Flow and Tableting Behaviors of Some Egyptian Kaolin Powders as Potential Pharmaceutical Excipients

The present work aimed at assessing the pharmaceutical tableting properties of some Egyptian kaolin samples belong to the Abu Zenima kaolin deposits (estimated at 120 million tons). Four representative samples were selected based on kaolinite richness and their structural order-disorder degree, and after purification, they were dried at 70 ºC and heated from room temperature up to 400 ºC (10 ºC/min). Mineralogy, micromorphology, microtexture, granulometry, porosimetry, moisture content, bulk and tapped density, direct and indirect flowability, and tableting characteristics are studied. Results indicated that purified kaolin samples were made up of 95–99% kaolinite, <3% illite, 1% quartz and 1% anatase. The powder showed mesoporous character (pore diameters from 2 to 38 nm and total pore volume from 0.064 to 0.136 cm3/g) with dominance of fine nanosized particles (<1 um–10 nm). The powder flow characteristics of both the ordered (Hinckley Index HI > 0.7, crystallite size D001 > 30 nm) and disordered (HI < 0.7, D001 < 30 nm) kaolinite-rich samples have been improved (Hausner ratio between 1.24 and 1.09) as their densities were influenced by thermal treatment (with some observed changes in the kaolinite XRD reflection profiles) and by moisture content (variable between 2.98% and 5.82%). The obtained tablets exhibited hardness between 33 and 44 N only from the dehydrated powders at 400 ºC, with elastic recovery (ER) between 21.74% and 25.61%, ejection stress (ES) between 7.85 and 11.45 MPa and tensile fracture stress (TFS) between 1.85 and 2.32 MPa, which are strongly correlated with crystallinity (HI) and flowability (HR) parameters. These findings on quality indicators showed the promising pharmaceutical tabletability of the studied Egyptian kaolin powders and the optimization factors for their manufacturability and compactability.This work has been funded by the Egyptian Cultural Affairs and Missions Sector (Plan 2013–2014), Ministry of Higher Education, in collaboration with the Group CTS-946 (Junta de Andalucía) and MINECO project CGL2016-80833-R (Spain), and the grant funded by Erasmus+ KA1 mobility program 2016/2017

Optimization of technological procedure for amygdalin isolation from plum seeds (Pruni domesticae semen)

The process of amygdalin extraction from plum seeds was optimized using central composite design (CCD) and multilayer perceptron (MLP). The effect of time, ethanol concentration, solid-to-liquid ratio, and temperature on the amygdalin content in the extracts was estimated using both mathematical models. The MLP 4-3-1 with exponential function in hidden layer and linear function in output layer was used for describing the extraction process. MLP model was more superior compared with CCD model due to better prediction ability. According to MLP model, the suggested optimal conditions are: time of 120 min, 100% (v/v) ethanol, solid-to liquid ratio of 1:25 (m/v) and temperature of 34.4 degrees C. The predicted value of amygdalin content in the dried extract (25.42 g per 100 g) at these conditions was experimentally confirmed (25.30 g per 100 g of dried extract). Amygdalin (>90%) was isolated from the complex extraction mixture and structurally characterized by FT-IR, UV, and MS methods

Evaluation of diclofenac sodium release from matrix pellets compressed into MUPS tablets

The purpose of the study was to screen the effects of formulation factors on the in vitro release profile of diclofenac sodium from matrix pellets compressed into multiple unit pellet system (MUPS) tablets using design of experiment (DOE). Extended release of diclofenac sodium was accomplished using Carbopol 71G as matrix substance. According to Fractional Factorial Design FFD 2(3-1) four formulations of diclofenac sodium MUPS matrix tablets were prepared. The process of direct pelletization and subsequently compression of the pellets into tablets was applied in order to investigate a different approach in formulation of matrix systems and to achieve a better control of the process factors over the principal response - the release of the drug. The investigated factors were X1-the percentage of polymer Carbopol 71G, X2-crushing strength of the tablet and X3-different batches of the diclofenac sodium. In vitro dissolution time profiles at 6 different sampling times were chosen as responses. Results of drug release studies indicated that drug release rates vary between different formulations, with a range of 1 to 8 h to complete dissolution. The most important impact on the drug release had factor X1-the percentage of polymer Carbopol 71G. The polymer percentage is suggested as release regulator for diclofenac sodium release from MUPS matrix tablets. All other investigated factors had no significant influence on the release profile of diclofenac sodium

Drug release control and system understanding of sucrose esters matrix tablets by artificial neural networks

Artificial neural networks (ANNs) were applied for system understanding and prediction of drug release properties from direct compacted matrix tablets using sucrose esters (SEs) as matrix-forming agents for controlled release of a highly water soluble drug, metoprolol tartrate. Complexity of the system was presented through the effects of SE concentration and tablet porosity at various hydrophilic-lipophilic balance (HLB) values of SEs ranging from 0 to 16. Both effects contributed to release behaviors especially in the system containing hydrophilic SEs where swelling phenomena occurred. A self-organizing map neural network (SOM) was applied for visualizing interrelation among the variables and multilayer perceptron neural networks (MLPs) were employed to generalize the system and predict the drug release properties based on HLB value and concentration of SEs and tablet properties, i.e., tablet porosity, volume and tensile strength. Accurate prediction was obtained after systematically optimizing network performance based on learning algorithm of MLP. Drug release was mainly attributed to the effects of SEs, tablet volume and tensile strength in multi-dimensional interrelation whereas tablet porosity gave a small impact. Ability of system generalization and accurate prediction of the drug release properties proves the validity of SOM and MLPs for the formulation modeling of direct compacted matrix tablets containing controlled release agents of different material properties

Optimization of drug release from compressed multi unit particle system (MUPS) using generalized regression neural network (GRNN)

The purpose of this study was development of diclofenac sodium extended release compressed matrix pellets and optimization using Generalized Regression Neural Network (GRNN). According to Central Composite Design (CCD), ten formulations of diclofenac sodium matrix tablets were prepared. Extended release of diclofenac sodium was acomplished using Carbopol(R) 71G as matrix substance. The process of direct pelletisation and subsequently compression of the pellets into MUPS tablets was applied in order to investigate a different approach in formulation of matrix systems and to achieve more control of the process factors over the principal response - the release of the drug. The investigated factors were X(1) -the percentage of polymer Carbopol(R) 71 G and X(2)- crushing strength of the MUPS tablet. In vitro dissolution time profiles at 5 different sampling times were chosen as responses. Results of drug release studies indicate that drug release rates vary between different formulations, with a range of 1 hour to 8 hours of dissolution. The most important impact on the drug release has factor X(1) -the percentage of polymer Carbopol(R) 71 G. The purpose of the applied GRNN was to model the effects of these two causal factors on the in vitro release profile of the diclofenac sodium from compressed matrix pellets. The aim of the study was to optimize drug release in manner wich enables following in vitro release of diclofenac sodium during 8 hours in phosphate buffer: 1 h: 15-40%, 2 h: 25-60%, 4 h: 35-75%, 8 h: <70%

In Vitro–In Vivo Correlation for Gliclazide Immediate-Release Tablets Based on Mechanistic Absorption Simulation

The aim of this study was to develop a drug-specific absorption model for gliclazide (GLK) using mechanistic gastrointestinal simulation technology (GIST) implemented in GastroPlusTM software package. A range of experimentally determined, in silico predicted or literature data were used as input parameters. Experimentally determined pH-solubility profile was used for all simulations. The human jejunum effective permeability (Peff) value was estimated on the basis of in vitro measured Caco-2 permeability (literature data). The required PK inputs were taken from the literature. The results of the simulations were compared with actual clinical data and revealed that the GIST-model gave accurate prediction of gliclazide oral absorption. The generated absorption model provided the target in vivo dissolution profile for in vitro–in vivo correlation and identification of biorelevant dissolution specification for GLK immediate-release (IR) tablets. A set of virtual in vitro data was used for correlation purposes. The obtained results suggest that dissolution specification of more than 85% GLK dissolved in 60 min may be considered as “biorelevant” dissolution acceptance criteria for GLK IR tablets

Application of dynamic neural networks in the modeling of drug release from polyethylene oxide matrix tablets

The main objective of this study was to demonstrate the possible use of dynamic neural networks to model diclofenac sodium release from polyethylene oxide hydrophilic matrix tablets. High and low molecular weight polymers in the range of 0.9-5 x 10(6) have been used as matrix forming materials and 12 different formulations were prepared for each polymer. Matrix tablets were made by direct compression method. Fractions of polymer and compression force have been selected as most influential factors on diclofenac sodium release profile. In vitro dissolution profile has been treated as time series using dynamic neural networks. Dynamic networks are expected to be advantageous in the modeling of drug release. Networks of different topologies have been constructed in order to obtain precise prediction of release profiles for test formulations. Short-term and long-term memory structures have been included in the design of network making it possible to treat dissolution profiles as time series. The ability of network to model drug release has been assessed by the determination of correlation between predicted and experimentally obtained data. Calculated difference (f(1)) and similarity (f(2)) factors indicate that dynamic networks are capable of accurate predictions. Dynamic neural networks were compared to most frequently used static network, multi-layered perceptron, and superiority of dynamic networks has been demonstrated. The study also demonstrated differences between the used polyethylene oxide polymers in respect to drug release and suggests explanations for the obtained results

Analysis of fluidized bed granulation process using conventional and novel modeling techniques

Various modeling techniques have been applied to analyze fluidized-bed granulation process. Influence of various input parameters (product, inlet and outlet air temperature, consumption of liquid-binder, granulation liquid-binder spray rate, spray pressure, drying time) on granulation output properties (granule flow rate, granule size determined using light scattering method and sieve analysis, granules Hausner ratio, porosity and residual moisture) has been assessed. Both conventional and novel modeling techniques were used, such as screening test, multiple regression analysis, self-organizing maps, artificial neural networks, decision trees and rule induction. Diverse testing of developed models (internal and external validation) has been discussed. Good correlation has been obtained between the predicted and the experimental data. It has been shown that nonlinear methods based on artificial intelligence, such as neural networks, are far better in generalization and prediction in comparison to conventional methods. Possibility of usage of SOMs, decision trees and rule induction technique to monitor and optimize fluidized-bed granulation process has also been demonstrated. Obtained findings can serve as guidance to implementation of modeling techniques in fluidized-bed granulation process understanding and control

Selection of the suitable polymer for supercritical fluid assisted preparation of carvedilol solid dispersions

Solid dispersions production is one of the substantial approaches for improvement of poor drug solubility. Additionally, supercritical fluid assisted method for preparation of solid dispersions can offer many advantages in comparison to the conventional melting or solvent-evaporation methods. Miscibility analysis provides valuable guidance for selection of the most appropriate polymeric carrier for dispersion of the drug of interest. In addition to the increased drug release rate, solid dispersions should have proper mechanical attributes in order to be successfully formulated in the final solid dosage form such as tablet. Therefore, several pharmaceutical grade polymers have been selected for development of BCS Class II drug carvedilol (CARV) solid dispersions. They were compared based on behavior in supercritical CO 2 and affinity towards CARV calculated from the miscibility analysis. By utilization of the supercritical CO 2 assisted method, solid dispersions of CARV with the selected (co)polymers (polyvinylpyrrolidone (PVP), hydroxypropyl methylcellulose (HPMC), Soluplus® and Eudragit®) were obtained. Properties of the prepared CARV-polymer dispersions were observed by the polarizing and scanning electron microscopy and analyzed by differential scanning calorimetry and Fourier transform infrared spectroscopy. CARV was additionally characterized by X-ray powder diffraction. Furthermore, in vitro dissolution studies and dynamic compaction analysis were performed on the selected samples of solid dispersions. Among the studied polymers, PVP and HPMC have been identified as polymers with the highest affinity towards CARV, based on the calculated δ p values. This has been also confirmed with the highest dissolution efficiency of CARV-PVP and CARV-HPMC solid dispersions. Solid state characterization indicated that CARV was dispersed either molecularly, or in the amorphous form, depending on interactions with each polymer. Determination of CARV-PVP and CARV-HPMC mechanical properties revealed that CARV-PVP solid dispersion has superior compactibility and tabletability. Therefore, CARV-PVP solid dispersion has been highlighted as the most appropriate for the further development of tablets as the final dosage form. Presented study provides an example for efficient approach for development of poorly soluble drug solid dispersion with satisfactory tableting properties.This is the peer-reviewed version of the following article: Đuriš, J., Milovanović, S., Medarević, Đ., Dobričić, V., Dapčević, A.,& Ibrić, S.. (2019). Selection of the suitable polymer for supercritical fluid assisted preparation of carvedilol solid dispersions. International Journal of Pharmaceutics Elsevier Science Bv, Amsterdam., 554, 190-200. [https://doi.org/10.1016/j.ijpharm.2018.11.015]Published version [http://technorep.tmf.bg.ac.rs/handle/123456789/4335