Jeffrey Fleitman

The approach   provides the required International Conference on Harmonization   validation elements as well as information on                          interactions, measurement uncertainty, control   strategy, and continuous improvement. Despite being less resource   intensive than the traditional validation approach,   quality is not compromised. Additionally, through judicious planning,   the MVbD approach can encompass early formulation design efforts   so that a wide range of formulations is taken into consideration when   defining the method-validation design space.

Analytical method   validation is a crucial part of formulation development. It is needed   to ensure that methods provide accuracy                          and precision in detecting formulation   differences in drug dissolution, stability, active, and impurity levels.   Method validation is also key to meeting requirements of cGMP,   compendia, and the International Conference on Harmonization (ICH) for   testing and releasing clinical or commercial dosage   forms. During early development, formulations are often changed to adapt   to new                          preclinical and clinical data. Revalidating each   of these new formulations is resource intensive and affects development   timelines. 

The ICH Q2   (R1) validation guidelines (1) do not provide crucial information such   as how formulation changes affect method                          performance or what method critical validation   parameters need to be monitored and controlled. An ICH approach may not   provide                          a good understanding of a method-measurement   uncertainty, which is needed to ensure that the overall process   capabilities                          are met and that appropriate in-process controls   and specifications are set. This sort of understanding is needed to   meet                          FDA’s process validation guidelines (2). Once   the product acceptance criteria are established, the influence of assay   variation                          can be determined relative to product acceptance   rates.

This article   describes a method-validation-by-design (MVbD) approach to validate a   method over a range of formulations. It                          uses both design-of-experiment (DOE) and   quality-by-design (QbD) principles to define a design space that allows   for formulation                          changes without revalidation. The approach   provides the required ICH validation elements as well as information on   interactions,                          measurement uncertainty, control strategy, and   continuous improvement. This approach is less resource intensive than   the traditional                          validation approach without compromising   quality. Additionally, through judicious planning, it can encompass   early formulation                          design efforts so that a wide range of   formulations can be used to define the method-validation design space. 

MVbD is not   specifically addressed in ICH; however, it is supported by the   principles presented in ICH Q8, 9, 10, 11 (3–6)                          as well as ICH Q2 (R1). There are other industry   guidelines (2), publications (7–9) and presentations (10–15) that   support                          a QbD/DOE approach for analytical method   development and validation, including FDA’s 21st Century Quality   Initiative, which                          was first presented in 2001 and later updated in   2005 (15), and the outcome from FDA’s QbD pilot. 

The use of DOE is   well established for determining method robustness, such as how much   the mobile-phase composition, column                          temperature, and flow rate can vary. DOE is also   well used in formulation screening. This approach broadens DOE to   include                          method validation over a range of formulations.

The MVbD approach applies to these QbD principles: 

 of the potential critical validation parameters. Systematic analysis (e.g., fishbone diagram) is done based on historical                                knowledge. The QbD/DOE output confirms those that are critical. 

. Once the required process capability is known, the required method accuracy and precision acceptance criteria are defined                                in the ATP. 

. DOE output   defines those parameters that have the most impact on the method   performance. Monitoring these parameters throughout                                development will help define acceptable   ranges. 

. MVbD provides the acceptance criteria that must be met to move to new technologies. 

. Knowledge gained is cycled backed to accelerate the next program. 

Traditionally, each   new drug-product formulation requires validation to support clinical   testing. In early development, the                       method is validated for linearity, accuracy, and   precision. Method linearity is determined across five concentrations   from                       50% to 150% of the nominal (100%) concentrations.   Accuracy and precision can be combined with the linearity study by doing                       six replicates at the 100% (nominal) concentration   and three replicates at the other concentrations (50%, 75%, 125%, and   150%),                       which gives a total of 18 sample preparations. If   there are five new formulations during development that require separate                       validation, a total of 90 sample preparations would   be needed. This approach is resource intensive and affects project   timelines.                       In this given example, only one                       formulation component is varied. As more components   are varied, the workload increases proportionally. 

It does not statistically define a design space

A QbD/DOE approach   validates a method over a range of formulations within a defined design   space. Movement within this design                       space does not require method revalidation with   each new formulation. The DOE approach detects any excipient–API   interactions                       and critical validation control parameters. A DOE   approach is not necessarily more labor intensive than the traditional   method-validation                       approach and additional information gained is well   worth the exercise. As described, the traditional approach requires at                       least 90 sample preparations to validate five   formulations over the course of the development. Using DOE to validate   across                       a broader range that includes these five   formulations would require only three determinations for each of the   five formulations,                       resulting in a total of 15 sample preparations. 

Step 1: Define the range of formulations and required process capability

.   A formulation screening DOE may be used to determine all possible   clinical formulations for the study. Manufacturing process                             capabilities need to be considered to define   the ATP (i.e., the necessary accuracy and precision to ensure acceptable   data).                             Full factorial, fractional factorial and/or   custom (d-optimal) design all may be used in developing the study   design. 

Step 2: High-performance liquid chromatography (HPLC) method selection

.   A method should be developed such that sample preparations of the   different formulation can be diluted to the same API concentrations.                             The diluted sample from each formulation is   injected onto the HPLC column under the same chromatography conditions.   Standardizing                             these conditions will facilitate DOE and   latter method-robustness studies. 

. The set up of the DOE can be accomplished with or without using DOE software. Inputs to the DOE include a number of varying                             factors, such as the different API levels and/or excipient and preservative levels. 

.   Data are analyzed for linearity, precision, and accuracy, including   confidence intervals. Any interactions are reported                             and a design space is defined. The design   space is defined by fitting a model to the factors used in the   evaluation of assay                             concentrations and other key factors that may   interfere or influence assay precision or bias. After fitting the   model, the                             design space may be visualized using contour   plot or profilers. 

To illustrate the MVbD process, a simulated case   study is presented where three factors are varied over a range of   formulations                       (see Table I). The API concentrations are varied   from 0.005%, 0.01%, 0.015%, 0.02%, and 0.025% (w/w). Preservative   concentrations                       are either 0.01% or 0.03% (w/w) and concentrations   of Excipient 3 are either 0.5% or 0.8% (w/w).

. The DOE   pattern is presented as 1, 2, 3, 4 or 5 --, -+, +- or ++. The numbers 1   through 5 represent the five API concentrations                       and the “+” or “–” represent with or without the   two possible preservative and excipient concentrations. Three replicates                       are done for each combination of API, preservative,   and excipient concentration. A total of 60 sample preparations is   required.                       The DOE typically suggests a randomized sample   preparation to avoid any sample preparation variability; however,   because the                       variability is low, a standard sample-preparation   scheme can be followed.

Figure 1 also presents the percent recovery data.   The required ICH statistics can be obtained from these data. For   example, 

 shows linearity data along with   typical linearity statistics. One key output is the root mean square   error (RMSE); this parameter                       identifies method variability that is critical for   understanding the method contribution to the overall process   variability. 

 also shows linearity plots of   percent recovery versus the two excipient concentrations. The difference   in slopes indicates                       interaction (i.e., a lower API recovery at a higher   level of Excipient 3 concentration), possibly due to interaction of the                       excipient with the API. Such information is   important for the formulator and method developer to consider if higher   preservative                       levels are needed.

 presents the mean percent recovery   for each combination of API, preservative, and excipient along with the   corresponding                       confidence limits. In the example describe, there   was a somewhat lower API recovery with the higher excipient levels,   which                       is further illustrated in 

.   It is obtained by mathematically modeling the primary main effects and   secondary two-factor interactions as well as possible                       polynomials using a DOE software. The operating   range is the white space shown in Figure 4; it is where the method   accuracy                       and precision meet the acceptance criteria. Adding   formulations outside this range would require confirmatory testing.

System linearity, quantitation limits (QL), and detection limits (DL) 

.   To ensure the sample amount injected is within the linear range of the   detector, a separate experiment can be done where                       the sample concentration is varied from 50% to 150%   of the nominal (100%) concentration. If the potency method is also used                       to quantitate impurities, a wider linearity study   can be performed from 100% to 0.03%. The QL and DL for the impurities   can                       be determined by the ratio of RMSE to the slope, as   described in ICH Q2 (R1). 

 shows there is less precision (i.e., higher standard deviation and   confidence values) at the higher API levels for lower                       amounts of Excipient                       3. This lower precision is approaching the   acceptance limit. Similarly, accuracy is approaching the acceptance   limit as the                       excipient and API levels increase. These parameters   need to be monitored in case the method needs to be optimized prior to                       transfer to the final manufacturing site(s). 

 shows a graph of the balance between precision and bias and its   influence on product acceptance/failure rates based on the                       DOE data. Combinations of method precision and bias   must fall within the designated white space to meet the acceptance   criteria.                       The equation for how precision and bias influence   product acceptance rates is as follows:

LSL = lower acceptance criteria, USL = upper acceptance criteria, bias = mean percent recovery - 100%, nominal = 100%, and                       precision = mean standard deviation. 

The prediction profiler (bottom graphs) in 

 allows the precision and bias to be varied to determine the impact on %   acceptance rates. This tool can be used to justify                       moving to new technologies and new specifications   by showing that the acceptance criteria will still be met. 

. The lower recovery of API observed at high levels of excipient concentrations adds to the formulator’s knowledge base. If                       higher excipient concentrations are needed in future formulations, this aspect needs to be considered. 

Regulatory strategy and potential hurdles

Since the MVbD approach is not specified in ICH regulations, not all regulatory agencies may accept this approach. Some filing                       strategies are briefly discussed. 

: The MVbD approach can be   presented in an investigational new drug (IND) application and discussed   at the end of Phase II                             chemistry, manufacturing, and controls (CMC)   meeting. The details can be present in a MVbD design protocol along with   a rationale                             for moving within the design space without   revalidation. Moving outside the design space could be justified through   the use                             of comparability protocol. 

:   Submission of the MVbD rationale, justification, and protocol early in   the development process is warranted since a face-to-face                             conversation may not be possible.   Alternatively, this information can be supplied when responding to any   agency questions. 

Articles

The authors describe a method-validation-by-design (MVbD) approach to validate a method over a range of formulations using both design-of-experiment and quality-by-design principles to define a design space that allows for formulation changes without revalidation.