Empirical search for factors affecting mean particle size of PLGA microspheres containing macromolecular drugs

BACKGROUND AND OBJECTIVES

Poly(lactic-co-glycolic acid) (PLGA) has become one of the most promising in design, development, and optimization for medical applications polymers. PLGA-based multiparticulate dosage forms are usually prepared as microspheres where the size is from 5 to 100 µm, depending on the route of administration. The main objectives of the study were to develop a predictive model of mean volumetric particle size and on its basis extract knowledge of PLGA containing proteins forming behaviour.

METHODS

In the present study, a model for the prediction of mean volumetric particle size developed by an rgp package of R environment is presented. Other tools like fscaret, monmlp, fugeR, MARS, SVM, kNNreg, Cubist, randomForest and piecewise linear regression are also applied during the data mining procedure.

RESULTS

The feature selection provided by the fscaret package reduced the original input vector from a total of 295 input variables to 10, 16 and 19. The developed models had good predictive ability, which was confirmed by a normalized root-mean-square error (NRMSE) of 6.8 to 11.1% in 10-fold cross validation training procedure. Moreover, the best models were validated using external experimental data. The superior predictiveness had a model obtained by rgp in the form of a classical equation with a normalized root-mean-squared error (NRMSE) of 6.1%.

CONCLUSIONS

A new approach is proposed for computational modelling of the mean particle size of PLGA microspheres and rules extraction from tree-based models. The feature selection leads to revealing chemical descriptor variables which are important in predicting the size of PLGA microspheres. In order to achieve better understanding in the relationships between particle size and formulation characteristics, the surface analysis method and rules extraction procedures were applied.

High-Energy Ball Milling as Green Process To Vitrify Tadalafil and Improve Bioavailability

In this study, the suitability of high-energy ball milling was investigated with the aim to vitrify tadalafil (TD) and improve its bioavailability. To achieve this goal, pure TD as well as binary mixtures composed of the drug and Soluplus (SL) were coprocessed by high-energy ball milling. Modulated differential scanning calorimetry (MDSC) and X-ray powder diffraction (XRD) demonstrated that after such coprocessing, the crystalline form of TD was transformed into an amorphous form. The presence of a single glass transition (T_g) for all the comilled formulations indicated that TD was dispersed into SL at the molecular level, forming amorphous molecular alloys, regardless of the drug concentration. The high values of T_g determined for amorphous formulations, ranging from 70 to 147 °C, foreshow their high stability during storage at room temperature, which was verified by XRD and MDSC studies. The stabilizing effect of SL on the amorphous form of TD in comilled formulations was confirmed. Dissolution tests showed immediate drug release with sustained supersaturation in either simulated gastric fluid of pH 1.2 or in phosphate buffer of pH 7.2. The beneficial effect of both amorphization and coamorphization on the bioavailability of TD was found. In comparison to aqueous suspension, the relative bioavailability of TD was only 11% for its crystalline form and 53% for the crystalline physical mixture, whereas the bioavailability of milled amorphous TD and the comilled solid dispersion was 128% and 289%, respectively. Thus, the results provide evidence that not only the presence of polymeric surfactant but also the vitrification of TD is necessary to improve bioavailability.

OPTIMIZATION OF FUROSEMIDE LIQUISOLID TABLETS PREPARATION PROCESS LEADING TO THEIR MASS AND SIZE REDUCTION

The great number of drug substances currently used in solid oral dosage forms is characterized by poor water solubility. Therefore, various methods of dissolution rate enhancement are an important topic of research interest in modem drug technology. The purpose of this study was to enhance the furosemide dissolution rate from liquisolid tablets while maintaining an acceptable size and mass. Two types of dibasic calcium phosphate (Fujicalin®/Emcompress®) and microcrystalline cellulose (Vivapur® 102/Vivapur® 12) were used as carriers and magnesium aluminometasilicate (Neusilin® US2) was used as a coating material. The flowable liquid retention potential for those excipients was tested by measuring the angle of slide. To evaluate the impact of used excipients on tablet properties fourteen tablet formulations were prepared. It was found that LS2 tablets containing spherically granulated dibasic calcium phosphate and magnesium aluminometasilicate exhibit the best dissolution profile and mechanical properties while tablets composed only with Neusilin® US2 was characterized by the smallest size and mass with preserved good mechanical properties and furosemide dissolution.

Computational Intelligence Modeling of the Macromolecules Release from PLGA Microspheres-Focus on Feature Selection

Poly-lactide-co-glycolide (PLGA) is a copolymer of lactic and glycolic acid. Drug release from PLGA microspheres depends not only on polymer properties but also on drug type, particle size, morphology of microspheres, release conditions, etc. Selecting a subset of relevant properties for PLGA is a challenging machine learning task as there are over three hundred features to consider. In this work, we formulate the selection of critical attributes for PLGA as a multiobjective optimization problem with the aim of minimizing the error of predicting the dissolution profile while reducing the number of attributes selected. Four bio-inspired optimization algorithms: antlion optimization, binary version of antlion optimization, grey wolf optimization, and social spider optimization are used to select the optimal feature set for predicting the dissolution profile of PLGA. Besides these, LASSO algorithm is also used for comparisons. Selection of crucial variables is performed under the assumption that both predictability and model simplicity are of equal importance to the final result. During the feature selection process, a set of input variables is employed to find minimum generalization error across different predictive models and their settings/architectures. The methodology is evaluated using predictive modeling for which various tools are chosen, such as Cubist, random forests, artificial neural networks (monotonic MLP, deep learning MLP), multivariate adaptive regression splines, classification and regression tree, and hybrid systems of fuzzy logic and evolutionary computations (fugeR). The experimental results are compared with the results reported by Szlȩk. We obtain a normalized root mean square error (NRMSE) of 15.97% versus 15.4%, and the number of selected input features is smaller, nine versus eleven.

The Relationship Between the Evolution of an Internal Structure and Drug Dissolution from Controlled-Release Matrix Tablets

In the last decade, imaging has been introduced as a supplementary method to the dissolution tests, but a direct relationship of dissolution and imaging data has been almost completely overlooked. The purpose of this study was to assess the feasibility of relating magnetic resonance imaging (MRI) and dissolution data to elucidate dissolution profile features (i.e., kinetics, kinetics changes, and variability). Commercial, hydroxypropylmethyl cellulose-based quetiapine fumarate controlled-release matrix tablets were studied using the following two methods: (i) MRI inside the USP4 apparatus with subsequent machine learning-based image segmentation and (ii) dissolution testing with piecewise dissolution modeling. Obtained data were analyzed together using statistical data processing methods, including multiple linear regression. As a result, in this case, zeroth order release was found to be a consequence of internal structure evolution (interplay between region's areas-e.g., linear relationship between interface and core), which eventually resulted in core disappearance. Dry core disappearance had an impact on (i) changes in dissolution kinetics (from zeroth order to nonlinear) and (ii) an increase in variability of drug dissolution results. It can be concluded that it is feasible to parameterize changes in micro/meso morphology of hydrated, controlled release, swellable matrices using MRI to establish a causal relationship between the changes in morphology and drug dissolution. Presented results open new perspectives in practical application of combined MRI/dissolution to controlled-release drug products.

DEVELOPMENT AND VERIFICATION OF NEW SOLID DENTAL FILLING TEMPORARY MATERIALS CONTAINING ZINC. FORMULA DEVELOPMENT STAGE

Caries is the most popular problem affecting teeth and this is the reason why so many temporary dental filling materials are being developed. An example of such filling is zinc oxide paste mixed with eugenol, Thymodentin and Coltosol F®. Zinc-oxide eugenol is used in dentistry because of its multiplied values: it improves heeling of the pulp by dentine bridge formation; has antiseptic properties; is hygroscopic. Because of these advantages compouds of zinc oxide are used as temporary fillings, especially in deep caries lesions when treatment is oriented on support of vital pulp. Temporary dental fillings based on zinc oxide are prepared ex tempone by simple mixing powder (Thymodentin) and eugenol liqiud together or a ready to use paste Coltosol F®. Quantitative composition depends mainly on experience of person who is preparing it, therefore, exact qualitative composition of dental fillings is not replicable. The main goal of the study was to develop appropriate dental fillings in solid form containing set amount of zinc oxide. Within the study, the influence of preparation method on solid dental fillings properties like mechanical properties and zinc ions release were examined.

Transparent computational intelligence models for pharmaceutical tableting process

Purpose

Pharmaceutical industry is tightly regulated owing to health concerns. Over the years, the use of computational intelligence (CI) tools has increased in pharmaceutical research and development, manufacturing, and quality control. Quality characteristics of tablets like tensile strength are important indicators of expected tablet performance. Predictive, yet transparent, CI models which can be analysed for insights into the formulation and development process.

Methods

This work uses data from a galenical tableting study and computational intelligence methods like decision trees, random forests, fuzzy systems, artificial neural networks, and symbolic regression to establish models for the outcome of tensile strength. Data was divided in training and test fold according to ten fold cross validation scheme and RMSE was used as an evaluation metric. Tree based ensembles and symbolic regression methods are presented as transparent models with extracted rules and mathematical formula, respectively, explaining the CI models in greater detail.

Results

CI models for tensile strength of tablets based on the formulation design and process parameters have been established. Best models exhibit normalized RMSE of 7 %. Rules from fuzzy systems and random forests are shown to increase transparency of CI models. A mathematical formula generated by symbolic regression is presented as a transparent model.

Conclusions

CI models explain the variation of tensile strength according to formulation and manufacturing process characteristics. CI models can be further analyzed to extract actionable knowledge making the artificial learning process more transparent and acceptable for use in pharmaceutical quality and safety domains.

Preformulation studies on solid self-emulsifying systems in powder form containing magnesium aluminometasilicate as porous carrier

The influence of alkaline and the neutral grade of magnesium aluminometasilicate as a porous solid carrier for the liquid self-emulsifying formulation with ibuprofen is investigated. Ibuprofen is dissolved in Labrasol, then this solution is adsorbed on the silicates. The drug to the silicate ratio is 1:2, 1:4, and 1:6, respectively. The properties of formulations obtained are analyzed, using morphological, porosity, crystallinity, and dissolution studies. Three solid self-emulsifying (S-SE) formulations containing Neusilin SG2 and six consisting of Neusilin US2 are in the form of powder without agglomerates. The nitrogen adsorption method shows that the solid carriers are mesoporous but they differ in a specific surface area, pore area, and the volume of pores. The adsorption of liquid SE formulation on solid silicate particles results in a decrease in their porosity. If the neutral grade of magnesium aluminometasilicate is used, the smallest pores, below 10 nm, are completely filled with liquid formulation, but there is still a certain number of pores of 40–100 nm. Dissolution studies of liquid SEDDS carried out in pH = 1.2 show that Labrasol improves the dissolution of ibuprofen as compared to the pure drug. Ibuprofen dissolution from liquid SE formulations examined in pH of 7.2 is immediate. The adsorption of the liquid onto the particles of the silicate causes a decrease in the amount of the drug released. Finally, more ibuprofen is dissolved from S-SE that consist of the neutral grade of magnesium aluminometasilicate than from the formulations containing the alkaline silicate.

Sensitive and precise HPLC method with back-extraction clean-up step for the determination of sildenafil in rat plasma and its application to a pharmacokinetic study

A sensitive HPLC method was developed and validated for the determination of sildenafil concentrations in rat plasma (200 μL) using a liquid-liquid extraction procedure and paroxetine as an internal standard. In order to eliminate interferences and improve the peak shape, a back-extraction into an acidic solution was utilized. Chromatographic separation was achieved on a cyanopropyl bonded-phase column with a mobile phase composed of 50 m m potassium dihydrogen phosphate buffer (pH 4.5) and acetonitrile (75:25, v/v), pumped at the flow rate of 1 mL/min. A UV detector was set at 230 nm. A calibration curve was constructed within a concentration range from 10 to 1500 ng/mL. The limit of detection was 5 ng/mL. The inter- and intra-day precisions of the assay were in the ranges 2.91-7.33 and 2.61-6.18%, respectively, and the accuracies for inter- and intra-day runs were within 0.14-3.92 and 0.44-2.96%, respectively. The recovery of sildenafil was 85.22 ± 4.54%. Tests confirmed the stability of sildenafil in plasma during three freeze-thaw cycles and during long-term storage at -20 and -80°C for up to 2 months. The proposed method was successfully applied to a pharmacokinetic study in rats.

Empirical modeling of the fine particle fraction for carrier-based pulmonary delivery formulations

In vitro study of the deposition of drug particles is commonly used during development of formulations for pulmonary delivery. The assay is demanding, complex, and depends on: properties of the drug and carrier particles, including size, surface characteristics, and shape; interactions between the drug and carrier particles and assay conditions, including flow rate, type of inhaler, and impactor. The aerodynamic properties of an aerosol are measured in vitro using impactors and in most cases are presented as the fine particle fraction, which is a mass percentage of drug particles with an aerodynamic diameter below 5 μm. In the present study, a model in the form of a mathematical equation was developed for prediction of the fine particle fraction. The feature selection was performed using the R-environment package "fscaret". The input vector was reduced from a total of 135 independent variables to 28. During the modeling stage, techniques like artificial neural networks, genetic programming, rule-based systems, and fuzzy logic systems were used. The 10-fold cross-validation technique was used to assess the generalization ability of the models created. The model obtained had good predictive ability, which was confirmed by a root-mean-square error and normalized root-mean-square error of 4.9 and 11%, respectively. Moreover, validation of the model using external experimental data was performed, and resulted in a root-mean-square error and normalized root-mean-square error of 3.8 and 8.6%, respectively.

Heuristic modeling of macromolecule release from PLGA microspheres

Dissolution of protein macromolecules from poly(lactic-co-glycolic acid) (PLGA) particles is a complex process and still not fully understood. As such, there are difficulties in obtaining a predictive model that could be of fundamental significance in design, development, and optimization for medical applications and toxicity evaluation of PLGA-based multiparticulate dosage form. In the present study, two models with comparable goodness of fit were proposed for the prediction of the macromolecule dissolution profile from PLGA micro- and nanoparticles. In both cases, heuristic techniques, such as artificial neural networks (ANNs), feature selection, and genetic programming were employed. Feature selection provided by fscaret package and sensitivity analysis performed by ANNs reduced the original input vector from a total of 300 input variables to 21, 17, 16, and eleven; to achieve a better insight into generalization error, two cut-off points for every method was proposed. The best ANNs model results were obtained by monotone multi-layer perceptron neural network (MON-MLP) networks with a root-mean-square error (RMSE) of 15.4, and the input vector consisted of eleven inputs. The complicated classical equation derived from a database consisting of 17 inputs was able to yield a better generalization error (RMSE) of 14.3. The equation was characterized by four parameters, thus feasible (applicable) to standard nonlinear regression techniques. Heuristic modeling led to the ANN model describing macromolecules release profiles from PLGA microspheres with good predictive efficiency. Moreover genetic programming technique resulted in classical equation with comparable predictability to the ANN model.

Generalized in vitro-in vivo relationship (IVIVR) model based on artificial neural networks

BACKGROUND

The aim of this study was to develop a generalized in vitro-in vivo relationship (IVIVR) model based on in vitro dissolution profiles together with quantitative and qualitative composition of dosage formulations as covariates. Such a model would be of substantial aid in the early stages of development of a pharmaceutical formulation, when no in vivo results are yet available and it is impossible to create a classical in vitro-in vivo correlation (IVIVC)/IVIVR.

METHODS

Chemoinformatics software was used to compute the molecular descriptors of drug substances (ie, active pharmaceutical ingredients) and excipients. The data were collected from the literature. Artificial neural networks were used as the modeling tool. The training process was carried out using the 10-fold cross-validation technique.

RESULTS

The database contained 93 formulations with 307 inputs initially, and was later limited to 28 in a course of sensitivity analysis. The four best models were introduced into the artificial neural network ensemble. Complete in vivo profiles were predicted accurately for 37.6% of the formulations.

CONCLUSION

It has been shown that artificial neural networks can be an effective predictive tool for constructing IVIVR in an integrated generalized model for various formulations. Because IVIVC/IVIVR is classically conducted for 2-4 formulations and with a single active pharmaceutical ingredient, the approach described here is unique in that it incorporates various active pharmaceutical ingredients and dosage forms into a single model. Thus, preliminary IVIVC/IVIVR can be available without in vivo data, which is impossible using current IVIVC/IVIVR procedures.

The influence of the API properties on the ODTs manufacturing from co-processed excipient systems

Directly compressible co-processed excipient systems facilitate orodispersible tablets (ODTs) manufacturing. Despite several excipient systems available, it is reported that the incorporation of high drug dose into the tablet mass may negatively affect both disintegration and mechanical properties. Therefore the influence of drug properties on the quality of orodispersible tablets was investigated. Fast dissolving tablet matrix was made of a co-processed excipient system F-Melt. Two grades of F-Melt that differed in composition, particle shape, and specific surface area were used to form tablet matrix. Ibuprofen, diclofenac sodium, and diltiazem hydrochloride were chosen as model drugs of different physicochemical properties such as solubility, particle size, and shape. Ninety formulations containing 12.5, 25, or 50 wt% of the model drug and F-Melt type C or M were prepared by direct compression. The quality of tablets was examined on the base of disintegration time, wetting time, mechanical resistance and texture analysis. The results showed that F-Melt grade, drug solubility, and its dose had an influence on the quality of tablets. From ninety formulations prepared, only four batches containing F-Melt type C and 12.5 wt% of ibuprofen, diclofenac sodium, or diltiazem hydrochloride could be classified as ODTs. Their disintegration time ranged from 41 to 144 s. In the case of F-Melt type M, tablets disintegrating within 101 s of friability below 1% could be prepared only if 12.5 wt% of diclofenac sodium was incorporated into the tablet mass.

Evaluation of co-processed excipients used for direct compression of orally disintegrating tablets (ODT) using novel disintegration apparatus

The compendial method of evaluation of orodispersible tablets (ODT) is the same disintegration test as for conventional tablets. Since it does not reflect the disintegration process in the oral cavity, alternative methods are proposed that are more related to in vivo conditions, e.g. modified dissolution paddle apparatus, texture analyzer, rotating shaft apparatus, CCD camera application, or wetting time and water absorption ratio measurement. In this study, three different co-processed excipients for direct compression of orally disintegrating tablets were compared (Ludiflash, Pharmaburst, F-Melt). The properties of the prepared tablets such as tensile strength, friability, wetting time and water absorption ratio were evaluated. Disintegration time was measured using the pharmacopoeial method and the novel apparatus constructed by the authors. The apparatus was based on the idea of Narazaki et al., however it has been modified. Magnetic resonance imaging (MRI) was applied for the analysis of the disintegration mechanism of prepared tablets. The research has shown the significant effect of excipients, compression force, temperature, volume and kind of medium on the disintegration process. The novel apparatus features better correlation of disintegration time with in vivo results (R(2) = 0.9999) than the compendial method (R(2) = 0.5788), and presents additional information on the disintegration process, e.g. swelling properties.

Analysis of wet granulation process with Plackett-Burman design--case study

According to Process Analytical Technology perspective, drug product quality should be ensured by manufacturing process design. Initial step of the process analysis is investigation of critical process parameters (CPPs). It is generally accepted to type the CPPs based on project team knowledge and experience [5]. This paper describes the use of Design of Experiments tool for selection of the CPPs. Seven factors of wet granulation process were investigated for criticality. Low and high levels of each factor represented maximal and minimal settings of wide operational ranges. Granulates were produced in line with Plackett-Burman experimental matrix, blended with extra-granular excipients and compressed into tablets. Semi-products and final products were tested. Out of specification result of any critical quality attribute was treated as critical failure. The high-shear granulation factors, i.e. quantity of binding solution, rotational speed of impeller and wet massing time were considered of critical importance. Operational ranges of the parameters were optimized. The process performance was confirmed in qualification trials.

Novel application of MRI technique combined with flow-through cell dissolution apparatus as supportive discriminatory test for evaluation of controlled release formulations

Dissolution studies cannot distinguish phenomena occurring inside the dosage forms when studying formulation with similar dissolution profiles-such formulations can behave differently when considering their physical changes. The application of flow-through dissolution apparatus integrated with magnetic resonance imaging (MRI) system for discriminative evaluation of controlled release dosage forms with similar dissolution profiles was presented. Hydrodynamically balanced systems (HBS) containing L: -dopa and various grades hydroxypropyl methylcelluloses were prepared. The dissolution studies of L: -dopa were performed at high field (4.7 T) MR system with MR-compatible flow-through cell. MRI was done with 0.14 x 0.14 x 1-mm spatial resolution and temporal resolution of 10 min to record changes of HBS parameters during dissolution in 0.1 M HCl. Structural and geometrical changes were evaluated using the following parameters: total area of HBS cross-section, its Feret's diameter, perimeter and circularity, area of hydrogel layer, and "dry core" area. While the dissolution profiles of L: -dopa were similar, the image analysis revealed differences in the structural and geometrical changes of the HBS. The mechanism of drug release from polymeric matrices is a result of synergy of several different phenomena occurring during dissolution and may differ between formulations, yet giving similar dissolution profiles. A multivariate analysis was performed to create a model taking into account dissolution data, data from MRI, information about chemical structure, and polymer viscosity. It provided a single model for all the formulations which was confirmed to be competent. The presented method has merit as a potential Process Analytical Technology tool.

An integrated system for dissolution studies and magnetic resonance imaging of controlled release, polymer-based dosage forms-a tool for quantitative assessment of hydrogel formation processes

Controlled release (CR) dosage forms are often based on polymeric matrices, e.g., sustained-release tablets and capsules. It is crucial to visualise and quantify processes of the hydrogel formation during the standard dissolution study. A method for imaging of CR, polymer-based dosage forms during dissolution study in vitro is presented. Imaging was performed in a non-invasive way by means of the magnetic resonance imaging (MRI). This study was designed to simulate in vivo conditions regarding temperature, volume, state and composition of dissolution media. Two formulations of hydrodynamically balanced systems (HBS) were chosen as model CR dosage forms. HBS release active substance in stomach while floating on the surface of the gastric content. Time evolutions of the diffusion region, hydrogel formation region and "dry core" region were obtained during a dissolution study of L-dopa as a model drug in two simulated gastric fluids (i.e. in fed and fasted state). This method seems to be a very promising tool for examining properties of new formulations of CR, polymer-based dosage forms or for comparison of generic and originator dosage forms before carrying out bioequivalence studies.

Development of a system for simultaneous dissolution studies and magnetic resonance imaging of water transport in hydrodynamically balanced systems: a technical note

An acid-buffering bioadhesive vaginal tablet was developed for the treatment of genitourinary tract infections. From the bioadhesion experiment and release studies it was found that polycarbophil and sodium carboxymethylcellulose is a good combination for an acid-buffering bioadhesive vaginal tablet. Sodium monocitrate was used as a buffering agent to provide acidic pH (4.4), which is an attribute of a healthy vagina. The effervescent mixture (citric acid and sodium bicarbonate) along with a superdisintegrant (Ac-Di-sol) was used to enhance the swellability of the bioadhesive tablet. The drugs clotrimazole (antifungal) and metronidazole (antiprotozoal as well as an antibacterial) were used in the formulation along with Lactobacillus acidophilus spores to treat mixed vaginal infections. From the ex vivo retention study it was found that the bioadhesive polymers hold the tablet for more than 24 hours inside the vaginal tube. The hardness of the acid-buffering bioadhesive vaginal tablet was optimized, at 4 to 5 kg hardness the swelling was found to be good and the cumulative release profile of the developed tablet was matched with a marketed conventional tablet (Infa-V). The in vitro spreadability of the swelled tablet was comparable to the marketed gel. In the in vitro antimicrobial study it was found that the acid-buffering bioadhesive tablet produces better antimicrobial action than marketed intravaginal drug delivery systems (Infa-V, Candid-V and Canesten 1).

Artificial neural networks in the modeling of drugs release profiles from hydrodynamically balanced systems

Artificial neural networks (ANNs) were used as modeling tools for prediction of various drugs release patterns from hydrodynamically balanced systems (HBS) composed with Metholose 90SH (hydroxypropylmethylcellulose--HPMC). The objective was to provide predictive and data-mining models of analyzed problem. It was found that ANNs are capable to accurately predict release patterns of different drugs from HBS based on the description of the formulation as well as chemical structure of the drug. Overall generalization error RMSE was 8.7 and after inclusion of pilot study in learning dataset it decreased to ca. 4.5. Sensitivity analysis of ANNs was applied to reduce native input vector from 77 to 7 inputs in order to improve the performance of predictive models. Simultaneously, it revealed crucial variables governing release of drugs from HBS.

The Macromolecular Polymers for the Preparation of Hydrodynamically Balanced Systems—Methods of Evaluation

Evaluation of macromolecular polymers used as excipients for the preparation of hydrodynamically balanced systems (HBS) was carried out. Hard gelatine capsules were filled with polymeric substances belonging to various chemical groups (chitosan, sodium alginate, hydroxypropylmethycellulose--HPMC). The following properties of the HBS were investigated: density, hydration, erosion and floating force. The solvent penetration process into the HBS was visualized using magnetic resonance imaging (MRI) technique. Densities of the HBS in hydrochloric acid (0.1 M) ranged from 0.37 g/cm3 to 0.71 g/cm3. Each polymer demonstrated different hydration/erosion abilities and floating properties. The maximum floating force (F(float max)) for capsules size 0, ranged from 26.7 mN (sodium alginate) to 64.7 mN (chitosan). HBS formulations also varied in time to reach maximum floating force (T(float max)). HPMC and sodium alginate formulation reached F(float max) within half an hour after immersion, while in the case of chitosan formulations (deacetylation degree (d.d.) 66% and d.d. 93%), the time was 184 minutes and 218 minutes respectively. The floating properties of the dosage forms were reliant on type of the polymer and the medium-fasted state simulated gastric fluid (FaSSGF) or fed state simulated gastric fluid (FeSSGF). The size of the HBS influenced the floating force value. The mechanisms of erosion and swelling of the polymeric matrices play a dominant role in flotation of the dosage forms.

Solid dispersion of ketoprofen in pellets

The formulation of ternary solid dispersions of ketoprofen with Macrogol and kollagen hydrolizate derivative as carriers was elaborated on the basis of the results of the experiments in which different methods of solid dispersion preparation (melting, solvent method, different cooling), different concentrations of drug/carriers and molecular weight of Macrogol were tested. The best solid dispersion consisted of: ketoprofen-Macrogol 6000-KLH(T) (1+8. 9+0.1) was chosen to formulate the pellets on the basis of the pharmaceutical availability of ketoprofen from solid dispersion and the physical chemical studies: thermomicroscopic, DSC and X-ray diffraction. The pellets were prepared by the extrusion and spheronization method. The mechanical properties of the pellets as well as ketoprofen released from pellets containing solid dispersion, in comparison with physical mixtures and the drug alone, were evaluated. The increase in the amount of released ketoprofen from solid dispersion pellets was 3.8-times greater than from the pellets containing the drug alone. The stability of solid dispersion pellets was satisfactory.

Erythromycin and penetrating agents containing ointment in acne vulgaris therapy

An ointment containing penetrating agents and erythromycin was devised. The rate of penetration of the antibiotic through the skin and the ointment efficacy in treatment of acne changes caused by Propionibacterium strains in animals were evaluated.