Characterizing the Formulation Design Space - Design of experiments is a valuable tool for identifying aspects of a formulation that are critical to product quality. - BioPharm International


Characterizing the Formulation Design Space
Design of experiments is a valuable tool for identifying aspects of a formulation that are critical to product quality.

BioPharm International
Volume 23, Issue 3


When designing an excipient robustness study, the following factors should be considered to appropriately select the formulation ranges, analytical methods, and experimental design used in the study:

Stage of development: The extent of robustness studies required is dependent on protein availability and resource commitment. Thorough robustness evaluations typically are performed in late-stage development, as the commercial formulation and configuration are being locked.

Manufacturing experience: Protein, excipient, and pH levels in historical lots can help give guidance for selecting appropriate ranges for the study. This is especially important for biotechnology products because purification processes such as the ultrafiltration–diafiltration (UF–DF) step typically used to formulate the product may result in pH and excipient levels different from the formulation buffer.16,17 The number of lots and manufacturing campaigns used to assess the manufacturing history also should be considered. If there is limited manufacturing experience before starting a robustness study, the ranges of the formulation components used in the study can be set relatively wide to cover potential future ranges. Ideally, the excipient robustness study is performed early enough in product development so that changes can be made to the target level and acceptable formulation ranges if warranted.

Target level and acceptable range: Development, characterization, and historical manufacturing data typically are used to set target levels and normal ranges in manufacturing batch records and a product specification before performing a robustness study. The formulation ranges selected for the study must go beyond the acceptance criteria defined in batch records and product specifications. Selecting wider ranges helps to confirm that variations within current normal operating ranges do not statistically or practically affect product stability. By evaluating wider than normal operating ranges, one also can define the acceptable range that does not effect the product (design space). The ranges selected for the study are limited by levels that may result in significant degradation (see below) or affect patient safety. Previously established levels of generally regarded as safe (GRAS) excipients can be found in the FDA inactive ingredients database and used as a guide. Results from the robustness study can then be used to justify the batch record ranges and product specification.

Results from development and characterization studies: Development experience can be used to ensure that ranges selected are not so wide that high levels of degradation are observed making interpretation of the data difficult. Data from previous studies that provide information on product impact from variations in formulation ranges (i.e., results from formulation screening studies) can be assessed and used to select appropriate ranges for the excipient robustness study.

Material availability to perform the study: Because parameters are evaluated simultaneously in a DOE study, the material requirements can become very large. The study design must be evaluated against the material availability and modified appropriately (if possible). Options to reduce material needs include the use of scale-down models of the bulk drug substance (BDS) and final drug product (FDP) container-closure configuration, reducing the testing frequency on long-term storage, and using accelerated stability data to establish robustness at earlier time-points. Screening designs also can be used (see below).

Experimental design: Examples of designs include screening, full factorial, fractional factorial, and response-surface designs. Screening designs are used to screen the effect of factors on different responses and are amenable to experiments with a large number of factors. A full factorial design allows the identification of the formulation components that have a significant effect on stability and all the interactions among the formulation components. However, depending on the number of formulation components, a fractional factorial design that also allows for the identification of significant factors, but not all the interactions, may be more appropriate. To identify curvature (for example, an optimal range for stability), a response surface design is required. Examples of response surface designs include central composite design (CCD) and Box-Behnken design. Central composite designs can be used when evaluating at least two factors, whereas Box-Behnken designs can be used only for three or more factors.

Table 1. Number of runs1 as a function of the number of factors (formulation components) evaluated for each type of experimental design
Central composite designs contain points in a factorial design (cube points) with additional points on the axes that allow curvature to be estimated (Figure 1). Box-Behken designs, on the other hand, contain points on the center of the edges of the cube and can allow for fewer design runs while still being able to analyze curvature in the responses. The main benefit of a Box-Behken design is that none of the factorial design points get run. This means combinations of extremes, for instance the high-high-high or low-low-low combinations in a 3-factor design, do not get run. Table 1 gives examples of the number of runs required for full-factorial and fractional factorial screening, central composite design, and Box-Behnken designs as a function of the number of factors being evaluated.

It is typical to use a screening design when there are a large number of factors that must be reduced to a manageable few. Sometimes follow-up screening designs are needed to separate confounded (confused) interaction terms from one another. After the important few factors are determined, a response surface design is used to get a complete understanding of the response and how it is correlated to the factors. Because of the use of DOEs, where the changes in the responses are directly correlated with the changes in the factors, the analysis of response surface designs provides a mathematical model that can be used in manufacturing as a control scheme.

In any design, it is important to run some replicate points—points that are run multiple times—to be able to asses the random error in the response models. It is typical, but not necessary, to make these replicates the center-point condition and to run somewhere between 3 and 6 center-point runs. The other benefit of using centerpoints for the error analysis is that these runs can be used to determine process stability through the use of a statistical process control (SPC) analysis.

Analytical capability: Analytical methods used must be sensitive and appropriate for detecting changes in the stability of the product within the experimental design space. In addition to the standard methods used to release the product, characterization methods provide a more thorough evaluation of the formulation design space. Similarly, replacing qualitative methods (such as visual inspection) with quantitative (such as turbidimetry and the USP tri-stimulus method for color analysis) also help to provide a more thorough understanding of the design space. Analytical method attributes such as repeatability, intermediate precision, the limit of detection (LOD), and limit of quantitation (LOQ) determined during method qualifications must be considered when interpreting results obtained from statistical analysis of the experimental design (see below).

Statistical software capability: Results obtained from statistical analysis of the experimental data are based on the input parameters. Accurate interpretation of the data may require consultations with statistical experts. In addition, the formulator's knowledge of degradation pathways and analytical method capabilities is needed to properly interpret the data. For example, methods with high data precision may result in identifying many statistically significant factors while the practical significance of the results observed requires the formulator's knowledge of quality attributes and what constitutes an acceptable change.

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