January 27, 2014

Determining Criticality, Part Two: DoE and Data-Driven Criticality

A practical roadmap in three parts that applies scientific knowledge, risk analysis, experimental data, and process monitoring throughout the three phases of the process validation lifecycle.

The most recent FDA (1) and International Conference on Harmonization (ICH) (2-4) guidance documents advocate a new paradigm of process validation based on process understanding and control of parameters and less on product testing. Consequently, the means of determining criticality has come under greater scrutiny. The FDA guidance points to a lifecycle approach to process validation (see Figure 1).

In Part I of this series, the author introduced the concept of continuum of criticality and applied it to the concepts of critical quality attributes (CQAs) and critical process parameters (CPPs). In the initial phase, the CQAs had their criticality risk level assigned according to the severity of risk to the patient. Applying a cause-and-effect matrix approach, the potential impact of each unit operation on the final product CQAs was assessed and each unit operation was thoroughly analyzed for its directly controllable inputs and outputs. Finally, a qualitative risk analysis or a formal failure mode effects and criticality analysis (FMECA) was conducted for each of the identified process parameters. The purpose of this assessment is to provide a focus for the downstream process characterization work required to complete process validation Stage 1 (process design).

This initial risk assessment is performed prior to the baseline characterization work and can be used as the primary means of determining the criticality of process parameters under the following conditions:

• When a platform process that possesses similar properties and process to another commercial product (e.g., new strength or new dosage form)
• When there is a significant body of published data on the process
• When experimental studies and commercial data are available, such as when the process validation lifecycle is applied to a legacy product to substantiate the initial assessment.

In these cases, this initial assessment can be further bolstered through the addition of an uncertainty component to the traditional risk score. For example, a high-risk critical parameter with low uncertainty (due to substantial supporting data) may not require further study, but a medium-risk parameter with high uncertainty may require further experimentation to quantify the risk to product performance.

The challenge facing most organizations is how to effectively evaluate the impact of potentially hundreds of process parameters on product performance to determine what is truly critical. Few companies have the time or resources to design experimental studies around all potentially critical process parameters. The initial risk assessment provides a screening tool to sort out the parameters that have low or no risk.

Design space and design of experiments
The goal is to increase process knowledge by providing a mechanistic understanding of the relationship between process parameters, raw material attributes, and CQAs. This is defined as both the demonstration of impact and the quantification of the contribution of each parameter to the product’s performance. Through this exercise, it will be possible to identify the process design space. The ICH guidance defines three elements--knowledge space, design space, and control space--to establish a process understanding (see Figure 2) (2). 

ICH Q8 defines design space as, “The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality.”

The design space is part of an enhanced process development approach referred to as quality by design (QbD). Prior to QbD, pharmaceutical development did not require the establishment of functional relationships between CPPs and CQAs. Consequently, process characterization experiments were primarily univariate (one factor at a time [OFAT]), showing that, for a given range of a process parameter (referred to as proven acceptable range or PAR), the CQAs meet acceptance criteria. While univariate experiments can provide some limited knowledge, a compilation of OFAT studies cannot typically define a design space because it cannot substantiate the importance or contribution of the parameter to the product CQA being evaluated. To do this, multivariate studies must be performed to account for the complexities of interactions when several CPPs vary across their control ranges.

Design spaces can be developed for each unit operation or across several or all unit operations. Although it may be simpler to develop for each unit operation, downstream unit operations may need to be included to sample and test the appropriate CQAs. For example, to perform a multivariate study on a fermentation unit operation, additional processing through cell lysis and purification unit operations is needed so that CQAs may be sampled and tested. The challenge faced by most development programs is how to efficiently and cost-effectively derive maximum process understanding in the fewest number of studies. To do this, a staged approach using multiple studies is most efficient.

A staged design of experiment approach
The following is an example of a simple staged design of experiment (DOE) approach. More complex DOE designs and strategies may be required, but these designs are typical:


Tags: process monitoring, critical parameters, attribute, quality, QbD, design space, control space, experimental data, risk analysis