Antisolvent precipitation technique: a very promising approach to crystallize curcumin in presence of polyvinyl pyrrolidon for solubility and dissolution enhancement

Curcumin with a vast number of pharmacological activities is a poorly water soluble drug which its oral bioavailability is profoundly limited by its dissolution or solubility in GI tract. Curcumin could be a good anticancer drug if its solubility could be increased. Therefore, the aim of the present study was to increase the dissolution rate of curcumin by employing antisolvent crystallization technique and to investigate the effect of polyvinyl pyrrolidone K30 (PVP) as colloidal particles in crystallization medium on resultant particles. Curcumin was crystalized in the presence of different amounts of PVP by antisolvent crystallization method and their physical mixtures were prepared for comparison purposes. The samples were characterized by scanning electron microscopy (SEM), differential scanning calorimetry (DSC), X-ray powder diffraction (XRPD) and Fourier transform infrared spectroscopy (FT-IR). The solubility and dissolution of the treated and untreated curcumin were also determined. Antisolvent crystallization of curcumin led to the formation of particles with no definite geometric shape. It was interesting to note that the DSC and XRPD studies indicated the formation of a new polymorph and less crystallinity for particles crystallized in the absence of PVP. However, the crystallized curcumin in the presence of PVP was completely amorphous. All crystalized curcumin samples showed much higher dissolution rate compared to untreated curcumin. The amount of curcumin dissolved within 10 for treated curcumin in the presence of PVP (1:1 curcumin:PVP) was 7 times higher than untreated curcumin and this enhancement in the dissolution for curcumin samples crystallized in the absence of PVP was around 5 times. Overall‚ the results of this study showed that antisolvent crystallization method in the absence or presence of small amounts of PVP is very efficient in increasing the dissolution rate of curcumin to achieve better efficiency for curcumin

Indomethacin electrospun nanofibers for colonic drug delivery: in vitro dissolution studies

Generally, although the conventional drug delivery systems, such as using only pHdependent polymers or time-dependent release systems are popular, the individuals’ variations of physiological conditions usually lead to premature or imperfect drug release from each of these systems. Therefore, a combination of pH- and time-dependent polymers could be more reliable for delivering drugs to the lower GI tract such as colon. To this end, electrospinning method was used as a fabrication approach for preparing electrospun nanofibers of indomethacin aimed for colon delivery. Formulations were prepared based on a 3 2 full factorial design. Independent variables were the drug:polymer ratio (with the levels of 3:5, 4.5:5 and 6:5 w/w) and Eudragit S:Eudragit RS w/w ratio (20:80, 60:40 and 100:0). The evaluated responses were drug release at pH 1.2, 6.4, 6.8 and 7.4. Combinations of Eudragit S (ES), Eudragit RS (ERS) and drug based on factorial design were loaded in 10 ml syringes. 3 Electrospinning method was used to prepare electrospun nanofibers from electrospinning solutions. Conductivity and the viscosity of the solutions were analyzed prior to electrospinning. After collection, the nanofibers were evaluated in terms of morphology and drug release. It was shown that the ratio of drug:polymer and polymer:polymer were pivotal factors to control the drug release from nanofibers. A formulation containing Eudragit S:Eudragit RS (60:40) and drug:polymer ratio of 3:5 exhibited the most appropriate drug release as a colon delivery system with a minor release at pH 1.2, 6.4 and 6.8 and major release at pH 7.4. Nanofibers resulted from this formulation were also more uniform and contained fewer amounts of beads. It was demonstrated that the electrospinning could be regarded as a modern approach for the preparation of colon drug delivery systems leading to marketable products

Exploring the Conformational Space and Classification of Proteins Using Robotics-Based and Machine Learning Methods

Proteins are essential molecules in living organisms that perform a broad scope of functionalities, such as catalyzing biochemical reactions, providing structural support, and acting as signaling molecules. Understanding the structure and dynamics of proteins is crucial to elucidate their functions and develop therapies for various diseases. However, the conformational space of proteins is vast, and experimentally exploring it is challenging and time-consuming. Therefore, computational methods are becoming increasingly important for investigating protein conformational changes and dynamics. These methods are also subject to considerable limitations, which are discussed in this dissertation, alongside potential techniques to address them. The goal of this Ph.D. research is to study the literature regarding protein conformational changes and develop efficient and effective methods to explore their conformational space, which is paramount to understanding how proteins function. The first two projects focus on using Rapidly-exploring Random Trees (RRT) and Monte Carlo (MC) simulations to efficiently sample and explore the protein conformational space. In the first project, we propose a new RRT*-based search algorithm that outperforms previous methods in terms of exploration efficiency. We further improve the search by integrating rigidity analysis information into the exploration process to help guide the search toward more low-energy conformations. We use topological data analysis in the second project to gain insights into the shape of protein conformational spaces and develop more practical exploration strategies. These methods are demonstrated on several benchmark protein systems and show significant improvements over existing techniques. The final two projects explore new directions in using machine learning techniques to analyze protein conformations. In the third project, we concentrate on designing a method for classifying protein families based on learned compressed representations. We show that these compact representations, which we call fingerprints, capture the relevant features of protein families. In contrast, in the last project, we use Variational Autoencoders (VAE) to explore the conformational space of proteins on molecular dynamics simulation data. Together, this work significantly contributes to advancing our understanding of protein conformational dynamics and developing new tools for studying protein structure-function relationships

Integrating Rigidity Analysis into the Exploration of Protein Conformational Pathways Using RRT* and MC

To understand how proteins function on a cellular level, it is of paramount importance to understand their structures and dynamics, including the conformational changes they undergo to carry out their function. For the aforementioned reasons, the study of large conformational changes in proteins has been an interest to researchers for years. However, since some proteins experience rapid and transient conformational changes, it is hard to experimentally capture the intermediate structures. Additionally, computational brute force methods are computationally intractable, which makes it impossible to find these pathways which require a search in a high-dimensional, complex space. In our previous work, we implemented a hybrid algorithm that combines Monte-Carlo (MC) sampling and RRT*, a version of the Rapidly Exploring Random Trees (RRT) robotics-based method, to make the conformational exploration more accurate and efficient, and produce smooth conformational pathways. In this work, we integrated the rigidity analysis of proteins into our algorithm to guide the search to explore flexible regions. We demonstrate that rigidity analysis dramatically reduces the run time and accelerates convergence

Comparison of alternative soil particle-size distribution models and their correlation with soil physical attributes

Complete descriptions of the particle-size distribution (PSD) curve should provide more information about various soil properties as opposed to only the textural composition (sand, silt and clay (SSC) fractions). We evaluated the performance of 19 models describing PSD data of soils using a range of efficiency criteria. While different criteria produced different rankings of the models, six of the 19 models consistently performed the best. Mean errors of the six models were found to depend on the particle diameter, with larger error percentages occurring in the smaller size range. Neither SSC nor the geometric mean diameter and its standard deviation correlated significantly with the saturated hydraulic conductivity (Kfs); however, the parameters of several PSD models showed significant correlation with Kfs. Porosity, mean weight diameter of the aggregates, and bulk density also showed significant correlations with PSD model parameters. Results of this study are promising for developing more accurate pedotransfer functions

Comparison of alternative soil particle-size distribution models and their correlation with soil physical attributes

Complete descriptions of the particle-size distribution (PSD) curve should provide more information about various soil properties as opposed to only the textural composition (sand, silt and clay (SSC) fractions). We evaluated the performance of 19 models describing PSD data of soils using a range of efficiency criteria. While different criteria produced different rankings of the models, six of the 19 models consistently performed the best. Mean errors of the six models were found to depend on the particle diameter, with larger error percentages occurring in the smaller size range. Neither SSC nor the geometric mean diameter and its standard deviation correlated significantly with the saturated hydraulic conductivity (Kfs); however, the parameters of several PSD models showed significant correlation with Kfs. Porosity, mean weight diameter of the aggregates, and bulk density also showed significant correlations with PSD model parameters. Results of this study are promising for developing more accurate pedotransfer functions

Improvement of Physico-mechanical Properties of Partially Amorphous Acetaminophen Developed from Hydroalcoholic Solution Using Spray Drying Technique

Objective(s): This study was performed aiming to investigate the effect of particle engineering via spray drying of hydroalcoholic solution on solid states and physico-mechanical properties of acetaminophen.   Materials and Methods: Spray drying of hydroalcoholic solution (25% v/v ethanol/water) of acetaminophen (5% w/v) in the presence of small amounts of polyninylpyrrolidone K30 (PVP) (0, 1.25, 2.5 and 5% w/w based on acetaminophen weight) was carried out. The properties of spray dried particles namely morphology, surface characteristics, particle size, crystallinity, dissolution rate and compactibility were evaluated. Results: Spray drying process significantly changed the morphology of acetaminophen crystals from acicular (rod shape) to spherical microparticle. Differential scanning calorimetery (DSC) and x-ray powder diffraction (XRPD) studies ruled out any polymorphism in spray dried samples, however, a major reduction in crystallinity up to 65%, especially for those containing 5% w/w PVP was observed. Spray dried acetaminophen particles especially those obtained in the presence of PVP exhibited an obvious improvement of the dissolution and compaction properties. Tablets produced from spray dried samples exhibited excellent crushing strengths and no tendency to cap. Conclusions: The findings of this study revealed that spray drying of acetaminophen from hydroalcoholic solution in the presence of small amount of PVP produced partially amorphous particles with improved dissolution and excellent compaction properties

Preparation and Characterization of a Novel Multiparticulate Dosage Form Carrying Budesonide-Loaded Chitosan Nanoparticles to Enhance the Efficiency of Pellets in the Colon

An attempt was made to conquer the limitation of orally administered nanoparticles for the delivery of budesonide to the colon. The ionic gelation technique was used to load budesonide on chitosan nanoparticles. The nanoparticles were investigated in terms of size, zeta potential, encapsulation efficiency, shape and drug release. Then, nanoparticles were pelletized using the extrusion–spheronization method and were investigated for their size, mechanical properties, and drug release. Pellets were subsequently coated with a polymeric solution composed of two enteric (eudragit L and S) and time-dependent polymers (eudragit RS) for colon-specific delivery. All formulations were examined for their anti-inflammatory effect in rats with induced colitis and the relapse of the colitis after discontinuation of treatment was also followed. The size of nanoparticles ranged between 288 ± 7.5 and 566 ± 7.7 nm and zeta potential verified their positive charged surface. The drug release from nanoparticles showed an initial burst release followed by a continuous release. Pelletized nanoparticles showed proper mechanical properties and faster drug release in acidic pH compared with alkaline pH. It was interesting to note that pelletized budesonide nanoparticles released the drug throughout the GIT in a sustained fashion, and had long-lasting anti-inflammatory effects while rapid relapse was observed for those treated with conventional budesonide pellets. It seems that there is a synergistic effect of nanoformulation of budesonide and the encapsulation of pelletized nanoparticles in a proper coating system for colon delivery that could result in a significant and long-lasting anti-inflammatory effect