HPMC: Hydroxy Propyl Methyl Cellulose; Na CMC: Sodium Carboxy Methyl Cellulose; EC: Ethyl Cellulose; HEC: Hydroxy Ethyl Cellulose; DCP: Dicalcium Phosphate; PVC: Poly Vinyl Chloride; HPC: Hydroxy Propyl Cellulose; MCC: Micro Crystalline Cellulose; PEG: Polyethylene Glycols; PVP: Polyvinyl Pyrrolidone

The use of optimization techniques employing design of experiments (DoE), however, permeated the field of pharmaceutical product/process development around four decades ago. The first literature report on the rational use of optimization appeared in 1967, when a tablet of sodium salicylate was optimized using a factorial designs (FD). Since then, these systematic approaches have been put into practice in the development of drug formulations at steady pace. Despite tremendous advancements in diverse drug delivery approaches, the oral route remains the most “natural” route of drug administration. In addition, because of the low cost of oral therapy, ease of administration, and improved patient compliance associated with oral route, more than 50% of drug delivery systems available commercially are oral ones. In this context, oral controlled release drug delivery systems are quite popular, offering a number of advantages over conventional dosage forms [1,2]. Generally, the controlled release drug delivery systems for oral use are solid dosage forms, based upon the mechanism of diffusion, dissolution, or a blend of both to control the release rate of drug. These include reservoir devices wherein a polymeric membrane surrounds a drug core and matrix devices wherein the dissolved or dispersed drug is distributed uniformly in an inert polymeric matrix. Most DoE literature reports in this category are focused on optimizing the levels of these release rate-controlling polymers. DoE optimization on oral controlled release matrix delivery devices started in the early 1980s. Such devices encompass the inert matrices such as hydrophilic, hydrocolloid, silicone elastomer, and lipid matrices. The common independent variables for all of these have been the quantities of the polymers or other ingredients, while the optimized responses invariably have been the parameters characterizing in vitro dissolution profile. The other response variables that have been optimized include disintegration time, bioavailability, and bioequivalence [3,4]. The literature reports on oral controlled release dosage forms have been compiled in various tables, categorized on the basis of various types of polymers (natural, semi-synthetic, synthetic) and the type of controlled release dosage form (matrices, dispersions, coated tablets). Table 1 depict the use of statistical experimental designs in optimization of oral sustained release (SR) matrices along with the selected drug candidate and various input variables (factors) studied. Table 1 reports the work on DoE optimization of oral controlled release drug delivery systems, where natural, synthetic or semi-synthetic polymers have been taken as factors, invariably to control or modify the release rate of the drug. The natural polymers used comprise isapghula husk, guar gum, xanthan gum, pectin, carrageenan, and alginic acid. Optimization reports on sustained release tablets formulated using synthetic polymers such as acrylates, poly-methacrylates, silicone elastomers, and polyethylene glycols (PEG), are shown in Table 1. Apart from polymer level, various other factors that have been optimized include tablet size, compression force, and amount of granulation liquid, lubricant, and glidants. Although most studies focused on optimizing drug release parameters, some studies involved optimization of dissolution evaluation conditions as well. Semisynthetic polymers that frequently have been employed include mainly the cellulose derivatives - i.e., hydroxyl propyl cellulose (HPC), hydroxyl propyl methylcellulose (HPMC), hydroxyl ethyl cellulose (HEC), sodium carboxy methyl cellulose (Sodium CMC), and ethyl cellulose (EC). Some studies on the gums involve treatment with acid or alkali to modify the swelling properties of the naturally existing gum and subsequent optimization of their proportion, to be used in SR matrices. Experimental designs have also successfully been employed in the case of the core-in-cup type of compressed SR matrices, studying role of non-swellable polymers and the other process variables in retarding the release of soluble (caffeine) and insoluble (ibuprofen) drugs employing factorial designs [5-7].

Table 1: Optimization reports on oral sustained release tablet formulations.

Design of experiments and optimization techniques in pharmaceutical research

The design of experiments (DOE) is an efficient procedure for planning experiments so that the data obtained can be analyzed to yield valid and objective conclusions. Experimental designs can be defined as the strategy for setting up experiments in such a manner that the information required is obtained as efficiently and precisely as possible. Well-chosen experimental designs maximize the amount of information that can be obtained for a given amount of experimental effort. Optimization of a formulation or process is finding the best possible composition or operating conditions. Determining such a composition or set of conditions is an enormous task, probably impossible and certainly unnecessary. Hence in practice, optimization may be considered as the search for a result that is satisfactory and at the same time the best possible within a limited field of search. The purpose of optimization is to determine quantitatively the influence of the different factors together on the response variables. The number of levels is usually limited to two, but sufficient experiments are carried out to allow for interaction between factors (Figure 1) [7].

Figure 1: Process Models for DOE.

Experimental designs have long been employed to optimize various industrial products and/or processes such as;

• Completely randomized designs (CRD)

• Randomized block designs (RBD)

• Screening Designs since 1946

• Simplex Lattice Design

• Latin squares designs (LSDs)

• Graeco-Latin squares designs

• Hyper-Graeco-Latin squares designs

• Factorial Designs (FDs) since 1926

• Full factorial designs

• Fractional factorial designs

• Plackett-Burman designs (PBDs)

• Central composite designs (CCDs) since 1951

• Face Centered Composite Design

• Rotatable Central Composite Design

• Box-Behnken designs (BBD)

• Response Surface Methodology (RSM)

• D-Optimal Design (D-OD)

• Simplex Centroid Design (SCD)

• Simplex Mixture Design (SMD) since 1958

• Sequential Simplex Design (SSDs)

• Artificial Neural Networks (ANN)

• Extreme Vertices Design (EVD)

• Doehlert Design

• Principal Component Analysis (PCA)

DOE Steps (Figure 2)

• Problem statement

• Choice of factors, levels, and ranges

• Choice of response variable(s)

• Choice of experimental design

• Performing the experiment

• Statistical analysis

• Conclusions and recommendations

Figure 2: Steps for Design of Experiments

DOE applications in process development

• Improve process yield

• Reduce variability

• Reduce development time

• Reduce overall costs

DOE objectives

• Determine influential variables (factors).

• Determine where to set influential factors to optimize response.

• Determine where to set influential factors to minimize response variability.

• Determine where to set influential factors to minimize the effect of the uncontrollable factors.

DOE applications in design

• Evaluate and compare alternatives

• Evaluate material alternatives

• Product robustness

• Determine key design parameter

An exhaustive literature search carried out by the authors in pharmaceutical journals and texts reveals that the DoE optimization techniques have been employed for almost all of these dosage forms, ranging from the simple conventional ones to that of the most intricate novel DDS. The updated literature reports unequivocally point out the increasing application of DoE techniques, with a significant shift in the focus of the formulator from optimization of the conventional formulations to that of the modern drug delivery devices [7].

With the advent of newer, sophisticated technologies, the task of drug delivery has become more intricate, involving a greater number of resources in terms of cost, time, and energy. To circumvent these developmental hiccups, adoption of DoE analytical tools is prudently called for. Particularly, when finding the correct compromise is not straight forward, a pharmaceutical scientist should mandatorily consider the use of optimization studies.

DoE techniques have been applied with fruition on almost all kinds of drug delivery systems, not only for optimizing the formulations but their processes too. Nevertheless, there are many new drug delivery applications awaiting demonstrations. The pivotal benefits of DoE have not been thoroughly investigated in some newer drug delivery areas such as gene delivery, peptide delivery, reverse micellar systems, dendrimer based delivery systems and the like. Understanding the formulation or process variables rationally using experimental designs will help in achieving the desired goals with phenomenal ease. Experimental designs can prove to be useful, even if the primary aim is not the selection of the optimum formulation, because it tends to reveal the degree of improvement in the product characteristics as a function of the change in any excipient or process parameter(s). The major impediment in using DoE has been to envision the entire exercise as a whole. The more the formulator knows about the system, the better it can be defined, and the higher the precision with which it can be modified. The difficulties in optimizing a pharmaceutical formulation are due to the difficulty in understanding the real relationship between casual and individual responses. DoE studies can come to the rescue of the formulator, yielding much better prognostic abilities. Once the empirical relationship between the cause and the effect is unraveled, the developmental or post-developmental thoughts can be realized quite rapidly as well as rationally. Defining the relationship between the formulation or process variables and quality traits of the formulation is almost an impossible task without the application of an apt design model. Trial and error methods, in this regard, can never allow the formulator to know how close any particular formulation is to optimal drug delivery solution. This would provide the desired impetus to the product development scientist, facilitating further evolution of research on oral controlled release drug delivery innovations and next generation product launches.

The literature search indubitably ratifies the steadily increasing popularity of DoE in drug delivery optimization. Verily, the number of optimization studies would be much higher in the drug industry, where DoE methods are applied much more frequently. Because only a miniscule fraction of industrial studies is reported, most investigations remain as only in-house information. Nevertheless, the DoE usage is far from being adopted as a standard practice. Many more endeavors have to be undertaken to highlight the enormous benefits of these techniques before this can happen as a global trend. With the easy availability and affordability of DoE software, these powerful tools can be implemented with the simple click of a mouse. However, there are some key issues that depend upon the experimenter but not upon the software. These include choosing suitable responses (output variables) and factors (input variables), setting appropriate factor ranges or levels, managing the experimentation, interpreting numeric outcomes and graphic manifestations of the findings, presenting the results, and finally deciding whether to continue further with process optimization or just run confirmatory experiment(s) to validate DoE. If the experimenter has not endeavored DoE as yet or if a significant jump in information and impact in production capability has not yet been obtained, it is the most opportune time to get started. Eventually, the day will come when the benefits of DoE would be harvested by drug industry and research to their fullest advantage. Providing a relatively pithy overview, this article thus endeavors to act as a disambiguation of knowledge, and knows how to guide and provide ideas to the product development scientists in formulating varied oral controlled release drug delivery systems. I hope that my effort is going to find new application or new idea in nearer future.

The author would like to thank Dr. M. R. Patel for his comments.