Application of Machine-Learning Algorithms for Better Understanding the Properties of Liquisolid Systems Prepared with Three Mesoporous Silica Based Carriers

The processing of liquisolid systems (LSS), which are considered a promising approach to improving the oral bioavailability of poorly soluble drugs, has proven challenging due to the relatively high amount of liquid phase incorporated within them. The objective of this study was to apply machine-learning tools to better understand the effects of formulation factors and/or tableting process parameters on the flowability and compaction properties of LSS with silica-based mesoporous excipients as carriers. In addition, the results of the flowability testing and dynamic compaction analysis of liquisolid admixtures were used to build data sets and develop predictive multivariate models. In the regression analysis, six different algorithms were used to model the relationship between tensile strength (TS), the target variable, and eight other input variables. The AdaBoost algorithm provided the best-fit model for predicting TS (coefficient of determination = 0.94), with ejection stress (ES), compaction pressure, and carrier type being the parameters that influenced its performance the most. The same algorithm was best for classification (precision = 0.90), depending on the type of carrier used, with detachment stress, ES, and TS as variables affecting the performance of the model. Furthermore, the formulations with Neusilin® US2 were able to maintain good flowability and satisfactory values of TS despite having a higher liquid load compared to the other two carriers.

Mucoadhesive buccal tablets with propranolol hydrochloride: Formulation development and in vivo performances in experimental essential hypertension

The objective of this study was to formulate extended-release mucoadhesive buccal tablets of propranolol hydrochloride in order to provide a prolonged absorption of propranolol hydrochloride from the buccal mucosa and to reduce presystemic metabolism and thus provide a better therapeutic effect. Besides, the aim was to perform comparative in vivo pharmacokinetic and hemodynamic studies of the developed extended-release (ER) propranolol hydrochloride 10 mg mucoadhesive buccal tablets and commercial immediate-release (IR) propranolol hydrochloride 10 mg tablets in spontaneously hypertensive rats. Formulation with 15% polyethylene oxide showed the highest degree of propranolol hydrochloride permeation, satisfactory mucoadhesiveness, and extended-release of propranolol hydrochloride, thus it was selected for further in vivo study. The pharmacokinetic study in rats showed the superiority of ER mucoadhesive buccal tablets over IR tablets in terms of propranolol hydrochloride absorption extent (AUC values: 70.32 ± 19.56 versus 31.69 ± 6.97 µg·h/mL), although lower maximum plasma propranolol hydrochloride concentration (C_max) was achieved. However, no statistically significant difference was observed in C_max between these treatments. The hemodynamic study showed that ER mucoadhesive buccal tablets provide a more pronounced decrease primarily in heart rate, but also in systolic and diastolic arterial pressure, as well as a longer heart rate reduction compared to IR tablets.

Tableting properties of microcrystalline cellulose obtained from wheat straw measured with a single punch bench top tablet press

The objective of this work was to study the relation between the manufacturing conditions of microcrystalline cellulose (MCC), its physicochemical properties and its tableting behavior. Two different preparation procedures were used to produce MCC from wheat straw, utilizing an acid hydrolysis method, either using only sulfuric acid or combination of sulfuric and hydrochloric acid. The tableting behavior of obtained MCC samples and mixtures of MCC with ibuprofen was studied using a dynamic powder compaction analyzer. It was observed that some of the obtained MCC samples showed better flowing properties than commercially available Vivapur® PH101 and also very high values of tensile strength, solid fraction and elastic recovery. This can be linked with its good compaction behavior, but on the other hand it can cause problems with the disintegration of the tablets. In mixtures with ibuprofen, MCC samples showed lower values of tensile strength, while on the other hand elastic recovery did not seem to be much affected, still exhibiting very high values. According to the obtained results, it can be concluded that MCC obtained from the agricultural waste could have satisfactory properties for tablet preparation by the direct compression method. Further studies are needed to optimize process conditions in order to achieve better physicochemical characteristics, especially in terms of elastic recovery.

Analytical and Computational Methods for the Estimation of Drug-Polymer Solubility and Miscibility in Solid Dispersions Development

The development of stable solid dispersion formulations that maintain desired improvement of drug dissolution rate during the entire shelf life requires the analysis of drug-polymer solubility and miscibility. Only if the drug concentration is below the solubility limit in the polymer, the physical stability of solid dispersions is guaranteed without risk for drug (re)crystallization. If the drug concentration is above the solubility, but below the miscibility limit, the system is stabilized through intimate drug-polymer mixing, with additional kinetic stabilization if stored sufficiently below the mixture glass transition temperature. Therefore, it is of particular importance to assess the drug-polymer solubility and miscibility, to select suitable formulation (a type of polymer and drug loading), manufacturing process, and storage conditions, with the aim to ensure physical stability during the product shelf life. Drug-polymer solubility and miscibility can be assessed using analytical methods, which can detect whether the system is single-phase or not. Thermodynamic modeling enables a mechanistic understanding of drug-polymer solubility and miscibility and identification of formulation compositions with the expected formation of the stable single-phase system. Advance molecular modeling and simulation techniques enable getting insight into interactions between the drug and polymer at the molecular level, which determine whether the single-phase system formation will occur or not.

Combined application of mixture experimental design and artificial neural networks in the solid dispersion development

This study for the first time demonstrates combined application of mixture experimental design and artificial neural networks (ANNs) in the solid dispersions (SDs) development. Ternary carbamazepine-Soluplus®-poloxamer 188 SDs were prepared by solvent casting method to improve carbamazepine dissolution rate. The influence of the composition of prepared SDs on carbamazepine dissolution rate was evaluated using d-optimal mixture experimental design and multilayer perceptron ANNs. Physicochemical characterization proved the presence of the most stable carbamazepine polymorph III within the SD matrix. Ternary carbamazepine-Soluplus®-poloxamer 188 SDs significantly improved carbamazepine dissolution rate compared to pure drug. Models developed by ANNs and mixture experimental design well described the relationship between proportions of SD components and percentage of carbamazepine released after 10 (Q10) and 20 (Q20) min, wherein ANN model exhibit better predictability on test data set. Proportions of carbamazepine and poloxamer 188 exhibited the highest influence on carbamazepine release rate. The highest carbamazepine release rate was observed for SDs with the lowest proportions of carbamazepine and the highest proportions of poloxamer 188. ANNs and mixture experimental design can be used as powerful data modeling tools in the systematic development of SDs. Taking into account advantages and disadvantages of both techniques, their combined application should be encouraged.

Design space approach in optimization of fluid bed granulation and tablets compression process

The aim of this study was to optimize fluid bed granulation and tablets compression processes using design space approach. Type of diluent, binder concentration, temperature during mixing, granulation and drying, spray rate, and atomization pressure were recognized as critical formulation and process parameters. They were varied in the first set of experiments in order to estimate their influences on critical quality attributes, that is, granules characteristics (size distribution, flowability, bulk density, tapped density, Carr's index, Hausner's ratio, and moisture content) using Plackett-Burman experimental design. Type of diluent and atomization pressure were selected as the most important parameters. In the second set of experiments, design space for process parameters (atomization pressure and compression force) and its influence on tablets characteristics was developed. Percent of paracetamol released and tablets hardness were determined as critical quality attributes. Artificial neural networks (ANNs) were applied in order to determine design space. ANNs models showed that atomization pressure influences mostly on the dissolution profile, whereas compression force affects mainly the tablets hardness. Based on the obtained ANNs models, it is possible to predict tablet hardness and paracetamol release profile for any combination of analyzed factors.