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Design of experiments for microencapsulation applications: A review
Accepted Manuscript Design of experiments for microencapsulation applications: A review
Filipa Paulo, Lúcia Santos PII: DOI: Reference:
S0928-4931(16)32143-9 doi: 10.1016/j.msec.2017.03.219 MSC 7731
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Materials Science & Engineering C
Received date: Revised date: Accepted date:
10 November 2016 7 January 2017 23 March 2017
Please cite this article as: Filipa Paulo, Lúcia Santos , Design of experiments for microencapsulation applications: A review. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Msc(2017), doi: 10.1016/j.msec.2017.03.219
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ACCEPTED MANUSCRIPT Design of experiments for microencapsulation applications: a review Filipa Paulo, Lúcia Santos* LEPABE – Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal
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*Corresponding author: Tel.: +351 22 5081682, Fax: +351 22 508 1440, e-mail address:
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[email protected]
Abstract: Microencapsulation techniques have been intensively explored by many research
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sectors such as pharmaceutical and food industries. Microencapsulation allows to protect the active ingredient from the external environment, mask undesired flavours, a possible
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controlled release of compounds among others. The purpose of this review is to provide a background of design of experiments in microencapsulation research context. Optimization
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processes are required for an accurate research in these fields and therefore, the right
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implementation of micro-sized techniques at industrial scale. This article critically reviews the use of the response surface methodologies in pharmaceutical and food microencapsulation
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also presented.
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research areas. A survey of optimization procedures in the literature, in the last few years is
Keywords: Microencapsulation; surface response methodology; design of experiments; screening design; pharmaceutical industry; food industry;
ACCEPTED MANUSCRIPT Contents 1.
Introduction .......................................................................................................................... 2
2.
Microencapsulation............................................................................................................... 3 2.1
Microencapsulation in pharmaceutical industry .......................................................... 4
2.2
Microencapsulation in food industry ............................................................................ 7
2.3 Challenges in microencapsulation in pharmaceutical and food industries ........................ 9 Design of experiments for microencapsulation applications.............................................. 11
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3.
3.1 Response surface methodology implementation ............................................................. 11
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3.1.1 Screening experiments: determination of factors and their levels ........................... 12 3.1.2 Choice of the experimental design............................................................................. 13
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3.1.3 Assessment of the predicted model .......................................................................... 15 3.1.4 Determination of the optimal conditions .................................................................. 15
4.
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3.1.5 Graphical representation of the model equation ...................................................... 16 Applications of response surface methodologies in microencapsulation techniques........ 19 4.1 Applications in pharmaceutical industry ........................................................................... 20
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4.2 Applications in food industry ............................................................................................ 27 Perspectives and future challenges .................................................................................... 35
6.
Conclusions ......................................................................................................................... 35
7.
Acknowledgements ............................................................................................................. 36
8.
Declaration of interest ........................................................................................................ 36
9.
References ........................................................................................................................... 37
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Introduction
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1.
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5.
The increased demand for added-value products has been substantially affecting trends in the global market. Therefore, new and innovative technological techniques as microencapsulation (Jyothi et al., 2010; Mali et al., 2012) and nanoencapsulation (Sulu et al., 2016) have been developed. Microencapsulation and nanoencapsulation are generally defined as a set of technologies that allows to entrap active ingredients also known as core materials using a surrounding material namely as encapsulating or shell material (Silva and Meireles, 2014). A clear distinction
ACCEPTED MANUSCRIPT between nanoencapsulation and microencapsulation is not consensual among authors, especially in terms of size: some authors consider that nanoparticles size varies between 1 and 1000 nm, however other researchers claim that nanoparticles size should range between 1 and 100 nm (Joye and McClements, 2014; Whelehan and Marison, 2011). Nevertheless, both technologies aim to create a physical barrier to protect the active ingredient from the external
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environment, allowing a possible controlled release of it. Usually, micro- and nanotechnologies are technically similar: some operational conditions are adapted in order to
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obtain microparticles or nanoparticles (Lassalle and Ferreira, 2007).
Nanotechnology is considered a promising technology to effectively entrap compounds
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(Bayram et al., 2008; Kilicay et al., 2016; Sulu et al., 2016) for a wide range of industrial sectors such as electronics, engineering, energy storage and biotechnology (Bo et al., 2014; Díez-
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Pascual and Díez-Vicente, 2014; Dikin et al., 2007; Liu andLosic, 2013), nevertheless microencapsulation is the focus of the present review because countless brand-new and
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reinvented microencapsulated products have been available on the retail market.
2. Microencapsulation
The microencapsulation technology was first presented by Green and Schleicher in 1950s with
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a patent registration for the preparation of capsules containing dyes, which were developed to be incorporated into paper for copying purposes (Ghosh, 2006; Green, 1956). Nowadays,
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microencapsulation as it was above described, allows to protect sensitive micro-sized substances from the external environment allowing a controlled release of these micro-sized substances (Benita, 2006; Ghosh, 2006; Kaur et al., 2013). The active ingredient also termed as core material can be temporary or permanently protected within a shell of a second material, designated as encapsulating or wall material (Carvalho et al., 2015; Casanova et al., 2016). The resulting products of microencapsulation techniques are designated microparticles (Figure 1). Microparticles can be distinguished in microspheres or microcapsules (Herrero-vanrell et
ACCEPTED MANUSCRIPT al., 2014) by their internal structure and morphology (Jyothi et al., 2010) even though, the terms are often used synonymously. Microspheres and microcapsules are differentiated in
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reservoir systems and matrix systems, respectively (Yasukawa et al., 2004).
(B)
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(A)
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Figure 1 - Differences between microcapsules (A) and microspheres (B) inner morphologies (adapted from Herrero-vanrell et al., 2014)
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This technological approach has been explored by pharmaceutical (68%), food (13%), cosmetic (8%), textile (5%), biomedical (3%), agricultural (2%) and electronic (1%) industries (Casanova
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and Santos, 2015; Ghosh, 2006; Kim et al., 2007; Umer et al., 2011).
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Microencapsulation aims to increase the effectiveness of selected substances in industry (Estevinho et al., 2013). Several authors have been discussing the main advantages of applying
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microencapsulation techniques in different industry sectors (Champagne and Fustier, 2007; Desai and Park, 2005; Patel and Patel, 2010; Gouin, 2004). Nevertheless, pharmaceutical and
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food industries are the main driving forces in microencapsulation advances.
2.1 Microencapsulation in pharmaceutical industry Currently, research on microencapsulation for pharmaceutical purposes is focused on finding new drug delivery systems (DDS) to obtain products to reach the market, reducing the adverse reactions and side effects, being suitable for the required administration mode, allowing sitespecific delivery, increasing shelf-life, improving patient compliance and allowing a possible controlled and sustained release of compounds (Agnihotri et al., 2012). Hence,
ACCEPTED MANUSCRIPT microencapsulation arises as a potential technological strategy to achieve the abovementioned goals.
Microparticles may be constituted by combinations of the active
pharmaceutical ingredients (APIs) and biomaterials. Regarding the microencapsulated APIs, these therapeutic agents can have a short half-life, can be quickly hydrolysed or degraded enzymatically in vivo, which is associated with a more strictly therapeutic regimen (multiple
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administrations). Therefore, microencapsulation techniques protect the API from degradation, allowing these compounds being appropriately released to obtain the required treatment
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concentration of API over time (Ma, 2014). Depending on the biomaterial properties,
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particularly if they are erodible or non-erodible, they can disappear from where they were
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administrated or remain there throughout the patient lifetime, respectively. Some examples of microencapsulated APIs are presented in Table 1.
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Table 1 – Examples of microencapsulated ingredients for pharmaceutical industry Class of active Active pharmaceutical pharmaceutical Main goal of the study References ingredient ingredient Description of process-related issues of Huang and Gentamicin proteins microencapsulation. Chung, 2001 Characterization of erythromycin-loaded Park and Kim, Erythromycin microspheres by double emulsion solvent Antibiotic 2004 evaporation technique. Development of a controlled release system Mundargi et Doxycycline for doxycycline microencapsulation for al., 2007 human periodontal pocket treatment. Lysozyme (group Analysis of stability parameters of the Pérez et al., Enzymes of glycoside microencapsulated compound (lysozyme). 2002 hydrolases) Evaluation of Ƴ-irradiation on pharmaceutical properties of PLGA based Igartua et al., SPf66 microspheres loaded with SPf66, a malaria 2008 Vaccine preventing vaccine. Evaluation of process-related issues of Bilati et al., Tatanus toxoid proteins microencapsulation. 2005 Preparation and characterization of Ruan and Pactitaxel microparticles loaded with pactitaxel Feng, 2003 intended for a controlled release. Anti-cancer agent Preparation and characterization of polyDisodium Wang et al., caprolactone based microparticles loaded norcantharidate 2008 with this anti-cancer agent. Anti-CD40 Preparation and characterization of antiGao et al.,
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Insulin
Gaignaux et al., 2012
Deoxyribonucleic acid (DNA)
Chen et al., 2002 Ahmed and Bodmeier, 2009
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Nucleotide
Liu et al., 2005
Investigation of a possible sustained release of a hydrophilic molecule for intra-articular administrations. Study the mechanism of ovalbumin-loaded microparticles formation by a double emulsion solvent evaporation technique. Preparation of DNA loaded and porous microspheres by leaching of fore formation for antisense therapy.
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Ovalbumin
Bilati et al., 2005
Han et al., 2001
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Clonidine
2011
Encapsulation and evaluation of rhEGF for chronic gastric ulcer healing.
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Protein
Rhi (recombinant human insulin) Recombinant human epidermal growth factor (rhEGF)
CD40 anti-body modified magnetic into polycaprolactone-polyethylene glycol- polycaprolactone microspheres. Evaluation of process-related issues of proteins microencapsulation. Evaluation the influence of process parameters on Rhi-loaded microparticles size distribution.
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antibody
An efficient DDS is the one that allows the API to reach the target site, in the required time and
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for the desired time. Four major factors are considered to achieve an efficient DDS:
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administration route, pattern of API release, method of delivery and production process also known as formulation process (Sinha and Trehan, 2003). Many of the non-microencapsulated
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API are administered repeatedly which makes the therapeutic regimen more frequent and always under medical supervision and as so, microencapsulation arises as a potential drug
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delivery strategy to overcome multiple issues associated to multiple administrations. Formulated microparticles must be biocompatible, stable, safe and demonstrate predictable degradation kinetics. However, other factors such as chemical modifications on the particle surface can optimize the system and thus be possible to use microencapsulation for drug delivery systems (Herrero-vanrell et al., 2014). Nevertheless, there are few microencapsulated pharmaceutical products available on the market (Stevenson, 2009). This can be explained have regard to the size control and size
ACCEPTED MANUSCRIPT distribution is difficult, resulting in low reproducibility of the production process, especially on a large scale. Thus, DDSs are difficult to be approved. Additionally, it is considered in the case of microencapsulation of APIs is difficult to maintain the bioactivity of the therapeutic agent during all processing steps (preparation, storage and release). The APIs can even lose their therapeutic capacity and even increase the unwanted side effects due to deactivation of the
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therapeutic agent (Varshochian et al., 2013). Despite the difficulties that have been encountered in the implementation of microencapsulation for DDSs, traditional therapies have
2.2 Microencapsulation in food industry
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been progressively replaced by more advanced technologies such as microencapsulation.
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The food industry is the second main driving force for microencapsulation progress. Increasingly demanding consumers and product requirements are the major motivations for
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microencapsulation research intended to food industry. In fact, demanding consumers have been required the addition of functional ingredients in the final product. Usually, these
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ingredients are environmental and/or processing instable and as so, microencapsulation arises
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as technological approach to overcome the above-mentioned problems and therefore obtain an effective protection of these instable ingredients. Additionally, these compounds may be
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prone to degradation in gastrointestinal conditions and consequently, an effective protection of these ingredients may be required. Functional ingredients can be used to regulate colour,
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flavour or texture of the final product. Additionally, they can be used as preservatives, being possible to extend their shelf life. As well as functional ingredients, bioactive ingredients have been encapsulated to preserve their stability during food processing and storage and additionally, to avoid undesired interactions between other ingredients present in the food matrix which could lead to a faster product degradation and loss of some proprieties. Thus, the unstable bioactive microencapsulated ingredients are protected and kept totally functional. Moreover, microencapsulation allows to potentiate specific flavors and aromas, to mask undesirable odours and tastes or even to increase ingredient bioactivities. Furthermore,
ACCEPTED MANUSCRIPT microencapsulation can enhance physico-chemical properties of food ingredients in order to allow an easier handling, provide a desired and adequate concentration of the active ingredient, to promote a uniform dispersion of the active ingredient in the food matrix and to avoid undesired reactions (Desai and Park, 2005; Lesmes and McClements, 2009). The microencapsulated active ingredients may be bioactive molecules (e.g. flavouring agents,
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sweeteners, colorants and vitamins) or living cells as probiotics (Fang and Bhandari, 2010).
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Some examples of microencapsulated ingredients for food industry are presented in Table 2.
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Clearly, bioactive molecules are the most commonly microencapsulated compounds. Many of the microencapsulated bioactive molecules present antioxidant capacity. These molecules are
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capable to oxidizing themselves instead or before others and consequently, protecting them. They may be used in pharmaceutical and food industries as active ingredients or supplements
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(preservatives) (Oroian and Escriche, 2015; Pokorny, 2007).
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Table 2 – Examples of microencapsulated ingredients for food industry Class of food Application field Examples References ingredient Lemon oil Bhandari et al., 1999 Flavouring agent Peppermint oil Koo et al., 2014 Vanilla oil Yang et al., 2014 Rocha-Selmi et al., Aspartame 2013 Sweetener Rocha-Selmi et al., Sucralose 2013 Xylitol Santos et al., 2015 Annatto De Marco et al., 2013 Bioactive molecule Donhowe and Kong, β-carotene Colorant 2014 Turmeric Donhowe and Kong, oleoresin 2014 Junyaprasert et al., Vitamin A 2001 Fat-soluble vitamins Vitamin D2 Shi and Tan, 2002 Sharipova et al., Vitamin E 2016 Water soluble vitamins Vitamin C Desai and Park, 2005 Bifidobacterium Hansen et al., 2002 spp. Living cells Probiotics Bifidobacterium Amine et al., 2014 longum
ACCEPTED MANUSCRIPT Lactobacillus acidophilus
Fávaro-Trindade and Grosso, 2002
It should be pointed out that microencapsulation of bioactive molecules, functional ingredients or living cells for food industry must consider several factors such as technological concerns (manufacturing and storage properties), economic feasibility and consumers satisfaction
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(Aguiar et al., 2016)
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2.3 Challenges in microencapsulation in pharmaceutical and food industries
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Much of inherent challenges of microencapsulation of ingredients are the same for both pharmaceutical and food industries and as so, this topic is going to be addressed for both
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industries.
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Several authors have been discussing the main advantages of applying microencapsulation techniques in pharmaceutical and food industry sectors. Numerous difficulties and
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disadvantages to industrial application of microencapsulation techniques have been pointed
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out for both application fields such as the poor entrapment of the active compound, the impossibility to scale-up some processes (Guerreiro et al., 2012; Khalil et al., 2013; Seremeta et
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al., 2013), the requirement of multi-step processes (Bitar et al., 2015; Zakeri-Milani et al., 2013) the downstream high concentration of undesired sub-products or residues (Destreé et
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al., 2008; Sajeesh and Sharma, 2006; Schubert and Muller-Goymann, 2003; Tolue et al., 2009; Wu et al., 2011; Yegin et al., 2006; Zhang et al., 2006), the long-time requirement of some processes (time-consuming processes) (Hao et al., 2013; Souguir et al., 2013), the requirement of high energy inputs and the demand of complex equipment (Hoyer et al., 2010; Kamiya et al., 2009; Shi et al., 2011; Silva et al., 2011). Many parameters can affect microencapsulated products and their final characteristics. These parameters may be divided into three classes: properties of materials, formulation parameters and operating conditions (Table 3). The main properties affected are microparticles mean size,
ACCEPTED MANUSCRIPT particle size distribution, microparticles surface morphology, product yield and encapsulation efficiency. The final properties of microparticles may affect the active compound release rate (Li et al., 2008) Table 3 – Parameters affecting microparticles final properties Factor
Studies
References
Evaluation the effect of wall materials on fish oil microencapsulation by spray-drying.
Pourashouri et al., 2014
Assessment the effect of wall material type and oil load on flaxseed oil microencapsulation by spray-drying.
Omar et al., 2009
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Type of factor
Stirring speed
Evaluation of the influence of stirring speed on drug release from microcapsules.
Wall material
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Core material
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Viscosity
Type of solvent
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Formulation parameters
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Properties of materials
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Use of additives
Investigation of the Effect of different wall materials on Red-Fleshed Pitaya (Hylocereus polyrhizus) seed oil microencapsulation by spray-drying. Evaluation the use of gum arabic, maltodextrin and a modified starch as wall materials on cardamom oleoresin microencapsulation by spray-drying. Assessment the effect of wall material on extra-virgin olive oil microencapsulation by spray-drying. Analysis of the solubility of core materials in aqueous polymeric solutions on curcumin microencapsulation by coacervation. Interpretation of the influence of viscosity and other physicochemical properties of acai (Euterpe oleraceae Mart.) microencapsulation by spray-drying. Analysis of the influence of using ethyl acetate as a dispersed solvent on microspheres properties. Investigation of the effect of salt addition on proteins microencapsulation.
Operation conditions Temperature
Consideration of the influence of spray-dryer air temperatures on mandarin oil microencapsulation. Assessment of the influence of air inlet temperature on the microencapsulation of flaxseed oil by spray drying.
Lim et al., 2012
Krishnan et al., 2005 Calvo et al., 2010 Aziz et al., 2007 Tonon et al., 2008
Sah, 1997 Pistel and Kissel, 2000 Jegat and Taverdet, 2000 Bringaslantigua et al., 2012 Tonon et al., 2011
ACCEPTED MANUSCRIPT 3. Design of experiments for microencapsulation applications Traditionally, a common approach to analyse which are the main influencing factors in microencapsulation area is studying the influence of one variable while the others remain constant. This methodology known as one-variable-at-time (OVAT) or interchangeably onefactor-at-time (OFAT) leads to non-optimized final products or processes (Frey et al., 2003).
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This may be explained by factor interactions that OVAT methodology does not count: different variables can interact and be responsible for a specific system behaviour (Kharia and Singhai,
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2013). Another disadvantage of OVAT methodology is the relative huge number of
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experiments required to be performed which makes OVAT methodology a material, reagent
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and time consuming approach (Bower, 2013).
Recently, multivariate statistical approaches as design of experiments (DOE) have been used to
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overcome the main disadvantages of OVAT procedures (Christolear, 2013). Among the most relevant multivariate statistical methods used in microencapsulation field, the surface
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response methodology (RSM) is the most used one. The application of RSM for
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microencapsulation optimization processes have been described essentially in pharmaceutical and food research sectors.
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3.1 Response surface methodology implementation The response surface methodology
is defined as a set of mathematical and statistical
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approaches to express relationships between factors and responses (Bas, 2007) . Factors also known as independent variables are independent parameters that can be independently changed. On the other hand, responses or dependent variables are the measured values from the experiments performed. The experiments belong to an experimental domain which is the experimental area under study. The maximum and minimum levels of experimental variables define the limits of the domain. Additionally, levels of a variable must be defined. Levels of variables are the values at which the experiment should be performed (Bezerra et al., 2008). Unlike OVAT methodology, RSM outlines the effect of factors alone or in combination
ACCEPTED MANUSCRIPT generating a mathematical model. The mathematical relationship between factors and responses is given by Equation (1) (Bas, 2007). (1) where
stands for the response,
stands for the unknown function of
and
the number of accounts for the
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independent variables,
, represent the factors being
statistical errors not considered in . Generally, the sources of
are measurement errors
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considering that follows a normal distribution with mean and variance equal to zero.
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The RSM optimization processes performed in four stages as depicted in Figure 2.
Figure 2 – The fundamental steps in a RSM optimization process
3.1.1 Screening experiments: determination of factors and their levels As was previously described many parameters can affect microparticles production and their properties. In many systems is arduous to determine the effects of all parameters; therefore, screening experiments may be a useful tool to identify factors and their interactions with relevant effects on the process. Generally, full or fractional designs are used in screening
ACCEPTED MANUSCRIPT experiments, essentially due to their efficiency: in the early stages of the optimization processes, a limited number of experiments may be representative (Hanrahan et al., 2007). After the selection of the most influencing parameters follows the determination of factors levels. Incidentally, this is a key point in the optimization process: levels incorrectly chosen can lead to a non-optimized response. The selected factors during the screening process usually
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have different units or ranges. Many examples can be given for each type of factor as presented in Table 2. If the factors under study are the wall material:core material ratio (%
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w/w), viscosity (mPa.s) and operation temperature (°C), then a normalization process has to be
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performed because the three parameters have different units and therefore they are not
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directly comparable. Consequently, a normalization process shall be performed before a regression analysis. Factors are coded (normalized) according to the following equation
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(Equation 2):
stands for the normalized factor, , the natural variable and
and
, the
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where,
(2)
maximum and minimum values for , respectively. Each factor varies between -1 and +1. The
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units of the selected factors are not more significant and therefore they are normalized. The most common approaches in screening experiments are the two-level full-factorial - designs where the 2 stands for the number of factor levels and
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designs, also known as
for the number of factors each with a high and low value. In fractional designs, also known as , the number of experiments is reduced by
(Bas, 2007; Bezerra et al., 2008; Hanrahan et
al., 2007). The choice of the screening design may affect the final response, actually, the screening design should be chosen regarding the goals and limitations of the optimization process.
3.1.2 Choice of the experimental design The simplest mathematical model that can be used in RSM is a linear function (Equation 3).
ACCEPTED MANUSCRIPT (3) where, factors,
is a constant term,
are the coefficients of the linear parameters,
represents the residues and
stands for
stands for the number of variables. For an accurate
application, the responses should be fitted in the linear model. However, a linear RSM model does not account for any curvature of the response. For factor interactions analysis, a second
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order model must be used (Equation 4).
Where
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(4)
stands for the coefficient of interactions between factors.
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If a maximum or a minimum has to be determined, a quadratic term ought to be added into
where,
(5)
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second order model, giving rise to the following model (Equation 5):
stands for the coefficient of the quadratic factor. According to Equation 5, the
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estimation of factors and consequently, the optimization process has to be carry out in at least three factor levels for all factors.
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The model presented in Equation 5 can be presented in a matrix notation (Equation 6)
(6)
The least squares method (MLS) allows to solve Equation 6 matrix system. The description of this method is not within the scope of this paper.
ACCEPTED MANUSCRIPT Nowadays, a huge variety of computer packages are available to solve RSM problems, so much so that computer packages may differ on specific inputs like the number of runs, blocks or even how experimental points are selected (Bas, 2007). The most known second-order models used in RSM are the central composite design (CCD), the face-centered composite design (FCCD) and the central rotatable composite design (CRCD).
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These systems are differentiated according to number of experiments required to run.
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3.1.3 Assessment of the predicted model
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A mandatory condition to use RSM is the predicted model has to describe the experimental domain. The reliability of the fitted model is usually tested through the analysis of variance
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(ANOVA). The ANOVA tests compare the variation due to changes in combinations of factors levels with the variation caused by measurement errors (random errors) of responses. Hereby,
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this evaluation is essential to determine if the model fits and describes the experimental domain (Cai et al., 2015; Gelman et al., 2005). Variation sources can be compared using a
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Fisher distribution (F-test) or alternatively, a Student’s distribution (t-test). Variations
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associated to the model and not to random errors, arise if the F-probability is less than 0.05 (correspondent to a 95% confidence level). A t-test, explores which factors and/or interactions
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have statistical meaning. If t-probability is smaller than 0.05, the factor or the interaction
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between factors are considered to be significant (Homem et al., 2016).
3.1.4 Determination of the optimal conditions Linear models (represented generically on Equation 3) allow to specify in which direction the original design should be studied in order to obtain optimal conditions. Nevertheless, if the experimental region cannot be changed, the optimization study should find out the optimal operational conditions by visual inspection (Bezerra et al., 2008). For quadratic models, the optimal conditions correspond to a maximum, minimum or a saddle point (critical points). The coordinates of a critical point are calculated deriving the quadratic
ACCEPTED MANUSCRIPT form of Equation 5 (Equation 7) in order to
and
and then equating them to zero. The
coordinates of the critical point are obtained solving Equations 8 and 9 in order to find the values of
and
. (7)
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(8)
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3.1.5 Graphical representation of the model equation
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(9)
The model equation predicted using a RSM can be visualized on response surface plots and
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their related contour plots. The response surface plots are three-dimensional graphs that
dimensional surfaces in the
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represent the relationship between factors and the response. Surface response plots are -dimensional space. If the system is represented by three
or more variables, the graphical representation is only possible if one or more variables are
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considered constant.
The two-dimensional depiction of the response surface plot is the related contour plot. The
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shape of lines in contour plots may predict the response type. A maximum or minimum response corresponds to ellipses or circles around the plot center. Further interpretations can
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be performed comparing both plots regarding the predicted model within the experimental domain. Concerning Figure 3, the surface plot (A) and the contour plot (B) are represented in a general two variables optimization of a maximum within the experimental region. Regarding Figure 4, the surface (A) and the contour (B) plots of a maximum point included in the experimental domain with a graphical plateau in relation to
are depicted. The variation of
levels does not affect the response. On the other hand, in Figure 5 is presented a maximum response outside the experimental domain. The initial design should be displaced in order to obtain the maximum response inside the experimental area. A minimum response within the
ACCEPTED MANUSCRIPT experimental domain is displayed on Figure 6, whereas a saddle point is depicted in Figure 7. Despite the result of Equation 8 and 9 are found in a saddle point, this is not an optimal response. The optimum response can be found through visual inspection.
Graphical
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representations presented in this article were made using the JMP 13 statistical software.
(B)
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Figure 3 – Surface response plot (A) and the contour plot (B) of a maximum response in which
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levels of all parameters affect the response
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Figure 4 – Surface response plot (A) and the contour plot (B) of a maximum response in which
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some parameters do not affect the response
(B)
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Figure 5 – Surface response plot (A) and the contour plot (B) of a maximum response outside
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the experimental area
(A)
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Figure 6 – Surface response plot (A) and the contour plot (B) of a minimum response in which levels of all parameters affect the response
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Figure 7 – Surface response plot (A) and the contour (B) of a saddle point
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4. Applications of response surface methodologies in microencapsulation techniques
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Innovate experimental designs and optimization methodologies have been applied in microencapsulation techniques to overcome the main barriers for the microencapsulation industrial application and moreover to improve and/or reformulated micro-enclosed products.
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Through representative examples, the application of RSM optimization studies in
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pharmaceutical and food industries are reviewed and listed in Tables 4 and 5. Likewise, studies about the application of design of experiments are comprehensively and critically reviewed and discussed.
Henceforth, for both research fields reviewed (Table 4 and Table 5), the specific application (the active ingredient, the coating material and the use of additives) is firstly presented. Afterwards, parameters related to the experimental optimization design (EOD) are reported namely the type of experimental screening design (ESD), the type of EOD and the number of runs performed. A widely variety of factorial designs can be performed. Notwithstanding, full
ACCEPTED MANUSCRIPT screening experiments do not have to be performed: a fractional factorial design may describe the system under study. Once a fractional design is applied, a reference for the fractional design is reported in both tables (application column) considering a general representation of . Therefore, the number of runs can be less than
if a fractional design is considered or
more if replicates are considered in modeling. Posteriorly, the factors ( ) and the response(s)
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are presented. Finally, the optimal settings are described: the desired values for both factors
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4.1 Applications in pharmaceutical industry
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and responses are presented.
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Applications of RSMs in microencapsulation of drugs are not widely described in the literature. Furthermore, the spectrum of drug classes evaluated according RSM is narrow: to the best of
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authors knowledge, only anti-viral drugs, analgesics and anti-inflammatories have been microencapsulated using optimal formulation and process conditions found through RSMs
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studies.
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The influence of using fatty and non-fatty additives to PLGA-loaded microspheres with acyclovir, an anti-viral drug intended for intravitreal administrations, using poly(D,L-lactide-co-
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glycolide), a synthetic polymer as coating material, was studied by Martínez-Sancho et al. (2003). Regarding this study, different formulations were assessed: additive-free formulations,
AC
polyethylene glycol-PLGA formulations, gelatine-PLGA formulations, Vitamin E-PLGA formulations, hydroxypropyl-methyl cellulose-PLGA formulations and isopropyl myristatePLGA formulations. Indeed, results showed that the higher encapsulation efficiency was obtained when gelatine was added (gelatine-PLGA based microparticles). Further considerations were considered by these authors, lately: an optimization process for microencapsulation of acyclovir in gelatine-PLGA, was presented. Regarding this optimization design, the microencapsulation technique applied was the
solvent evaporation, using
gelatine as a stabilizer in the external aqueous phase ( ). As per, in this study was applied a 52
ACCEPTED MANUSCRIPT - experimental screening design. The factors under study were the aciclovir content (mg) and the gelatine content (mg). The responses studied were the product yield (%), the encapsulation efficiency (%), the initial burst release, the cumulative amount of aciclovir released from 1 to 14 days (μg/mg of microspheres) and the cumulative amount of aciclovir released at the end of the assay (μg/mg of microspheres) in 30 experiments. For the
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experimental optimization design a central rotatable composite 22 + star design (CRCD) was applied leading to an optimized formulation. This optimized formulation was made up of 80
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mg of acyclovir and gelatine, each, conducting to a maximum product yield and encapsulation
SC
efficiency around 70%. A constant release rate was verified during 63 days. The cumulative
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amount of aciclovir released from 1 to 14 days and the cumulative amount of aciclovir released at the end of the assay was 26.96 μg.mg-1 and 118.83 μg.mg-1 of microspheres, respectively.
optimization design of experiments.
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Thereby, a main conclusion can be drawn as preliminary studies may be useful to develop an
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A full factorial design optimization of an anti-inflammatory drug release from
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polycaprolactone-polyethylene glycol-polycaprolactone (PCL–PEG–PCL) microspheres was studied by Azouz et al. (2016). In accordance with this study, an
solvent evaporation
CE
technique was applied in a 16 set of experiments, testing an 24-experimental screening design. The factors analysed were the shaking speed (rpm), the required time of contact (hr), this is,
AC
the time required for dichloromethane evaporation, the polyvinyl alcohol (PVA) concentration (%) and the drug (ibruprofen) concentration (%). The solvent chosen for the organic phase formation was dichloromethane and PVA was used as a stabilizer in the external aqueous phase with the main goal to study the encapsulation efficiency (%) as EOD response. To the purpose of optimization, the simplest model, a linear model, was employed. The optimal conditions were found considering a shaking speed of 200 rpm, time of contact of 0.5 hr, a PVA concentration of 0.1% and drug concentration of 35% leading to an encapsulation efficiency of 88.86%. Even though, the microencapsulation of ibuprofen has been widely reported (Bidone
ACCEPTED MANUSCRIPT et al., 2009; Hussein et al., 2007; Newa et al., 2007; Thompson et al., 2007), the improvement of observed results using this methodology is difficult to evaluate due to lack of comparative investigations. Nonetheless, another main conclusion can be drawn: even though preliminary studies may be useful, an optimization design can be performed without preceding investigations.
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Lastly, 3 experimental factorial designs were develop by Billon et al., conducted 3 produce acetaminophen microparticles by spray-drying. For the experimental screening design, a
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fractional 25-1 was used, evaluated in a 16 set of experiments each. The EOD applied was the
SC
CRCD and the factors evaluated were feed rate (mL.min-1), the inlet temperature (°C), the drug
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concentration in the feed (g.L-1), the polymer concentration (g.L-1), the additive concentration (g.L-1) and the SiO2 concentration (g.L-1) used as an additive to all formulations. As responses,
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was evaluated the product yield (%) and the residual moisture content of the spray-dried product (%). Regarding the first set of experiments, tartic acid (TA) was added to the polymer
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solution and both drug and the polymer were dissolved in the distilled water. Alternatively, in
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the second round of experiments, oxalic acid (OA) was added to the polymer solution. Differentially, polyvinylpyrrolidone (PVP) was added to the polymer solution and both polymer and drug were suspended, in the third set of trials. Comparing the use of carboxylic acids as
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additives (TA or OA), the RSM optimization presented the same values for the operating
AC
settings (feed rate, inlet temperature, drug concentration in the feed, polymer concentration, additive concentration and SiO2 concentration). Even though the studied output responses were similar, the product yield was higher when TA was used instead of OA (82.5±1.95 against 80.8±0.40). Nevertheless, the residual moisture was negatively higher when TA was used instead of OA (1.03±0.094 versus 1.02±0.08). It can be concluded that although the residual content was slightly higher for formulations with TA, the product yield was improved. Additionally, three central points were considered to evaluate the validity of the linear model for the three formulations.
ACCEPTED MANUSCRIPT Comparing the optimal experience with carboxylic acids and the third set of experiments (PVP used as additive to the polymer and a suspension was feed to the spray-dryer), even though the required input parameters were slightly higher, the final product yield (86.8±1.91) was improved and the residual moisture content remained practically constant (1.02±0.08). Therefore, it can be concluded that a fractional factorial design with centre points (imbedded
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in the central composite design) allows to estimate the curvature (this is, second order interactions between factors) effectively. In this type of model, the use of three centre points
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seems to be useful for TA or OA additives content estimation: probably without analysing the
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curvature of system behaviours, equal results would be obtained for both carboxylic acids due
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to their similar chemical behaviour and as so, this model allowed to analyse differences between two carboxylic acid additives. As it was used a rotatable design for optimization, the
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predicted values of product yield and residual moisture are function of the distance of points from the centre of design and not function of the direction that points span from the centre.
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Clearly, the CRCD was useful because more accurate values for product yield and residual
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moisture specially for carboxylic acids were obtained because results are not only function of the distance from the centre of space design but additionally, a function of the direction. For a coherence of results and for practical reasons, the use of the CRCD was maintained for PVP
CE
experiments analysis (the third round of experiments).
AC
Comparing all the presented formulations in the pharmaceutical experimental domain, the CRCD was the most used due to the accuracy of results. Nevertheless, a linear model was applied for optimization of ibuprofen microencapsulation and optimal formulation conditions were found, nevertheless, further DOEs ought to be performed to evaluate the accuracy of the model tested.
ACCEPTED MANUSCRIPT
Table 4 – Examples of DOE applications within microencapsulation field for pharmaceutical industry Application Active ingredient Coating material Additives
Aciclovir Poly(D,L-lactide-coglycolide) Gelatine
EOD
ESD EOD (NoE)
52 CRCD (30)
Factor(s)
Ibuprofen PCL–PEG–PCL n.d.
Acetaminophen TA-CMC Colloidal silicon dioxide
I R
Response(s)
C S U
-Aciclovir content (mg) -Gelatine content (mg)
N A
T P E
2 LM (16)
-Shaking speed (rpm) -Time of contact (hr) -PVA concentration (%) -IBF concentration (%)
25-1 CRCD (16)
-Feed rate (mL.min-1) -Inlet temperature (°C) -Drug concentration in the feed (g.L-1) -Polymer concentration (g.L-1)
A
C C
References
Optimal formulation
-Aciclovir content: 80 mg -PY (%) -Gelatine content: 80 mg -EE (%) -PY: 70.14% -Initial burst release -EE: 70.77% -Cumulative amount of aciclovir -Initial burst release: constant release rate during released from 1 to 14 days 63 days (µg.mg of microspheres-1) -Cumulative amount of aciclovir released from 1 -Cumulative amount of aciclovir to 14 days: 26.96 µg.mg-1 of microspheres released at the end of the assay -Cumulative amount of aciclovir released at the (µg.mg of microspheres-1) end of the assay: 118.83 µg.mg-1 of microspheres -Shaking speed: 200 rpm -Time of contact: 0.5 hr -EE (%) -PVA concentration: 0.1% -IBF concentration: 35% -EE: 88.86% -Feed rate: 20 mL.min-1 -Inlet temperature: 140 °C -PY (%) -Drug concentration in the feed: 6 g.L-1 -Residual moisture content of -Polymer concentration: 6 g.L-1 spray-dried product (%) -Additive concentration: 1.8 g.L-1 -SiO2 concentration: 8 g.L-1
D E
4
T P
M
MartínezSancho et al., 2004
Azouz et al., 2016
Billon et al., 2000
ACCEPTED MANUSCRIPT
-Additive concentration (g.L-1) -SiO2 concentration (g.L-1)
T P
I R
Acetaminophen TA-CMC Colloidal silicon dioxide
C S U
N A
Acetaminophen OA-CMC Colloidal silicon dioxide
Acetaminophen MCC Colloidal silicon dioxide
-PY: 82.5% -Residual moisture content of spray-dried product: 1.03% -Feed rate: 20 mL.min-1 -Inlet temperature: 140 °C -Drug concentration in the feed: 6 g.L-1 -Polymer concentration: 6 g.L-1 -Additive concentration: 1.8 g.L-1 -SiO2 concentration: 8 g.L-1 -PY: 82.5% -Residual moisture content of spray-dried product: 1.03% -Feed rate: 20 mL.min-1 -Inlet temperature: 140 °C -Drug concentration in the feed: 6 g.L-1 -Polymer concentration: 6 g.L-1 -Additive concentration: 1.8 g.L-1 -SiO2 concentration: 8 g.L-1 -PY: 80.8% -Residual moisture content of spray-dried product: 1.02% -Feed rate: 30 mL.min-1 -Inlet temperature: 160 °C -Drug concentration in the feed: 10 g.L-1 -Polymer concentration: 10 g.L-1 -Additive concentration: 0.3 g.L-1 -SiO2 concentration: 18 g.L-1 -PY: 86.8% -Residual moisture content of spray-dried
D E
T P E
A
C C
M
ACCEPTED MANUSCRIPT
product: 1.02% n.d. – not defined; CMC - Carboxymethyl cellulose; CRCD – Central Rotatable Composite Design; EE – Encapsulation efficiency; EOD – Experimental Optimization Design; ESD – Experimental Screening Design; IBF – ibuprofen; LM – Linear model; MCC - Microcrystalline Cellulose; NoE – Number of Experiments; OA - Oxalic acid; PCL- Polycaprolactone; PEG - Polyethylene glycol; PY – product yield; TA -Tartic acid;
T P
I R
C S U
N A
D E
T P E
A
C C
M
ACCEPTED MANUSCRIPT
4.2 Applications in food industry Multiple DOEs have been applied to optimize the microencapsulation of active ingredients for food industry. Even though the pharmaceutical industry has been further exploring
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microencapsulation, to the authors best knowledge, publications about DOEs for microencapsulation of active ingredients intended for food industry are more available.
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Mostly active ingredients are essential oils, as presented in Table 6 (fish oil, lemon myrtle oil,
SC
holy basil essential oil, flaxseed oil and rosemary essential oil). Notwithstanding, DOEs have been reported for the microencapsulation of other compounds in the food industry as
NU
extracts, derivatives and probiotics.
MA
A RSM optimization for fish oil microencapsulation using a spray-dryer was reported by Aghbashlo et al. (2012) including a face-centred full central composite design (FCCD)
D
evaluating three factors (aqueous phase content in the emulsion, oil proportion in total solids
PT E
of the emulsion and the emulsification time) and two responses (exergy efficiency and encapsulating efficiency). Each factor ranged from three set levels and three repetitions of the centre point were performed to evaluate the curvature of the model. Only 17 experiences
CE
were performed, nevertheless, careful conclusions can be drawn considering responses are
AC
rightly represented by a fractional higher polynomial model (second order model) with repetitions of the centre point. An optimal formulation was found with 87.12% of aqueous phase content in the emulsion, 10.82% of oil concentration in the total solids of the emulsion, considering an emulsification time of 13.23 minutes, resulting in 6.17% and 88.67% in exergy efficiency and encapsulating efficiency, respectively. The same type of second-order polynomial optimization design (FCCD) was studied by Huynh et al.,about lemon myrtle oil microencapsulation by spray-drying. The factors studied were input conditions for the spray-dryer: the feed concentration (% w/w), the oil concentration (%
ACCEPTED MANUSCRIPT w/w of feed concentration) and the outlet drying air temperature (°C). The responses analysed were oil retention (%) and the surface oil content (mg/100 g of powder). It was run 40 experiences. The optimal operating setting was found: 40% w/w, 18% w/w and 65 °C, respectively for the feed concentration, the oil concentration and the outlet drying air temperature giving an oil retention of 83.82% and a surface oil content of 23.04 mg/100 g of
PT
powder. The same DOE methodology was applied to the optimization of holy basil essential oil
RI
microencapsulation by Sutaphanit and Chitprasert. These authors, in a 13-round experiment
SC
studied the influence of gelatine concentration (% w/v), the amount of holy basil essential oil
NU
(mL) on the product yield (%), encapsulating efficiency (%) and final oil content. The optimal values for the factors were 11.75% w/v and 31 mL for gelatine concentration and holy basil
MA
essential oil amount, respectively. The responses obtained were 98.80%,95.41% and 66.50% for product yield, encapsulation efficiency and final oil content, correspondingly.
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Similarly, a FCCD was used in a 12-round experiment to study the effect of solids content and
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oil load on the specific responses of microencapsulation process of rosemary essential oil as moisture content, wettability (s), hygroscopicity (%), total oil content (%) and oil retention (%). The results show that the wall materials concentration, the oil load, the wettability, the
CE
hygroscopicity, the total oil and the oil retention of 20.9%, 29.4% w/w, 1315 s, 11%, 11% and
AC
39%, respectively.
Omar et al., presented a research article on RSM methodology to obtain optimal conditions for flaxseed oil microencapsulation. It was applied a FCCD to study the following factors: the concentration of lecithin (% w/w), the concentration of oil loading (% w/w) and the concentration of xanthan gum (% w/w) on selected responses (encapsulation efficiency (%) and the flaxseed oil droplet (nm)). In a 17-round experiment, the optimal conditions were found corresponding to a lecithin concentration of 1.14% w/w, an oil loading concentration of
ACCEPTED MANUSCRIPT 22.78% w/w and 0.10% w/w concentration of the polymer (xanthan gum) leading to an encapsulation efficiency of 92.3% and a flaxseed oil droplet size of 446.9 nm. In all above-mentioned studies about essential oils microencapsulation, the FCCD was applied. As it is shown in Table 5, all the studies have imbedded a central composite design (CCD) and as so, some of the experiments were used to estimate the curvature of the design.
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Additionally, a FCCD, requires 3 levels for each factor which allows to analyse the second-order interactions. The other two types of CCD (the circumscribed design and the inscribed design)
RI
require five levels for each factor. It may seem that using FCCD diminishes the accuracy of the
SC
design. In fact, the curvature analysis account only three levels but the number of experiments
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is widely reduced. For example, if two factors FCCD design was applied to a system under study, the numbers of runs required would be 9, but if two factors circumscribed CCD or
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inscribed CCD were used, the number of experiments run would be 25. Even though a loss of curvature accuracy is evident using a FCCD, nevertheless, the amount of active ingredient and
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other reagents, time and utilities are retrieved.
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The same RSM methodology is also describe for optimization processes of other bioactive compounds such as peanut sprout extract and starch oleate derivatives from native corn. An optimization process for peanut sprout extract microencapsulation by RSM using a FCCD as
CE
a model design was presented by Lee et al., . Specifically, was used 2 coating materials:
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triacylglycerol was used as first coating material and maltodextrin as the secondary coating material. Additionally, were added polyglycerol as a primary emulsifier and polyoxyethylene sorbitan monolaurate as secondary emulsifier. In a set of 31 experiments, the effects of the ratio between the core material and the first coating material, the ratio between the core material and the secondary coating material, the polyglycerol concentration (% w/v) and the polyoxyethylene sorbitan monolaurate concentration (% w/v) were assessed on the product yield (%). A FCCD showed optimal conditions: core material:triacylglycerol ratio of 1:2, core material: maltodextrin ratio of 1:1.23, polyglycerol concentration of 1.25% w/v,
ACCEPTED MANUSCRIPT polyoxyethylene sorbitan monolaurate concentration of 1.21% w/v, resulting in a product yield of 98.7%. An optimization process of starch oleate derivatives form native corn and hydrolysed corn starch microencapsulation using a RSM was presented by Kshirsagar and Singhal. The oleic acid content (g), the required time for the process (min) and the temperature (°C) were the
PT
factors under study. A unique response was analysed: the degree of substitution. The degree of substitution is an effective approach to assess the modification level of a polymer. It reveals
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the number of substitutions in repetition structures of a polymer. A CRCD was applied in a 20-
SC
round experiment design. Optimal results were found for all formulations. A maximum 0.021
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degree of substitution was found using native starch and an oleic acid content, time of process and temperature of 1.2 g, 201 min. and 160 °C, respectively. On the other hand, a maximum
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degree of substitution was found using starch oleate derivatives from hydrolysed corn starch. The sample analysed with the longer glucose chain demonstrates a lower substitution value
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and higher process conditions comparing to the shorter glucose chain. The maximum degree
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of substitutions was 0.0353 and 0.0503 for the longer glucose chain and the shorter glucose chain, associated to process variables of 1.53 g of oleic acid content, 334 min of processing at 150 °C for the longer glucose chain and 1.36 g of oleic acid content, 300 min of processing at
CE
129 °C. In this study, a rotatable design was applied and as so, the number of runs to estimate
AC
the curvature of the response were five. As the response is very specific, at a sequence level of polymer chain, a deeper study of the type of interactions (polynomial design) was required. A simple CCD would not give an accurate relationship between the response and factors unlike a rotatable design: as the number of runs to estimate the curvature of the system was increased, the accuracy of interactions was improved. Lastly, optimization of microencapsulation of living cells was proposed by Anekella and Orsat. Actually, microencapsulation of living cells is still a challenger in microencapsulation research field. Nevertheless, these authors chosen a combination of widely available probiotics
ACCEPTED MANUSCRIPT (Lactobacillus acidophilus and Lactobacillus rhamnosus) in raspberry juice. The carbon source chosen for these organisms growth was maltodextrin. A CCD was employed to study selected processing factors (inlet temperature, ratio between total solids and maltodextrin and inlet feed ratio) associated to the microencapsulation technique used (the spray-drying) in three selected responses: recovery, probiotics survival percentage and colour. Optimal responses of
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48.7% of recovery, 87.17% of survival percentage and 57.21 ΔE were found, using 100 °C as inlet temperature, 1:1 of total solids: maltodextrin ratio, 40 mL.min-1 of the feed rate as
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processing settings. Even though a simple second-order polynomial was applied, the
SC
assessment of factors interactions is fundamental when it is studied living cells due to the
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combining challenging characteristics of living cells and microencapsulation techniques: living cells are mostly thermosensitive and they may undergo oxidative and osmotic stresses but
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generally, microencapsulation techniques present some demanding steps that may expose the formed microparticles to the above-mentioned physico-chemical challenges (Rokka and
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CE
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Rantamäki, 2010).
ACCEPTED MANUSCRIPT
Table 5 – Examples of DOE applications within microencapsulation field for food industry Application Active ingredient Coating material Additives Fish oil n.d. n.d.
Lemon myrtle oil n.d. n.d. Holy basil essential oil n.d. Gelatin
ESD EOD (NoE)
3
3 FCCD (17) n.d. FCCD (40)
n.d. FCCD (13)
EOD Factor(s) -Aqueous phase content (%) -Oil concentration in total solids of emulsion (%) -Emulsification time (min)
-ExE (%) -EE (%)
-Feed concentration (% w/w) -Oil concentration (% w/w of feed concentration) -Outlet drying air temperature (°C)
-Oil retention (%) -Surface oil content (mg/100 g of podwer)
D E
-PY (%) -EE (%) -Final oil content (%)
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-Gelatine concentration (% w/v) -Holy basil essential oil amount (mL)
E C
5 FCCD (12)
-Wall materials concentration (%) -Oil load % (w/w)
-Moisture content -Wettability (s) -Hygroscopicity (%) -Total oil (%) -Oil retention (%)
Flaxseed oil n.d. Lecithin
n.d. FCCD (17)
-Concentration of lecithin (% w/w) -Concentration of oil loading (%
-EE (%) -Flaxseed Oil Droplet (nm)
C A
References
Optimal formulation
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-Aqueous phase content: 87.12% -Oil concentration in total solids of emulsion: 10.82% -Emulsification time: 13.23 min -ExE: 6.17% -EE: 88.67% -Feed concentration: 40% w/w -Oil concentration: 18 % w/w of feed concentration -Outlet drying air temperature: 65 °C Oil retention: 83.82% -Surface oil content: 23.04 mg/100 g of podwer -Gelatine concentration: 11.75% w/v -Holy basil essential oil amount: 31 mL -PY: 98.80% -EE: 95.41% -Final oil content: 66.50% -Wall materials concentration: 20.9% -Oil load: 29.4% w/w -Wettability: 1315 s -Hygroscopicity: 11% -Total oil: 11% -Oil retention:39% -Concentration of lecithin: 1.14% w/w -Concentration of oil loading: 22.78% w/w -Concentration of xanthan gum: 0.10% w/w
C S U
N A
M
Rosemary essential oil n.d. n.d.
2
T P
Response(s)
Aghbashlo et al., 2012
Huynh et al., 2008
Sutaphanit and Chitprasert, 2014
Fernandes et al., 2014
Omar et al., 2009
ACCEPTED MANUSCRIPT
(emulsifier) Xanthan gum (emulsifier) Peanut sprout extract Triacylglycerol (first coating material) Maltodextrin (secondary coating material) Polyglycerol polyricinoleates (primary emulsifier) Polyoxyethylene sorbitan monolaurate (secondary emulsifier) Starch oleate derivatives from native corn n.d. n.d. Starch oleate derivatives from hydrolyzed corn starch with longer glucose chain
w/w) -Concentration of xanthan gum (% w/w)
24 CCD (31)
-EE: 92.3% -Flaxseed Oil Droplet: 446.9 nm
T P
I R
-Core material: triacylglycerol ratio -Core material: maltodextrin ratio -Polyglycerol polyricinoleates concentration (% w/v) -Polyoxyethylene sorbitan monolaurate concentration (% w/v)
C S U
-Core material: triacylglycerol ratio: 1:2 -Core material: maltodextrin ratio: 1:1.23 -Polyglycerol polyricinoleates concentration: 1.25% w/v -Polyoxyethylene sorbitan monolaurate concentration: 1.21% w/v -PY: 98.7%
-PY (%)
D E
N A
M
T P E
53 CCD (20)
C C
-Oleic acid content (g) -Time of the process (s) -Degree of substitution -Temperature of the process (°C)
A
Lee et al., 2013
-Oleic acid content 1.2 g -Time of the process: 201 min. -Temperature of the process: 160 °C -Degree of substitution: 0.021 -Oleic acid content 1.53 g -Time of the process: 334 min. -Temperature of the process: 150 °C -Degree of substitution: 0.0353
Kshirsagar and Singhal, 2007
ACCEPTED MANUSCRIPT
n.d. n.d. Starch oleate derivatives from hydrolyzed corn starch with shorter glucose chain n.d. n.d. Probiotics of raspberry juice n.d. Maltodextrin (carbon source and parameter to assess the prebiotic potential)
T P
-Oleic acid content 1.36 g -Time of the process: 300 min. -Temperature of the process: 129 °C -Degree of substitution: 0.0503
I R
C S U
n.d. CCD (20)
-Inlet temperature (°C) -Total solids: maltodextrin ratio (product of starch hydrolysis) -Feed rate (mL.min-1)
N A
-Recovery (%) -Survival percentage (%) -Color (ΔE)
T P E
D E
M
-Inlet temperature: 100 °C -Total solids: maltodextrin ratio: 1:1 -Feed rate: 40 mL.min-1 -Recovery: 48.7% -Survival percentage: 87.17% -Color: 57.21
Anekella and Orsat, 2013
n.d. – not defined; CCD – Central Composite Design; EE – Encapsulation efficiency; EOD – Experimental Optimization Design; ESD – Experimental Screening Design; ExE - Exergy efficiency; FCCD – Face Centered Composite Design; NoE – Number of Experiments; PY – product yield;
A
C C
ACCEPTED MANUSCRIPT 5.
Perspectives and future challenges
Microencapsulation techniques have been under study for a long time but optimization processes are relatively recent. Microencapsulation for pharmaceutical and food industries still faces technological challenges, especially for the microencapsulation of sensitive bioactive
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compounds as probiotics. Design of experiments will be a useful tool to face and overcome the actual barriers for industrial application of microencapsulation. Specially, response surface
RI
methodologies are time, reagents and utilities saving research methods but mainly, they are
SC
results oriented approaches.
Moreover, to the best of authors knowledge no research articles about optimization processes
NU
are available on literature about microencapsulation in cosmetics field even though some
MA
bioactive compound for food industry may also be microencapsulated for cosmetic purposes. Nevertheless, optimization methods are required for a successful implementation of this
Conclusions
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6.
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technological approach within this research field.
The response surface methodology is an innovate technological approach for optimization
CE
processes. This technique when applied to microencapsulation research has several
AC
advantages. A huge amount of information can be obtained with a reduced number of experiments. Consequently, the reduced number of experiments is associated to efficiency: less number of reagents are required and less time are required for a specific study. As computer
tools
for
RSM
implementation
are
straightforward,
research
within
microencapsulation field can be largely developed. Unlike traditional methods, RSM allows to analyse the relationship between factors. As matter of fact, it is a key point in microencapsulation: many microencapsulation parameters (materials, formulation or operation) can act in addition, antagonistically or synergistically.
ACCEPTED MANUSCRIPT According to the literature review performed by the authors, the number of experiments required for microencapsulation processes are relatively small (maximum of 40, including replicates). The most studied factors were the ratio between the wall and the core material, emulsifier concentrations and operation settings as inlet temperature in a spray-dryer. The most considered responses were the encapsulation efficiency and the product yield. Clearly,
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the studies presented in this article, demonstrate the importance of design of experiments for an accurate research in two microencapsulation domains (pharmaceutical and food
SC
RI
industries).
The model equation is a useful tool for the researcher to identify the main parameters
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affecting the microencapsulation process under study. However, for RSM application, factors
7.
Acknowledgements
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and data have to fit in a second-order polynomial model.
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This work was financially supported by the projects POCI-01-0145-FEDER-006939 (Laboratory
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for Process Engineering, Environment, Biotechnology and Energy – UID/EQU/00511/2013) funded by the European Regional Development Fund (ERDF), through COMPETE2020 -
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Programa Operacional Competitividade e Internacionalização (POCI) and by national funds, through FCT - Funda o ara a C n a e a e nolo a and
E 01 0145 FEDE 000005 –
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LEPABE-2-ECO-INNOVATION, supported by North Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).
8.
Declaration of interest
The authors declare no conflict of interest.
ACCEPTED MANUSCRIPT 9.
References
Aghbashlo, M., Mobli, H., Ra, S., & Madadlou, A. (2012). Optimization of emulsification procedure for mutual maximizing the encapsulation and exergy efficiencies of fish oil microencapsulation. Powder Technology, 225, 107–117.
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Agnihotri, N., Mishra, R., Goda, C., & Arora, M. (2012). Microencapsulation – A Novel Approach
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n Dru Del very : A ev ew. Journal of Pharmaceutical Sciences, 2(1), 1–20.
Aguiar, J., Estevinho, B. N., & Santos, L. (2016). Microencapsulation of natural antioxidants for l at on - The specific case of coffee antioxidants - A review. Trends in Food Science
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food a
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and Technology, 58, 21–39
Ahmed, A. R., & Bodmeier, R. (2009). Preparation of preformed porous PLGA microparticles
Biopharmaceutics, 71(2), 264–270.
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and antisense oligonucleotides loading. European Journal of Pharmaceutics and
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Amine, K. M., Champagne, C. P., Salmieri, S., Britten, M., St-Gelais, D., Fustier, P., & Lacroix, M.
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(2014). Effect of palmitoylated alginate microencapsulation on viability of Bifidobacterium longum during freeze-drying. LWT - Food Science and Technology, 56(1), 111–117.
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Graphical abstract
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Highlights 1. Nowadays, microencapsulation is a high-tech approach for the global market. 2. The most important industrial sectors for microencapsulation applications are pharmaceutical, food and cosmetics industries. 3. Design of experiments is a powerful tool in microencapsulation research field. 4. Response surface methodology theoretical concepts allow to optimize microencapsulation procedures. 5. Applications of design of experiments in microencapsulation research areas are widely used
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in pharmaceutical and food research.
Filipa Paulo, Lúcia Santos PII: DOI: Reference:
S0928-4931(16)32143-9 doi: 10.1016/j.msec.2017.03.219 MSC 7731
To appear in:
Materials Science & Engineering C
Received date: Revised date: Accepted date:
10 November 2016 7 January 2017 23 March 2017
Please cite this article as: Filipa Paulo, Lúcia Santos , Design of experiments for microencapsulation applications: A review. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Msc(2017), doi: 10.1016/j.msec.2017.03.219
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ACCEPTED MANUSCRIPT Design of experiments for microencapsulation applications: a review Filipa Paulo, Lúcia Santos* LEPABE – Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal
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*Corresponding author: Tel.: +351 22 5081682, Fax: +351 22 508 1440, e-mail address:
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[email protected]
Abstract: Microencapsulation techniques have been intensively explored by many research
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sectors such as pharmaceutical and food industries. Microencapsulation allows to protect the active ingredient from the external environment, mask undesired flavours, a possible
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controlled release of compounds among others. The purpose of this review is to provide a background of design of experiments in microencapsulation research context. Optimization
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processes are required for an accurate research in these fields and therefore, the right
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implementation of micro-sized techniques at industrial scale. This article critically reviews the use of the response surface methodologies in pharmaceutical and food microencapsulation
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also presented.
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research areas. A survey of optimization procedures in the literature, in the last few years is
Keywords: Microencapsulation; surface response methodology; design of experiments; screening design; pharmaceutical industry; food industry;
ACCEPTED MANUSCRIPT Contents 1.
Introduction .......................................................................................................................... 2
2.
Microencapsulation............................................................................................................... 3 2.1
Microencapsulation in pharmaceutical industry .......................................................... 4
2.2
Microencapsulation in food industry ............................................................................ 7
2.3 Challenges in microencapsulation in pharmaceutical and food industries ........................ 9 Design of experiments for microencapsulation applications.............................................. 11
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3.
3.1 Response surface methodology implementation ............................................................. 11
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3.1.1 Screening experiments: determination of factors and their levels ........................... 12 3.1.2 Choice of the experimental design............................................................................. 13
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3.1.3 Assessment of the predicted model .......................................................................... 15 3.1.4 Determination of the optimal conditions .................................................................. 15
4.
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3.1.5 Graphical representation of the model equation ...................................................... 16 Applications of response surface methodologies in microencapsulation techniques........ 19 4.1 Applications in pharmaceutical industry ........................................................................... 20
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4.2 Applications in food industry ............................................................................................ 27 Perspectives and future challenges .................................................................................... 35
6.
Conclusions ......................................................................................................................... 35
7.
Acknowledgements ............................................................................................................. 36
8.
Declaration of interest ........................................................................................................ 36
9.
References ........................................................................................................................... 37
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Introduction
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1.
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5.
The increased demand for added-value products has been substantially affecting trends in the global market. Therefore, new and innovative technological techniques as microencapsulation (Jyothi et al., 2010; Mali et al., 2012) and nanoencapsulation (Sulu et al., 2016) have been developed. Microencapsulation and nanoencapsulation are generally defined as a set of technologies that allows to entrap active ingredients also known as core materials using a surrounding material namely as encapsulating or shell material (Silva and Meireles, 2014). A clear distinction
ACCEPTED MANUSCRIPT between nanoencapsulation and microencapsulation is not consensual among authors, especially in terms of size: some authors consider that nanoparticles size varies between 1 and 1000 nm, however other researchers claim that nanoparticles size should range between 1 and 100 nm (Joye and McClements, 2014; Whelehan and Marison, 2011). Nevertheless, both technologies aim to create a physical barrier to protect the active ingredient from the external
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environment, allowing a possible controlled release of it. Usually, micro- and nanotechnologies are technically similar: some operational conditions are adapted in order to
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obtain microparticles or nanoparticles (Lassalle and Ferreira, 2007).
Nanotechnology is considered a promising technology to effectively entrap compounds
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(Bayram et al., 2008; Kilicay et al., 2016; Sulu et al., 2016) for a wide range of industrial sectors such as electronics, engineering, energy storage and biotechnology (Bo et al., 2014; Díez-
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Pascual and Díez-Vicente, 2014; Dikin et al., 2007; Liu andLosic, 2013), nevertheless microencapsulation is the focus of the present review because countless brand-new and
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reinvented microencapsulated products have been available on the retail market.
2. Microencapsulation
The microencapsulation technology was first presented by Green and Schleicher in 1950s with
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a patent registration for the preparation of capsules containing dyes, which were developed to be incorporated into paper for copying purposes (Ghosh, 2006; Green, 1956). Nowadays,
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microencapsulation as it was above described, allows to protect sensitive micro-sized substances from the external environment allowing a controlled release of these micro-sized substances (Benita, 2006; Ghosh, 2006; Kaur et al., 2013). The active ingredient also termed as core material can be temporary or permanently protected within a shell of a second material, designated as encapsulating or wall material (Carvalho et al., 2015; Casanova et al., 2016). The resulting products of microencapsulation techniques are designated microparticles (Figure 1). Microparticles can be distinguished in microspheres or microcapsules (Herrero-vanrell et
ACCEPTED MANUSCRIPT al., 2014) by their internal structure and morphology (Jyothi et al., 2010) even though, the terms are often used synonymously. Microspheres and microcapsules are differentiated in
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reservoir systems and matrix systems, respectively (Yasukawa et al., 2004).
(B)
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(A)
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Figure 1 - Differences between microcapsules (A) and microspheres (B) inner morphologies (adapted from Herrero-vanrell et al., 2014)
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This technological approach has been explored by pharmaceutical (68%), food (13%), cosmetic (8%), textile (5%), biomedical (3%), agricultural (2%) and electronic (1%) industries (Casanova
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and Santos, 2015; Ghosh, 2006; Kim et al., 2007; Umer et al., 2011).
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Microencapsulation aims to increase the effectiveness of selected substances in industry (Estevinho et al., 2013). Several authors have been discussing the main advantages of applying
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microencapsulation techniques in different industry sectors (Champagne and Fustier, 2007; Desai and Park, 2005; Patel and Patel, 2010; Gouin, 2004). Nevertheless, pharmaceutical and
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food industries are the main driving forces in microencapsulation advances.
2.1 Microencapsulation in pharmaceutical industry Currently, research on microencapsulation for pharmaceutical purposes is focused on finding new drug delivery systems (DDS) to obtain products to reach the market, reducing the adverse reactions and side effects, being suitable for the required administration mode, allowing sitespecific delivery, increasing shelf-life, improving patient compliance and allowing a possible controlled and sustained release of compounds (Agnihotri et al., 2012). Hence,
ACCEPTED MANUSCRIPT microencapsulation arises as a potential technological strategy to achieve the abovementioned goals.
Microparticles may be constituted by combinations of the active
pharmaceutical ingredients (APIs) and biomaterials. Regarding the microencapsulated APIs, these therapeutic agents can have a short half-life, can be quickly hydrolysed or degraded enzymatically in vivo, which is associated with a more strictly therapeutic regimen (multiple
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administrations). Therefore, microencapsulation techniques protect the API from degradation, allowing these compounds being appropriately released to obtain the required treatment
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concentration of API over time (Ma, 2014). Depending on the biomaterial properties,
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particularly if they are erodible or non-erodible, they can disappear from where they were
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administrated or remain there throughout the patient lifetime, respectively. Some examples of microencapsulated APIs are presented in Table 1.
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Table 1 – Examples of microencapsulated ingredients for pharmaceutical industry Class of active Active pharmaceutical pharmaceutical Main goal of the study References ingredient ingredient Description of process-related issues of Huang and Gentamicin proteins microencapsulation. Chung, 2001 Characterization of erythromycin-loaded Park and Kim, Erythromycin microspheres by double emulsion solvent Antibiotic 2004 evaporation technique. Development of a controlled release system Mundargi et Doxycycline for doxycycline microencapsulation for al., 2007 human periodontal pocket treatment. Lysozyme (group Analysis of stability parameters of the Pérez et al., Enzymes of glycoside microencapsulated compound (lysozyme). 2002 hydrolases) Evaluation of Ƴ-irradiation on pharmaceutical properties of PLGA based Igartua et al., SPf66 microspheres loaded with SPf66, a malaria 2008 Vaccine preventing vaccine. Evaluation of process-related issues of Bilati et al., Tatanus toxoid proteins microencapsulation. 2005 Preparation and characterization of Ruan and Pactitaxel microparticles loaded with pactitaxel Feng, 2003 intended for a controlled release. Anti-cancer agent Preparation and characterization of polyDisodium Wang et al., caprolactone based microparticles loaded norcantharidate 2008 with this anti-cancer agent. Anti-CD40 Preparation and characterization of antiGao et al.,
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Insulin
Gaignaux et al., 2012
Deoxyribonucleic acid (DNA)
Chen et al., 2002 Ahmed and Bodmeier, 2009
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Nucleotide
Liu et al., 2005
Investigation of a possible sustained release of a hydrophilic molecule for intra-articular administrations. Study the mechanism of ovalbumin-loaded microparticles formation by a double emulsion solvent evaporation technique. Preparation of DNA loaded and porous microspheres by leaching of fore formation for antisense therapy.
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Ovalbumin
Bilati et al., 2005
Han et al., 2001
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Clonidine
2011
Encapsulation and evaluation of rhEGF for chronic gastric ulcer healing.
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Protein
Rhi (recombinant human insulin) Recombinant human epidermal growth factor (rhEGF)
CD40 anti-body modified magnetic into polycaprolactone-polyethylene glycol- polycaprolactone microspheres. Evaluation of process-related issues of proteins microencapsulation. Evaluation the influence of process parameters on Rhi-loaded microparticles size distribution.
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antibody
An efficient DDS is the one that allows the API to reach the target site, in the required time and
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for the desired time. Four major factors are considered to achieve an efficient DDS:
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administration route, pattern of API release, method of delivery and production process also known as formulation process (Sinha and Trehan, 2003). Many of the non-microencapsulated
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API are administered repeatedly which makes the therapeutic regimen more frequent and always under medical supervision and as so, microencapsulation arises as a potential drug
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delivery strategy to overcome multiple issues associated to multiple administrations. Formulated microparticles must be biocompatible, stable, safe and demonstrate predictable degradation kinetics. However, other factors such as chemical modifications on the particle surface can optimize the system and thus be possible to use microencapsulation for drug delivery systems (Herrero-vanrell et al., 2014). Nevertheless, there are few microencapsulated pharmaceutical products available on the market (Stevenson, 2009). This can be explained have regard to the size control and size
ACCEPTED MANUSCRIPT distribution is difficult, resulting in low reproducibility of the production process, especially on a large scale. Thus, DDSs are difficult to be approved. Additionally, it is considered in the case of microencapsulation of APIs is difficult to maintain the bioactivity of the therapeutic agent during all processing steps (preparation, storage and release). The APIs can even lose their therapeutic capacity and even increase the unwanted side effects due to deactivation of the
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therapeutic agent (Varshochian et al., 2013). Despite the difficulties that have been encountered in the implementation of microencapsulation for DDSs, traditional therapies have
2.2 Microencapsulation in food industry
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been progressively replaced by more advanced technologies such as microencapsulation.
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The food industry is the second main driving force for microencapsulation progress. Increasingly demanding consumers and product requirements are the major motivations for
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microencapsulation research intended to food industry. In fact, demanding consumers have been required the addition of functional ingredients in the final product. Usually, these
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ingredients are environmental and/or processing instable and as so, microencapsulation arises
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as technological approach to overcome the above-mentioned problems and therefore obtain an effective protection of these instable ingredients. Additionally, these compounds may be
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prone to degradation in gastrointestinal conditions and consequently, an effective protection of these ingredients may be required. Functional ingredients can be used to regulate colour,
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flavour or texture of the final product. Additionally, they can be used as preservatives, being possible to extend their shelf life. As well as functional ingredients, bioactive ingredients have been encapsulated to preserve their stability during food processing and storage and additionally, to avoid undesired interactions between other ingredients present in the food matrix which could lead to a faster product degradation and loss of some proprieties. Thus, the unstable bioactive microencapsulated ingredients are protected and kept totally functional. Moreover, microencapsulation allows to potentiate specific flavors and aromas, to mask undesirable odours and tastes or even to increase ingredient bioactivities. Furthermore,
ACCEPTED MANUSCRIPT microencapsulation can enhance physico-chemical properties of food ingredients in order to allow an easier handling, provide a desired and adequate concentration of the active ingredient, to promote a uniform dispersion of the active ingredient in the food matrix and to avoid undesired reactions (Desai and Park, 2005; Lesmes and McClements, 2009). The microencapsulated active ingredients may be bioactive molecules (e.g. flavouring agents,
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sweeteners, colorants and vitamins) or living cells as probiotics (Fang and Bhandari, 2010).
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Some examples of microencapsulated ingredients for food industry are presented in Table 2.
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Clearly, bioactive molecules are the most commonly microencapsulated compounds. Many of the microencapsulated bioactive molecules present antioxidant capacity. These molecules are
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capable to oxidizing themselves instead or before others and consequently, protecting them. They may be used in pharmaceutical and food industries as active ingredients or supplements
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(preservatives) (Oroian and Escriche, 2015; Pokorny, 2007).
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Table 2 – Examples of microencapsulated ingredients for food industry Class of food Application field Examples References ingredient Lemon oil Bhandari et al., 1999 Flavouring agent Peppermint oil Koo et al., 2014 Vanilla oil Yang et al., 2014 Rocha-Selmi et al., Aspartame 2013 Sweetener Rocha-Selmi et al., Sucralose 2013 Xylitol Santos et al., 2015 Annatto De Marco et al., 2013 Bioactive molecule Donhowe and Kong, β-carotene Colorant 2014 Turmeric Donhowe and Kong, oleoresin 2014 Junyaprasert et al., Vitamin A 2001 Fat-soluble vitamins Vitamin D2 Shi and Tan, 2002 Sharipova et al., Vitamin E 2016 Water soluble vitamins Vitamin C Desai and Park, 2005 Bifidobacterium Hansen et al., 2002 spp. Living cells Probiotics Bifidobacterium Amine et al., 2014 longum
ACCEPTED MANUSCRIPT Lactobacillus acidophilus
Fávaro-Trindade and Grosso, 2002
It should be pointed out that microencapsulation of bioactive molecules, functional ingredients or living cells for food industry must consider several factors such as technological concerns (manufacturing and storage properties), economic feasibility and consumers satisfaction
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(Aguiar et al., 2016)
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2.3 Challenges in microencapsulation in pharmaceutical and food industries
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Much of inherent challenges of microencapsulation of ingredients are the same for both pharmaceutical and food industries and as so, this topic is going to be addressed for both
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industries.
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Several authors have been discussing the main advantages of applying microencapsulation techniques in pharmaceutical and food industry sectors. Numerous difficulties and
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disadvantages to industrial application of microencapsulation techniques have been pointed
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out for both application fields such as the poor entrapment of the active compound, the impossibility to scale-up some processes (Guerreiro et al., 2012; Khalil et al., 2013; Seremeta et
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al., 2013), the requirement of multi-step processes (Bitar et al., 2015; Zakeri-Milani et al., 2013) the downstream high concentration of undesired sub-products or residues (Destreé et
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al., 2008; Sajeesh and Sharma, 2006; Schubert and Muller-Goymann, 2003; Tolue et al., 2009; Wu et al., 2011; Yegin et al., 2006; Zhang et al., 2006), the long-time requirement of some processes (time-consuming processes) (Hao et al., 2013; Souguir et al., 2013), the requirement of high energy inputs and the demand of complex equipment (Hoyer et al., 2010; Kamiya et al., 2009; Shi et al., 2011; Silva et al., 2011). Many parameters can affect microencapsulated products and their final characteristics. These parameters may be divided into three classes: properties of materials, formulation parameters and operating conditions (Table 3). The main properties affected are microparticles mean size,
ACCEPTED MANUSCRIPT particle size distribution, microparticles surface morphology, product yield and encapsulation efficiency. The final properties of microparticles may affect the active compound release rate (Li et al., 2008) Table 3 – Parameters affecting microparticles final properties Factor
Studies
References
Evaluation the effect of wall materials on fish oil microencapsulation by spray-drying.
Pourashouri et al., 2014
Assessment the effect of wall material type and oil load on flaxseed oil microencapsulation by spray-drying.
Omar et al., 2009
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Type of factor
Stirring speed
Evaluation of the influence of stirring speed on drug release from microcapsules.
Wall material
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Core material
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Viscosity
Type of solvent
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Formulation parameters
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Properties of materials
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Use of additives
Investigation of the Effect of different wall materials on Red-Fleshed Pitaya (Hylocereus polyrhizus) seed oil microencapsulation by spray-drying. Evaluation the use of gum arabic, maltodextrin and a modified starch as wall materials on cardamom oleoresin microencapsulation by spray-drying. Assessment the effect of wall material on extra-virgin olive oil microencapsulation by spray-drying. Analysis of the solubility of core materials in aqueous polymeric solutions on curcumin microencapsulation by coacervation. Interpretation of the influence of viscosity and other physicochemical properties of acai (Euterpe oleraceae Mart.) microencapsulation by spray-drying. Analysis of the influence of using ethyl acetate as a dispersed solvent on microspheres properties. Investigation of the effect of salt addition on proteins microencapsulation.
Operation conditions Temperature
Consideration of the influence of spray-dryer air temperatures on mandarin oil microencapsulation. Assessment of the influence of air inlet temperature on the microencapsulation of flaxseed oil by spray drying.
Lim et al., 2012
Krishnan et al., 2005 Calvo et al., 2010 Aziz et al., 2007 Tonon et al., 2008
Sah, 1997 Pistel and Kissel, 2000 Jegat and Taverdet, 2000 Bringaslantigua et al., 2012 Tonon et al., 2011
ACCEPTED MANUSCRIPT 3. Design of experiments for microencapsulation applications Traditionally, a common approach to analyse which are the main influencing factors in microencapsulation area is studying the influence of one variable while the others remain constant. This methodology known as one-variable-at-time (OVAT) or interchangeably onefactor-at-time (OFAT) leads to non-optimized final products or processes (Frey et al., 2003).
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This may be explained by factor interactions that OVAT methodology does not count: different variables can interact and be responsible for a specific system behaviour (Kharia and Singhai,
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2013). Another disadvantage of OVAT methodology is the relative huge number of
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experiments required to be performed which makes OVAT methodology a material, reagent
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and time consuming approach (Bower, 2013).
Recently, multivariate statistical approaches as design of experiments (DOE) have been used to
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overcome the main disadvantages of OVAT procedures (Christolear, 2013). Among the most relevant multivariate statistical methods used in microencapsulation field, the surface
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response methodology (RSM) is the most used one. The application of RSM for
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microencapsulation optimization processes have been described essentially in pharmaceutical and food research sectors.
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3.1 Response surface methodology implementation The response surface methodology
is defined as a set of mathematical and statistical
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approaches to express relationships between factors and responses (Bas, 2007) . Factors also known as independent variables are independent parameters that can be independently changed. On the other hand, responses or dependent variables are the measured values from the experiments performed. The experiments belong to an experimental domain which is the experimental area under study. The maximum and minimum levels of experimental variables define the limits of the domain. Additionally, levels of a variable must be defined. Levels of variables are the values at which the experiment should be performed (Bezerra et al., 2008). Unlike OVAT methodology, RSM outlines the effect of factors alone or in combination
ACCEPTED MANUSCRIPT generating a mathematical model. The mathematical relationship between factors and responses is given by Equation (1) (Bas, 2007). (1) where
stands for the response,
stands for the unknown function of
and
the number of accounts for the
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independent variables,
, represent the factors being
statistical errors not considered in . Generally, the sources of
are measurement errors
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considering that follows a normal distribution with mean and variance equal to zero.
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The RSM optimization processes performed in four stages as depicted in Figure 2.
Figure 2 – The fundamental steps in a RSM optimization process
3.1.1 Screening experiments: determination of factors and their levels As was previously described many parameters can affect microparticles production and their properties. In many systems is arduous to determine the effects of all parameters; therefore, screening experiments may be a useful tool to identify factors and their interactions with relevant effects on the process. Generally, full or fractional designs are used in screening
ACCEPTED MANUSCRIPT experiments, essentially due to their efficiency: in the early stages of the optimization processes, a limited number of experiments may be representative (Hanrahan et al., 2007). After the selection of the most influencing parameters follows the determination of factors levels. Incidentally, this is a key point in the optimization process: levels incorrectly chosen can lead to a non-optimized response. The selected factors during the screening process usually
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have different units or ranges. Many examples can be given for each type of factor as presented in Table 2. If the factors under study are the wall material:core material ratio (%
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w/w), viscosity (mPa.s) and operation temperature (°C), then a normalization process has to be
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performed because the three parameters have different units and therefore they are not
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directly comparable. Consequently, a normalization process shall be performed before a regression analysis. Factors are coded (normalized) according to the following equation
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(Equation 2):
stands for the normalized factor, , the natural variable and
and
, the
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where,
(2)
maximum and minimum values for , respectively. Each factor varies between -1 and +1. The
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units of the selected factors are not more significant and therefore they are normalized. The most common approaches in screening experiments are the two-level full-factorial - designs where the 2 stands for the number of factor levels and
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designs, also known as
for the number of factors each with a high and low value. In fractional designs, also known as , the number of experiments is reduced by
(Bas, 2007; Bezerra et al., 2008; Hanrahan et
al., 2007). The choice of the screening design may affect the final response, actually, the screening design should be chosen regarding the goals and limitations of the optimization process.
3.1.2 Choice of the experimental design The simplest mathematical model that can be used in RSM is a linear function (Equation 3).
ACCEPTED MANUSCRIPT (3) where, factors,
is a constant term,
are the coefficients of the linear parameters,
represents the residues and
stands for
stands for the number of variables. For an accurate
application, the responses should be fitted in the linear model. However, a linear RSM model does not account for any curvature of the response. For factor interactions analysis, a second
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order model must be used (Equation 4).
Where
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(4)
stands for the coefficient of interactions between factors.
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If a maximum or a minimum has to be determined, a quadratic term ought to be added into
where,
(5)
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second order model, giving rise to the following model (Equation 5):
stands for the coefficient of the quadratic factor. According to Equation 5, the
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estimation of factors and consequently, the optimization process has to be carry out in at least three factor levels for all factors.
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The model presented in Equation 5 can be presented in a matrix notation (Equation 6)
(6)
The least squares method (MLS) allows to solve Equation 6 matrix system. The description of this method is not within the scope of this paper.
ACCEPTED MANUSCRIPT Nowadays, a huge variety of computer packages are available to solve RSM problems, so much so that computer packages may differ on specific inputs like the number of runs, blocks or even how experimental points are selected (Bas, 2007). The most known second-order models used in RSM are the central composite design (CCD), the face-centered composite design (FCCD) and the central rotatable composite design (CRCD).
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These systems are differentiated according to number of experiments required to run.
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3.1.3 Assessment of the predicted model
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A mandatory condition to use RSM is the predicted model has to describe the experimental domain. The reliability of the fitted model is usually tested through the analysis of variance
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(ANOVA). The ANOVA tests compare the variation due to changes in combinations of factors levels with the variation caused by measurement errors (random errors) of responses. Hereby,
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this evaluation is essential to determine if the model fits and describes the experimental domain (Cai et al., 2015; Gelman et al., 2005). Variation sources can be compared using a
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Fisher distribution (F-test) or alternatively, a Student’s distribution (t-test). Variations
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associated to the model and not to random errors, arise if the F-probability is less than 0.05 (correspondent to a 95% confidence level). A t-test, explores which factors and/or interactions
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have statistical meaning. If t-probability is smaller than 0.05, the factor or the interaction
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between factors are considered to be significant (Homem et al., 2016).
3.1.4 Determination of the optimal conditions Linear models (represented generically on Equation 3) allow to specify in which direction the original design should be studied in order to obtain optimal conditions. Nevertheless, if the experimental region cannot be changed, the optimization study should find out the optimal operational conditions by visual inspection (Bezerra et al., 2008). For quadratic models, the optimal conditions correspond to a maximum, minimum or a saddle point (critical points). The coordinates of a critical point are calculated deriving the quadratic
ACCEPTED MANUSCRIPT form of Equation 5 (Equation 7) in order to
and
and then equating them to zero. The
coordinates of the critical point are obtained solving Equations 8 and 9 in order to find the values of
and
. (7)
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(8)
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3.1.5 Graphical representation of the model equation
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(9)
The model equation predicted using a RSM can be visualized on response surface plots and
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their related contour plots. The response surface plots are three-dimensional graphs that
dimensional surfaces in the
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represent the relationship between factors and the response. Surface response plots are -dimensional space. If the system is represented by three
or more variables, the graphical representation is only possible if one or more variables are
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considered constant.
The two-dimensional depiction of the response surface plot is the related contour plot. The
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shape of lines in contour plots may predict the response type. A maximum or minimum response corresponds to ellipses or circles around the plot center. Further interpretations can
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be performed comparing both plots regarding the predicted model within the experimental domain. Concerning Figure 3, the surface plot (A) and the contour plot (B) are represented in a general two variables optimization of a maximum within the experimental region. Regarding Figure 4, the surface (A) and the contour (B) plots of a maximum point included in the experimental domain with a graphical plateau in relation to
are depicted. The variation of
levels does not affect the response. On the other hand, in Figure 5 is presented a maximum response outside the experimental domain. The initial design should be displaced in order to obtain the maximum response inside the experimental area. A minimum response within the
ACCEPTED MANUSCRIPT experimental domain is displayed on Figure 6, whereas a saddle point is depicted in Figure 7. Despite the result of Equation 8 and 9 are found in a saddle point, this is not an optimal response. The optimum response can be found through visual inspection.
Graphical
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representations presented in this article were made using the JMP 13 statistical software.
(B)
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Figure 3 – Surface response plot (A) and the contour plot (B) of a maximum response in which
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levels of all parameters affect the response
ACCEPTED MANUSCRIPT (A)
(B)
Figure 4 – Surface response plot (A) and the contour plot (B) of a maximum response in which
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some parameters do not affect the response
(B)
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Figure 5 – Surface response plot (A) and the contour plot (B) of a maximum response outside
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the experimental area
(A)
(B)
Figure 6 – Surface response plot (A) and the contour plot (B) of a minimum response in which levels of all parameters affect the response
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ACCEPTED MANUSCRIPT
(B)
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(A)
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Figure 7 – Surface response plot (A) and the contour (B) of a saddle point
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4. Applications of response surface methodologies in microencapsulation techniques
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Innovate experimental designs and optimization methodologies have been applied in microencapsulation techniques to overcome the main barriers for the microencapsulation industrial application and moreover to improve and/or reformulated micro-enclosed products.
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Through representative examples, the application of RSM optimization studies in
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pharmaceutical and food industries are reviewed and listed in Tables 4 and 5. Likewise, studies about the application of design of experiments are comprehensively and critically reviewed and discussed.
Henceforth, for both research fields reviewed (Table 4 and Table 5), the specific application (the active ingredient, the coating material and the use of additives) is firstly presented. Afterwards, parameters related to the experimental optimization design (EOD) are reported namely the type of experimental screening design (ESD), the type of EOD and the number of runs performed. A widely variety of factorial designs can be performed. Notwithstanding, full
ACCEPTED MANUSCRIPT screening experiments do not have to be performed: a fractional factorial design may describe the system under study. Once a fractional design is applied, a reference for the fractional design is reported in both tables (application column) considering a general representation of . Therefore, the number of runs can be less than
if a fractional design is considered or
more if replicates are considered in modeling. Posteriorly, the factors ( ) and the response(s)
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are presented. Finally, the optimal settings are described: the desired values for both factors
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4.1 Applications in pharmaceutical industry
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and responses are presented.
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Applications of RSMs in microencapsulation of drugs are not widely described in the literature. Furthermore, the spectrum of drug classes evaluated according RSM is narrow: to the best of
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authors knowledge, only anti-viral drugs, analgesics and anti-inflammatories have been microencapsulated using optimal formulation and process conditions found through RSMs
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studies.
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The influence of using fatty and non-fatty additives to PLGA-loaded microspheres with acyclovir, an anti-viral drug intended for intravitreal administrations, using poly(D,L-lactide-co-
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glycolide), a synthetic polymer as coating material, was studied by Martínez-Sancho et al. (2003). Regarding this study, different formulations were assessed: additive-free formulations,
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polyethylene glycol-PLGA formulations, gelatine-PLGA formulations, Vitamin E-PLGA formulations, hydroxypropyl-methyl cellulose-PLGA formulations and isopropyl myristatePLGA formulations. Indeed, results showed that the higher encapsulation efficiency was obtained when gelatine was added (gelatine-PLGA based microparticles). Further considerations were considered by these authors, lately: an optimization process for microencapsulation of acyclovir in gelatine-PLGA, was presented. Regarding this optimization design, the microencapsulation technique applied was the
solvent evaporation, using
gelatine as a stabilizer in the external aqueous phase ( ). As per, in this study was applied a 52
ACCEPTED MANUSCRIPT - experimental screening design. The factors under study were the aciclovir content (mg) and the gelatine content (mg). The responses studied were the product yield (%), the encapsulation efficiency (%), the initial burst release, the cumulative amount of aciclovir released from 1 to 14 days (μg/mg of microspheres) and the cumulative amount of aciclovir released at the end of the assay (μg/mg of microspheres) in 30 experiments. For the
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experimental optimization design a central rotatable composite 22 + star design (CRCD) was applied leading to an optimized formulation. This optimized formulation was made up of 80
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mg of acyclovir and gelatine, each, conducting to a maximum product yield and encapsulation
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efficiency around 70%. A constant release rate was verified during 63 days. The cumulative
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amount of aciclovir released from 1 to 14 days and the cumulative amount of aciclovir released at the end of the assay was 26.96 μg.mg-1 and 118.83 μg.mg-1 of microspheres, respectively.
optimization design of experiments.
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Thereby, a main conclusion can be drawn as preliminary studies may be useful to develop an
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A full factorial design optimization of an anti-inflammatory drug release from
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polycaprolactone-polyethylene glycol-polycaprolactone (PCL–PEG–PCL) microspheres was studied by Azouz et al. (2016). In accordance with this study, an
solvent evaporation
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technique was applied in a 16 set of experiments, testing an 24-experimental screening design. The factors analysed were the shaking speed (rpm), the required time of contact (hr), this is,
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the time required for dichloromethane evaporation, the polyvinyl alcohol (PVA) concentration (%) and the drug (ibruprofen) concentration (%). The solvent chosen for the organic phase formation was dichloromethane and PVA was used as a stabilizer in the external aqueous phase with the main goal to study the encapsulation efficiency (%) as EOD response. To the purpose of optimization, the simplest model, a linear model, was employed. The optimal conditions were found considering a shaking speed of 200 rpm, time of contact of 0.5 hr, a PVA concentration of 0.1% and drug concentration of 35% leading to an encapsulation efficiency of 88.86%. Even though, the microencapsulation of ibuprofen has been widely reported (Bidone
ACCEPTED MANUSCRIPT et al., 2009; Hussein et al., 2007; Newa et al., 2007; Thompson et al., 2007), the improvement of observed results using this methodology is difficult to evaluate due to lack of comparative investigations. Nonetheless, another main conclusion can be drawn: even though preliminary studies may be useful, an optimization design can be performed without preceding investigations.
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Lastly, 3 experimental factorial designs were develop by Billon et al., conducted 3 produce acetaminophen microparticles by spray-drying. For the experimental screening design, a
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fractional 25-1 was used, evaluated in a 16 set of experiments each. The EOD applied was the
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CRCD and the factors evaluated were feed rate (mL.min-1), the inlet temperature (°C), the drug
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concentration in the feed (g.L-1), the polymer concentration (g.L-1), the additive concentration (g.L-1) and the SiO2 concentration (g.L-1) used as an additive to all formulations. As responses,
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was evaluated the product yield (%) and the residual moisture content of the spray-dried product (%). Regarding the first set of experiments, tartic acid (TA) was added to the polymer
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solution and both drug and the polymer were dissolved in the distilled water. Alternatively, in
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the second round of experiments, oxalic acid (OA) was added to the polymer solution. Differentially, polyvinylpyrrolidone (PVP) was added to the polymer solution and both polymer and drug were suspended, in the third set of trials. Comparing the use of carboxylic acids as
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additives (TA or OA), the RSM optimization presented the same values for the operating
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settings (feed rate, inlet temperature, drug concentration in the feed, polymer concentration, additive concentration and SiO2 concentration). Even though the studied output responses were similar, the product yield was higher when TA was used instead of OA (82.5±1.95 against 80.8±0.40). Nevertheless, the residual moisture was negatively higher when TA was used instead of OA (1.03±0.094 versus 1.02±0.08). It can be concluded that although the residual content was slightly higher for formulations with TA, the product yield was improved. Additionally, three central points were considered to evaluate the validity of the linear model for the three formulations.
ACCEPTED MANUSCRIPT Comparing the optimal experience with carboxylic acids and the third set of experiments (PVP used as additive to the polymer and a suspension was feed to the spray-dryer), even though the required input parameters were slightly higher, the final product yield (86.8±1.91) was improved and the residual moisture content remained practically constant (1.02±0.08). Therefore, it can be concluded that a fractional factorial design with centre points (imbedded
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in the central composite design) allows to estimate the curvature (this is, second order interactions between factors) effectively. In this type of model, the use of three centre points
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seems to be useful for TA or OA additives content estimation: probably without analysing the
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curvature of system behaviours, equal results would be obtained for both carboxylic acids due
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to their similar chemical behaviour and as so, this model allowed to analyse differences between two carboxylic acid additives. As it was used a rotatable design for optimization, the
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predicted values of product yield and residual moisture are function of the distance of points from the centre of design and not function of the direction that points span from the centre.
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Clearly, the CRCD was useful because more accurate values for product yield and residual
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moisture specially for carboxylic acids were obtained because results are not only function of the distance from the centre of space design but additionally, a function of the direction. For a coherence of results and for practical reasons, the use of the CRCD was maintained for PVP
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experiments analysis (the third round of experiments).
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Comparing all the presented formulations in the pharmaceutical experimental domain, the CRCD was the most used due to the accuracy of results. Nevertheless, a linear model was applied for optimization of ibuprofen microencapsulation and optimal formulation conditions were found, nevertheless, further DOEs ought to be performed to evaluate the accuracy of the model tested.
ACCEPTED MANUSCRIPT
Table 4 – Examples of DOE applications within microencapsulation field for pharmaceutical industry Application Active ingredient Coating material Additives
Aciclovir Poly(D,L-lactide-coglycolide) Gelatine
EOD
ESD EOD (NoE)
52 CRCD (30)
Factor(s)
Ibuprofen PCL–PEG–PCL n.d.
Acetaminophen TA-CMC Colloidal silicon dioxide
I R
Response(s)
C S U
-Aciclovir content (mg) -Gelatine content (mg)
N A
T P E
2 LM (16)
-Shaking speed (rpm) -Time of contact (hr) -PVA concentration (%) -IBF concentration (%)
25-1 CRCD (16)
-Feed rate (mL.min-1) -Inlet temperature (°C) -Drug concentration in the feed (g.L-1) -Polymer concentration (g.L-1)
A
C C
References
Optimal formulation
-Aciclovir content: 80 mg -PY (%) -Gelatine content: 80 mg -EE (%) -PY: 70.14% -Initial burst release -EE: 70.77% -Cumulative amount of aciclovir -Initial burst release: constant release rate during released from 1 to 14 days 63 days (µg.mg of microspheres-1) -Cumulative amount of aciclovir released from 1 -Cumulative amount of aciclovir to 14 days: 26.96 µg.mg-1 of microspheres released at the end of the assay -Cumulative amount of aciclovir released at the (µg.mg of microspheres-1) end of the assay: 118.83 µg.mg-1 of microspheres -Shaking speed: 200 rpm -Time of contact: 0.5 hr -EE (%) -PVA concentration: 0.1% -IBF concentration: 35% -EE: 88.86% -Feed rate: 20 mL.min-1 -Inlet temperature: 140 °C -PY (%) -Drug concentration in the feed: 6 g.L-1 -Residual moisture content of -Polymer concentration: 6 g.L-1 spray-dried product (%) -Additive concentration: 1.8 g.L-1 -SiO2 concentration: 8 g.L-1
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4
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MartínezSancho et al., 2004
Azouz et al., 2016
Billon et al., 2000
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-Additive concentration (g.L-1) -SiO2 concentration (g.L-1)
T P
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Acetaminophen TA-CMC Colloidal silicon dioxide
C S U
N A
Acetaminophen OA-CMC Colloidal silicon dioxide
Acetaminophen MCC Colloidal silicon dioxide
-PY: 82.5% -Residual moisture content of spray-dried product: 1.03% -Feed rate: 20 mL.min-1 -Inlet temperature: 140 °C -Drug concentration in the feed: 6 g.L-1 -Polymer concentration: 6 g.L-1 -Additive concentration: 1.8 g.L-1 -SiO2 concentration: 8 g.L-1 -PY: 82.5% -Residual moisture content of spray-dried product: 1.03% -Feed rate: 20 mL.min-1 -Inlet temperature: 140 °C -Drug concentration in the feed: 6 g.L-1 -Polymer concentration: 6 g.L-1 -Additive concentration: 1.8 g.L-1 -SiO2 concentration: 8 g.L-1 -PY: 80.8% -Residual moisture content of spray-dried product: 1.02% -Feed rate: 30 mL.min-1 -Inlet temperature: 160 °C -Drug concentration in the feed: 10 g.L-1 -Polymer concentration: 10 g.L-1 -Additive concentration: 0.3 g.L-1 -SiO2 concentration: 18 g.L-1 -PY: 86.8% -Residual moisture content of spray-dried
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T P E
A
C C
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ACCEPTED MANUSCRIPT
product: 1.02% n.d. – not defined; CMC - Carboxymethyl cellulose; CRCD – Central Rotatable Composite Design; EE – Encapsulation efficiency; EOD – Experimental Optimization Design; ESD – Experimental Screening Design; IBF – ibuprofen; LM – Linear model; MCC - Microcrystalline Cellulose; NoE – Number of Experiments; OA - Oxalic acid; PCL- Polycaprolactone; PEG - Polyethylene glycol; PY – product yield; TA -Tartic acid;
T P
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C S U
N A
D E
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ACCEPTED MANUSCRIPT
4.2 Applications in food industry Multiple DOEs have been applied to optimize the microencapsulation of active ingredients for food industry. Even though the pharmaceutical industry has been further exploring
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microencapsulation, to the authors best knowledge, publications about DOEs for microencapsulation of active ingredients intended for food industry are more available.
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Mostly active ingredients are essential oils, as presented in Table 6 (fish oil, lemon myrtle oil,
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holy basil essential oil, flaxseed oil and rosemary essential oil). Notwithstanding, DOEs have been reported for the microencapsulation of other compounds in the food industry as
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extracts, derivatives and probiotics.
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A RSM optimization for fish oil microencapsulation using a spray-dryer was reported by Aghbashlo et al. (2012) including a face-centred full central composite design (FCCD)
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evaluating three factors (aqueous phase content in the emulsion, oil proportion in total solids
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of the emulsion and the emulsification time) and two responses (exergy efficiency and encapsulating efficiency). Each factor ranged from three set levels and three repetitions of the centre point were performed to evaluate the curvature of the model. Only 17 experiences
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were performed, nevertheless, careful conclusions can be drawn considering responses are
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rightly represented by a fractional higher polynomial model (second order model) with repetitions of the centre point. An optimal formulation was found with 87.12% of aqueous phase content in the emulsion, 10.82% of oil concentration in the total solids of the emulsion, considering an emulsification time of 13.23 minutes, resulting in 6.17% and 88.67% in exergy efficiency and encapsulating efficiency, respectively. The same type of second-order polynomial optimization design (FCCD) was studied by Huynh et al.,about lemon myrtle oil microencapsulation by spray-drying. The factors studied were input conditions for the spray-dryer: the feed concentration (% w/w), the oil concentration (%
ACCEPTED MANUSCRIPT w/w of feed concentration) and the outlet drying air temperature (°C). The responses analysed were oil retention (%) and the surface oil content (mg/100 g of powder). It was run 40 experiences. The optimal operating setting was found: 40% w/w, 18% w/w and 65 °C, respectively for the feed concentration, the oil concentration and the outlet drying air temperature giving an oil retention of 83.82% and a surface oil content of 23.04 mg/100 g of
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powder. The same DOE methodology was applied to the optimization of holy basil essential oil
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microencapsulation by Sutaphanit and Chitprasert. These authors, in a 13-round experiment
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studied the influence of gelatine concentration (% w/v), the amount of holy basil essential oil
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(mL) on the product yield (%), encapsulating efficiency (%) and final oil content. The optimal values for the factors were 11.75% w/v and 31 mL for gelatine concentration and holy basil
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essential oil amount, respectively. The responses obtained were 98.80%,95.41% and 66.50% for product yield, encapsulation efficiency and final oil content, correspondingly.
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Similarly, a FCCD was used in a 12-round experiment to study the effect of solids content and
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oil load on the specific responses of microencapsulation process of rosemary essential oil as moisture content, wettability (s), hygroscopicity (%), total oil content (%) and oil retention (%). The results show that the wall materials concentration, the oil load, the wettability, the
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hygroscopicity, the total oil and the oil retention of 20.9%, 29.4% w/w, 1315 s, 11%, 11% and
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39%, respectively.
Omar et al., presented a research article on RSM methodology to obtain optimal conditions for flaxseed oil microencapsulation. It was applied a FCCD to study the following factors: the concentration of lecithin (% w/w), the concentration of oil loading (% w/w) and the concentration of xanthan gum (% w/w) on selected responses (encapsulation efficiency (%) and the flaxseed oil droplet (nm)). In a 17-round experiment, the optimal conditions were found corresponding to a lecithin concentration of 1.14% w/w, an oil loading concentration of
ACCEPTED MANUSCRIPT 22.78% w/w and 0.10% w/w concentration of the polymer (xanthan gum) leading to an encapsulation efficiency of 92.3% and a flaxseed oil droplet size of 446.9 nm. In all above-mentioned studies about essential oils microencapsulation, the FCCD was applied. As it is shown in Table 5, all the studies have imbedded a central composite design (CCD) and as so, some of the experiments were used to estimate the curvature of the design.
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Additionally, a FCCD, requires 3 levels for each factor which allows to analyse the second-order interactions. The other two types of CCD (the circumscribed design and the inscribed design)
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require five levels for each factor. It may seem that using FCCD diminishes the accuracy of the
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design. In fact, the curvature analysis account only three levels but the number of experiments
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is widely reduced. For example, if two factors FCCD design was applied to a system under study, the numbers of runs required would be 9, but if two factors circumscribed CCD or
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inscribed CCD were used, the number of experiments run would be 25. Even though a loss of curvature accuracy is evident using a FCCD, nevertheless, the amount of active ingredient and
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other reagents, time and utilities are retrieved.
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The same RSM methodology is also describe for optimization processes of other bioactive compounds such as peanut sprout extract and starch oleate derivatives from native corn. An optimization process for peanut sprout extract microencapsulation by RSM using a FCCD as
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a model design was presented by Lee et al., . Specifically, was used 2 coating materials:
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triacylglycerol was used as first coating material and maltodextrin as the secondary coating material. Additionally, were added polyglycerol as a primary emulsifier and polyoxyethylene sorbitan monolaurate as secondary emulsifier. In a set of 31 experiments, the effects of the ratio between the core material and the first coating material, the ratio between the core material and the secondary coating material, the polyglycerol concentration (% w/v) and the polyoxyethylene sorbitan monolaurate concentration (% w/v) were assessed on the product yield (%). A FCCD showed optimal conditions: core material:triacylglycerol ratio of 1:2, core material: maltodextrin ratio of 1:1.23, polyglycerol concentration of 1.25% w/v,
ACCEPTED MANUSCRIPT polyoxyethylene sorbitan monolaurate concentration of 1.21% w/v, resulting in a product yield of 98.7%. An optimization process of starch oleate derivatives form native corn and hydrolysed corn starch microencapsulation using a RSM was presented by Kshirsagar and Singhal. The oleic acid content (g), the required time for the process (min) and the temperature (°C) were the
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factors under study. A unique response was analysed: the degree of substitution. The degree of substitution is an effective approach to assess the modification level of a polymer. It reveals
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the number of substitutions in repetition structures of a polymer. A CRCD was applied in a 20-
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round experiment design. Optimal results were found for all formulations. A maximum 0.021
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degree of substitution was found using native starch and an oleic acid content, time of process and temperature of 1.2 g, 201 min. and 160 °C, respectively. On the other hand, a maximum
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degree of substitution was found using starch oleate derivatives from hydrolysed corn starch. The sample analysed with the longer glucose chain demonstrates a lower substitution value
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and higher process conditions comparing to the shorter glucose chain. The maximum degree
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of substitutions was 0.0353 and 0.0503 for the longer glucose chain and the shorter glucose chain, associated to process variables of 1.53 g of oleic acid content, 334 min of processing at 150 °C for the longer glucose chain and 1.36 g of oleic acid content, 300 min of processing at
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129 °C. In this study, a rotatable design was applied and as so, the number of runs to estimate
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the curvature of the response were five. As the response is very specific, at a sequence level of polymer chain, a deeper study of the type of interactions (polynomial design) was required. A simple CCD would not give an accurate relationship between the response and factors unlike a rotatable design: as the number of runs to estimate the curvature of the system was increased, the accuracy of interactions was improved. Lastly, optimization of microencapsulation of living cells was proposed by Anekella and Orsat. Actually, microencapsulation of living cells is still a challenger in microencapsulation research field. Nevertheless, these authors chosen a combination of widely available probiotics
ACCEPTED MANUSCRIPT (Lactobacillus acidophilus and Lactobacillus rhamnosus) in raspberry juice. The carbon source chosen for these organisms growth was maltodextrin. A CCD was employed to study selected processing factors (inlet temperature, ratio between total solids and maltodextrin and inlet feed ratio) associated to the microencapsulation technique used (the spray-drying) in three selected responses: recovery, probiotics survival percentage and colour. Optimal responses of
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48.7% of recovery, 87.17% of survival percentage and 57.21 ΔE were found, using 100 °C as inlet temperature, 1:1 of total solids: maltodextrin ratio, 40 mL.min-1 of the feed rate as
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processing settings. Even though a simple second-order polynomial was applied, the
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assessment of factors interactions is fundamental when it is studied living cells due to the
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combining challenging characteristics of living cells and microencapsulation techniques: living cells are mostly thermosensitive and they may undergo oxidative and osmotic stresses but
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generally, microencapsulation techniques present some demanding steps that may expose the formed microparticles to the above-mentioned physico-chemical challenges (Rokka and
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Rantamäki, 2010).
ACCEPTED MANUSCRIPT
Table 5 – Examples of DOE applications within microencapsulation field for food industry Application Active ingredient Coating material Additives Fish oil n.d. n.d.
Lemon myrtle oil n.d. n.d. Holy basil essential oil n.d. Gelatin
ESD EOD (NoE)
3
3 FCCD (17) n.d. FCCD (40)
n.d. FCCD (13)
EOD Factor(s) -Aqueous phase content (%) -Oil concentration in total solids of emulsion (%) -Emulsification time (min)
-ExE (%) -EE (%)
-Feed concentration (% w/w) -Oil concentration (% w/w of feed concentration) -Outlet drying air temperature (°C)
-Oil retention (%) -Surface oil content (mg/100 g of podwer)
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-PY (%) -EE (%) -Final oil content (%)
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-Gelatine concentration (% w/v) -Holy basil essential oil amount (mL)
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5 FCCD (12)
-Wall materials concentration (%) -Oil load % (w/w)
-Moisture content -Wettability (s) -Hygroscopicity (%) -Total oil (%) -Oil retention (%)
Flaxseed oil n.d. Lecithin
n.d. FCCD (17)
-Concentration of lecithin (% w/w) -Concentration of oil loading (%
-EE (%) -Flaxseed Oil Droplet (nm)
C A
References
Optimal formulation
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-Aqueous phase content: 87.12% -Oil concentration in total solids of emulsion: 10.82% -Emulsification time: 13.23 min -ExE: 6.17% -EE: 88.67% -Feed concentration: 40% w/w -Oil concentration: 18 % w/w of feed concentration -Outlet drying air temperature: 65 °C Oil retention: 83.82% -Surface oil content: 23.04 mg/100 g of podwer -Gelatine concentration: 11.75% w/v -Holy basil essential oil amount: 31 mL -PY: 98.80% -EE: 95.41% -Final oil content: 66.50% -Wall materials concentration: 20.9% -Oil load: 29.4% w/w -Wettability: 1315 s -Hygroscopicity: 11% -Total oil: 11% -Oil retention:39% -Concentration of lecithin: 1.14% w/w -Concentration of oil loading: 22.78% w/w -Concentration of xanthan gum: 0.10% w/w
C S U
N A
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Rosemary essential oil n.d. n.d.
2
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Response(s)
Aghbashlo et al., 2012
Huynh et al., 2008
Sutaphanit and Chitprasert, 2014
Fernandes et al., 2014
Omar et al., 2009
ACCEPTED MANUSCRIPT
(emulsifier) Xanthan gum (emulsifier) Peanut sprout extract Triacylglycerol (first coating material) Maltodextrin (secondary coating material) Polyglycerol polyricinoleates (primary emulsifier) Polyoxyethylene sorbitan monolaurate (secondary emulsifier) Starch oleate derivatives from native corn n.d. n.d. Starch oleate derivatives from hydrolyzed corn starch with longer glucose chain
w/w) -Concentration of xanthan gum (% w/w)
24 CCD (31)
-EE: 92.3% -Flaxseed Oil Droplet: 446.9 nm
T P
I R
-Core material: triacylglycerol ratio -Core material: maltodextrin ratio -Polyglycerol polyricinoleates concentration (% w/v) -Polyoxyethylene sorbitan monolaurate concentration (% w/v)
C S U
-Core material: triacylglycerol ratio: 1:2 -Core material: maltodextrin ratio: 1:1.23 -Polyglycerol polyricinoleates concentration: 1.25% w/v -Polyoxyethylene sorbitan monolaurate concentration: 1.21% w/v -PY: 98.7%
-PY (%)
D E
N A
M
T P E
53 CCD (20)
C C
-Oleic acid content (g) -Time of the process (s) -Degree of substitution -Temperature of the process (°C)
A
Lee et al., 2013
-Oleic acid content 1.2 g -Time of the process: 201 min. -Temperature of the process: 160 °C -Degree of substitution: 0.021 -Oleic acid content 1.53 g -Time of the process: 334 min. -Temperature of the process: 150 °C -Degree of substitution: 0.0353
Kshirsagar and Singhal, 2007
ACCEPTED MANUSCRIPT
n.d. n.d. Starch oleate derivatives from hydrolyzed corn starch with shorter glucose chain n.d. n.d. Probiotics of raspberry juice n.d. Maltodextrin (carbon source and parameter to assess the prebiotic potential)
T P
-Oleic acid content 1.36 g -Time of the process: 300 min. -Temperature of the process: 129 °C -Degree of substitution: 0.0503
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C S U
n.d. CCD (20)
-Inlet temperature (°C) -Total solids: maltodextrin ratio (product of starch hydrolysis) -Feed rate (mL.min-1)
N A
-Recovery (%) -Survival percentage (%) -Color (ΔE)
T P E
D E
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-Inlet temperature: 100 °C -Total solids: maltodextrin ratio: 1:1 -Feed rate: 40 mL.min-1 -Recovery: 48.7% -Survival percentage: 87.17% -Color: 57.21
Anekella and Orsat, 2013
n.d. – not defined; CCD – Central Composite Design; EE – Encapsulation efficiency; EOD – Experimental Optimization Design; ESD – Experimental Screening Design; ExE - Exergy efficiency; FCCD – Face Centered Composite Design; NoE – Number of Experiments; PY – product yield;
A
C C
ACCEPTED MANUSCRIPT 5.
Perspectives and future challenges
Microencapsulation techniques have been under study for a long time but optimization processes are relatively recent. Microencapsulation for pharmaceutical and food industries still faces technological challenges, especially for the microencapsulation of sensitive bioactive
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compounds as probiotics. Design of experiments will be a useful tool to face and overcome the actual barriers for industrial application of microencapsulation. Specially, response surface
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methodologies are time, reagents and utilities saving research methods but mainly, they are
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results oriented approaches.
Moreover, to the best of authors knowledge no research articles about optimization processes
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are available on literature about microencapsulation in cosmetics field even though some
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bioactive compound for food industry may also be microencapsulated for cosmetic purposes. Nevertheless, optimization methods are required for a successful implementation of this
Conclusions
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6.
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technological approach within this research field.
The response surface methodology is an innovate technological approach for optimization
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processes. This technique when applied to microencapsulation research has several
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advantages. A huge amount of information can be obtained with a reduced number of experiments. Consequently, the reduced number of experiments is associated to efficiency: less number of reagents are required and less time are required for a specific study. As computer
tools
for
RSM
implementation
are
straightforward,
research
within
microencapsulation field can be largely developed. Unlike traditional methods, RSM allows to analyse the relationship between factors. As matter of fact, it is a key point in microencapsulation: many microencapsulation parameters (materials, formulation or operation) can act in addition, antagonistically or synergistically.
ACCEPTED MANUSCRIPT According to the literature review performed by the authors, the number of experiments required for microencapsulation processes are relatively small (maximum of 40, including replicates). The most studied factors were the ratio between the wall and the core material, emulsifier concentrations and operation settings as inlet temperature in a spray-dryer. The most considered responses were the encapsulation efficiency and the product yield. Clearly,
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the studies presented in this article, demonstrate the importance of design of experiments for an accurate research in two microencapsulation domains (pharmaceutical and food
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industries).
The model equation is a useful tool for the researcher to identify the main parameters
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affecting the microencapsulation process under study. However, for RSM application, factors
7.
Acknowledgements
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and data have to fit in a second-order polynomial model.
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This work was financially supported by the projects POCI-01-0145-FEDER-006939 (Laboratory
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for Process Engineering, Environment, Biotechnology and Energy – UID/EQU/00511/2013) funded by the European Regional Development Fund (ERDF), through COMPETE2020 -
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Programa Operacional Competitividade e Internacionalização (POCI) and by national funds, through FCT - Funda o ara a C n a e a e nolo a and
E 01 0145 FEDE 000005 –
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LEPABE-2-ECO-INNOVATION, supported by North Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).
8.
Declaration of interest
The authors declare no conflict of interest.
ACCEPTED MANUSCRIPT 9.
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Graphical abstract
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Highlights 1. Nowadays, microencapsulation is a high-tech approach for the global market. 2. The most important industrial sectors for microencapsulation applications are pharmaceutical, food and cosmetics industries. 3. Design of experiments is a powerful tool in microencapsulation research field. 4. Response surface methodology theoretical concepts allow to optimize microencapsulation procedures. 5. Applications of design of experiments in microencapsulation research areas are widely used
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in pharmaceutical and food research.