Selection of Better Method for the Preparation of Nanoparticles by Applying Analytic Hierarchy Process

 

Saminathan C, P Venkatesan, S Madhusudhan

Department of Pharmacy, Faculty of Engineering and Technology, Annamalai University, Annamalai Nagar, Tamilnadu, India 608002

*Corresponding Author E-mail:

 

ABSTRACT:

The aim of present study was to choose the better technique to achieve reproducibility and consistency with good entrapment efficiency for the preparation of nanoparticles by employing Analytic Hierarchy Process as tool. A number of techniques are available for the preparation of nanoparticles. AHP technique was applied to make choice amongst alternative nanoencapsulation techniques (Solvent Evaporation Technique (SET)/Nano Precipitation Method (NPM)/Solvent Diffusion Method (SDM)/Salting Out Method (SOM)) and thereby opt the best technique. The solution of the problem involves finding the composite score that reflects the relative priorities of all the alternatives at the lowest level of the hierarchy. The composite score is used for the final ranking of the alternatives. The composite score favored the selection of SET (score=0.469) over NPM (score=0.365), SDM (score=0.174), SOM (score=0.076) for Nano encapsulation technique.

 

KEYWORDS: Analytical hierarchy Process, Multi Criteria Decision Making, Nanoencapsulation, Solvent Evaporation Technique, Nano Precipitation Method, Solvent Diffusion Method and Salting out Method.

 

 

 

INTRODUCTION:

The last few decades have witnessed dramatic developments in pharmaceutical sciences. Much research effort in developing novel drug delivery systems has been focused on controlled release and sustained release dosage forms1-3. The pharmaceutical formulations with novel drug delivery systems have been introduced with the course of optimizing the bioavailability through the modulation of the time course of the drug concentration in blood4-7.

 

All sustained and controlled release products show the common goal of improving drug therapy over that achieved with their non-sustained and controlled release counter parts8-10. One of the more recent and interesting result of pharmaceutical research is the fact that absorption rate of a drug can be decreased by reducing its rate of release from the dosage form.

 

The products so formulated are designed as sustained action, sustained release, Prolonged action, depot, retarded release, delayed action and timed-release medication11,12. This has been due to various factors viz prohibitive cost of developing new drug entities, expiration of existing international patents, discovering of new polymeric materials suitable for prolonging the drug release, improvement in therapeutic efficacy and safety achieved by these delivery systems13,14. Various approaches are available for achieving novel drug delivery dosage forms such as targeted delivery system, nanoparticles, prodrugs, transdermal system, ocular systems, intravaginal and intrauterine systems, injection and implants, nanoencapsulation, matrix devices, reservoir devices. One of the most effective approach is nanoencapsulation of the drug15-17.

 

A number of techniques are available for the preparation of nanoparticles that include co-acervation phase separation, emulsion solvent evaporation method, multiorifice centrifugal process, spray drying and spray congealing, polymerization, pan coating, electrostatic deposition18-24. The choice of an appropriate nanoencapsulation techniques mainly depend on the nature of the polymer used. The method of preparation and its choice are equivocally determined by technique related factors viz the particle size, requirement, reproducibility of the release profile and method25-28.

 

In nanoencapsulation technique, the overall goal is to achieve a quality nanoparticle with reproducibility and consistency with good entrapment efficiency. This is influenced by a number of factors such as process information of the equipment and method, operation skills of the nanoencapsulator, sensitivity of the equipment etc. hence while choosing technique, consideration of cost factor alone may not be justifiable. It is more rational and appropriate to analyse both qualitative and quantitative parameters and then to make a decision. When two or more alternatives are in hand and one has to select the best, then the appropriate approach is to use a multi–criteria decision making (MCDM) method. It is important to incorporate all the factors that could influence nanoencapsulation in decision making process while choosing technique.

 

In the present study analytic hierarchy process (AHP), a MCDM tool has been used to select the better technique among Solvent Evaporation Technique (SET), Nano Precipitation Method (NPM), Solvent Diffusion Method (SDM) and Salting out Method (SOM) for preparation of nanoparticles3-5.

 

Analytic Hierarchy Process (AHP):

AHP developed by Saaty is one of the very effective MCDM Model [21].

This has been employed very successfully in many situations where a decision situation is characterized by a multitude of complementary and conflicting factors 29-31.

General methodology, excellent analytical-mathematical treatments of AHP are available in literature 32-35.

The basic steps of analytic hierarchy process model are given below

1.     List the set of different alternatives (Aj, 1<= i<=n)

2.     Identify the factors that may be intrinsic as well as extrinsic, which may have an impact on the selection of alternatives for nanoencapsulation technique for nanoparticles formulation. For each of these impacts identify the criteria (Ci, 1<= i<=m) and the quantifiable indicates to the criteria for a possible measure.

3.     Develop a graphical representation of the problem in terms of the overall goal, the factors, the criteria and decision alternatives. Such a graph depicts the hierarchy of the problem.

4.     Assign weights to each alternative on the basis of its relative importance of its contribution to each criterion. This is carried out through a pair wise comparison of the alternatives for each criterion. The scale of pair wise comparison may be used for preparing the pair wise comparison matrix elements Mkij for each criterion Ck (where Mkif is evaluated when Ai is compared with Aj.

5.     Once the pair wise comparison matrix has been formed for a criterion Ck the normalized priority of each alternative is synthesized.

This is carried out by following steps:

·       Sum the values in each column of Mk.

·       Divide each element in the column by its column total which results in a normalized pair wise matrix.

·       Compute the average of the elements in each row of normalized comparison matrix thus providing an estimate of the relative priorities of the alternatives. This result in a priority vector PMk1 denotes the priority for alternative Ai with respect to criterion Ck.

6.     In addition to the pair wise comparison of the n alternative use the same pair wise Comparison procedure to set priorities for all the criteria in terms of the importance of each in contributing towards the overall goal. Let Lij denote each element of the resulting pair wise comparison matrix, when Ci is compared with Cj.

7.     The priority vector PL is synthesized similar to step 5(PLi denotes the priority for criterion Ci

8.     Calculate the overall priority for alternative Ai denoted by Pi as follows: 

 

9.     Choose the alternative that has the highest priority

      According to Saaty a key step in the AHP model is the establishment of priorities through the use of pairwise comparison procedure and the quality of the ultimate decision relates to the consistency of judgments that he decision maker demonstrates during the pairwise comparisons. The consistency is determined using the eigenvalue (MW­ = lmax W is solved). The eigenvector provides priority and eigenvalues give a measure of consistency of judgment. The consistency index (CI) derived from the departure of lmax from n is compared with corresponding average values for random entries yielding the consistency ratio (CR).

 

Here M = matrix; w = n dimensional eigenvector associated with the largest eigenvalue lmax of the comparison matrix M.

 

Multiply each CI by the priority of the corresponding criterion and adding them together finds the consistency of the entire hierarchy. The result is then divided by the same type of expression using the random CI corresponding to the dimensions of each matrix weighted by the priorities as before. Saaty has shown that lmax is always greater than or equal to n, the closer the value of lmax is to n, the more consistent are the observed values of matrix. A zero value of CR would indicate perfect consistency whereas large values indicating increasing levels of inconsistency. The CR should be about 10% or less to be acceptable, if not, the quality of the judgment should be improved, perhaps by revising the manner in which questions are asked in making pairwise comparisons. If this should fail to improve consistency then, it is likely that the problem should be more accurately structured; that is, grouping similar elements under more meaningful criteria. The Cl for a matrix of size n is given by the formula

                                  lmax - n

                   CI  =  ľľľľ

                                  n – 1

                  

                     CR = CI  /RI

 

METHODOLOGY AND EXPERIMENTAL WORK:

The study was conducted with an objective to choose the better system between four alternatives, namely SET, NPM, SDM and SOM for carrying out nanoencapsulation. To identify major system evaluation criteria, a group was constituted and a brainstorming session was conducted. The active participants of the group were selected based on their expertise and experience in nanoencapsulation technique and a group leader with good experience in brainstorming technique and decision-making 16, 36 (in this study the group leader is well experienced and knowledgeable in nanoencapsulation technique). The group leader is also familiar with AHP model. After this exercise the group identified the factors/attributes such as Process Information (PI) of the equipment and method, Operational skill (OS) of the nanoencapsulator, supplier (SUP) of the equipment, technical information (TEI) about the equipment, technical status (TES) of the equipment, machine (MAC) inbuilt operational flexibility, etc.

 

All attribute sub-attributes were associated for example under the attribute Process Information sub-attributes such as production scale and process condition are considered since these sub-attributes contribute a lot in achieving the overall goal to formulate nanoparticles with reproducibility and consistency release profile.

 

AHP hierarchy for selection of best technique for the preparation nanoparticles by employing AHP were shown in fig 01.

 

The figure 01 represents four levels of hierarchy. The highest level, [L 1], is the focus of the problem.

 

This is turn is split into a set of attributes, PI, OS, SUP, TEI, TES and MAC corresponding to an intermediate level of hierarchy [L 2]. This in turn into another set of sub attributes such as PS, PC etc., corresponding to a lower level of hierarchy, [L 3], the last or the lowest level of hierarchy, [L4], consists of the decision alternative, SET/NPM/SDM/SOM of the technique.

 

Using the AHP model the priority weights, [PR_WT], to the attributes and sub-attributes are calculated and the results are presented in table 01.

 

Table 01 gives the pair wise comparison of the attributes by the decision maker using the Saaty’s 9-point scale. It is seen here that, PI is most important (priority = 0.421) followed by OS (priority = 0.261) and so on. In the next level of comparison, sub-attributes were compared with each other with respect to an attribute at a higher level. For instance, within PI the sub-attributes PS, PC were compared table 01. Similarly, in all the other tables of V (VC, VD, VE, VF, and VG) the priorities of the sub-attributes were computed. Table 01 (A, B, C, D, E and F) gives pairwise comparison of the alternatives (SET/NPM/SDM/SOM) with respect to each of the sub-attributes. For example, under the attribute Process Information (PI) the alternatives are compared with respect to the sub-attributes PS and PC.

 

In table 01 the factors are compared with respect to the overall objective of the problem. Here, PI is equally important when it is compared with itself and therefore assigned a value of 1. PI is moderately important when compared with OS and therefore assigned a value of 3 and PI is extremely (or absolutely) important when compared with MAC and is assigned a value of 9. Similarly, the other factors are considered. The lower half of the diagonal of the pair wise comparison matrix is the reciprocal of the upper half of the matrix. The CR for all the matrices were checked and found to be less than 0.10. On same lines, all the tables have been formulated using the expert judgment (group leader) and saaty’s nine-point comparison scale. In table 01 training [TR], the comparison of alternatives on the sub-attribute, SET and NPM has been assigned a higher priority weight of 0.469 over other alternatives namely SDM with 0.174 and SOM with 0.076. This is assigned on the basis that SET and NPM is requires lesser training to handle the instrument compared to SDM and SOM. Table 01 consolidates the results of overall composite score of each of the alternatives (SET/NPM/SDM/SOM).

 

 

Figure 01: AHP Hierarchy structure for Nanoencapsulation Technique selection

 

Table 01: Composite score of each of the alternatives.

#

Attributes

Notation

PR_WT

Sub-attributes

PR_WT

PR_WT

SET

NPM

SDM

SOM

1.

Process Information

PI

0.421

 

 

 

 

 

 

 

 

 

 

PS

0.25

0.581

0.217

0.051

0.110

 

 

 

 

PC

0.75

0.549

0.323

0.052

0.058

2.

Operation skill

OS

0.261

 

 

 

 

 

 

 

 

 

 

NET

0.435

0.558

0.263

0.056

0.121

 

 

 

 

KN

0.435

0.558

0.263

0.056

0.121

 

 

 

 

TR

0.080

0.469

0.469

0.063

0.063

3.

Supplier

SUP

0.153

 

 

 

 

 

 

 

 

 

 

AV

0.314

0.530

0.311

0.096

0.061

 

 

 

 

EX

0.363

0.350

0.350

0.039

0.049

 

 

 

 

SE

0.171

0.350

0.350

0.039

0.049

 

 

 

 

SP

0.171

0.350

0.350

0.039

0.049

 

 

 

 

MO

0.171

0.424

0.424

0.061

0.040

4.

Technical information

TEI

0.094

 

 

 

 

 

 

 

 

 

 

LT

0.250

0.420

0.282

0.134

0.054

 

 

 

 

MA

0.750

0.420

0.282

0.134

0.054

5.

Technical status

TES

0.046

 

 

 

 

 

 

 

 

 

 

ET

0.833

0.638

0.230

0.055

0.073

 

 

 

 

GR

0.167

0.638

0.230

0.055

0.073

6.

Machine

MAC

0.025

 

 

 

 

 

 

 

 

 

 

VE

0.750

0.511

0.343

0.094

0.044

 

 

 

 

CO

0.250

0.511

0.343

0.094

0.044

 

Composite rating

 

 

 

 

0.469

0.365

0.174

0.076

 

 

RESULTS AND DISCUSSION:

In this study, AHP technique was applied to make choice amongst alternative nanoencapsulation techniques (SET/NPM/SDM/SOM) and thereby opt the best technique. The composite score is used for the final ranking of the alternatives. The solution of the problem involves finding the composite score that reflects the relative priorities of all the alternatives at the lowest level of the hierarchy. The composite score favored the selection of SET (score=0.469) over NPM (score=0.365), SDM (score=0.174), SOM (score=0.076) for nanoencapsulation technique.

 

CONCLUSION:

In today competitive scenario, an effective framework for formulation of nanoparticles using AHP as MCDM tool is presented in this study here. This approach is a systematic one and it includes both quantitative and qualitative factors. Software for computing priority weights can be easily developed else commercial software (expert choice) is available. The factors considered here are illustrative only and these may vary from case to case. The proposed approach can be extended to other situations like selection of alternatives such as tablets formulation machines, characterization technique such as pharmacokinetic studies, release behavior, drug content, (microbial versus instrumental for the determination of potency of antibiotics, blenders for mixing powders, liquid, semisolids and site selection for pharmaceutical plants.          

 

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Received on 10.05.2019           Modified on 16.06.2019

Accepted on 15.07.2019         © RJPT All right reserved

Research J. Pharm. and Tech. 2019; 12(11):5320-5324.

DOI: 10.5958/0974-360X.2019.00922.3