Downloaded 44 times
More Related Content
Similar to Computers in Pharmaceutical emulsion development.
Computers in Pharmaceutical emulsion development.
- 1. Submitted to: Ms. RichaSood Asst. Professor, Department of Pharmaceutics, COPS, DSU Banglore. Presented by: Arpitha.B. M M Pharm (II SEM), Department of Pharmaceutics, COPS, DSU Banglore COMPUTERS IN PHARMACEUTICAL EMULSION DEVELOPMENT
- 2. CONTENTS • INTRODUCTION • APPLICATIONOF COMPUTER AIDED TECHNIQUES IN DEVELOPMENT OF PHARMACEUTICAL EMULSION COPS, DSU Dept. of Pharmaceutics 225-04-2020
- 3. INTRODUCTION • Due totheir complex composition, preparation and stability issues of emulsions were selected to showcase various computer-aided tools in pharmaceutical formulation development. • Successful development of an emulsion formulation is dependent on both formulation ingredients and processing parameters, which is especially significant for more complex formulation types, such as self-emulsifying systems or double emulsions. • The examples provided illustrate techniques used to define a design space, select the appropriate formulation ingredient, and optimize the formulation composition as well as process parameters, according to the quality-by-design (QbD) concept. • Importantly, methods that allow simultaneous optimization of multiple factors are also presented. COPS, DSU Dept. of Pharmaceutics 325-04-2020
- 4. APPLICATION OF COMPUTERAIDED TECHNIQUES IN DEVELOPMENT OF PHARMACEUTICAL EMULSION • Emulsions are disperse systems made of two immiscible liquids. One liquid is dispersed into the other, in the presence of surface active agents, such as emulsifier(s). The two immiscible liquids are usually oil and water, and the main types of simple emulsions are oil-in-water (o/w) or water-in-oil (w/o). • In the pharmacy, emulsions have a great potential as vehicles for active ingredients for different routes of administration (topical, parenteral, oral). • Emulsions are thermodynamically unstable systems, and different phenomena during storage could occur, including gravitational separation (creaming/sedimentation), flocculation, coalescence, Ostwald ripening, and phase inversion. • Stability and properties of emulsions are influenced by different factors. Formulation and process optimization techniques would be useful for finding the ideal emulsion formulation. • The main parameters relating to the stability, effectiveness, and safety of the pharmaceutical emulsion should be optimized simultaneously. COPS, DSU Dept. of Pharmaceutics 425-04-2020
- 5. • Prinderre etal. (1998) applied factorial design methods to optimize the stability and suggested the required hydrophilic lipophilic balance (HLB) of o/w emulsions prepared with sodium lauryl sulfate as the surfactant. The independent variables and their levels (low/high) were mixing rate (rpm) (500/900), homogenization (no/yes), and mixing time (min) (10/20). • Dependent variables were the average size of the droplets, the emulsion viscosity, and the conductivity. Experimental design determined the required HLB with good approximation in five runs for the average diameter and viscosity studies, while the conductivity study needed at least eight runs. • Simovic et al. (1999) investigated the influence of the processing variables on performance of o/w emulsion gels stabilized by a polymeric emulsifier (Permulen TR- 2NF) • Two factors at three level experimental design at two sets was applied. • Rahali et al. (2009) found the optimal preservative combination and concentration for preparing topical emulsions by using a D-optimal experimental design (mixture design). In this study, three preservatives were tested, benzoic acid, sorbic acid, and benzyl alcohol. • The preservative effects were evaluated using the antimicrobial preservative efficacy test (challenge test) of the European Pharmacopeia (EP). The results of this study were analyzed with the help of Design Expert® software. COPS, DSU Dept. of Pharmaceutics 525-04-2020
- 6. • Other authors(Kumar et al., 2011) applied the ANN (artificial neural network) model to optimize the fatty alcohol concentration in the formulation of an o/w emulsion. • Emulsion was composed of purified water, liquid paraffin, sodium lauryl sulfate, and fatty lauryl alcohol. Predictions from ANNs are accurate and allow quantification of the relative importance of the inputs. • Furthermore, by varying the network topology and parameters, it was possible to obtain output values (zeta potential, viscosity, conductivity, and particle size) that were close to experimental values. • The ANN model predicted results and the actual output values were compared. An R2 value of 0.84 for the model suggested adequate modeling, which is supported by the correlation coefficient value of 0.9445. • Multiple (or double) emulsions are even more complex dispersion systems, also known as ‘emulsions of emulsions’. • The most common multiple emulsions are of the w/o/w type, although, for some specific applications, o/w/o emulsions can also be prepared. • Usually, the multiple emulsions have been produced in a two-step-production process: the first one for production of the primary emulsion, and the second for production of the multiple emulsions. In w/o/w emulsions, oil globules, containing small droplets of water, are dispersed in an aqueous continuous phase. COPS, DSU Dept. of Pharmaceutics 625-04-2020
- 7. • Advantages ofthese types of emulsion systems : • relatively high entrapment capacity for hydrophilic compounds • protection of the encapsulated substances towards degradation • the ability to introduce incompatible substances into the same system, and sustained release of active substance. • These characteristics make them potentially interesting for application in pharmaceutics and cosmetics. However, in practice, significant problems may arise because of their thermodynamic instability and strong tendency for coalescence, flocculation, and creaming. The stability of w/o/w emulsion may be affected by a number of factors, including the method of preparation, osmotic balance between the internal and external water phase, phase volumes ratio, type, and concentration of the emulsifiers. COPS, DSU Dept. of Pharmaceutics 725-04-2020
- 8. • In thework of Onuki et al. (2004), formulation optimization of the w/o/w multiple emulsion incorporating insulin was performed, based on statistical methods such as the and the response surface evaluation. • As model formulations, 16 types of emulsions were prepared according to the orthogonal experimental design. To optimize the formulation, the influences of five factors on characteristics of the emulsion were evaluated first. Inputs were amounts of gelatin, insulin, oleic acid, volume ratio of the outer water phase, and agitation time of the second emulsification process. • Outputs were inner droplet size, viscosity, stability, and pharmacological effect. Based on Analysis of Variance (ANOVA), it was concluded that the most predominant contribution of all causal factors was the volume ratio of the outer water phase. • As for the optimization study, the optimum formulation with respect to pharmacological hypoglycemic effect in rats and stability of emulsion was estimated using a simultaneous optimization technique, in which a multivariate spline interpolation (MSI) was incorporated. • A two-factor composite second-order spherical experimental design was used to select model formulation. The data measured for the model formulations were analyzed by a computer program (dataNESIA, Yamatake, Tokyo, Japan). The authors (Onuki et al., 2004) reported that the optimum formulation had pharmacological activity and stability as high as a pharmaceutical formulation. COPS, DSU Dept. of Pharmaceutics 825-04-2020
- 9. • The secondemulsification step could be critical for the production of multiple emulsions. For this reason, Lindenstruth and Müller (2004) examined the second emulsification step in the formulation of w/o/w multiple emulsions. • Unvaried primary w/o emulsion, with diclofenac sodium as the active ingredient in the inner water phase, was used during the investigation. In the second step, a central composite design was used, and the process parameters pressure and temperature were varied. • The multiple droplet size and the encapsulation rate of totally 10 emulsions were determined after 1, 3, and 5 homogenization cycles, to investigate the influence of process parameters. For statistical analysis, the Statistica® program was used. • It was shown that the pressure and temperature, as process parameters in the second step, influenced the size of multiple droplets in the w/o/w multiple emulsion. Further experiments with different w/o emulsions resulted in w/o/w multiple emulsions with different encapsulation rates of diclofenac sodium. COPS, DSU Dept. of Pharmaceutics 925-04-2020
- 10. • In thework of Wei et al. (2008), formulation optimization of emulsifiers for preparing w/o/w multiple emulsions was performed in respect of stability by using the ANN technique. The emulsifiers used were sorbitan monooleate (Span 80) and polysorbate 80 (Tween 80). • The stability of multiple emulsions was expressed by the percentage of reserved emulsion volume of freshly prepared sample after centrifugation. Individual properties of multiple emulsions, such as droplet size, phase angle δ, viscosity of the primary, and the multiple emulsions were also considered. COPS, DSU Dept. of Pharmaceutics 1025-04-2020
- 11. Application of computer-aidedtechniques in development of microemulsion drug carriers • Microemulsions are thermodynamically stable and optically isotropic transparent colloidal systems consisting of water, oil, and surfactant. Although they are clear, low viscous liquids, the different types of microstructures are identified (i.e. w/o, o/w, and bicontinuous), all organized on the level below 100 nm. • The microstructure of microemulsions is determined by physicochemical properties and concentrations of the constituents. • Such unique systems, as well as their water-free preconcentrates, so-called self-microemulsifying oil/surfactant mixtures, are of increasing interest as potential drug delivery vehicles with long term stability, considerable 25-04-2020 COPS, DSU Dept. of Pharmaceutics 11
- 12. • capacity fordrug solubilization, and great potential for bioavailability enhancement (Fanun, 2009). • Development of such carriers requires a complex strategy balancing all relevant aspects. In systems consisting of water, oil, and tenside(s), a diverse range of colloidal systems and coarse dispersions may form (e.g. emulsions, microemulsions, micelles, lyotropic liquid crystals), depending on physicochemical properties and quantitative ratios of constituents and temperature. • Microemulsions and self-microemulsifying drug delivery systems (SMEDDS) form only in well balanced mixtures of the selected excipients and within the specific concentration ranges of the constituents at given temperatures and pressures (i.e. the microemulsion area). • The analysis of the influence of formulation variables on the area of microemulsion systems is usually performed within the phase behavior studies. Pharmaceutically applicable microemulsions consist of five (surfactant, cosurfactant, oil, water, and drug) or more components. • Complete phase behavior differentiation in such multicomponent mixtures requires a large number of experiments (Alany et al., 2009; Friberg and Aikens, 2009). Furthermore, characterization of a microstructure is a difficult task, due to its dynamic character as well as nanoscale organization (Tondre, 2005). 25-04-2020 COPS, DSU Dept. of Pharmaceutics 12
- 13. • Microemulsions andself-microemulsifying drug delivery systems (SMEDDS) form only in well balanced mixtures of the selected excipients and within the specific concentration ranges of the constituents at given temperatures and pressures (i.e. the microemulsion area). • The analysis of the influence of formulation variables on the area of microemulsion systems is usually performed within the phase behavior studies. Pharmaceutically applicable microemulsions consist of five (surfactant, cosurfactant, oil, water, and drug) or more components. Complete phase behavior differentiation in such multicomponent mixtures requires a large number of experiments (Alany et al., 2009; Friberg and Aikens, 2009). • Furthermore, characterization of a microstructure is a difficult task, due to its dynamic character as well as nanoscale organization (Tondre, 2005). ANN models were introduced as useful tools for accurate differentiation and prediction of the microemulsion area from the qualitative and 25-04-2020 COPS, DSU Dept. of Pharmaceutics 13
- 14. quantitative composition ofdifferent microemulsion-forming systems • The pioneer studies of Richardson et al. (1996, 1997) demonstrated the use of ANNs to identify the physico- chemical properties of the cosurfactant with relevance for microemulsion formation in the four-component system lecithin, (surfactant)/isopropyl myristate (oil)/triple distilled water/cosurfactant. • The different types of cosurfactants (i.e. short- and medium-chain alcohols, amines, acids, and ethylene glycol monoalkyl ethers) were employed. The BP feed-forward algorithm of learning and four computed cosurfactant molecule properties (molecular volume (v), areas for its head group (aψ) and hydrophobe (aϕ), and computed octanol/water logP value), were selected. • The output was presence (+ 1) or absence (− 1) of microemulsion formation in a particular mixture 25-04-2020 COPS, DSU Dept. of Pharmaceutics 14
- 15. • In arelated study, Agatonovic-Kustrin and Alany (2001) estimated the influence of the cosurfactant on phase behavior of the five-component systems (ethyl oleate (oil)/a mixture of sorbitan monolaurate and polyoxyethylene 20 sorbitan monooleate (surfactant + cosurfactant)/deionized water/n- alcohols (1-propanol, 1- butanol, 1-hexanol, and 1-octanol) or 1,2-alkanediols (1,2-propandiol, 1,2- pentanediol, 1,2-hexanediol, and 1,2-octanediol)(cosurfactant)). • A supervised network with a multilayer perceptron (MLP) architecture with a BP learning rule (Neural Networks®, StatSoft Inc, Tulsa, USA), was used to correlate phase behavior of the investigated systems with cosurfactant descriptors (inputs), which were preselected by a genetic algorithm (GA) (Pallas 2.1, Compu Drug Int., San Francisco, USA and ChemSketch 3.5 freeware, ACD Inc., Toronto, Canada). 25-04-2020 COPS, DSU Dept. of Pharmaceutics 15
- 16. • The datarequired for ANN training and testing were extracted from the pseudo-ternary diagrams generated previously by Aboofazeli et al. (1994), together with the additional data from four pseudo ternary phase diagrams constructed at a fixed weight ratio of surfactant-to-cosurfactant 1:1. • The trained ANN (the in-house software YANNI) with the final architecture involving 6 input neurons, a single hidden layer of 14 neurons, and 1 output neuron, was shown to be highly successful in predicting phase behavior for the investigated systems from the computed values of v, aψ, aϕ, and logP, achieving mean success rates of 96.7 and 91.6% for training and test data, respectively. • These investigations pointed to the potential of the trained ANN to screen out cosurfactants considering only the molecule features and gave an idea for more general networks, trained with data on systems involving other oils, surfactants, and surfactant-to-cosurfactant ratios 25-04-2020 COPS, DSU Dept. of Pharmaceutics 16
- 17. • The fundamentalgoal in SMEDDS development is to optimize the surfactant/cosurfactant/oil mixture, in order to achieve sufficient drug solubility and infinite dilutability with water phase. However, there is a risk of disturbing the thermodynamic stability on dilution with the subsequent drug precipitation (Kyatanwar et al., 2010). • The study of Mendyik and Jachowicz (2006) describes the development of the system of 11 ANN models suitable for further prediction of phase behavior of microemulsion-forming systems analyzing only the properties and contents of the components. The ANN models were developed by digitalization of phase diagrams of 118 systems, published in the relevant literature. • The inputs were the concentrations of the constituents (surfactant, cosurfactant, oil, water) and their molecule features (ionic strength of the water phase, HLB value of the surfactant, density of the oil, etc.). 25-04-2020 COPS, DSU Dept. of Pharmaceutics 17
- 18. • The outputsconfirmed whether the microemulsion is present or not for a particular quantitative and qualitative composition. The ANN- based optimization technique was extended with Neuro-Fuzzy Modeling (NFM). • There have been varied numbers of hidden layers of the MLP network (from 1 to 5) and up to 100 nodes in neuro-fuzzy systems, by using neural networks simulator Nets2004 written by the authors. • After the architecture search step, 10 of the best ANNs were selected to become the expert committee. In addition, a so-called second-order ANN was employed to combine outputs of the above-mentioned ANNs and to produce the final decision of the system. • The developed expert system was applied in selection of the surfactant/cosurfactant/water mixtures (microemulsion preconcentrates) with the high capacity for water solubilization. The performance of the system was estimated on 77% of properly classified data records. 25-04-2020 COPS, DSU Dept. of Pharmaceutics 18
- 19. • The studyof Podlogar et al. (2008) demonstrated that ANN modeling could be effective in minimizing the experimental efforts characterizing complex structural features of microemulsions. • Two evolutionary ANNs (Yao, 1991) have been constructed by introducing GA to the feed-forward ANN, one being able to predict the type of microemulsion from its composition and the second to predict the type of microemulsion from the differential scanning calorimetry (DSC) curve. • The components of the microemulsion-forming system were isopropyl myristate (oil), polyoxyethylene (20) sorbitan monopalmitate (Tween® 40) (surfactant), glyceryl caprylate (Imwitor® 308) (cosurfactant), and twice distilled water. • The type of microemulsion microstructure (i.e. o/w, bi-continuous, w/o) was differentiated by measuring the freezing peak of the water in DSC thermograms. 25-04-2020 COPS, DSU Dept. of Pharmaceutics 19
- 20. • The datapool used to train both ANNs included the composition of 170 microemulsion samples and DSC curves. • To determine the type of microemulsion from its composition, a feed forward network was programmed, with the final architecture involving 4 input neurons, a single hidden layer of 12 neurons, and 5 output neurons. • To determine the type of microemulsion from its DSC curve, a second feed-forward ANN with 1 hidden layer was constructed, containing 100 input neurons, a single layer of 5 hidden neurons, and 5 output neurons. Both ANNs showed an accuracy of 90% in predicting the type of microemulsion from the previously untested compositions. 25-04-2020 COPS, DSU Dept. of Pharmaceutics 20
- 21. REFERENCE • Computer aidedapplications in pharmaceutical technology, edited by Jelena Djuris, published by woodhead series. COPS, DSU Dept. of Pharmaceutics 2125-04-2020
- 22. COPS, DSU Dept. ofPharmaceutics 2225-04-2020