Since its inception in 2002 by USFDA, the Quality by design (QbD) approach has brought a major shift in pharmaceutical manufacturing. Its core idea that the quality cannot be increased by performing inspection and various tests on the final product but must be built into the product during manufacturing has forced the pharma manufacturers to revisit their manufacturing practices.
QbD is a systematic approach based on sound science and quality risk management. To adopt it, you must begin with the following steps:
- Create a quality target product profile (QTPP) that identifies the critical quality attributes (CQAs) of the drug product.
- Understand the product design and identification of critical material attributes (CMAs) of the product.
- Understand the process design and identification of critical process parameters (CPPs).
- Link the CMAs and CPPs to CQAs.
- Establish a control strategy that includes developing specifications for the API, excipients, the final product, and developing controls for each step of the manufacturing process.
- Improve process capability.
Before we dive deep into the QbD, let us first understand a few terms associated with it and what do they mean.
Critical material attributes (CMAs) – These are chemical, physical, biological, or microbiological characteristics of an input material that should be within a range or limit to ensure that the desired quality of output is achieved. Any variation in a CMA can result in a poor quality of output. For example, variation in type or quantity of disintegrant can affect the tablet hardness and disintegration time subsequently affecting the drug release profile of the tablet.
Critical process parameters (CPPs) – These are operating process parameters that are critical when a change in any parameter can impact CQAs.
Critical quality attributes (CQAs) – These are physical, chemical, biological, or microbiological properties of an output material including finished drug product that should be within an appropriate limit, range, or distribution to ensure the desired product quality.
Embracing QbD for granulation
Wet granulation has remained one of the prominent batch manufacturing operations for pharma companies. The scale-up of high shear wet granulation is, however, a complex process because it involves multiple rate stages such as nucleation, coalescence, consolidation, and attrition. Also, wet granulation significantly impacts final product properties such as content uniformity, dissolution, flow, and hardness. The scale-up of high shear granulation has been extensively studied and it seems like the QbD approach can play a major role in executing the scale-up efficiently. QbD not only helps to understand the material attributes that are important for wet granulation but also assesses the impact of each attribute on the quality of granules.
|CMAs in wet granulation||CPPs in wet granulation|
|Bulk density||Water amount|
|Rheology||Binder addition time|
|Dried granule size distribution||Impeller speed|
|Porosity||Binder addition rate|
|Moisture content||Mode of binder addition|
|Morphology||Wet massing time|
|Type of binders|
|Type of disintegrants|
|Type of diluents|
Following two major approaches have been used to ensure successful scale-up of high shear wet granulation.
Attribute-based approach – It involves adjusting parameter values based on in-process measurements of granulation viscosity or particle size. In this approach, various granule attributes such as pore size distribution, compaction properties, granule hardness, particle size distribution, bulk and tap density are kept constant and equipment process parameters are adjusted while scaling up. Process analytical tool (PAT) is used to monitor various process parameters including binder amount or wet massing time and progress in the granulation process. It provides a feedback type control on process parameters and eventually stipulates the endpoint of the granulation process.
Parameter-based approach – In the parametric approach, small-scale parameters are used to determine process parameters at large. This approach focuses on conducting a process parameter ranging study for different parameters such as water amount, wet massing time, impeller speed, spray related factors, etc., after which CPPs can be identified and scaling up can be done.
Apart from these two approaches, QbD also focuses on using mechanistic models rather than depending only on the empirical approach. A mechanistic model allows for simple scaling-up with an enhanced understanding of mechanistic and fundamental related aspects of high shear granulation with a decreased need for experimentation. Different mechanistic models such as population balance modeling (PBM), regime-map based modeling, discrete element modeling (DEM), or a combination of these models are commonly used.
Population balance modeling (PBM) – It is a mechanistically derived model and uses many balance methods around each granule size fraction where the change in the number of granules in a given size is equal to the number of granules formed minus the number of granules leaving the size fraction.
Discrete element modeling (DEM) – It is a numerical simulation-based model that tracks the position of each granule particle within a specified area or geometry of the granulator machine and simultaneously estimates the force and velocity exerted on each particle. DEM gives a lot more detailed information relating to particle dynamics in comparison to PBM. DEM model has the capability of analysing sensitivity around process parameters and formulation such as impeller speed, viscosity, contact angle, etc. Such sensitive analysis helps to establish a well-defined design space for formulation.
Overall, mathematical modeling of the wet granulation process is an attractive approach in overcoming the challenges associated with wet granulation process scale-up and development.
The pharma industry has remained fairly conservative owing to their essential role in the healthcare system and the high influence of regulatory bodies. Naturally, many pharma companies would like to stick to their conventional manufacturing practices rather than opting in for a complex and time-consuming approach such as QbD. However, they also have to keep in mind that the FDA and the patients are now more concerned about consistent product quality. And that can be achieved only when the pharma manufacturers embrace scientifically proven approaches like QbD. Not only it will facilitate the FDA approval process but it can take the efficiency of the pharma operations to the next level.
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