Application of Design of Experiments (DoE) in Pharmaceutical Product and Process Optimization

CHAPTER 3

Application of Design of Experiments (DoE) in Pharmaceutical Product and Process Optimization Sarwar Beg*, Suryakanta Swain†, Mahfoozur Rahman‡, Md Saquib Hasnain§, Syed Sarim Imam¶ *

Department of Pharmaceutics, School of Pharmaceutical Education and Research (SPER), Jamia Hamdard (Hamdard University), New Delhi, India † Southern Institute of Medical Sciences, College of Pharmacy, Department of Pharmaceutics, SIMS Group of Institutions, Guntur, India ‡ Department of Pharmaceutical Sciences, Shalom Institute of Health and Allied Sciences, Sam Higginbottom University of Agriculture, Technology & Sciences (SHUATS), Allahabad, India § Department of Pharmacy, Shri Venkateshwara University, Gajraula, India ¶ Department of Pharmaceutics, Glocal School of Pharmacy, Glocal University, Saharanpur, India

1 INTRODUCTION Design of experiments (DoE) is a systematized approach of performing the experimentation by utilizing the principles of science and statistics, which helps in establishing relationship between the input factors and output responses.1 In other words, it helps in establishing cause-and-effect relationships among the factors and response(s). The said information is required to manage the input control for rationally optimizing the end results in the form of output. In simplest form, an experimental design aims at predicting the outcome on the basis of model built with the help of planned set of experiments by introducing a change of the preconditions, which is represented by one or more independent variables, also referred to as “input variables.” The change in one or more independent variables can result in a change in one or more dependent variables, also referred to as “output variables” or “response variables.” The experimental design may also identify variables that must be held constant to prevent external (uncontrollable) factors affecting the results. Besides, experimental designs not only involve the selection of suitable independent, dependent, and control variables, but also planning the experiments under statistically optimal conditions.2

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2 A BRIEF HISTORY OF EXPERIMENTAL DESIGNS The evolution of the concept of experimental design was made almost a century back by Ronald A. Fisher in the 1920s through his pioneering work dealt with agricultural applications of statistical methods. Later, Fisher published his findings on methodology for designing experiments in his innovative books: The Arrangement of Field Experiments (1926) and The Design of Experiments (1935). At that time, Fisher was interested in conducting research on statistical data analysis at the Rothamsted Agricultural Experimental Station based at London, England. He recognized flaws in the method of experimentation without rational planning and analysis without any scientific assumptions and facts. Hence, Fisher systematically introduced statistical principles for design and analysis of experiments, and proposed the concept of factorial design and analysis of variance. The era was later referred as first generation of experimental designs.3 Although applications of statistical design in industrial settings were started in the 1930s, the second-generation era of experimental design was initiated with the introduction of the response surface methodology (RSM) of designing experiments by George E.P. Box and K.B. Wilson in 1951. After this, the RSM designs were very popular in the manufacturing sectors and beyond, in agricultural and chemical industries. Later, Kiefer and Wolfowitz in 1959 proposed the approach designing in a different perspective by utilizing multifactorial optimality criteria. Further, the work of Genichi Taguchi in 1987 and 1991 was emphatically practiced in manufacturing process characterization and robustness evaluation.2 Later, the third era was marked with the development of computer software for evaluation of designs, where designed experiments became more widely used in the discrete segments of the industries, including automotive and aerospace manufacturing, electronics and semiconductors, and many more.

3 EVOLUTION OF SYSTEMATIC QUALITY APPROACHES The evolutionary changes in industrial sector were observed in the 1990s, where whole industrial started implementing designed experiments for effective quality in manufacturing processes. There is not a single area of science and engineering that has not successfully employed statistically designed experiments. An article appeared in Forbes magazine on March 11, 1996, entitled “The New Mantra: MVT,” where MVT stands for

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multivariate testing, a term used to describe the experiments encountering multiple factors. This generated fuel for applying MVT principles in diverse manufacturing sectors including the pharmaceutical field. Later, a news article also appeared in The Wall Street Journal on September 3, 2003, entitled “New Prescription for Drug Makers: Update the Plants,” deeply criticized pharmaceutical manufacturing standards by indicating that pharmaceutical industry has a little secret: even as it invents futuristic new drugs, its manufacturing techniques lag far behind those of potato-chip and laundry-soap makers.4

4 ICH INITIATIVES AND QUALITY BY DESIGN Being inspired from the Forbes Mantra and The Wall Street Journal, the International Conference on Harmonization (ICH) later instituted the quality practices into the pharmaceutical development. In this regard, the ICH published a series of quality guidances such as Q8 (Pharmaceutical Development), Q9 (Quality Risk Management), and Q10 (Pharmaceutical Quality System) for implementation of systematic quality principles.5–7 Among these guidelines, Q8 particularly describes the principles of quality by design (QbD), where DoE is considered as a pivotal element for optimizing the product and process performance. Fig. 1 illustrates the integrated quality approach revolving around the ICH Q8–Q10 guidances.

Fig. 1 Implementation of QbD approach requires integrated application of the ICH Q8, Q9, Q10, Q11, and Q12 guidelines.

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5 ONE-FACTOR-AT-A-TIME (OFAT) APPROACH OF EXPERIMENTATION The one-factor-at-a-time (OFAT) method consists of selecting a starting point, or baseline set of levels, for each factor, and then successively varying each factor over its range with the other factors held constant at the baseline level. After all tests are performed, a series of graphs are usually constructed showing how the response variable is affected by varying each factor with all other factors held constant. The major disadvantage of the OFAT strategy is that it fails to consider any possible interaction between the factors. An interaction is the failure of one factor to produce the same effect on the response at different levels of another factor.1

6 COMPARISON OF TRADITIONAL OFAT VS MODERN DoE The comparative advantages of the OFAT and DoE approaches have been discussed in several literature reports. The OFAT approach is always bound with so many experimental hassles and usually requires very high number of experiments. Moreover, it requires utilization of huge amount of time, money, and resources. It is impossible to establish cause-and-effect relationship among the factors using the OFAT approach, which many a times provide the pseudo or apparently optimal solution far away from the true optimum solution. On the contrary, DoE has significant merits over the OFAT approach, which facilitate significant saving of the resources and provide the best solution by performing minimal experimentation to produce the maximal outcomes. Table 1 provides a comparative advantages and disadvantages of the OFAT and DoE approaches.

7 GENERAL CONSIDERATIONS ON DESIGNING EXPERIMENTS The approach of designing experiments is a multistep process and requires defining the objective of experimental study, identification of influential factors and response variables, establishment of cause-and-effect relationship, mathematical modelization, and selection of the optimum formulation. In simplest form, an experimental design aims at predicting the outcome by introducing a change of conditions on the basis of planned objectives. Main concerns in experimental design include the establishment of validity, reliability, and replicability.

Application of DoE in Pharmaceutical Product and Process Optimization

Table 1 Comparison of OFAT and DoE methodology Attributes OFAT

47

DoE

Optimum formulation selection Resourceeconomics

Usually results only in suboptimal solutions

Yields the best possible formulation

Highly resource-intensive, as it leads to unnecessary runs and batches

Timeeconomics

Highly time-consuming, as each product is individually evaluated for its performance Inept to reveal possible interactions

Economical, as it furnishes information on product/process performance using minimal trials Can simulate the product or process behaviour using model equations Estimates any synergistic or antagonistic interaction among constituents Changes in the optimized formulation can easily be incorporated, as all response variables are quantitatively governed by a set of input variables

Interaction among the variables Scale-up and post approval changes

Very difficult to design formulation slightly differing from the desired formulation

8 FUNDAMENTAL OF APPLYING DoE The fundamental principles underlying experimental designs include establishment of relationship between the input and output factors in a system. The process or system can be represented by the model shown in Fig. 2. Some of the variables x1, x2,…, xn in a system can be controllable, which are regarded as controllable factors, whereas other variables y1, y2,…,yn are uncontrollable. Usually, an experimental design helps in determining the influence of factors on the output response of the system.

9 VITAL PRINCIPLES OF EXPERIMENTAL DESIGNS There are three basic principles of experimental designs, which govern the rationality for accurate prediction of the response variables. These include randomization, replication, and blocking. Every experimental design used these principles for generating the design matrix.

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Experimental space

Controllable factors X1

Input

X2

X3

Xn

Transfer function (T)

Output

yn y1 y2 y3 Uncontrollable factors

Fig. 2 General principle underlying experimental design-based optimization of products and processes.

9.1 Randomization It is the cornerstone underlying the use of statistical methods in experimental design. It helps in the allocation of the experiments and the order in which the individual runs of the experiment are to be performed are randomly determined. Statistical methods require that the observations (or errors) be independently distributed random variables. Randomization usually makes this assumption valid. By properly randomizing the experiment, one can use the “averaging out” the effects of extraneous factors that may be present.

9.2 Replication It indicates an independent repeat run of each factor combination. In the experimental design practice, replication is used for creating the repeated number of observations for a particular combination of factors. There is no specific limit for use of number of replicates in a particular design. Moreover, the replicates should be performed in a randomized order for avoiding experimental designs. Also, the principles of randomization and replication are applied simultaneously during execution of experimental designs.

9.3 Blocking This design technique is used to improve the precision with which comparisons among the factors of interest are carried out. Many a times, blocking is used to reduce or eliminate the variability transmitted from nuisance factors,

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which may influence the experimental response. Generally, a block is a set of relatively homogeneous experimental conditions. For example, an experiment in a chemical process may require two batches of raw material to make all the required runs. However, there could be differences between the batches due to supplier-to-supplier variability, and if we are not specifically interested in this effect, we would think of the batches of raw material as a nuisance factor.

10 STEPS IN PERFORMING DoE Various steps involved in design, execution, and analysis of experiment have been provided below. Fig. 3 illustrates a schematic layout of steps which can be used for efficient designing, planning, and analyzing the experimental data using DoE principles.

10.1 Defining the Problem It is necessary to articulate the objectives of the experimental process. It is usually helpful to prepare a list of specific problems that are to be addressed by the experiment. It is also important to keep all the objectives of the experiment in mind.

Fig. 3 Flowchart depicting planning an experimental analysis which should not be used as a hard-and-fast rule for analyzing all DoE. (Adapted from https://www.itl.nist.gov/.)

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10.2 Establishment of Cause-and-Effect Relationship The cause-and-effect diagrams are useful for establishing the information generated during preexperimental planning phase. The cause-and-effect diagram is also known as a fishbone diagram because the effect of interest or the response variable is drawn along the spine of the diagram and the potential causes or design factors are organized in a series of ribs. The diagram uses the traditional causes of measurement, materials, people, environment, methods, and machines to organize the information and potential design factors.

10.3 Factor Screening When a system or process is new, it is usually important to identify the vital factors which have the most influence on the response(s) of interest. Often there are a lot of factors involved in any pharmaceutical product or process, and the experimenters do not know much about the system. Hence, it is better to perform screening if we want to efficiently get the desired performance from the system. Fig. 4 portrays a schematic approach used for performing factor screening study. Unknown factors

Known factors Factor screening

Trivial many

Screening Vital few

Factor effects & interactions

No

Curvature? Yes

Factor optimization

Response surface models

Confirm? Celebrate

No

Get back

Yes

Fig. 4 Schematic flow chart for use of experimental design in product and process optimization.

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10.4 Selection of the Factor Ranges When considering the factors that may influence the performance of a formulation system, the experimenter usually discovers that these factors can be classified as either potential design factors or nuisance factors. The specific ranges are assigned to the factors for executing the experimental designs. The selection of factor levels or ranges is very crucial in order to observe the meaningful impact of the factors on the response variables. The range for a factor can be identified on the basis of prior experimental knowledge or experience. Once the experimenter has selected the design factors, he or she must choose the ranges over which these factors will be varied and the specific levels at which runs will be made. When the objective of the experiment is factor screening, it is usually best to keep the number of factor levels low. Generally, two levels work very well in factor screening studies. On the other hand, when the objective of the experiment is factor optimization, it is usually best to keep the factor levels high. In this case, it is best to use three levels work for each of the factors.

10.5 Selection of the Response Variable(s) In selecting the response variable(s), the experimenter should be certain that this variable really provides useful information about the process under study. Most often, the average or standard deviation (or both) of the measured characteristic will be the response variable. The experimenters must decide how each response will be measured, and address issues to achieve accuracy in the measured response variable.

10.6 Factor Optimization After the system has been characterized and we are reasonably certain that the important factors have been identified, the next objective is usually optimization. An optimization experiment is usually a follow-up after a screening experiment. Use of customized optimization designs (also called as response surface designs) is very helpful to produce the optimal settings of the factors. Fig. 4 portrays a schematic approach used for performing factor screening study.

10.7 Performing the Experiment While running the experiment, it is vital to monitor the process carefully to ensure that everything is being done accurately. Errors in experimental procedure at this stage will usually destroy experimental validity. One of

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the most common mistakes that I have encountered is that the people conducting the experiment failed to set the variables to the proper levels on some runs. Someone should be assigned to check factor settings before each run.

10.8 Factor-Response Confirmation In a confirmation experiment, the formulators usually try to verify that the system operates or behaves in a manner that is consistent with some theory or past experience. For example, if theory or experience indicates that a particular new material is equivalent to the one currently in use and the new material is desirable, then a confirmation experiment would be conducted to verify that substituting the new material results in no change in product characteristics that impact its use.

10.9 Mathematical Modeling in Experimental Design The model is just a quantitative relationship (equation) between the response and the important design factors. In many cases, a low-order polynomial model will be appropriate. A first-order model in two variables is where y is the response, the x’s are the design factors, the y’s are the unknown parameters that will be estimated from the data in the experiment, and is a random error term that accounts for the experimental error in the system that is being studied. The first-order model is also sometimes called a main effects model. First-order models are used extensively in screening or characterization experiments. A common extension of the first-order model is to add an interaction term, say where the cross-product term x12 represents the two-factor interaction between the design factors. Because interactions between factors are relatively common, the first-order model with interaction is widely used. Higher-order interactions can also be included in experiments with more than two factors if necessary. Another widely used model is the second-order model. The second-order models are often used in optimization experiments. In selecting the design, it is important to keep the experimental objectives in mind. In many engineering experiments, we already know at the outset that some of the factor levels will result in different values for the response.

10.10 Statistical Data Analysis Statistical methods should be used to analyze the data so that results and conclusions are objective rather than judgmental in nature. If the experiment has

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been designed correctly and performed according to the design, the statistical methods required are not elaborate. There are many excellent software packages designed to assist in data analysis, and many of the programs used can be helpful to provide statistical analysis and effortless interpretation of the outcomes. It is also usually very helpful to present the results of many experiments in terms of an empirical model, that is, an equation derived from the data that express the relationship between the responses. Also, the graphical analysis of the results provides much better interpretation of the outcome from the performed experiment.

10.11 Selection of Experimental Designs The selection of experimental design exclusively depends on the nature of experiments such as factor screening or characterization, and factor optimization. Other considerations like number of factors and sample size (number of replicates), selection of a suitable run order for the experimental trials, and determination of whether or not blocking or other randomization restrictions, etc. determines the selection of experimental design. Design selection also depends on the selection of empirical model to describe the statistical cause-and-effect relationship.

10.12 Types of Experimental Designs The experimental designs can be classified into two types, such as screening designs and response surface designs, which have been discussed below in detail. 10.12.1 Screening Designs Screening designs are an efficient way to identify the significant main effects of the factors. The term “screening” refers to an experimental plan that is intended to find the few significant factors from a list of many potential ones. Alternatively, a screening design is primarily used to identify significant main effects, rather than interaction effects. Low-resolution designs (III or IV) like fractional factorial design (FFD), Taguchi design, and Plackett-Burman design are primarily used for the purpose of screening. Ultimately, these screening designs provide very limited experimental runs, thus allows limited expenditure of time and resources. The details about each of the above listed screening experimental designs have been provided below. Fig. 5 portrays the depiction of various screening experimental designs.

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E1 E2

I1 I3 I2

(B)

(A)

(C) Fig. 5 Examples of screening designs (A) fractional factorial design, (B) Taguchi design, (C) and Plackett-Burman design.

10.12.1.1 Fractional Factorial Design

It belongs to a type of factorial designs, which can be typically used for factor screening. FFDs are expressed using the notation Xk p, where X is the number of levels of each factor investigated, k is the number of factors investigated, and p describes the size of the fraction of the full factorial used. Formally, p is the number of generators, assignments as to which effects or interactions are confounded, that is, cannot be estimated independently of each other. For example, a 25–2 design is 1/4 of a two level, five factor factorial design, which requires only 8 runs instead of full 32 runs. FFDs are recommended when number of factors in an experiment range between 3 and 7. Two-factor levels are used for screening purpose. The code +1 is used for high level and code 1 is used for low level. However, a three-level factor, with intermediate coded value 0 can also be used when screening designs are augmented for the purpose. 10.12.1.2 Taguchi Design

Genichi Taguchi, a Japanese engineer, proposed several approaches to experimental designs that are sometimes called “Taguchi methods.” These

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methods utilize two-, three-, and mixed-level FFDs. Taguchi refers to experimental design as “off-line quality control” because it is a method of ensuring good performance in the design stage of products or processes. This design primarily relies on the use of orthogonal arrays, which provide a set of well balanced (minimum) experiments serve as objective functions for optimization. Taguchi design starts with minimum of three factors and produces four experimental runs at two levels without requisite of any center point runs. 10.12.1.3 Plackett-Burman Design

This design is used for screening experiments, because it only identifies main effects and generates minimal experimental runs (also referred as “saturated designs”), while two-factor interactions are highly confounded. This design has run numbers that are a multiple of 4. Plackett-Burman design starts with minimum of 11 factors and produces 12 experimental runs without requisite of any center point runs. It also exist for 20-run, 24-run, and 28-run (and higher) designs. With a 20-run design you can run a screening experiment for up to 19 factors, up to 23 factors in a 24-run design, and up to 27 factors in a 28-run design. These Resolution III designs are known as saturated main effect designs because all degrees of freedom are utilized to estimate main effects. 10.12.2 Response Surface Designs Response surface designs are particularly used for optimization of the factors identified from the risk assessment and/or screening study. These designs produce enough experimental runs to identify both the main effects and interaction effects of the factors. Select instances of the response surface designs include full factorial design, central composite design (CCD), Box-Behnken design (BBD), optimal design, and mixture designs. The details about each of the above-listed designs have been provided below. Fig. 6 portrays the depiction of various response surface experimental designs. 10.12.2.1 Full Factorial Design

A full factorial experiment is an experiment whose design consists of two or more factors, each with discrete possible values or “levels,” and whose experimental units take on all possible combinations of these levels across all such factors. These designs are represented by Xk, where X indicates number of factors and k indicates number of levels. A full factorial design may also be called a fully crossed design. A full factorial design generates experimental runs based on the factorial points and generates a linear

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Fig. 6 Examples of response surface designs (A) full factorial design, (B) central composite design, (C) Box-Behnken design, (D) optimal design, and (E) mixture design.

polynomial model. Moreover, use of additional center points also helps in increasing the power for better prediction of the design space. Such an experiment design allows the experimenter to study the effect of each factor on the response variables, as well as the effects of interactions between the factors on the response variables. 10.12.2.2 Central Composite Design

CCD is an effective statistical design which provides information exclusively on the effect of experiment variables. It is widely used for response surface optimization of the experiments, which employs second-order (quadratic) model for the response variable without using a complete three-level factorial experiment. CCD is considered as an augmented form of three-level factorial design coupled with star points or axial points. It is employed when factorial designs detect the presence of curvature in the data, thus requires augmentation from an erstwhile linear design to the quadratic response surface design. 10.12.2.3 Box-Behnken Design

BBD is an independent quadratic design in that it does not contain an embedded factorial or FFD. In this design, the treatment combinations are at the midpoints of edges of the process space and at the center. These

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designs are rotatable (or near rotatable) and require three levels of each factor. The designs have limited capability for orthogonal blocking compared with the CCDs. However, it can be used as an alternate choice for fitting quadratic models that requires three levels of each factor and is quite rotatable to provide symmetry to the design. 10.12.2.4 Optimal Designs

The optimality of a design depends on the statistical model and is assessed with respect to a statistical criterion, which is related to the variance-matrix of the estimator. Specifying an appropriate model and specifying a suitable criterion function both require understanding of statistical theory and practical knowledge with designing experiments. Further, optimal designs are of different types such as D-optimal, I-optimal, and A-optimal. These designs utilize three levels for each of the selected factors and are most commonly used for factor optimization study. 10.12.2.5 Mixture Designs

In a mixture experiment, the independent factors are proportions of different components of a blend. In another way, the fact that the proportions of the different factors must sum to 100% complicates the design as well as the analysis of mixture experiments. Mixture designs can be of different types such as simplex-lattice designs, simplex-centroid designs, and optimal designs. Among these variants of mixture designs, optimal designs are most commonly used for optimization of factors. Further, optimal designs are of different types such as D-optimal, I-optimal, and A-optimal. These designs utilize three levels for each of the selected factors and are most commonly used for factor optimization study.

11 DoE APPLICATIONS IN THE PHARMA PRODUCT DEVELOPMENT Pharmaceutical drug products are the intricate devices containing one or more active ingredients and a plethora of excipients, which requires multiple step processes to convert these raw materials into finished dosage forms.8,9 A wide variety of pharmaceutical products are available till date, which differ each other in multiple ways such as device geometry, use of excipients, and manufacturing processes. Verily, the pharmaceutical development of drug products encounters multiple challenges with respect to inconsistency in product quality and robustness. In this regard, the systematic development of drug products requires rational optimization of the influential product and

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process parameters. In this regard, the application of DoE tools in the systematic development of various oral and non-oral drug delivery systems have been described in the following sections.10

11.1 Oral Drug Delivery Systems These systems particularly include solid and liquid pharmaceutical dosage forms. Solid dosage forms include tablets, capsules, powders, granules, pellets, microspheres, etc., while liquid dosage forms include dry syrups, emulsions, suspensions, etc. Several reports are available on the DoE optimization of these drug delivery systems. Table 2 provides a succinct account on the literature instances on drug delivery optimization of various oral dosage forms, which enlist use of drugs, excipients, and manufacturing processes.

Table 2 Select literature instances on DoE-based optimization of oral drug delivery systems Experimental Drug design Key findings Reference Immediate/modified release tablets

Cinnarizine

Central composite design

Rosuvastatin

PlackettBurman design

Meloxicam

Response surface design

Deacetyl mycoepoxydience

Central composite design

Enhancement made in the bioavailability, due to the prolonged gastric residence time Improved biopharmaceutical performance in the term of particle size, entrapment efficiency and drug loading capacity Resulted to positive effects on disintegration time, wetting time, and mechanical strength Got improved dissolution and oral bioavailability of the DM.

11

Improved stability, extending the core’s shelf-life, and providing a sustained and controlled release

15

12

13

14

Microspheres/microparticulate systems

Lacidipine

Central composite design

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Table 2 Select literature instances on DoE-based optimization of oral drug delivery systems—cont’d Experimental Drug design Key findings Reference

Riboflavin sodium phosphate Etoposide

Paclitaxel

Response surface design Factorial design Taguchi design

It provides optimum encapsulation and better gastric stability Received better stability and enhanced intracellular drug delivery Improved drug loading capacity and reversal from cancer drug resistance

16

Obtained size less than100 nm and >80% drug release in 15 min and 5.7-folds enhancement in AUC. It founds optimum particle size and better drug release

19

It founds optimum drug absorption and enhanced pharmacodynamic potential in regulating of serum lipid levels It received 4.27-fold higher oral bioavailability of quercetin, 1.5 fold higher in case of resveratrol and 2.8-fold higher in case of genistein over to free antioxidants suspension

21

17

18

Nanoparticulate-based systems

Rosuvastatin calcium

D-optimal mixture design

Valsartan

Lovastatin

Central composite design Facecentered cubic design

Quercetin, resveratrol and genistein

Response surface design

20

22

11.2 Non-Oral Drug Delivery Systems These systems particularly include dosage forms meant for administration through routes other than oral route, such as parenteral, topical, transdermal, ocular, otic, and inhalational preparations. A variety of dosage forms have been developed for administration through aforementioned routes. Several reports are available on the DoE optimization of these drug delivery systems. Table 3 provides a succinct account on the literature instances on drug

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Reference

Topical drug delivery systems

Isotretinoin

Aceclofenac

Lornoxicam

Response surface design Response surface design Central composite design

Topical gel

Topical gel

Topical liposomal gel

The optimized SLNs showed entrapment of 89.49  4.1%, while the size was found to be in the nano-range (i.e., 75.3  2.4 nm) The resulted gel formulation got improved permeation profile and exhibited excellent anti-inflammatory action, when compared with marketed gel The optimized formulation showed particle size range from 100 to 200 nm with sustained release action

23

24

25

Transdermal drug delivery systems

Dexibuprofen

Diflunisal

Glimepiride

Response surface design Central composite design PlackettBurman design

Transdermal patches

Nanolipidic carriers

Transdermal glimepiride liposomal films.

The resulted optimized formulation has uniform thickness, relatively low moisture uptake and highly acceptable drug loading The optimized SNLC has particle size of lesser than 200 nm, >86.77% entrapment, better skin retention and significantly higher paw edema The optimized formulation obtained maximum entrapment capacity and optimum drug release

26

27

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Table 3 Select literature instances on DoE-based optimization of non-oral drug delivery systems Experimental Drug design Dosage form Key findings

Injectable drug delivery systems

Valrubicin

Injectable nanoparticle

It founds particle size lesser than 100 nm and formulation were stable for up to 24 h.

29

PLGA nanoparticles

Encapsulation efficiency as high as 70% with immediate and long-term sustained release properties

30

Solid lipid nanoparticles

It provides improved efficiency and hybrid gel systems prolonged their action in a sustained manner

31

Nanostructured Lipid Carriers

The optimized formulation showed optimum stability and increase in curcumin permeation ( 2.5-fold) across the rabbit cornea in comparison to the control

32

Microparticles inhaler

It founds the particle size of 13.068 μm, better dissolution and permeability

33

In situ gelling microemulsion via intranasal route Nanoemulsion

The optimized formulation founds 97% drug loading and release the drug to 6 h. Further it showed higher flux across goat nasal mucosa Salegeline nanoemulsion administered intranasally into Parkinson induced rat, results to significant improvement in behavioral activities as compared to oral administered drug

34

Ocular drug delivery systems

Moxifloxacin Hydrochloride Curcumin

Response surface design Central composite design

Intranasal systems

Levodopa

Lorazepam

Selegiline

Response surface design Response surface design Response surface design

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Small interfering RNA

Response surface design Response surface design

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delivery optimization of various non-oral dosage forms, which enlist use of drugs, excipients, and manufacturing processes.

12 CONCLUSION Since the development of pharmaceutical products is facing lot of regulatory hiccups owing to inconsistent product quality and lack of adequate product performance, the federal agencies across the globe are constantly coercing the pharma manufacturers to adopt the systematic tools. In this regard, DoE is considered as an indispensable tool for ultimately building quality into the products by rational planning and meaningful execution of the experiments. A pharmaceutical scientist can maximally utilize the essence of DoE tools to harness the best to optimize quality by improved understanding of the product and process performance.

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