Design and development of an ideal pharmaceutical product invariably comprise multiple objectives. Such is particularly true for more intricate drug delivery systems (DDS) involving a plethora of drugs, excipients, polymers and processes. For decades, this task has been conducted through trial and error, supplemented with the previous experience, knowledge, and wisdom of the formulator.
The traditional approach of developing a formulation or process essentially entailed studying the influence of the corresponding composition and process variables changing One Variable at a Time (OVAT), while keeping others as constant. The technique, at times, is also referred to as Changing One Single variable at a Time (COST) or OFAT (i.e. One Factor at a Time) or "shotgun" approach. During the OVAT studies, the first variable is fixed at a favorable value, and the next is examined until no further product/process improvement. This customary OVAT approach has been proved to be not only too expensive in terms of time, money, and effort, but also unfavourable to fix errors, unpredictable, and at times even unsuccessful, as enumerated in Box 1.
The modern formulation optimization approaches, on the other hand, employing Design of Experiments (DoE), can be used in the development of diverse DDS to improve such irregularities. Such systematic approaches are far more advantageous, as enlisted explicitly in Box 2.
DoE has successfully attained the "true" optimal formulation, invariably missed by the OVAT techniques, as illustrated in Figure 1.
These systematic DoE approaches have been fast permeating into product development research at an alarming pace, as illustrated in Figure 2.
Basic terminology
DoE, with respect to drug formulations or pharmaceutical processes, is a phenomenon of finding "the best" possible composition or operating conditions. Of the numerous technical terms employed during DoE optimization, the vital ones are summarized in Box 3.
Methodology
The gamut of DoE methodology provides explicit information on diverse DoE aspects organized in a seven-step sequence, as described in Figure 3.
Experimental designs
There are myriad experimental designs to choose from for response surface modelling (RSM) and factor screening. Figure 4 portrays a typical 3-D response surface plot drawn using an experimental design.
Search for optimum
Optimization of one response or the simultaneous optimization of multiple responses can be accomplished either graphically or numerically.
Graphical optimization: Known popularly as response surface analysis, graphical optimization displays the area of feasible response values in the factor space by search methods or overlay plots. Brute force search methods are employed for choosing the upper and lower limits of the responses of interest in two steps viz. feasibility search and grid search. Overlay plots are the 2-D contour plots superimposed over each other to search for the best compromise visually, as depicted in Figure 5.
Numerical Optimization: In case of multiple responses, numerical optimization is usually preferred vis-à-vis graphical techniques to uncover a feasible region. Desirability function involves a way of overcoming the difficulty of multiple opposing responses. Objective function methods, on the other hand, are used to seek an optimum formulation by solving for a maximum or a minimum in the presence of equality and/or inequality constraints.
The merits of DoE optimization techniques are galore and their acceptability upbeat. Figure 6 gives a bird's eye view of various DDS systematically developed and optimized using DoE till date.
Computer use in optimization
Putting the rational approaches of DoE into practice, however, usually involves a great deal of mathematical and statistical intricacies. With the availability of powerful and economical hardware and that of the comprehensive DoE software (like Design Expert, Minitab, FACTOP etc.), the computational hiccups have been greatly streamlined.
Computer software have been used almost at every step during the entire optimization cycle, ranging from design selection, factor screening, RSM, design matrix generation, plotting response surfaces and contour plots, optimum search, result interpretation, and finally, DoE validation.
Epilogue
Of late, use of DoE has become an integral and regular phenomenon globally as a leading edge approach in rational drug delivery. It is a pivotal developmental tool in diverse industrial processes today. Employing DoE makes it much simpler to modify existing formulations and meet redefined objectives. From federal perspectives too, DoE is an indispensible component of overall Quality-by-Design (QbD) paradigm, required to be implemented at different levels of industrial processing. Nonetheless, its enormous potential has not been fully harvested in drug delivery development, research and industry so far. Regardless of some reports and publications, we are yet to make the most of this revolutionary practice for mundane product development of various DDS. A journey of thousand miles has to start with a single determined step. Hence, let us all resolve to begin the useful DoE sojourn to the destination of success, marked with quality, precision, alacrity and economy.
Dr Bhupinder Singh is Professor (Pharmaceutics & Pharmacokinetics), University Institute of Pharmaceutical Sciences,UGC Centre of Advanced Studies & Dean, Alumni Relations and Rishi Kapil is Research Associate, Univ Institute of Pharmaceutical Sciences, Panjab University, Chandigarh.