous solid dispersions seems to be demanded. In this paper, we describe the amorphization of ezetimibe, a lipid-lowering drug, in the spray drying process and investigate the effect of polyvinylpyrrolidone-co-poly(vinyl acetate) (PVP/VA) content on the physical stability and dissolution characteristics of the drug. Fully amorphous systems were obtained when the concentration of the polymer in solid dispersion was as low as 20%. The amorphization led to the dissolution enhancement by even 70%, with a noticeable sudden increase at around 40% of PVP/VA content and very small variations for systems having 66-90% PVP/VA. It was also correlated to wettability characteristics of solid dispersions, which may suggest that in the vicinity of 40% of the polymer content, the behavior of the system becomes independent of the PVP/VA content.

Molecular Simulation and Statistical Learning Methods toward Predicting Drug-Polymer Amorphous Solid Dispersion Miscibility, Stability, and Formulation Design

Amorphous solid dispersions (ASDs) have emerged as widespread formulations for drug delivery of poorly soluble active pharmaceutical ingredients (APIs). Predicting the API solubility with various carriers in the API-carrier mixture and the principal API-carrier non-bonding interactions are critical factors for rational drug development and formulation decisions. Experimental determination of these interactions, solubility, and dissolution mechanisms is time-consuming, costly, and reliant on trial and error. To that end, molecular modeling has been applied to simulate ASD properties and mechanisms. Quantum mechanical methods elucidate the strength of API-carrier non-bonding interactions, while molecular dynamics simulations model and predict ASD physical stability, solubility, and dissolution mechanisms. Statistical learning models have been recently applied to the prediction of a variety of drug formulation properties and show immense potential for continued application in the understanding and prediction of ASD solubility. Continued theoretical progress and computational applications will accelerate lead compound development before clinical trials. This article reviews in silico research for the rational formulation design of low-solubility drugs. Pertinent theoretical groundwork is presented, modeling applications and limitations are discussed, and the prospective clinical benefits of accelerated ASD formulation are envisioned.