Optimized synthesis of polyglutaraldehyde nanoparticles using central composite design

Adding vitamin E-TPGS to the formulation of Genexol-PM: specially mixed micelles improve drug-loading ability and cytotoxicity against multidrug-resistant tumors significantly

Genexol-PM, produced by Samyang Company (Korea) is an excellent preparation of paclitaxel (PTX) for clinical cancer treatment. However, it cannot resolve the issue of multidrug resistance (MDR)—a significant problem in the administration of PTX to cancer patients. To increase the efficacy of Genexol-PM against MDR tumors, a mixed micelle capable of serving as a vehicle for PTX was developed, and two substances were chosen as carrier materials: 1) Polyethylene glycol–polylactic acid (PEG-PLA), the original vehicle of Genexol-PM. 2) Vitamin E-TPGS, an inhibitor of P-glycoprotein (P-gp). P-gp has been proven to be the main cause of MDR. In vitro evaluation indicated that the mixed micelle was an ideal PTX delivery system for the treatment of MDR tumors; the mixed micelle also showed a significantly better drug-loading coefficient than Genexol-PM.

Optimization of preparation of DHAQ-loaded PEG-PLGA-PEG nonaparticles using central composite design

Mitoxantrone (DHAQ)-loaded poly (ethylene glycol)-poly (lactic acid-co-glycolic acid) -poly (ethyleneglycol) (PELGE) nanoparticles (NP) were fabricated using an emulsification/solvent evaporation technique. A central composite design (CCD) was applied to evaluate the joint influence of three formulation variables: the amounts of polymer, concentration of the DHAQ, and the ratio of the organic phase (inner-phase) and the aqueous phase (outer-phase). In this study, we optimize the preparation technology on the basis of the single factor evaluation. The optimal conditions for the preparation of DHAQ-loaded nanoparticle were found to be: the concentration of PELGE was 9 mg/mL, the concentration of inner-phase of DHAQ was 27.5 mg/L, and the ratio of inner-phase/outer-phase was 8.5/1. The results showed that CCD is an ideal technique for formulation studies. The entrapment efficiency ratio (ER) was 90% and particle sizes are less than 500 nm. The nanoparticles, as examined by transmission electron microscopy (TEM), have a smooth and spherical surface. The DHAQ could be loaded into PELGE copolymers. In this study, the DHAQ nanoparticle-polymer delivery system was established by using PELGE polymers as carrier material.

Adaptive Neuro-Fuzzy Modeling of Poorly Soluble Drug Formulations

PURPOSE

The purpose of this study was to evaluate the efficiency of a neuro-fuzzy logic-based methodology to model poorly soluble drug formulations and predict the development of the particle size that has been proven to be an important factor for long-term stability.

METHODS

An adaptive neuro-fuzzy inference system was used to model the natural structures within the data and construct a set of fuzzy rules that can subsequently used as a predictive tool. The model was implemented in Matlab 6.5 and trained using 75% of an experimental data set. Subsequently, the model was evaluated and tested using the remaining 25%, and the predicted values of the particle size were compared to the ones from the experimental data. The produced adaptive neuro-fuzzy inference system-based model consisted of four inputs, i.e., acetone, propylene glycol, POE-5 phytosterol (BPS-5), and hydroxypropylmethylcellulose 90SH-50, with four membership functions each. Moreover, 256 fuzzy rules were employed in the model structure.

RESULTS

Model training resulted in a root mean square error of 4.5 x 10(-3), whereas model testing proved its highly predictive efficiency, achieving a correlation coefficient of 0.99 between the actual and the predicted values of the particle size (mean diameter).

CONCLUSIONS

Neuro-fuzzy modeling has been proven to be a realistic and promising tool for predicting the particle size of drug formulations with an easy and fast way, after proper training and testing.

Neural network based optimization of drug formulations

A pharmaceutical formulation is composed of several formulation factors and process variables. Several responses relating to the effectiveness, usefulness, stability, as well as safety must be optimized simultaneously. Consequently, expertise and experience are required to design acceptable pharmaceutical formulations. A response surface method (RSM) has widely been used for selecting acceptable pharmaceutical formulations. However, prediction of pharmaceutical responses based on the second-order polynomial equation commonly used in an RSM, is often limited to low levels, resulting in poor estimations of optimal formulations. The purpose of this review is to describe the basic concept of the multi-objective simultaneous optimization technique, in which an artificial neural network (ANN) is incorporated. ANNs are being increasingly used in pharmaceutical research to predict the nonlinear relationship between causal factors and response variables. Superior function of the ANN approach was demonstrated by the optimization for typical numerical examples.

Enhanced brain targeting by synthesis of 3',5'-dioctanoyl-5-fluoro-2'-deoxyuridine and incorporation into solid lipid nanoparticles

To overcome the limited access of the drug 5-fluoro-2'-deoxyuridine (FUdR) to the brain, 3',5'-dioctanoyl-5-fluoro-2'-deoxyuridine (DO-FUdR) was synthesized and incorporated into solid lipid nanoparticles (DO-FUdR-SLN). DO-FUdR-SLN were prepared by a thin-layer ultrasonication technique and a central composite design (CCD) was applied to optimize the formulation. The median particle size of DO-FUdR-SLN was 76 nm with drug loading of 29.02% and entrapment efficiency of 96.62%. The in vitro drug release was studied by a bulk-equilibrium reverse dialysis bag technique in phosphate-buffered saline (pH 7.4) containing 0.3% pancreatic enzyme at 37 degrees C. The concentrations of FUdR in various organs were determined by reversed-phase high-performance liquid chromatography after intravenous administration of DO-FUdR-SLN, DO-FUdR or FUdR. The brain area under the concentration-time curve of DO-FUdR-SLN and DO-FUdR were 10.97- and 5.32-fold higher than that of FUdR, respectively. These results indicated that DO-FUdR-SLN had a good brain targeting efficiency in vivo. SLN can improve the ability of the drug to penetrate through the blood-brain barrier and is a promising drug targeting system for the treatment of central nervous system disorders.

Characterization of polyvinylalcohol microspheres of diclofenac sodium: application of statistical design

Microspheres of polyvinylalcohol (PVA) containing diclofenac sodium were prepared by an emulsion-chemical cross-linking method. A statistical design was used to study the variables that affect the preparation of microspheres and to study the release profile of diclofenac from the microspheres. To account for the drug content, a mass balance study of the process was performed. A high concentration of polyvinylalcohol, a high stirring speed, and a low level of glutaraldehyde were found to be important to obtain spherical and discrete microspheres. The concentration of polyvinylalcohol and the amount of heavy liquid paraffin were found to be critical factors in influencing the t50 value. Almost 98% of the total diclofenac sodium added was accounted for in mass balance studies.

Artificial neural network as a novel method to optimize pharmaceutical formulations

One of the difficulties in the quantitative approach to designing pharmaceutical formulations is the difficulty in understanding the relationship between causal factors and individual pharmaceutical responses. Another difficulty is desirable formulation for one property is not always desirable for the other characteristics. This is called a multi-objective simultaneous optimization problem. A response surface method (RSM) has proven to be a useful approach for selecting pharmaceutical formulations. However, prediction of pharmaceutical responses based on the second-order polynomial equation commonly used in RSM, is often limited to low levels, resulting in poor estimations of optimal formulations. The aim of this review is to describe the basic concept of the multi-objective simultaneous optimization technique in which an artificial neural network (ANN) is incorporated. ANNs are being increasingly used in pharmaceutical research to predict the non-linear relationship between causal factors and response variables. The usefulness and reliability of this ANN approach is demonstrated by the optimization for ketoprofen hydrogel ointment as a typical numerical example, in comparison with the results obtained with a classical RSM approach.

Application of central composite designs to the preparation of polycaprolactone nanoparticles by solvent displacement

Cyclosporin A (CyA) is a good candidate for incorporation in colloidal carriers such as nanoparticles (NP) that would diminish the adverse effects associated with its use under conventional pharmaceutical dosage forms and improve bioavailability after oral administration. In this study a composite rotational experimental design was used to evaluate the joint influence of five formulation variables: temperature of the aqueous phase, needle gauge, volume of the organic phase, and the amounts of polymer and surfactant on the micromeritic characteristics of the CyA-loaded NP obtained by the method of Fessi et al. The percentage of drug encapsulated in the NP was also evaluated for each formulation, and the yield, which was expressed as the ratio between the experimentally measured quantity of drug in the formulation and the theoretical content, was determined because CyA undergoes surface absorption. Potential variables such as stirring speed (500 rpm), final drug concentration (100 micrograms/mL), or injection rates (GRi = 0.379 mL/s) were maintained constant. The ANOVA corresponding to the experimental design showed that the amounts of polymer and surfactant, and the diameter of the needle used in the preparation of NP, significantly affected the percentage of entrapped drug (I2 = 0.8916). The mean particle size was significantly affected by all the formulation variables tested except for the amount of surfactant dissolved in the external aqueous phase (r2 = 0.9518). Neither the yield (mean value of 99.61%) nor the size distribution parameters (polydispersity and coefficient of variation) presented good correlation coefficients for the equations obtained, although some variables showed statistical significance. A second study was carried out to investigate the effects on the drug-loaded NP characteristics of varying the global injection rates (GRi) for the organic phase into the aqueous medium. The results showed a dramatic decrease in both particle size and drug incorporation in the carrier as the rate of mixing increased. From the results of both the experimental design and the second study, a theoretical model for nanoparticle formation is proposed that considers the most significant variables, and an empirical relationship to predict mean particle size is presented. Thus, particle size can be controlled by the injection rates (GRi), the needle gauge, and the polymer concentration.

Formulation optimization of indomethacin gels containing a combination of three kinds of cyclic monoterpenes as percutaneous penetration enhancers

A computer optimization technique based on response surface methodology was applied for the optimization of a hydrogel formulation containing indomethacin as a model drug. As the penetration enhancer, a combination of three cyclic monoterpenes, limonene, menthol, and cineole, was employed. Pharmacokinetic parameters, from an in vivo percutaneous absorption study on rats of model formulations prepared according to the composite experimental design for five factors, were determined as prime response variables. The skin damage evoked by each formulation was microscopically judged and graded as the response variable concerning skin safety. The response variables were predicted by multiple regression equations comprising combinations of the five formulation factors. The regression equations for the response variables assembled as a simultaneous optimization problem based on the generalized distance function. The simultaneous optimum was predicted as a function of individual optima within a 95% confidence region. The predicted response values for the optimum formulation have been successfully validated in a repeated in vivo percutaneous absorption study.

A new attempt to solve the scale-up problem for granulation using response surface methodology

Scale-up from lab to production is always problematic for the development of pharmaceuticals. In granulation, an optimal formulation of binder solution determined in a lab scale is often different than that in a production scale. A new mathematical procedure to solve this scale-up problem is assessed. Granules were prepared in the two manufacturing scales (2- and 5-kg scale) by using a high-speed mixer granulator. In the manufacturing process, the binder solution plays an essential role in the formation of granules with desired physical properties, in close conformity with the manufacturing scale. A computerized optimizing technique based on a response surface methodology was developed to study the scale-up problem in the manufacturing of granules. For this purpose, a new mathematical function was introduced for the first time, which is namely an integrated optimization function. A universal optimal formulation unaffected by manufacturing scale could be obtained by minimizing the integrated optimization function. Predicted values such as yield, mean granule size, and uniformity of granule size agreed well with experimental ones on both scales. Furthermore, the optimized characteristics measured at the production scale coincided well with those obtained at laboratory scale, suggesting that this approach could be very useful in minimizing scale-up problems.

Synthesis and inverse emulsion polymerization of aminated acrylamidodextran

A chemically modified form of dextran was prepared, having a polymerizable moiety (acrylamide) and a reactive functional group (primary amine). Dextran was activated with 4-nitrophenyl-chloroformate (24 mol per polysaccharide, 9.8 mol per 100 glucose residues); 9.8% glucose residues were converted to aliphatic carbonates and 5.2% were converted to cyclic carbonates. The activated dextran was coupled with trityldiaminoethane (8 mol per 100 glucose residues), reactivated with 4-nitrophenylchloroformate, then coupled with acryloamidodiaminohexane (6.8 mol per 100 glucose residues). The trityl group was removed by hydrolysis with trifluoroacetic acid to yield the required aminated acryloamidodextran. The modified dextran was shown to be polymerizable by inverse emulsion polymerization. Submicron particles were successfully prepared, containing functional amine groups suitable for preparing drug conjugates or for modifying the surface properties of this biomaterial.

Optimized formulation of magnetic chitosan microspheres containing the anticancer agent, oxantrazole

A combined emulsion/polymer cross-linking/solvent evaporation technique was used to prepare magnetic chitosan microspheres (MCM) containing the anticancer drug, oxantrazole. A central composite experimental design was used to simultaneously evaluate a variety of formulation factors on a number of response variables, such as the percentage of oxantrazole entrapped in the MCM. In association with the study design, statistical optimization procedures indicated the factors that significantly influence MCM preparation and what levels of the factors are needed to produce optimum MCM. Entrapment of anticancer agents into biodegradable microspheres is difficult because of low aqueous drug solubility and porosity of the particles. The latter effect was circumvented by a chitosan cross-linking step that resulted in approximately 3% (w/w) oxantrazole entrapment in the MCM via the optimization procedures. The combined formulation and statistical optimization strategy provide a basis to develop other microparticulate systems and led to a dosage form that can be used for future in vivo investigations.

Albumin microspheres. I: Physico-chemical characteristics

Albumin microspheres are biodegradable particles which can be readily radiolabelled and synthesized in the size range of 1 to 200 microns. During the last 30 years extensive efforts have been made towards the design and development of this carrier for the purpose of diagnosis and drug delivery. This review presents a thorough discussion on the physico-chemical characteristics of albumin microspheres.

Factorial design based optimization of the formulation of albumin microspheres containing adriamycin

The use of factorial design in the formulation of adriamycin-associated albumin microspheres, using the heat-stabilization technique, is illustrated. The effect of stabilization temperature, protein concentration and stabilization time on the entrapment and recovery of adriamycin in microspheres have been investigated using a 2 x 4 x 4 factorial design. The associated drug content in unwashed and four times washed microspheres was determined using HPLC. Maximum drug association and drug recovery were obtained from microspheres synthesised using 25 per cent w/v albumin solution and stabilized at 120 degrees C for 2.5 min. Under these conditions, the entrapped and total associated drug content of the microspheres was about 4 per cent and 12 per cent w/w respectively, and the drug recovery was about 75 per cent. The in vitro dissolution study carried out using dynamic dialysis revealed that the release of adriamycin from these particles follows a bi-phasic pattern. The results demonstrate that use of short stabilization time, low protein concentration and low stabilization temperature are required for the formulation of microspheres with high adriamycin content.