Nowadays, Quality by Design (QbD) has become an essential part of any process research and development (PR&D) aimed at advancing product and process quality in industry. The methodology was embraced by the FDA for any development related to drug substances, drug products and analytical methods.
QbD is based on Juran’s concept of “planning quality into the product” at the design stage itself, rather than “complying product to quality or quality by QC”. Designing quality into the product can be achieved by having proper process knowledge: good understanding of the relationship between three important parameters:
Critical quality attributes (CQAs: which product attributes need to be defined, measured and monitored to ensure the final process outputs remain within acceptable quality limits e.g. potency, purity, etc.)
Critical process parameters (CPPs: process parameters which affect product quality and need to be monitored and controlled within predefined limits e.g. temperature, stirring rate, etc.)
Critical material attributes (CMAs: which input material attributes should be within an appropriate limit to ensure the desired quality of output material e.g. particle size, impurities, etc.).
One of the major hurdles to any robust process is inadequate understanding of the process, which results in inconsistent product quality.
Process robustness can be (partially) achieved by the implementation of risk-based statistical tools such as Six Sigma and QbD during development, scale-up to pilot and commercial production. The quality risk tools should be able to mitigate the risks caused by various process variables (CPP/CMA).
There are many sources of variation in every process. Certain sources of variation can be controlled and are called assignable causes of variation e.g. variations in raw material quality. However, there are certain sources of variation which cannot be controlled: room temperature (without air-conditioning), production shift changes, age of the reactors, focus of the operators, .. just to name a few. These sources are called common causes of variation and are inherent to any process.
QbD assists to eliminate the assignable causes of variation by designing the process in such a way that the (critical) process parameters can be varied within certain limits without negatively affecting product quality. The methodology also helps to minimize the effects of common causes of variation by randomization and blocking of experiments during the design of experiment (DoE) studies. However, this subject is not discussed in this blog as it would lead us too far.
QbD is a risk mitigation tool that ensures the API quality remains the same for every batch produced, which in turn is beneficial for patient safety.
Process development of an API using QbD
A possible sequence of events for drug substance development according to QbD principles is outlined below (Figure 1).
Figure 1. Possible sequence of steps for development using QbD.
Step 1. Categorization of Drug Properties
All the quality characteristics of the drug product are placed into physicochemical, analytical, and safety categories, as all of these are treated separately.
Step 2. Risk Assessment 1: Identification of CQA’s
The risk assessment tool known as failure mode and effect analysis (FMEA) is applied to screen the quality characteristics. This stage is important as it shortlists the characteristics which are critical for the patient and it provides an understanding of what ensures the quality, safety and efficacy of a specific drug product. These shortlisted quality characteristics become the CQAs. This process is denoted as FMEA-1 in Figure 2.
Note: for a commercial or generic molecule, step 1 & 2 can be omitted as the CQA’s are identical to the specifications set by the customer or as given by the pharmacopeia.
Step 3. Identification of CPPs and CMAs
Once the CQAs are identified, it becomes imperative to identify the PPs and MAs that can affect those CQAs. A second risk assessment (FMEA-2 in Figure 1) is performed to identify the most important PPs and MAs (= input variables for DoE), either by brainstorming or based on past experimental data. The following three criteria apply:
Unit operation with highest risk priority number (RPN)
If there is no control strategy for any given unit operation
If the effect of any unit operation on the CQA is not known
Note: FMEA is the simplest of all risk assessment tools. The regulators are flexible about the choice of risk assessment tool. Even an in-house developed tool can be used if it serves the purpose.
Step 4. Optimization of the Effects of the Input Variables on the CQAs
Next, a DoE is planned to gain understanding about the relationship between the selected input variables and the CQAs. The output is the set of input variables that affect the CQA significantly and are termed CPPs and CMAs. It is possible that only a few of the input variables that were initially selected might actually affect the CQAs or that the same CPPs affect more than one CQA. The design space obtained from DoE provides a region within which any CPP or CMA can be varied without affecting the CQAs (NOR, PAR). This becomes the basis for the control strategy.
Step 5. Control Strategy
Once the operating ranges of the CPPs are identified (from the design space), it is important to
ensure that all of the CPPs remain within their ranges by providing proper control (e.g., by the use of process analytical tools (PATs) such as ReactIR, pH control, etc.). Suppliers should be included during the QbD phase to ensure that manufacturers have control over the CMAs for all starting materials.
Additionally, CPPs and CQAs are monitored during commercialization using control charts to capture any deviations in the process. Reasons for positive deviations must be incorporated in the process, whereas reasons for negative deviations must be eliminated. This forms the basis of continuous improvement during the entire life cycle of the product.
Note: as a general practice, only the CQAs are monitored by the control charts. However, one needs to understand that as the CQAs are the outcome of the CPPs (Figure 2), it is imperative to monitor both.
Step 6. Risk Assessment 3: Risk Re-evaluation
After identification of the CPPs/CMAs, the design space and the control strategy, it is time for the third risk assessment (FMEA-3 in Figure 1), in which the risk to the CQAs is re-evaluated to determine whether it has been reduced after optimization with respect to the risk that existed during FMEA-2 i.e. if a reduction of the RPN number is achieved.
Step 7. Continuous Improvement: Monitoring and Improving the Process
Even after all of the above steps have been performed, it is rarely observed that commercialization happens without any hiccups, as the process takes its own time to mature. If the process capability of any process is not under control, it would lead to out-of-specification (OOS) or out-of-trend (OOT) batches.
Some of the deviations are good for the process (e.g., a yield increase) and some are bad (e.g., an increase in the impurity levels). The root causes for all OOS/OOT batches are investigated and a corrective and preventive action (CAPA) plan is proposed and implemented. This is an iterative process during the entire life cycle of the product. Hence, QbD runs during the entire life cycle of the product and is a continuous journey of gaining more and more knowledge about the process.
The main purpose of QbD is to reduce the variation in the process so that the CQAs remain well within the specification limits, as shown by Figure 2. The CQA or the customer’s requirement is represented by upper and lower specification limits (USL and LSL, respectively), whereas the process capability is represented by upper and lower control limits (UCL and LCL, respectively).
Figure 2. Ultimate goal of QbD. The CQAs or the customer’s requirements are represented by specification limits, whereas the process capability is represented by control limits.
A successful product development strategy requires thorough understanding of QbD principles and tools. Design of experiments, risk assessment tools, PAT and process simulation software are the major tools for the establishment of QbD principles. Scientific and risk-based product development is carried out with the help of QbD which forms a happy marriage of chemistry and engineering. QbD significantly reduces the cost and resource drain due to exhaustive validation exercises.
 Org. Process Res. Dev. 2015, 19, 1634−1644
 ICH Q8 R2