QbD in API Manufacturing


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Quality by Design: Concepts

Quality by design is an essential part of the modern approach to pharmaceutical quality. There is much confusion among pharmaceutical scientists in generic drug industry about the appropriate element and terminology of quality by design. This paper discusses quality by design for generic drugs and presents a summary of the key terminology. The elements of quality by design are examined and a consistent nomenclature for quality by design, critical quality attribute, critical process parameter, critical material attribute, and control strategy is proposed. Agreement on these key concepts will allow discussion of the application of these concepts to abbreviated new drug applications to progress.


The Food and Drug Administration (FDA) (1–3) and pharmaceutical industry (4–6) are talking about quality by design, and there are many important terms that are used as part of this discussion. However, industry comments indicate that there is still much confusion in the generic industry as to the meaning of quality by design and its associated nomenclature. In this paper, we provide a consistent set of definitions to provide a clearer understanding of quality by design for abbreviated new drug applications (ANDAs).

In order to describe quality by design, we must first define what we mean by quality. In a 2004 paper, Janet Woodcock (Director for the Center for Drug Evaluation and Research) defined pharmaceutical quality as a product that is free of contamination and reproducibly delivers the therapeutic benefit promised in the label to the consumer (1). Traditionally, the relationship of product attributes to product quality has not been well understood, and thus FDA has ensured quality via tight specifications based on observed properties of exhibit or clinical trail batches and constraining sponsors to use a fixed manufacturing process. In this approach, specifications are valued not because they are related to product quality, but because they are able to detect differences batch to batch that may potentially have therapeutic consequences.

FDA’s emphasis on quality by design began with the recognition that increased testing does not improve product quality (this has long been recognized in other industries). The following equation indicates where quality comes from:

Pharmaceutical Quality = f (drug substance, excipients, manufacturing, packaging).

In order for quality to increase, it must be built into the product. To do this requires understanding how formulation and manufacturing process variables influence product quality; this is the function f in the equation above.


We start with the assertion that Quality by Design (QbD) is a systematic approach to pharmaceutical development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management (7). It means designing and developing formulations and manufacturing processes to ensure a predefined quality. Thus, QbD requires an understanding how formulation and process variables influence product quality. Relevant documents from the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH), ICH Q8 (8), Pharmaceutical Development, along with ICH Q9 (9), Quality Risk Management, and ICH Q10 (10), Pharmaceutical Quality Systems, indicate on an abstract level how quality by design acts to ensure drug product quality. Especially for ANDA sponsors, who were not actively involved in the ICH processes, there is a need for more concrete descriptions of quality by design.

Over the past several years, pharmaceutical scientists have provided several more specific definitions of what are the elements of quality by design (2,4) and a draft of an annex to ICH Q8 has been released (7). These discussions have generally focused on the development of new drugs. Drawing on these discussions and some specific aspects of the development of generic products, a QbD development process may include (Fig. 1):

  • Begin with a target product profile that describes the use, safety and efficacy of the product
  • Define a target product quality profile that will be used by formulators and process engineers as a quantitative surrogate for aspects of clinical safety and efficacy during product development
  • Gather relevant prior knowledge about the drug substance, potential excipients and process operations into a knowledge space. Use risk assessment to prioritize knowledge gaps for further investigation
  • Design a formulation and identify the critical material (quality) attributes of the final product that must be controlled to meet the target product quality profile
  • Design a manufacturing process to produce a final product having these critical material attributes.
  • Identify the critical process parameters and input (raw) material attributes that must be controlled to achieve these critical material attributes of the final product. Use risk assessment to prioritize process parameters and material attributes for experimental verification. Combine prior knowledge with experiments to establish a design space or other representation of process understanding.
  • Establish a control strategy for the entire process that may include input material controls, process controls and monitors, design spaces around individual or multiple unit operations, and/or final product tests. The control strategy should encompass expected changes in scale and can be guided by a risk assessment.
  • Continually monitor and update the process to assure consistent quality

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Fig. 1
Overview of QbD

Design of experiments (DOE), risk assessment, and process analytical technology (PAT) are tools that may be used in the QbD process when appropriate. They are not check-box requirements.

The difference between QbD for NDA and ANDA products is most apparent at the first step of the process. For an NDA, the target product profile is under development while for the ANDA product the target product profile is well established by the labeling and clinical studies conducted to support the approval of the reference product.


The first aspects of QbD are an articulation of the design goals for the product.

Definition of TPP

FDA published a recent guidance defining a Target Product Profile (TPP) (11): “The TPP provides a statement of the overall intent of the drug development program, and gives information about the drug at a particular time in development. Usually, the TPP is organized according to the key sections in the drug labeling and links drug development activities to specific concepts intended for inclusion in the drug labeling.” When ICH Q8 (8) says that pharmaceutical development should include “…identification of those attributes that are critical to the quality of the drug product, taking into consideration intended usage and route of administration”, the consideration of the intended usage and route of administration would be through the TPP.

The TPP is a patient and labeling centered concept, it can be thought of as the “user interface” of the drug product. Thus a generic version and its reference product would be expected to have the same TPP. A generic product may use a different formulation or design to implement the TPP. The characteristics and performance tests of a drug product would depended on the particular implementation and may differ between a generic and reference product. For a new drug, changes to the TPP may require new safety or efficacy data, but changes to product characteristics or performance that result from a reformulation may not.

Many aspects of the TPP constrain or determine the actions of formulation and process development scientists. These can include the route of administration, dosage form and size, maximum and minimum doses, pharmaceutical elegance (appearance), and target patient population (pediatric formulations may require chewable tablets or a suspension). Common aspects of drug product quality are implicitly in the TPP. If the label states a tablet contains 100 mg of active ingredient, this is a claim relating to the assay and content uniformity. It is the role of a pharmaceutical scientist to translate the qualitative TPP into what we define as the target product quality profile (TPQP) for further use in a quality by design process.

Definition of TPQP

The target product quality profile (TPQP) (12) is a quantitative surrogate for aspects of clinical safety and efficacy that can be used to design and optimize a formulation and manufacturing process. International Society of Pharmaceutical Engineers (ISPE) Product Quality Lifecycle Implementation (PQLI) calls this the Pharmaceutical Target Product Profile (4). It should include quantitative targets for impurities and stability, release profiles (dissolution) and other product specific performance requirements. Product specific examples include resuspendability for an oral suspension, adhesion for a transdermal system, and viscosity for a topical cream. Generic products would include bioequivalence to the RLD as part of the TPQP.

The TPQP is not a specification because it includes tests such as bioequivalence or stability that are not carried out in batch to batch release. The TPQP should only include patient relevant product performance. For example, if particle size is critical to the dissolution of a solid oral product, then the TPQP should include dissolution but not particle size. Particle size would be a critical material attribute and thus included in the process description and control strategy. The TPQP should be performance based and not mechanism based.

Examples of a TPQP can be found in the mock quality overall summary (QOS) presented on the Office of Generic Drugs website (13,14) although the term TPQP was not clearly stated in the mock QOS. Another example of a TPQP is presented in the European Mock P2 (15) that was developed to facilitate a scientific and regulatory dialogue between the Industry Association European Federation of Pharmaceutical Industries Associations, and Regulatory Authorities on the presentation of enhanced product and process understanding in regulatory dossiers. The European Mock P2 uses the nomenclature Target Product Profile, but their Table I fits our definition of a TPQP. They claim that the TPQP is a definition of product intended use and a pre-definition of quality targets (with respect to clinical relevance, efficacy and safety) and thus summarizes the quality attributes of the product required to provide safety and efficacy to the patient.

Definition of CQA

The ISPE PQLI (4) defines critical quality attributes (CQAs) as physical, chemical, biological or microbiological properties or characteristics that need to be controlled (directly or indirectly) to ensure product quality. ICH Q8 (R1) defines CQAs as physical, chemical, biological or microbiological properties or characteristics that should be within an appropriate limit, range, or distribution to ensure the desired product quality (7). CQA has been used by some (16) to describe elements of the TPQP (such as dissolution) while others (17) have used CQA to describe mechanistic factors (such as particle size and hardness) that determine product performance. Thus CQA is used to describe both aspects of product performance and determinants of product performance.

It was stated that the ICH working definition of CQA was: “A CQA is a quality attribute (a physical, chemical, biological or microbiological property or characteristic) that must be controlled (directly or indirectly) to ensure the product meets its intended safety, efficacy, stability and performance” (18). This CQA definition implies that the intended safety, efficacy, stability and performance are not CQAs. Safety and efficacy clearly fall under the domain of the TPP. But if stability and performance are not CQA and not part of the TPP, then what are they? We are thus compelled to acknowledge that there is an intermediate category of product performance (or surrogates for quality) that we have defined as the TPQP.

As shown in Fig. 2, it seems more precise to consider the TPP, TPQP, and material attributes as separate categories. The use of CQA can be reserved for cases where there is a need to refer collectively to the targets of a QbD approach. CQA is generally assumed to be an attribute of the final product, but it is also possible to indicate a CQA of an intermediate or a raw material.

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Fig. 2
An Illustration of How Under QbD the Identification of Critical Process Parameters and Critical Material Attributes is Linked to the TPQP and Finally to TPP that Represents the Clinical Safety And Efficacy

Although many people have identified dissolution as a critical quality attribute, we consider that a set of critical material attributes (CMAs) that are independent of each other provide specific goals with which to evaluate a manufacturing process. For example a dissolution test may depend on particle size and hardness. Particle size and hardness are CMAs which can be directly linked to raw materials and manufacturing process parameters. Independent CMAs are the best way to provide a mechanistic link of the product quality to the critical process parameters in the manufacturing process. At the 2005 Drug Information Association meeting, Reed discussed dissolution in detail and indicated the greater value of have very specific CQAs (19). Others (20) have commented negatively that processing behavior of materials is usually evaluated in performance tests (flowability) rather than focusing on fundamental material properties. Differentiating between CMAs (properties) and multi-faceted performance tests is part of the movement away from quality by testing to quality by design.

The evolution of ICH Q8 is also consistent with making a distinction between CMA and performance tests. The 2004 Q8 draft (21) put CQA and performance tests into the same pile of physiochemical and biological properties:

The physicochemical and biological properties relevant to the performance or manufacturability of the drug product should be identified and discussed. These could include formulation attributes such as pH, osmolarity, ionic strength, lipophilicity, dissolution, redispersion, reconstitution, particle size distribution, particle shape, aggregation, polymorphism, rheological properties, globule size of emulsions, biological activity or potency, and/or immunological activity. Physiological implications of formulation attributes such as pH should also be addressed.

However, the final version of Q8 (8) made clear that this section was focus on product performance:

The physicochemical and biological properties relevant to the safety, performance, or manufacturability of the drug product should be identified and discussed. This includes the physiological implications of drug substance and formulation attributes. Studies could include, for example, the development of a test for respirable fraction of an inhaled product. Similarly, information supporting the selection of dissolution vs. disintegration testing (or other means to ensure drug release) and the development and suitability of the chosen test could be provided in this section.

Other examples may help to show the benefit of this analysis. Consider alcohol induced dose dumping. The TPP would be the labeling statement (supported by clinical data) that the product does not dose-dump when taken with alcohol. A performance test in the TPQP would be an in vitro dissolution test in alcohol. The CMA would be the thickness of a tablet coat. Defining the CMAs on this mechanistic physical property level makes it the best link to the manufacturing process variables.


What is a Process Parameter?

There is confusion about what is a process parameter. Previously, some have defined a critical process parameter (CPP) as any measurable input (input material attribute or operating parameter) or output (process state variable or output material attribute) of a process step that must be controlled to achieve the desired product quality and process consistency. In this view, every item in Fig. 3 would be a process parameter.

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Fig. 3
An Example of Identification of Process Parameters and Material Attributes Prior to Pharmaceutical Development

We propose that process parameter be understood as referring to the input operating parameters (mixing speed, flow rate) and process state variables (temperature, pressure) of a process or unit operation. Under this definition, the state of a process depends on its CPPs and the CMAs of the input materials. Monitoring and controlling output material attributes can be a better control strategy than monitoring operating parameters especially for scale up. For example, a material attribute, such as moisture content, should have the same target value in the pilot and commercial processes. An operating parameter, such as air flow rate, would be expected to change as the process scale changes.

For a given unit operation, there are four categories of parameters and attributes

  • input material attributes
  • output material attributes
  • input operating parameters
  • output process state conditions

What is an Unclassified Process Parameter?

We recognize that there are many material attributes and process parameters that are important and even essential to product quality, but it is of little value to define all parameters as critical. Thus we propose three categories for attributes or parameters: unclassified, critical, or non-critical. The criticality of an unclassified parameter is undetermined or unknown. Sponsors’ pharmaceutical development studies can provide the additional data needed to classify an unclassified parameter as critical or non-critical. For a process or dosage form we expect wide agreement on the set of attributes or parameters that need classification. Prior experience and standard texts will guide this process. Figure  33  provides an example identification of unclassified process parameters (UPP) at the beginning of a development process.

These UPP may later be classified as critical or non-critical. For example, in the granulation process, the impeller speed should clearly be identified as an unclassified process parameter because if impeller speed were zero the process step would not be successful. However, this does not mean that impeller speed is always a critical parameter. If development studies demonstrated the granulation was not affected by realistic changes in impeller speed, it would not be identified as critical. An application that did not include the results of pharmaceutical development studies investigating the criticality of the UPP would have a large number of UPP remaining in the final submission.

What is a Critical Process Parameter?

A parameter is critical when a realistic change in that parameter can cause the product to fail to meet the TPQP. Thus, whether a parameter is critical or not depends on how large of a change one is willing to consider. A simple example is that an impeller speed of zero will always fail. Thus the first step in classifying parameters is to define the range of interest which we call the potential operating space (POS). The POS is the region between the maximum and minimum value of interest to the sponsor for each process parameter. The POS can also be considered as the extent of the sponsor’s quality system with respect to these parameters. This definition is at the discretion of the application that sponsor must balance the trade-offs in its definition. The POS defines the scope of the application and the sponsor’s quality system so that going outside of the POS must need an amendment or supplement to the application. Thus sponsors benefit from defining a large feasible POS. The cost of a large POS is the need for the pharmaceutical development (in the form of prior knowledge, process models or experimental data) to cover the POS and the increased chance that a parameter will be found critical in the large POS. The only constraint on the narrowness of the POS is that the POS must encompass the variability of the process parameters around their target values.

Our criteria for identifying critical and non-critical parameters are that a parameter is non-critical when there is no trend to failure within the POS and there is no evidence of interactions within the proven acceptable range (PAR)(see explanatory footnote on first page of article), which is the range of experimental observations that lead to acceptable quality. A sponsor has the option of conducting experimental observations over the entire POS; in this case the POS could be equivalent to the PAR. Alternatively a sponsor may use prior knowledge, mechanistic models and trends from the PAR to draw conclusions about sensitivity over a POS that is larger than the PAR. If the lack of interaction part of the test cannot be met, then the parameter remains a UPP. A parameter is critical when there is an observation of failure or a trend to failure predicted within the POS. If the interaction between two parameters is significant enough to predict a potential failure in the POS, then both parameters should be considered as critical.

The most definitive way to identify critical and non-critical parameters is by scientific investigations involving controlled variations of the parameters. The focus in the process development report is on the additional studies that build this knowledge. These studies can be conducted on pilot or lab scale and do not need to be conducted under current Good Manufacturing Practice. When the sensitivity of process parameters is established, this can be used to design appropriate control strategies.

However, it may not be possible (due to economic and time constraints) to conduct scientific investigations on all UPP. We believe that prior knowledge and experience with the unit operations can be used to classify some UPP. The prior knowledge can be used in a formal risk assessment process to prioritize unclassified parameters for further experimental study. This is potentially a challenging issue for FDA review, if the reviewer does not agree with the risk assessment used to classify parameters as non-critical, then all further conclusions may be in doubt because a potential critical variable was left out of the experimentation that was used to develop a design space.

Our criteria for identifying critical and non-critical process parameters are based on the sensitivity of product characteristics to changes in the process parameters. Other approaches presented in the literature link the classification as critical to the variability in a process parameter (22,23). The variability of a process parameter impacts the control strategy that will be used, but we concur with ISPE PQLI that control of a variable does not render it non-critical (4). Table I summarizes the proposed classification of process parameters.

Table I

Classification of Process Parameters

Parameter type Definition Sensitivity
Non-critical process parameter (non-CPP) Not critical • No failure in target product quality profile (TPQP) observed or predicted in the potential operating space (POS), and
• No interactions with other parameters in the proven acceptable range (PAR)
Unclassified process parameter (UPP) Criticality unknown • Not established
• The default in the absence of pharmaceutical development
Critical process parameter (CPP) Critical (control needed to ensure quality • Failure in target product quality profile (TPQP) observed or predicted in the potential operating space (POS), or
• Interactions with other parameters in the proven acceptable range (PAR)
Table I
Classification of Process Parameters

Uniqueness of Critical Process Parameters

Because of the broadness of the CPP definition it is possible for two investigators to examine the same process and come to a different set of CPP. The set of CPP is not unique, but the chosen set must be sufficient to ensure product quality.

Different sets of CPP can have several origins. One is that the definition of operating parameters depends on the engineering systems installed on a piece of process equipment. For example, one fluid bed dryer may define the product temperature as an operating parameter and have an internal control system (a thermostat) that maintains that temperature, while another fluid bed dryer may have inlet air flow rate and inlet air temperature indicated as operating parameters. The batch record for the first unit might indicate a fixed temperature, while the second unit would have a design space that indicated the combination of inlet air flow rate and inlet air temperature that would insure the appropriate product temperature.

Another source of differences in the set of CPP comes from the balance between control of operating parameters and material attributes. Morris (24) indicates that set of CPP and CMA (which he refers to as process critical control points (PCCP)) can affect the scale up process

  • PCCPs are preserved throughout scale-up, the magnitude of the responses may not scale directly, but the variables being monitored reflect the “state” of the process
  • Monitoring material properties makes scaling less equipment dependent (as opposed to only monitoring equipment properties) equipment differences (scale and type) may have an effect, however, differences in the material should reflect significant changes in the PCCPs


A control strategy may include input material controls, process controls and monitoring, design spaces around individual or multiple unit operations, and/or final product specifications used to ensure consistent quality. A control strategy is what a generic sponsor uses to ensure consistent quality as they scale up their process from the exhibit batch presented in the ANDA to commercial production.

Every process has a control strategy right now. Figure 44 shows a simplified quality assurance diagram under the current regulatory evaluation system. In this system, product quality is ensured by fixing the process to produce the active ingredient, raw material testing, performing the drug product manufacturing process as described in a fixed batch record, in-process material testing, and end product testing.

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Fig. 4
An Example of Control Strategy for Pre-QbD Process

The quality of raw materials including drug substance and excipients is monitored by testing. If they meet specifications or other standards such as USP for drug substance or excipients, they can be used for manufacturing of the products. As the drug substance specification alone may not be sufficient to ensure quality, the drug substance manufacturing process is also tightly controlled. Potentially significant changes to the drug substance manufacturing process will require the drug product manufacturer to file supplements with the FDA.

The finished drug products are tested for quality by assessing if they meet specifications. In addition, manufacturers are usually expected to conduct extensive in process tests, such as blend uniformity or tablet hardness. Manufacturer are also not permitted to make changes to the operating parameters (a large number of UPPs) specified in the batch record or other process changes without filling supplements with the FDA.

This combination of fixed (and thus inflexible) manufacturing steps and extensive testing is what ensures quality under the current system. A combination of limited characterization of variability (only three pilot lots for innovator products and one pilot lot for generic products), a failure of manufactures to classify process parameters as critical or non-critical, and cautiousness on the part of regulator leads to conservative specifications. Significant industry and FDA resources are being spent debating issues related to acceptable variability, need for additional testing controls, and establishment of specification acceptance criteria. The rigidity of the current system is required because manufacturers may not understand how drug substance, excipients, and manufacturing process parameters affect the quality of their product or they do not share this information with FDA chemistry, manufacturing and controls (CMC) reviewers. Thus the FDA CMC reviewers must act conservatively.

A QbD based control strategy is shown in Fig. 5. Pharmaceutical quality is assured by understanding and controlling formulation and manufacturing variables to assure the quality of the finished product. The end product testing only confirms the quality of the product. In this example, PAT provides tools for realizing the real time release of the finished product although its use is not required under the paradigm of the Quality by Design.

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Fig. 5
An Example of Control Strategy for QbD Process

Implications of Process Parameter Classification

The classification of process parameters as critical or non-critical is essential to evolve the control strategy toward the QbD based goal. Full classification of all parameters as either non-critical or critical can lead to reduced end-product testing. It is the uncertainty about the UPP that leads to extensive testing.

Without development studies, UPP may need to be constrained at fixed values or narrow ranges (used to produce acceptable exhibit batches) because they might be critical. The presence of UPP also leads to inclusion of extensive release and in-process tests into the control strategies. The goal of development studies is to move parameter from unclassified (criticality unknown) to either non-critical or critical. This classification is an important step toward a flexible manufacturing process because unclassified parameters classified as non-critical may be monitored and controlled via monovarient ranges or as part of a sponsor’s quality system (see Table II). For non-critical parameters it may be possible to designate a normal operation range (NOR) up to (or beyond) the proven acceptable range (PAR) depending on trends and prior knowledge. The superposition of NOR for non-critical parameters would be considered as part of the design space.

Table II

Table II

Table II

Impact of Classification of Process Parameters on Control Strategy

Parameter type Potential control strategies
Non-critical process parameter (non-CPP) • Univarient range in batch record (may extend beyond existing data)
• Under control of sponsors quality system
Unclassified process parameter (UPP) • Extensive release testing because of uncertainty
• Fix at exhibit batch value or narrow range (to be tested in validation) to ensure no interactions
Critical process parameter (CPP) • Reduced release testing when all critical process parameters are identified, monitored and controlled
• Proven acceptable range if no evidence of multivariate interactions
• Design space to allow multivariate changes
• Feedback control based on measurement of material attributes
Impact of Classification of Process Parameters on Control Strategy

The ranges of critical parameters must be constrained to a multidimensional design space or fixed at values of all parameters known to be acceptable. Univariate PAR can be used for critical parameters only when there is evidence that there are no significant interactions between the CPP. However the establishment of this knowledge about CPPs may render them lower risk than UPP. A control strategy appropriate to the known CPP may also have less need for release testing than one for a process with many UPPs.

Design Space

In the presence of interacting critical process parameters a design space is one approach to ensure product quality although it is not a check-box requirement. The current definition of design space is “The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality.” (8) This definition evolved from early ICH Q8 drafts where design space was defined as “the established range of process parameters that has been demonstrated to provide assurance of quality” (21). The change emphasizes the multidimensional interaction of input variables and closely binds the establishment of a design space to a conduct of a DOE that includes interactions among the input variables. A design space may be constructed for a single unit operation, multiple unit operations, or for the entire process.

Submission of a design space to FDA is a pathway obtaining the ability to operate within that design space without further regulatory approval. A design space is a way to represent the process understanding that has been established. The benefits of having a design space are clear; one challenge to the effective use of a design space is the cost of establishing it.

In a typical design space approach a sponsor identifies the unclassified parameters and then does a DOE on some of the unclassified parameters with the other unclassified parameters fixed. Thus the end is a regulatory situation where there is some space for the selected parameters but no flexibility for the other parameters. This operating parameter based design space is limited to the equipment used to develop the design space. It might change on scale up or equipment changes.

In the development of a design space, the key issue to efficiency is demonstrating or establishing that the unclassified parameters left out of the DOE are truly non-critical process parameters and are thus by our definition non-interacting. Before attempting to establish a design space, effort should be invested to reduce the number of unclassified process parameters. This may involve a screening DOE to rule out significant interactions between process parameters. When they are non-interacting, univariate ranges for non-critical parameters are appropriate and can be added to the design space presentation without additional studies.

It is best to exploit the non-uniqueness of CPPs to define the design space in terms of scale independent (dimensionless) parameters and material attributes. Understanding the design space in terms of material attributes allows scale up and equipment changes to be linked to previous experiments. The scalability of the design space can be evaluated in the transfer from lab to exhibit batch manufacturing.

Feedback Control and PAT

Application of PAT (25) may be part of a control strategy. ICH Q8(R) (7) identifies one use of PAT as ensuring that the process remains within an established design space. In a passive process, PAT tools provide continuous monitoring of CPP to demonstrate that a process is maintained in the design space. In process testing of CMA can also be conducted online or inline with PAT tools. Both of these applications of PAT are more efficient ways to detect failures. In a more robust process, PAT can enable active control of CPP, and if there is variation in the environment or input materials the operating parameters can be adjusted to keep the CMA under control to ensure quality.

A PAT system that combines continuous monitoring of CMA (instead of CPP) can potentially be combined with feedback control of process parameters to provide an alternative to design space based control strategies. A problem with design space is that it can limit flexibility. A design space is usually a specified space of process parameters that has been demonstrated to provide acceptable quality. There may be sets of process parameters that lead to acceptable quality but were not explored in the establishment of the design space. Thus, pursuit of a design space can be movement in the opposite direction from a flexible and robust manufacturing process. Direct assessment of product quality via PAT may support more flexibility and robustness than is represented by the design space. When CMA can be actively monitored and feedback control applied to the CPP, then variation in the environment or input materials can be counteracted by new values of the CPP (even values outside of a design space that represents prior experience) to keep the CMA within desired limits. When direct assessment of product quality by PAT is established, it may be more valuable to invest pharmaceutical development resources toward an active control system than toward documentation of a design space.


Quality by design is an essential part of the modern approach to pharmaceutical quality. This paper clarifies the use of QbD for ANDAs including:

  • Emphasis on the importance of the Target Product Quality Profile in articulating a quantitative performance target for QbD.
  • Identification of critical material attributes that provide a mechanistic link of the product quality to the manufacturing process.
  • Clarification that critical process parameters are operating parameters and should be combined with critical material attributes to describe the relation between unit operation inputs and outputs.
  • A definition of non-critical, unclassified, and critical that provides a way to classify process parameters and in-process material attributes
  • The role of the control strategy as the mechanism for incremental implementation of QbD elements into practice
  • An efficient path to a design space through the identification of non-interacting process variables and their exclusion from formal experimental designs.


Opinions expressed in this manuscript are those of the authors and do not necessarily reflect the views or policies of the FDA.

The PAR is the range of experimental observations that lead to acceptable quality. A sponsor has the option of conducting experimental observations over the entire POS; in this case the POS could be equivalent to the PAR. Alternatively a sponsor may use prior knowledge, mechanistic models and trends from the PAR to draw conclusions about sensitivity over a POS that is larger than the PAR.


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20. Hlinak A. J., Kuriyan K., Morris K. R., Reklaitis G. W., Basu P. K. Understanding critical material properties for solid dosage form design. J. Pharm. Innovation. 2006;1:12–17. doi: 10.1007/BF02784876.[Cross Ref]
21. FDA CDER. Draft guidance for industry. Q8 pharmaceutical development, version 4.3 (draft), Nov. 2004.
22. T. Parks. A science and risk-based approach for establishing critical process parameters and critical intermediate quality attributes. Available at:$File/12+-+CPP+Poster.ppt. (accessed 11/21/2007).
23. R. Nosal. Industry perspective of risk-based CMC assessment under QbD. AAPS Annual Meeting, Oct. 2006.
24. K. R. Morris. Risked-based development and CMC question-based review: Asking the right questions for process understanding, control and filing. FDA Advisory Committee for Pharmaceutical Science. Available at: (accessed 11/21/2007).
25. FDA CDER. Guidance for industry: PAT-a framework for innovative pharmaceutical development, manufacturing, and quality assurance. Sept. 2004.

Torcetrapib (CP-529,414, Pfizer) was a drug being developed to treat hypercholesterolemia (elevated cholesterol levels) and preventcardiovascular disease.

The concept and application of quality by design (QbD) principles has been and will undoubtedly continue to be an evolving topic in the pharmaceutical industry. However, there are few and limited examples that demonstrate the actual practice of incorporating QbD assessments, especially for active pharmaceutical ingredients (API) manufacturing processes described in regulatory submissions. We recognize there are some inherent and fundamental differences in developing QbD approaches for drug substance (or API) vs drug product manufacturing processes. In particular, the development of relevant process understanding for API manufacturing is somewhat challenging relative to criteria outlined in ICH Q8 (–254–1.html) guidelines, which are primarily oriented toward application of QbD for drug product manufacturing. ……………………………………………J Pharm Innov (2007) 2:71–86

The adoption of quality by design in small-molecule drug development and manufacturing continues to evolve as the industry seeks ways to augment process understanding for APIs. T he science- and risk-based approach in quality by design (QbD) entails greater product and process understanding as a means to ensure product quality. These concepts are embodied in ICH guidelines Q8 (R2) Pharmaceutical Development, Q9 Quality Risk Management, Q10 Pharmaceutical Quality System, and most recently, Q11 Development and Manufacture of Drug Substances (Chemical Entities and Biotechnological/ Biological Entities) (1–4), which offer a lifecycle approach to continual improvement to drug manufacturing. Traditional versus enhanced approaches ICH Q11 focuses specifically on the development and manufacture of drug substances.

It specifies that a company can follow “traditional” or “enhanced”approaches or a combination of both in developing a drug substance (4). In the traditional approach, set points and operating ranges for process parameters are defined, and the drug-substance control strategy is typically based on process reproducibility and testing to meet established acceptance criteria (4). In an enhanced approach, risk management and scientific knowledge are used more extensively to identify and understand process parameters and unit operations that affect critical quality attributes (CQAs) (4).

In an effort to establish a consensus and develop consistency, industry and regulators have frequently described quality by design (QbD) by dividing it into three fundamental, interrelated concepts: control strategy, design space, and criticality.1 Figure 1 describes a QbD approach for developing design space, establishing control strategy, and delineating criticality for an active pharmaceutical ingredient (API) that essentially serves as a map for how these conceptual elements were used to establish design space for the torcetrapib API manufacturing process. A preliminary assessment of the QbD strategy for the manufacture of the API typically begins early in development when chemists and engineers evaluate synthetic route selection as well as intermediate quality attributes (QAs) impacting API specifications. As a default, established API specification limits serve as a primary control standard for QAs and surrogate control in the absence of a process control strategy and relevant intermediate specifications.


The API specification, by default, serves as a predictor of critical QAs (CQAs) because the combination of their measurements may directly correlate to potential impact to the safety and efficacy of the drug product and thus to the

patient. API CQAs may include physical characteristics beyond such things as the impurity specification of the API, e.g., particle size, polymorphic form, and salt selection are Mrelevant for drug product manufacture.3 The analytical

control strategy for an API manufacturing process that evolves during development is routinely focused with the attention on the formation and purge of impurities and their cascade effects on the multiple process steps, including the

potential impact to the API’s CQAs. To establish design space, a formal, prospective risk assessment is executed in accordance with ICH Q9 (B in Fig. 1). A process risk assessment is performed as a precedent to formally develop a design space for the commercial manufacturing process so that potential critical process parameters (CPPs) can be identified. In general, a process risk assessment considers prior knowledge, mechanistic understanding of the chemistry, and relevant chemical manufacturing experiences.





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Starting and Raw Materials

Before parameters and ranges can be evaluated in any multivariate designed experiment, the appropriate quality of SMs (or key intermediates) and raw materials must be established. For the torcetrapib manufacturing process, some of the specifications of compound 4 were deemed CQAs because of their direct impact on controlling the relative genotoxic impurities in steps 5 and 6. In addition, ECF (raw material) is a commodity chemical used in step 5 that is incorporated into the structure of the API. Fate and purge development work, batch history, and appropriate communications with vendors are a few methods to establish appropriate specifications for SMs and raw materials. Appropriate specifications were established for each of these materials before any of the multivariate designs were initiated for torcetrapib, and by default, some of these specifications were deemed CQAs. The validity of a multivariate experimental design used to establish a design space depends on understanding the functional relationship between these CQAs/specifications and the API CQAs.




We have provided a case study of a QbD effort, including a risk assessment, for the torcetrapib drug substance process. Fundamentally, different from the drug product, API processes have multiple steps. Understanding the functional relationship between FAs, QAs, and process parameters as they progress through the manufacturing process is the most universally challenging aspect of QbD for API development. Analytical specifications and control strategy aspects of the QbD plan remain the foundation for change throughout the evolution of the manufacturing process (from phase I to launch).

The role of the chemist and engineer during the course of development is to effectively eliminate as many of the CQAs and CPPs as possible from the commercial manufacturing process through continuous improvement efforts. Designed experiments generate the data required to establish a design space for commercial manufacturing processes, while providing the process understanding that facilitates sound business decisions. First principles ofchemistry can expand  this “toolbox” to include kinetic models, computer predictive programs, and more diverse concepts of prior knowledge



The enhanced approach further includes the development of appropriate control strategies applicable over the lifecycle of the drug substance that may include the establishment of design space(s) (4). Manufacturing process development should include, at a minimum, according to ICH Q11:Identifying potential CQAs associated with the drug substance so that those characteristics having an impact on drug-product quality can be studied and controlled tDefining an appropriate manufacturing process tDefining a control strategy to ensure process performance and drugsubstance quality (4).

An enhanced approach to manufacturing process development would additionally include: tA systematic approach to evaluating, understanding, and refining the manufacturing process, including identifying—through prior knowledge, experimentation and risk assessment—the material attributes (e.g., of raw materials, starting materials, reagents, solvents, process aids, intermediates) and process parameters that can have an effect on drug-substance CQAs tDetermining the functional relationships that link material attributes and process parameters to drug-substance CQAs (4).

The enhanced approach, in combination with quality risk management, is used to establish an appropriate control strategy. Those material attributes and process parameters important to drug-substance quality should be addressed by the control strategy. The risk assessment can include an assessment of manufacturing process capability, attribute detectability, and severity of impact as they relate to drug-substance quality (4). For example, when assessing the link between an impurity in a raw material or intermediate and drugsubstance CQAs, the ability of the drugsubstance manufacturing process to remove that impurity or its derivatives should be considered in the assessment (4).

The risk related to impurities can usually be controlled by specifications for raw material/intermediates and/or robust purification capability in downstream steps. It is important to understand the formation, fate (whether the impurity reacts and changes its chemical structure), and purge (whether the impurity is removed via crystallization, extraction, etc.) as well as their relationship to the resulting impurities that end up in the drug substance as CQAs (4). The process should be evaluated to establish appropriate controls for impurities as they progress through multiple process operations (4).

Regulatory expectations In March 2011, the European Medicines Agency and Food and Drug Administration launched a three-year pilot program for a parallel assessment by both agencies of certain quality and chemistry, manufacturing and control (CMC) sections of new drug applications submitted to FDA and marketing authorization applications (MAAs) submitted to EMA that are relevant to QbD, such as development, design space, and real-time release testing.

Image result for API Quality by Design Example from the Torcetrapib Manufacturing Process

The objective of the pilot, which is scheduled to end Mar. 31, 2014, is to ensure consistent implementation between the European Union and the United States of ICH guidelines Q8, Q9, Q10, and Q 11 and to facilitate sharing of regulatory decisions (5–7). In August 2013, the agencies reported that the first EMA–FDA parallel assessment of QbD elements of an initial MAA was successfully finalized as well as some consultative advice procedures.

In a question-and-answer format, EMA and FDA reported on their expectations as they relate to quality target product profiles (QTPPs), CQAs, classification of criticality, and application of QbD in analytical method development (7). With respect to the QTPP, the agencies specified that they expect applicants to provide the QTPP, which describes prospectively the quality characteristics of a drug product that should be achieved to ensure the desired quality, safety, and efficacy of the drug product. With respect toCQAs, the agencies expect applicants to provide a list of CQAs for the drug substance, finished product, and excipients when relevant. This list should also include the acceptance limits for each CQA and a rationale for designating these properties as a CQA.

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Furthermore, there should be a discussion of how the drug substance and excipient CQAs relate to the finished product CQAs based on prior knowledge, risk assessment, or experimental data. The basis of the control strategy is to ensure that the drug substance and finished product CQAs are consistently within the specified limits (7). Another issue was whether the agencies would accept a three-tier classification of criticality for process parameters.

The agencies responded that ICH Q8 (R2) specifies that a critical process parameter is one whose variability has an impact on a CQA and needs to be monitored or controlled to ensure the process produces the desired quality. EMA and FDA cited a regulatory submission in which the applicant proposed an approach to risk assessment and determination of criticality that included a three-tier classification (“critical,” “key,” and “noncritical”) for quality attributes and process parameters. Using this classification, a “critical” factor was defined as a factor that led to failure during experimentation.

A factor that had not led to failure within the range studied, but still may have an impact on product quality, was considered as a “key” factor. The agencies said that they do not support the use of the term “key process parameters (KPP)” because it is not ICH terminology and there is differing use of the term “key” among applicants.

Although FDA and EMA said they are amenable to this terminology in the pharmaceutical development section to communicate development findings, they are not in describing the manufacturing process, process control, and control of critical steps and intermediates, where the description of all parameters that have an impact on a CQA should be classified as critical (7).

The agencies further specified that process manufacturing descriptions be comprehensive and describe process steps in a sequential manner, including batch size(s) and equipment type. The critical steps and points at which process controls, intermediate tests, or final product controls are conducted should be identified (7). Steps in the process should have the necessary detail in terms of appropriate process parameters along with their target values or ranges.

The process parameters that are included in the manufacturing process description should not be restricted to the critical ones; all parameters that have been demonstrated during development as needing to be controlled or monitored during the process to ensure that the product is of the intended quality need to be described (7).

The agencies also commented on QbD as it relates to analytical methods using risk assessments and statistically designed experiments to define analytical target profiles (ATP) and method operational design ranges (MODR) for analytical methods (7). “There is currently no international consensus on the definition of ATP and MODR,” noted the agencies. “Until this is achieved, any application that includes such proposals will be evaluated on a case-by-case basis” (7).

Image result for API Quality by Design Example from the Torcetrapib Manufacturing Process

The agencies noted, however, that an ATP can be acceptable as a qualifier of the expected method performance by analogy to the QTPP as defined in ICH Q8 (R2), but the agencies would not consider analytical methods that have different principles (e.g., highperformance liquid chromatography and near-infrared [NIR] spectroscopy) equivalent solely on the basis of conformance with the ATP. “An applicant should not switch between these two types of methods without appropriate regulatory submission and approval,” they said.

The agencies also noted that similar principles and data requirements could apply for MODRs. For example, data to support an MODR could include: appropriately chosen experimental protocols to support the proposed operating ranges/conditions and demonstration of statistical confidence throughout the MODR. Issues for further reflection include the assessment of validation requirements as identified in ICH Q2 (R1) throughout the MODR and confirmation of system suitability across all areas of the MODR (7).

The agencies further indicated that future assessment of the pilot program will include other lessons learned in areas such as design-space verification, the level of detail in submissions for design space and risk assessment, continuous process verification, and continuous manufacturing. QbD at work A review of recent literaure reveals some interesting applications of QbD in drug-substance development and manufacturing.

For example, scientists at Bristol-Myers Squibb reported on a process-modeling method using a QbD approach in the development of the API ibipinabant, a cannabinoid receptor 1 antagonist being developed to treat obesity (8). In its development, the molecule had volume requirements of 6 kg for toxicology studies and formulation development, which later increased to 175 kg for late-stage clinical trials. The researchers used mechanistic kinetic modeling to understand and control undesired degradation of enantiomeric purity during API crystallization. They implemented a work flow, along with kinetic and thermodynamic process models, to support the underlying QbD

Image result for QbD in API Manufacturing

Those material attributes and process parameters that are found to be important to drug-substance quality should be addressed by the control strategy.

In August 2013, FDA and the European Medicines Agency reported on lessons learned from an EMA–FDA parallel assessment of QbDelements examined from submissions in their pilot program.


approach and reported on the use of risk assessment, target quality specifications, operating conditions for scale-up, and plant control capabilities to develop a process design space (8). Subsequent analysis of process throughput and yield defined the target operating conditions and normal operating ranges for a specific pilot-plant implementation. Model predictions were verified from results obtained in the laboratory and at the pilot-plant scale (8).

Future efforts were focused on increasing fundamental process knowledge, improving model confidence, and using a risk-based approach to re-evaluate the design space and select operating conditions for the future scale-up (8). Scientists at Merck & Co. reported on their work in applying QbD to set up an improved control strategy for the final five steps in the production route of a legacy steroidal contraceptive, which has been produced for more than 20 years within its facilities (9).Image result for QbD in API Manufacturing

A generic ultra-high-performance liquid chromatography method was developed according to QbD principles to create a range of proven acceptance criteria for the assay and side-product determination for the final five steps in the production route of the API (9). Scientists at Eli Lilly reported on a systematic approach consisting of a combination of first-principles modeling and experimentation for the scaleup from bench to pilot-plant scale to estimate the process performance at different scales and study the sensitivity of a process to operational parameters, such as heat-transfer driving force, solvent recycle, and removed fraction of volatiles (10).

This approach was used to predict process outcomes at the laboratory and pilot-plant scale and to gain a better understanding of the process. The model was also used further to map the design space (10). In other work, scientists reported on the application of latent variablesbased modeling to a reaction process in a small-molecule synthesis based on continuous-flow hydrogenation (11). In another study, scientists reported on using a QbD approach for designing improved stability studies (12).

Also, scientists at UCB and the Institut des Sciences Moléculaires de Marseille in France recently reported on the feasibility of using online NIR spectroscopy as a process analytical technology tool to monitor in real time the API and residual solvent content to control the seeding of an API crystallization process at industrial scale. A quantitative method was developed at laboratory scale using statistical design of experiments and multivariate data analysis (13).

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1. ICH, Q8 (R2) Pharmaceutical Development (2009).

2. ICH, Q9 Quality Risk Management (2005).

3. ICH, Q10 Pharmaceutical Quality System (2008).

4. ICH, Q11 Development and Manufacture of Drug Substances (Chemical Entities and Biotechnological/Biological Entities)(2012).

5. FDA, “FDA, EMA Announce Pilot for Parallel Assessment of Quality by Design Applications,” Press Release, Mar. 16, 2011.

6. EMA, “European Medicines Agency and US Food and Drug Administration Announce Pilot Program for Parallel Assessment of Quality by Design Applications,” Press Release, Mar. 16, 2011.

7. EMA, “EMA–FDA Pilot Program for Parallel Assessment of Quality-by-Design Applications: Lessons Learnt and Q&A Resulting from the First Parallel Assessment,” Press Release, Aug. 20, 2013.

8. S.B. Brueggemeier et al., Org. Proc. Res. Dev. 16 (4) 567-576 (2012).

9. J. Musters et al., Org. Process Res. Dev. 17 (1) 87-96 (2013).

10. I. Figueroa, S. Vaidyaraman and S. Viswanath, Org. Process Res. Dev., online, DOI: 10.1021/op4001127, July 22, 2013.

11. Z. Shi, N. Zaborenkdo, and D.E. Reed, J. Pharm. Innov. 8 (10) 1-10 (2013).

12. S.T. Colgan, J. Pharm. Innov. 7 (3-4) 205-213 (2012). 13. C. Schaefer et al., J. Pharm. Biomed. Anal., online, jpba.2013.05.015. May 20, 2013. PT

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Image result for QbD in API Manufacturing




All of us talk about using common sense for everything we do. However, in certain instances we do things that are quite contrary because we are in our comfort zone. Many times the world tells us that we did not use our “common sense” or pay attention to the feedback or there is a time lapse. When this happens, the world wants to rationalize our intentions and motives.

Even infants use their “common sense” to figure out the virtues of moving from crawling to walking to running in about one year. They understand the exhilaration and benefits of their newfound freedom. Similar euphoria is experienced when we have technology innovations or exceed financial goals.

In the manufacturing world common sense tells us that producing quality product the first time without interruptions (QbD) is more satisfying compared to the process where we have to stop and examine the progress at intermediate steps (QbA). It seems that the pharmaceutical manufacturing industry has considerable hesitation (1) in it’s ability to move from Qb (Quality by) A (Analysis) to D (Design). This observation is based on my review of discussions on different websites, in print and at conferences.


After more than five years of discussions we are still talking about how to get to “D”. This suggests that there are some reasons for not getting there. Pharma either is not able to overcome “B (bureaucracy) and “C (consternation) in its drive from “A” to “D” or has defiantly decided to live with analysis of raw materials, intermediates and final products to ensure we produce consistent quality products. Regulatory bodies have jumped into the fray by proposing methods of “how to” of QbD (2,3). They might be useful for new processes and products.

My question to all readers is how we are going to have a QbD process if we do not understand and apply fundamentals of chemistry and chemical engineering to our processes. Regulatory guidance is not going to deliver QbD. There are just too many guidances and they could complicate matters.

Had pharma moved from “A to D” we would be discussing its virtues, values and the amounts of monies saved and the  reduction in compliance paper work. In defense of the pharmaceutical industry it has not lost “common sense” in its move from QbA to QbD. There are opportunities that have been missed and it is up to the technocrats to show their value. I believe that the industry is also victim of its own success.

We do need to understand and explore some of the hurdles that are in the way to move from “A” to “D”. These could be that we do not understand value of improved profits (most unlikely) or do not have financial justification (likely) or are reluctant to move out of our comfort zone to a simpler lifestyle of less work and regulation (likely). Actually the causes are a combination of all of the above plus more. They need to be discussed.

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Current Discussion

Much of the “how to” of the QbD discussion is geared towards formulations. APIs are excluded from the discussion. It is suggested that modeling, six sigma, design of experiments and other statistical tools along with Process Analytical Technologies (PAT) will facilitate “A” to “D” move. I believe that we are putting the cart before the horse or spinning wheels. These methods have value and will help “only” after we have a complete understanding and command of the interaction of chemistry, chemicals, process variables and unit operations.

Modeling without understanding of process fundamentals is time consuming, expensive and is not going to get us to “D”. Since significant data is needed for mathematical analysis, without process understanding we are creating a difficult situation for ourselves. If the modelers believe that volumes of data and its analysis, without understanding and control of the process variables, will deliver an economic and sustainable process that will produce quality product, which would not require repeated analysis of intermediates and final product and we will able to move the process from “A” to “D”, then they know something some of us do not know or understand.

In addition, there is a perception that various analytical instruments and tools will automatically deliver quality product. These tools tell us “the state of the process” but do not fix the process unless correct process control schemes are incorporated. Only our understanding along with proper use of these tools will create an optimum process. I think our misbeliefs have created a difficult situation. Simplicity and economic methods are needed to deliver quality products.

Some additional reasons for our reluctance to convert existing processes to QbD processes can be our fear of the unknown such as regulatory re-approval hurdles and associated costs or the profits are so high that everyone is comfortable with their current levels or, as said earlier,(1) we cannot justify innovation. Another reason for lack of QbD is a lack of economies of scale in our processes.

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QbD is an opportunity. We need to take simple steps and have to have small wins to bring common sense back into our daily fold of process development, scale up and commercialization. Without wins we will be stuck in “analysis paralysis” mode and “A” to “D” movement will become the fad of the day and like any other fashion will disappear. Small wins are an inspiration for big wins.

The manufacture of a drug dose is a two-step process. The first step is the manufacture of the active pharmaceutical ingredient and the second step is its formulation to a dispensable dose. Since APIs are left out of pharmaceutical manufacturing conversations, discussion in this paper is focused on their manufacturing. The considerations are applicable to formulations also. Without a quality API and correct dose the drug would not deliver the desired effect. Under or over dosing would have detrimental effects.

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Pharmaceutical current state

Business and manufacturing aspects – At branded (ethical) and generic pharmaceutical companies, we are always in a hurry to get the product to the market. Depending on the dosage, drug efficacy and the sale price API, their annual volume can vary from less than thousand pounds to few thousand pounds. Volume at a branded company during the patent life belongs to one company but it could be produced at multiple sites i.e. lower economies of scale.

Since the APIs are fine/specialty chemicals, it is natural for any chemist or chemical engineer to produce them in the same equipment where other similar chemicals are produced. The practice of producing APIs, due to similarity of chemistries and equipment needs, began at fine/specialty chemical plants more than hundred years ago.

From a financial perspective this practice makes sense as it avoids investment for every product. Since process inefficiencies are absorbed through product pricing, businesses do not have to be concerned about low process yields or lack of having sustainable processes. Thus, the need for an optimum process has never been a pharma business concern i.e. having a QbD process has never been an issue.

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Regulatory Aspects – Since the drugs are for human consumption, their quality and consistency is paramount. This need has created different regulatory bureaucracies. As explained earlier, different products due to their low volume are produced in the same equipment where other products are produced. Regulatory bodies in order to prevent cross contamination established “good manufacturing practices”. These guidelines are minimal at best.

All of the above necessitate that record keeping of every step including raw materials, intermediates, equipment cleaning and testing be done so that no mistakes happen and if they happen, they can be tracked and corrected. This has become a gargantuan task and more time is spent on cleaning the equipment and paperwork than having the correct and optimum process. QbA has become a way of life.

Due to lack of competition the pharmaceutical industry has not been proactive in developing innovative manufacturing technologies because the current practices have delivered the desired profits. By not having the optimum process and equipment that should be used for a designated API manufacturing, companies have relied on in-process analysis to replicate laboratory milestones.  However, with efficient and optimized processes the industry can exceed these guidelines. It is very possible that better technologies can reduce the paperwork but this option has never been shared, if explored. Reduced paperwork will improve profits.

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As patents expire, additional companies enter the business due to the potential for high profits. Entry of “me-too” companies (definition: those who produce many of the same or similar products as their competitors) will lower production volumes per site from the “pre-patent expire” levels. Most of these companies will be from the developing countries. Since these new entrants attain their expected profits, the need for efficient processes and manufacturing technology innovation is not a priority.

The financial investment for process innovation and improvements is difficult to justify at low production levels. This exacerbates the “A” to “D” move further. Thus the pharmaceutical industry, branded and generic, will lumber along with the QbA methodology unless the business philosophy changes or is threatened. This is discussed later.

To recap, the pharmaceutical industry, unless it makes changes in its business model, is not ready and equipped to implement QbD for existing and new products – especially the API manufacturing step. This is also true for the formulated products.

Suggested regulatory guidances/guidelines are not a gospel but an indication of the kind of data that needs to be generated by the companies to create a quality product. From web and print chatter I can see that without understanding and command of the process, such an effort will add costs and confusion. Since no one, including pharmaceutical companies and regulatory bodies know the costs and related benefits for existing products, I am not sure how many would venture to move from “A” to “D”.  Simplicity is needed.

If we want QbD practices to become the pharmaceutical manufacturing norm, industry will have to change (1). If we do not change voluntarily, business circumstances might force the change. We have seen this in many industries and need to pay attention. Creative destruction (4) is needed for pharmaceutical companies as well as regulatory bodies. Having QbD processes is not difficult and/or expensive. It requires a different mindset and a revamp of our thinking.

QbD processes can be arrived at two different ways. For lack of better names for these methods I have labeled them “Me-Too” and “Consolidated” model. They are discussed below.

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“Me-Too” Model

“Me-Too” companies have to rely on “square plug in a round hole (5)” as their manufacturing philosophy. The process is fitted in the existing equipment and most likely is a copy of their lab process that has been submitted for regulatory approval. These processes are generally inefficient, unsustainable and require monitoring of everything as described above.

If the companies want to stay the course of producing every product that is approved, there are two paths to QbD. First is to audit the processes, operating methods and production planning to see how the processes can be simplified to minimize QbA practices. The second path for QbD is to have equipment that is “right sized” for the process. Of course chemical engineering practices have to be applied in each case.

“Right sizing” requires investing in new equipment and commercialization of the optimum process. Manpower and financial investment along with re-approval of the products might be needed. It is very likely few companies will want to go this route, as they might not be able to justify such an investment for the existing products.

Modular equipment might help to reduce the investment but would require careful equipment selection, sizing and production planning. If not done right, companies would have even worse asset utilization compared to current conditions. Many companies may consider these alternates too challenging and complex and might stay with the current QbA methodology.

In a recent article (6) Figure 2 compares the costs of API manufacture in the West, India and China. A review suggests opportunities. Companies in all three regions purchase their raw materials from the same source. Thus their material costs are about the same. Companies in each region have opted for different margins. These margins can be significantly improved through QbD practices.

The reduction of conversion cost from 50 to 35% in the West through process simplification, which is not out the realm of possibilities, can significantly improve profit margins (7,8). If similar improvements are adopted in processes in India and China, their margins will also improve, making them more aggressive for further expansion in API and formulated products. Simplification will result in cycle time reduction, for example, production capacity will increase with no or minimal investment. I do not believe anyone would mind a “free” side benefit.

“Me-Too” companies can adopt QbD for their new products. They will have to think through these practices at the laboratory level and carry them to commercialization. The lessons of benefits can be extended to their existing products.

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“Consolidated” Model

Companies adopting this or a similar model will be producing a limited number of products instead of many in the “Me-Too” model. Since the number of products is limited they will supply a higher volume percentage of the global needs. Manufacturing technologies for these products will be the most innovative and sustainable. These technologies will be simple to execute and will be QbD based. Some of the products in their basket could be produced continuously.

Companies in this model would have some of the following attributes for their API manufacturing and their formulation practices.

1. Have a technology that is the best, revolutionary and simple.

2. Have complete command of the process and require no or minimal in-process quality analysis.

3. Produce better than 50% or a very large percentage of the global needs for the API and possibly the formulated product.

4. Process equipment is designed for the product.

5. Dedicated product lines at a site to produce multiple products that are independent of each other.

Companies in this model will be creating procedures and manufacturing methods that will far exceed what is outlined in regulatory guidelines. They would be able to show case what is possible with excellent processes.

Due to economies of scale they will be purchasing their raw materials in significantly large quantities, and would be able to get price discounts to achieve higher profit margins. QbD processes will reduce their paperwork, as there will be significantly reduced changeovers. Reduced paperwork is extra profit. Regulatory compliance will be easier.

Compared to the “Me-Too” model the “Consolidated” model will result in better business practices resulting in minimal supply shortages, better cash flow, lower inventories, lower investment in total equipment and infrastructure and definitely less regulatory paper work. Due to better and sustainable technology this model will have a better conversion yield and a lower cost.

Irrespective of what path companies choose to attain QbD, companies will have to incorporate and exploit chemicals, their chemistry and chemical engineering principles to get there.(9) Companies will have to select options. They can apply QbD to new products (least expensive) or existing products (more expensive compared to new products). QbD in the long run will be a win-win.

Expenses to achieve QbD processes will be offset through savings that will come from efficient processes and reduced paperwork. If companies want to stay with QbA methods it will be an option of their choosing but then they also will have to live with the threat of being driven out of business at some point.

QbA –> QbD is an imagination and creativity challenge (10), which can be met if the companies want to. There are two unknowns in the “A” to “D” move. They are: 1) what would be the combined cost of each re-approval (pharmaceutical companies and regulatory) and would the savings offset the expense and 2) do the regulatory bodies have sufficient and knowledgable manpower to handle the paperwork and inspections. My conjecture is that no one has the answer and any improvement or change will have to be justified on a case by case basis (11).



1. Malhotra, Girish: Who and What Killed QbD, Pharmaceutical Processing, Volume 26, No. 4, May 2011, pg 10-14

2. Quality by Design for ANDAs: An Example for Modified Release Dosage Forms Accessed February 13, 2012

3.  Process Validation: General Principles and Practices Accessed February 13, 2012

4. Malhotra, Girish: Pharmaceutical Manufacturing: Is it antithesis of creative destruction? October 2008

5. Malhotra Girish: Square Plug In A Round Hole: Does This Scenario Exist in Pharmaceuticals? Chemical Week, August 25, 2010

6. Pollack, P., Bardot, A, and Dach, R., API Manufacturing: Facts and Fiction, Contract Pharma, January/February 2012 Vol. 14, No. 1 Pgs. 56-66

7. Malhotra, Girish: Chemical Process Simplification: Improving Productivity and Sustainability, John Wiley & Sons,ISBN: 978-0-470-48754-9, January 2011

8. Malhotra, Girish: Can the Combination of Creative Destruction and “Steve Jobs Traits” Lead to Pharma QbD Spring? Profitability through Simplicity (, April 15, 2012

9. Malhotra, Girish: Focus on Physical Properties To Improve Processes, Chemical Engineering, Vol 119, No. 4, April 2012, Pg 63-66

10. Malhotra, Girish: QbA –>QbD: An imagination and creativity challenge, Chemica OGGI/ Chemistry TODAY March/April 2011, Vol. 29 Number 2, Pgs. 42-44

11. Malhotra, Girish: Neglected Tropical Disease (Infectious Diseases) Drugs: What are they telling us about Innovations! March 7, 2012

QbD in API Manufacturing

With a quality-by-design approach, robust processes consistently can help deliver quality product.

With a quality-by-design approach, robust processes consistently deliver quality product. Small drug manufacturers looking to adopt a quality-by-design (QbD) approach aim to scientifically determine product and process characteristics that will meet specific criteria set after careful analysis of the intended drug application.

These critical quality attributes (CQAs) of the final drug product (and often of the API) include the physico chemical properties and performance behavior of the formulated drug (drug substance). The manufacture of small-molecule APIs often requires the production of multiple intermediates using different processes. To consistently achieve these CQAs, it is necessary to ensure that each of these processes is robust. Critical process parameters (CPPs) are, therefore, important components of an effective control strategy for ensuring that the API process consistently delivers product with the appropriate CQAs.

Linking CQAs with CPPs
While true CQAs are found in the final specification for a drug product, there are several different aspects of a formulated drug, and particularly the API, that are affected by process parameters. These aspects, therefore, must be linked to the CPPs for each manufacturing process required for the preparation of an API, according to Chris Senanayake, vice-president of chemical development for Boehringer Ingelheim Pharmaceuticals. These characteristics include, but are not limited to, total purity, individual impurity levels, the polymorph(s), solvates, pH, water content, and particle size.

For the API, the most common CQAs are related to impurities from chemical processes and the solid-state properties (e.g., particle size, polymorphism) of the drug substance, according to Gert Thurau, group leader, Technical Regulatory at Roche. It is essential to control impurities for the safety of the drug product, while control of the solid-state properties of the drug substance is necessary to achieve consistent product properties, including bioavailability. “Once these attributes have been defined, it is possible to define what is important to the process and build a process that comes closer to quality by design and is, therefore, highly robust,” he says.

The assay and impurity profile of an API are essentially inter-related and are important because low impurity levels translates to safety for patients, while a high assay indicates appropriate efficacy, according to Vishwanath Nadig, associate director of quality assurance for Dr. Reddy’s Custom Pharmaceutical Services (CPS). Nadig adds that moisture content is also important because, in some cases, the presence of moisture may accelerate degradation of the drug substance.

These CQAs should be selected such that if the final product has these attributes, then it will work for its intended purpose. CPPs should then be selected because they have a relationship to the final CQAs and, when controlled, will assure the manufacture or a product with the right CQAs, according to Senanayake. “CPPs are the process parameters that affect the critical quality attributes of the API, and it is important to determine the range for each critical process parameter expected to be used during routine manufacturing and process control,” adds Mark TePaske, director of regulatory affairs, quality, and compliance for Cambrex.

Most importantly, it is essential to establish appropriate CPPs for each individual process step because they will ultimately affect the quality of the drug, which in turn can impact efficacy, according to Nadig. “Establishing and meeting CPPs and CQAs allows quality to be built into the process and affords robustness beyond release testing,” adds TePaske.  He also notes that well-defined CPPs ensure that a process operates in a state of control and in a validated state, which is crucial, because process validation is a cGMP requirement.

The definition of the CQAs for the final drug product and API is the first step in a comprehensive risk-based approach to product and process development, according to Thurau. “The starting point of the analysis is typically the quality target product profile, which leads to defining the critical quality attributes for the final product, and in turn, the critical quality attributes for the drug substance. Since the most important CQAs of the final API are the process-related impurities, which mostly result from the starting and raw materials, it is essential to understand the chemistry of the individual process steps,” he explains. In addition to risk assessment, Senanayake adds that prior knowledge, designed experiments, and regulatory expectations all play a role in determining CQAs and CPPs.

Risk-assessment approaches

Once CQAs are defined, typically during the initial scale-up phase, it is necessary to identify the appropriate CPPs. “At Boehringer Ingelheim, we believe that using a design of experiment (DOE) approach is the best method for demonstrating the interdependency of CPPs and the CQAs for individual process steps,” Senanayake says. Along with general risk assessments, the use of design failure mode and effects analyses (FMEA) is also effective for defining critical parameters of drug products, drug substances, and production processes, according to Nadig. “When applied appropriately, these two approaches are sufficient for defining the important attributes,” he says.

While orthogonal experiments can also be used, Senanayake notes that with DOE, knowledge is gained about the design space for each process that cannot be obtained using screening experiments, although they are useful when determining certain aspects of processes, such as the most effective solvents or catalysts. TePaske adds that once defined, critical attributes should be affirmed experimentally and control limits established, with processes, procedures, equipment and/or facilities revised as needed.

It should be noted that the risk-assessment methods used today are much more rigorous than the processes used in the past. “Historically, process and CQA definition was driven by the process development chemist, and the depth and breadth of the investigations were chemist-dependent,” observes TePaske. As a result, process development was primarily viewed as a laboratory exercise.  He goes on to note that current approaches are more disciplined, and studies are defined by multidisciplinary project teams. “These teams review and reconcile findings and risk assessments, and the concerns of the production, materials, quality, engineering, and other departments involved in manufacturing are integrated into the development process.

In effect, CQA definition and process development have evolved from a laboratory exercise to a global preparation for process validation and commercial GMP manufacturing.” He concludes that this team-oriented approach is the best practice because it integrates the needs of all of the disciplines involved in commercial manufacturing and virtually eliminates the possibility that CQAs and CPPs that cannot be performed in the available facilities and equipment will be defined.

Connecting CPPs to CQAs
Based on the results of the quality risk assessment, various reaction and work-up conditions can be investigated, and the products from each process step can be carefully analyzed with regard to their impurity profiles and other properties, according to Thurau. “For example, the structures of the impurities can be determined, and consequently the process conditions can be adjusted to minimize the impurities and/or their fate in downstream steps can be determined. The result is the elimination of the impurities or the determination of safe limits for them,” he explains. He stresses that it is important in this part of the process to be systematic, and to consistently maintain a link between the CPPs being evaluated and the drug product and drug substance CQAs.

Barriers to developing CPPs
While there are many benefits to a QbD approach and the establishment of CPPs that are linked to final product and drug substance CQAs, there are challenges to implementing such an approach. For Senanayake, the time and resources required remain the biggest barrier to obtaining a useful understanding of a process. “These barriers must be overcome, though, because having thorough knowledge of a process once it has reached commercial manufacturing is critical for success, and therefore, it is necessary to take the time to fully understand the design space,” he asserts.  Thurau agrees that the major limitations are practical in nature and can typically be overcome.

He does, however, note that there are some open questions on the definitions and differences in the applicability of the concept by various health authorities. “As registrations for products developed and manufactured using the QbD approach become more detailed and explicit about criticality, the hope is that the increased knowledge will lead to more flexible approaches to change management (do and tell) as opposed to the current set approach (tell and do).

Both Nadig and TePaske point to the need for participation of knowledgeable team members from across different functions of the organization as a challenging aspect of QbD and the establishment of effective CQAs and CPPs. “The multidisciplinary approach requires up-front involvement of disciplines whose primary job responsibility has historically been manufacturing and requires a new way of thinking,” says TePaske. He also adds that the risk-assessment approach often focuses on what can be done, which can stifle innovation.

Practical use of CPPs
Despites these barriers and limitations, many companies have recognized the value of a QbD approach and the importance of linking critical process parameters for individual process steps with the critical quality attributes of small-molecule APIs and the corresponding formulated drug products. Boehringer Ingelheim, for example, is monitoring process robustness at each development stage and focusing on understanding how this robustness translates to the commercial scale using batch histories, according to Senanayake. In addition, the company is trying to complete DOEs on the important steps in its manufacturing processes. “Our success to date is demonstrated by our validation efforts and the control charts we have established for individual steps,” he says.

At Dr. Reddy’s CPS, when defining CPPs for each process step, a cross-functional team meets to discuss the key sensitivities of the process parameters and material attributes, which are first weighted in importance, and then tolerances on the variables are established, according to Nadig. DOE screening is then performed to identify the critical parameters, followed by optimization and definition of the design space to understand what will happen to the CQAs under different parameter settings. “Importantly, we are trying to engage senior members from cross-functional teams during these pre-planning meetings so that an exhaustive risk assessment is conceptualized and a mitigation plan worked out,” he comments. The company also emphasizes the importance of clear communications, participation with the customer, and giving weight to all stakeholder concerns throughout the process.

Potential consequences
The consequences of not establishing CPPs that are effectively linked to CQAs can be severe. “If the proper controls for an intermediate step in the manufacturing process are not in place, then it is possible for a material to be produced that does not meet specifications for unknown reasons. Such a situation can in turn result in a recall if the intermediate is carried through the entire commercial process. In addition, without proper controls, processes often deliver poor quality product or product of inconsistent quality,” asserts Senanayake. In addition, according to Thurau, if inappropriate process parameters or in-process material attributes are established, the quality of the final product may appear high, but it is the result of testing quality into it, and not of process design and process understanding. Finally, TePaske stresses that operation in a state of control is fundamental to process validation and continued process performance in a validated state.

Quality by Design—Bridging the Gap between Concept and Implementation

As Europe strives to firmly incorporate quality-by-design principles, there are several key issues that still need to be addressed.

The drive to embed quality-by-design (QbD) principles into the pharmaceutical regulatory framework of the European Union has reached a key point 10 years after the European Medicines Agency (EMA) first backed QbD concepts. A relatively small number of marketing approval applications made in Europe have supporting QbD data, with EMA conceding that application dossiers with QbD information are far from becoming a standard approach. Nonetheless, the pharmaceutical industry has been internally adopting the QbD concepts laid down in the guidelines of the International Conference on Harmonization (ICH) covering pharmaceutical development (Q8), quality risk management (Q9), pharmaceutical quality systems (Q10), and the development and manufacture of drug substances (Q11) (1-4).

The international pharmaceutical companies in particular have benefited from applying QbD principles to their manufacturing operations. These companies have seen increased production efficiencies and yields as a result, with fewer off-specification outputs and a decrease in production recalls. Some of the leading pharmaceutical companies have been pace setters in including QbD data in authorization dossiers for approval of new medicines. Most pharmaceutical manufactures, however, have been reluctant to tie their marketing-authorization applications to this higher standard of quality management.

The need of global regulatory alignment
QbD is now at a “critical step” between regulatory support for its concepts and a much deeper implantation of its principles, Georges France, external relations head of quality at Novartis, noted at a joint quality-by-design workshop of EMA and Parenteral Drug Association (PDA) in London in late January 2014. According to France, the next step involves a streamlined system of regulatory review of QbD applications that needs to be extended to a “global regulatory alignment.”

The workshop, which included participants from FDA and the Japanese Pharmaceuticals and Medical Devices Agency (PMDA), revealed that there are, however, a number of big hurdles to widespread adoption of QbD despite considerable progress in its acceptance by the industry in recent years. One of the speakers at the meeting, Christine Moore, acting director of FDA’s Office of New Drug Quality Assessment (ONDQA), summarized the main concerns as being the classifying of criticality, levels of details required in process descriptions and in risk assessments, design-space verification, and changes to non-critical process parameters (non-CPPs).

Other major issues emerging at the conference, which focused on six case studies of QbD submissions evaluated by EMA, included real-time release testing (RTRT), application of QbD to continuous manufacture and biopharmaceutical production, dealing with post-approval changes to QbD processes, and the need for international harmonization of assessment methods. Regulators at the meeting complained that one big difficulty comes from the differences in terminology as well as the definitions used by pharmaceutical companies in their submissions, or during consultations on QbD matters.

“We have dealt with doubts about the terminology of terms by asking companies to verify what they mean before we review submissions,” commented one regulator speaking from the floor. “But the use of different definitions by companies for the same terms is something that should be sorted out between regulators.”

Some differences in terminology appear to have stemmed from companies adopting QbD concepts internally to gain greater operational efficiencies but in the absence of making QbD submissions with marketing-authorization applications, some of these companies have lost touch with the terminology in the ICH QbD guidelines.

Lessons from a QbD dossier
In a QbD dossier drawn up by GlaxoSmithKline (GSK), which was the subject of a case study presented at the meeting, the company used terms created before the ICH guidelines were finalized. As a result, “ICH terminology was not always followed, which in some situations, made it difficult to follow the information in the dossier in relation to guideline requirements,” said Gorm Herlev Joergensen, head of pharmabiotech, Danish Health and Medicines Authority, and Theodora Kourti, senior technical director, GSK, who jointly presented the case study (5). With some aspects of QbD, the ICH guidelines do not provide definitions. Non-CPPs, for example, are not defined in the guidelines.

Regulators at the meeting also stressed the need for QbD submissions to provide sufficient data for the sake of clarity and also to enable companies to justify properly the adoption of certain approaches to quality issues. The GSK dossier was praised by its regulatory assessors for the thoroughness of the data provided. The assessors, however, warned about the inclusion of complex statistical calculations in QbD submissions for this case study.

According to the case study, evaluation of statistical calculations, such as multivariate analysis, and the choice of experimental models on which QbD approaches are based, are challenging and require advanced statistical knowledge. Common quality assessors and GMP inspectors, however, usually do not have such knowledge, the study noted.

Design space verification
Both regulators and industry executives at the meeting conceded that QbD requires more resources to gather extra data and to answer the questions triggered by the data. This is particularly the case when drawing up design spaces that set the combination of variables and process parameters needed to ensure quality but have to be based on a scientific understanding of how these factors interact.

As a result, “industry experience to date suggests that design spaces for more complex products (e.g., biopharmaceuticals) may be harder to get approved,” said Tone Agasoester of the Norwegian Medicines Agency and Graham Cook, senior director, process knowledge/quality by design at Pfizer, in a joint presentation on design space verification (6).

A key issue with design spaces is that they are often developed at a small scale at the laboratory or pilot level. Despite that, design spaces have to show that they match the quality requirements needed at commercial scale. The incentive behind the resources devoted to design space development is the greater operating flexibility they allow in the post-approval phase so that process variations do not have to be authorized.

Innovation is key
Process innovation with a greater assurance of quality is a driving force behind QbD but it has to have the support of a simplified procedure for the approval of variations. “We need to be more innovative if we are to be successful in delivering consistent and reliable quality for all products,” said David Tainsh, chief product quality officer at GSK, and Keith Pugh, expert pharmaceutical assessor at the UK Medicines and Healthcare products Regulatory Agency (MHRA), in a joint presentation on innovation (7).

The barriers to innovation include limited availability of new skillsets, an unwillingness to deploy new technology, perceptions of high regulatory hurdles, and the absence of a procedure to manage complex lifecycle management changes, said the speakers. Above all, they added that there is “a lack of an overall vision on how to modernize pharmaceutical manufacture.”

Among the novel products to which QbD principles are having to be applied are advanced therapeutics, oligonucleotides, and microneedles, while innovative methods of manufacture and control include continuous processing, synthetic biochemistry, discrete manufacture, and analytics.

Tainsh and Pugh cited, as an example of innovation, liquid dispensing with process analytical technologies (PAT), such as near infrared (NIR) inspection, ultraviolet (UV) solution analysis, droplet weight checks, and pad inspections. The future “desired state” would be an enabling regulatory strategy and a procedure that keeps pace with innovation with increased scientific dialogue between regulators and industry. It would even include regulators being trained in new skillsets. There would also have to be “enhanced scientific and risk-based approaches to QbD to match new technologies with a simplified and streamlined variations process,” said Tainsh and Pugh.

There have been signs of an acceleration in the numbers of QbD submissions. Yoshihiro Matsuda, of the PMDA’s office of standards and guidelines development, told the meeting that after averaging approximately three QbD applications annually for a few years, the number jumped to 11 submissions per year in 2011-2012 and to six submissions up to mid-2013. EMA has reported that after averaging approximately five submissions per year since 2008, QbD applications went up to eight submissions last year. The rise in QbD applications from a low base could be evidence of more pharmaceutical innovations stimulating a higher uptake of QbD principles. There is, however, still a long way to go in Europe before QbD principles are firmly incorporated into regulatory procedures.

1. ICH, Q8 Pharmaceutical Development, Step 4 version (August 2009).
2. ICH, Q9 Quality Risk Management, Step 4 version (November 2005).
3. ICH, Q10 Pharmaceutical Quality System, Step 4 version (June 2008).
4. ICH, Q11 Development and Manufacture of Drug Substances, Step 4 version (May 2012).
5. G.H. Joergensen and T. Kourti, Case Study 4: Challenge of implementation of model-based and PTA-based RTRT for new products, presentation at EMA-PDA QbD Workshop (London, Jan. 2014).
6. T. Agasoester and G. Cook, Case Study 2: Development and verification of design space, presentation at EMA-PDA QbD Workshop (London, Jan. 2014).
7. D. Tainsh and K. Pugh, Innovation in medicines and manufacturing, future opportunities, presentation at EMA-PDA QbD Workshop (London, Jan. 2014)

New Developments in Scale-Up and QbD to Ensure Control Over Product Quality

Quality by Design and Design Space

The concept of Quality by Design (QbD) is described in the ICH guideline Q8 (R1) [1]. The guideline, which is written for the development of the drug product, includes the following definition of QbD: a systematic approach to development that includes incorporation of prior knowledge, results of studies using design of experiments, use of quality risk management and use of knowledge risk management throughout the life cycle of the drug product. Since the introduction of ICH Q8, much progress has been made in introducing the QbD concept for the manufacturing process of the active pharmaceutical ingredient (API) as well. This article focuses on the final API crystallization.

Design of experiments and quality risk management, as part of process development, are not new but have been used in the pharmaceutical industry in a more limited way. Typically, in the late stage of a drug project, experiments called “method qualification (MQ)” or “estimation of parameter ranges” (EPR) have been performed. Normally, this work includes a series of experiments where the primary focus is to show that the process yields a consistent result- without any exceptions- within a certain process parameter range. The obtained parameter intervals, the traditional design space, are normally the only data that are submitted to the regulatory authorities.


A consequence of this narrow filing strategy has been that even relatively small changes in the process must go through a post-approval process. When applying the new QbD principles and submitting a QbD-file, data and information are given on a wider “design space” as well as an even larger “knowledge space.” Schematically, this is illustrated in Figure 1. Notice that the knowledge space can be unsymmetrical and include areas (life cycle management (LCM) design space) which may be used in a future process. By submitting a wider range of data, which shows sensitivity and responses to changes in certain process parameters as well as describing a deeper understanding of the process, it will become easier to adjust and optimize the process to for instance new starting materials or changes in scale and type of equipment. Consequently, the regulatory flexibility is much larger.

What Does QbD for API’s Mean?

Dr M Nasr, Dir. ONDQA, FDA stated in 2005 a recommendation for how to proceed with QbD work not only for the drug product but also for the API’s [2]. He suggested the following: (i) design the drug substance with patient needs and drug product & process design in mind, (ii) Start by determining critical quality attributes (CQA:s) impacting safety and efficacy (e.g. impurity content), drug product performance (e.g. dissolution, stability) and manufacturability (e.g. content uniformity), (iii) For each unit operation, make sure you understand how process parameters affect the CQA, i.e. determine operating ranges of process parameters and input material attributes that achieve desired quality (the design space) and establish appropriate process controls to minimize effects of variability on CQA.

Quality Risk Assessment (QRA)

The quality risk assessment includes a step by step analysis of the process either by a topdown or bottom-up approach. The top-down approach aims to identify which process steps may have an influence on the CQA. This is useful in the early stages of process development. The bottom-down procedure, which should not be applied until the process is clearly defined, identifies possible failures in every process step and operation, and the consequences of the failure on the CQA are estimated. Typical examples of process failures are temperatures, concentrations, amounts or times that are out of the recommended ranges. By estimation of the severity, probability and detectability of the process failures, the most important ones are identified and the problem may be avoided by a change in design or by introducing necessary process controls.

Why Apply QbD Principles?

The application of QbD and quality risk assessment may initially mean more experimental and paper work, especially when the research and development organization is not used to the new way of working. However, it is clear that QbD has many advantages. First, and most important, it may give an improved regulatory flexibility with less post approval work. Eventually this will save both money and work. Secondly, it offers an effective tool for faster and more focused process development. Finally, as a consequence of the risk analysis performed, any non-robust process steps will be identified and possibly eliminated.

The Design of a Robust Crystallization Process

By introducing the above suggested QbD principles with regular risk assessment sessions early in the drug project, many potential future crystallization problems can be avoided. The most important decision – the choice of solid form- should be based on comprehensive salt- and polymorph screenings, careful API characterization including formulation properties of different solid forms. A complete knowledge about the phase diagram in the chosen process system i.e. understanding the stability limits for the polymorphs, will save a lot of problems. The choice of crystallization procedure is also important. A cooling crystallization in a single solvent or well-defined solvent mixture is more likely to deliver consistent batches than a direct crystallization of a sparingly soluble salt. Anti-solvent crystallizations are very efficient and flexible but often require accurate control of volumes and charging rate of the anti-solvent. This can be difficult when applied immediately after a reaction step.

Phase Diagrams

In many cases, it is possible to isolate the desired form (polymorph, hydrate, salt or co-crystal) of the API under conditions where it is thermodynamically stable. For simple cooling crystallizations containing principally the API and a fixed solvent composition, the thermodynamics can be described completely by a temperature-solubility curve. This can be used to define the screening temperature and isolation temperature, hence the yield and productivity of the cooling crystallization [3]. Because this information is thermodynamic, it is independent of scale – the main requirement on scale-up is that the system achieves equilibrium before filtration commences.

A temperature-solubility curve is like a map. The coordinates of temperature and composition define locations within the map, and the solubility curve separated regions on the map that have different properties, as the coastline separates sea from land. In many API crystallizations, there is more than one solid or more than one solvent. The compositions of such systems cannot be described by one concentration parameter. Examples of systems containing three components include racemic solutions, salt formation, cocrystal formation, anti-solvent additions and hydrate formation from mixed solvents. The ‘maps’ that are most useful in such cases are isothermal, with the axes describing the two ratios necessary to define the composition of the system. These isothermal ternary phase diagrams are familiar to geologists and have been used extensively to describe the behavior of racemic solutions [4, 5]. More recently, they have also been applied to formation of hydrates [6], co-crystals [7] and salts [8]. Here we describe two in-house examples where phase diagrams have been used to define the operating region for a process. Figure 2 shows these phase diagrams, which use the same color coding. The blue regions represent solutions. The red regions represent compositions where two solid phases are both present at equilibrium – these regions are generally to be avoided. In the green and yellow regions there is only one solid present, and in the yellow regions this is the desired solid.

Example 1: Making an Anhydrous Form

In this example, the desired form was anhydrous, but a hydrated form was also known. A basic requirement of the final crystallization was to avoid hydrate formation. The simplest way to do this would have been to exclude water from the crystallization. However, in this case water was essential, to achieve sufficient solubility, and to ensure the removal of inorganic impurities. Moreover, the process included the separation of an aqueous layer followed by distillation, so the amount of water present at the end of the crystallization was different from the amount added initially.

Figure 2, left shows a schematic isothermal ternary phase diagram for such a system. Following the trajectory of the arrow corresponds to starting with a suspension of solid anhydrate in a saturated solution in dry solvent at point A and adding water. Initially the added water resides in the solvent. On entering the red region, the hydrate begins to crystallize. As more water is added, the solution composition stays constant at composition C while the anhydrous form turns over to the hydrate. Once this turnover is complete, we enter the green region in which the solvent composition continues to change as more water is added. In order to be sure that the anhydrous form will be isolated, the water: organic solvent ratio in the system must be less than at point C. This critical water content at the isolation temperature was determined experimentally by a series of slurry experiments using in-line technology to monitor the course of transformations [9].

The stable phase at the end of the crystallization is controlled by keeping the water composition of the system within the range specified by the phase diagram. This could have been verified by in-process tests, but a more elegant solution was to use the batch temperature. Modeling of the distillation process showed that the batch temperature at the end of distillation was sufficiently sensitive to the water content. Ensuring that this temperature was at least 106° C translated to a water level at isolation sufficiently low to avoid the hydrate.

Example 2: Forming a Salt

The solubilities of the acid, base and salt in a fixed solvent composition define which stoichiometries of acid and base will yield the crystalline salt. In this example, the desired product is the 1:1 salt and no other salt stoichiometries are known. The salt is crystallized by mixing solutions of the acid and base at elevated temperature, then seeding and cooling the solution to ensure a low supersaturation process. To avoid impurity formation under acidic conditions, the process is designed with a small excess of base. The system is allowed to reach equilibrium at the final temperature before isolation and so a ternary phase diagram such as that shown in figure 2, right applies.

The key features of this schematic phase diagram arise because the base is less soluble than the acid in the process solvent. Imagine starting from a slurry of the base in the solvent at point F and adding acid to move along the line shown. Initially, the solubility of the base increases, until the system composition enters the red region of the diagram. Now the solution composition is constant at point B, and as further acid is added the amount of solid base decreases as the amount of salt solid increases. Composition B is a eutectic composition and the system is buffered. When the entire base has dissolved, we enter the yellow region which is the correct region for isolation of the pure salt. The line between B and the solid salt separates these regions and defines an edge of failure for the process.

The eutectic point B can be measured by allowing slurries of the salt and base to reach equilibrium at the isolation temperature of the process. The concentration of the process is fixed and so the acid/base composition at the edge of failure can be defined. Slurry experiments showed that at the process composition (represented schematically by X), an acid: base ratio less than 0.89:1 would be required to allow crystalline base to be isolated with the salt. Ensuring an acid charge of greater than 0.89 mole equivalents with respect to the base in the process would therefore avoid contamination of the isolated salt with crystalline base.

Process Analytical Technologies (PAT)

Process analytical technologies have become increasingly important in the pharmaceutical industry in recent years [10]. A diverse range of in-line tools can be used to monitor solution composition and solid form in crystallization processes from laboratory to plant scales. Application of these techniques in process development and scale up experiments is useful in developing process understanding and in many cases this removes the need for in-line monitoring at production scale. PAT can be used to reduce the number of experiments required to develop a process by optimizing the information acquired during experiments. It also allows access to information unobtainable by offline methods.

Solid form can be monitored directly using spectroscopic techniques such as near infrared (NIR) and Raman spectroscopies and indirectly by monitoring changes in the particle size by in-line chord length distribution measurements (in-line CLD measurement). In-line CLD measurement is now a standard technique to monitor crystal nucleation and growth. Short-lived metastable polymorphs or solvates are difficult to capture with off-line XRPD analysis but can be observed with in-line CLD measurement or Raman spectroscopy. Solution composition and supersaturation can be monitored by UV and IR spectroscopy. PAT allow the time of any changes in a process to be captured accurately, avoid changes which may occur during sampling and allow monitoring to be carried out throughout the process. The examples described here illustrate the use of PAT in process development, scale-up and manufacture.

Example 3: Use of In-Line CLD Measurement in Process Development

Early in development a recrystallization process for a basic API was required. The solubility of the API was measured in 10 solvents with diverse properties at 20°C and a solvent with suitable solubility was selected. From this data, a seeded cooling crystallization from isopropanol was expected to be feasible.

A trial process was monitored using in-line CLD measurement and the data are shown in figure 3a. The dissolution temperature was noted and this information was used to define the seed point. The seed were added at 12:30 at 10°C below the dissolution temperature. The total counts increase and remain elevated during the hold at 70°C indicating that the seed crystals do not dissolve. No crystal growth is observed during the hold after seeding but crystal growth occurs during the cooling step and then to a lesser extent during the hold following cooling. Some attrition may also be occurring at this stage. Monitoring by in-line CLD measurement allows us to determine the dissolution temperature and so select a seed point and in the same experiment confirm that adding seed at this temperature is successful. Monitoring during larger scale experiments allows us to determine the effect of scale on the kinetics of the process.

Example 4: Use of In-Line CLD Measurement on Scale-Up

In this case study, the monitoring of one such process on scale up allowed detection of an unknown solid form. The in-line CLD measurement data in figure 3b for a seeded cooling crystallization of an API crystallization at 100l scale showed unexpected dissolution and crystal growth on cooling to 0°C. This occurred after successful seeding and crystal growth of the desired anhydrous form. At 6:00 the crystals of the anhydrous form dissolve and a new solvated crystalline form grows. In the subsequent isolation and drying, the solvated form reverts to the desired anhydrous form. The solvated form would not have been detected without inline monitoring. Undetected, this solvated form would remove the ability to control the particle properties in the crystallization. The use of in-line CLD measurement to monitor the crystallization during development improved process understanding.

Example 5: The Use of In-Line Raman Techniques to Identify Unstable Solvates

Long drying times are sometimes difficult to explain but may be caused by small amounts of solvates that exist as long as the substance is in contact with the solvent. By introducing in-line measurement with Raman spectroscopy during the crystallization of an API a solvate was identified. Raman spectra of the slurry contain information from both the solution and the solid and the solid-state information needs to be extracted by mathematical treatment of the spectra. The characteristic peaks from the solid phase can be obtained by subtracting the corresponding mother liquor spectra as shown in Figure 4. A small, but significant difference over time is then seen around 1150 cm-1. This indicates that the solvate is a metastable form that in the current process only exists in the slurry for a few hours. A rapid transformation to a more stable form occurs and the solvate is not a problem.

Understanding the Process

Once the solvent and supersaturation technique are decided it is important to investigate the robustness of the process by a systematic variation of the process parameters to capture any kinetic effects. Typical examples are solvent composition, solution concentration, temperatures and crystallization times. It is also necessary to check the influence of the solution purity before crystallization. Repeat the crystallization with very pure material to identify any possible future effects that may occur due to optimized chemistry and improved work-up procedures. Specially designed edge-of-failure experiments will identify combinations of process parameters that must be avoided.

Example 6: Understanding Polymorphism

Example 6 discusses the application of factorial design to identify both safe and unsafe process regions. Substance X exists in two crystal modifications A and B. The wanted polymorphic form (form B) was identified as a critical quality attribute (CQA) and to minimize future CQA failures it was necessary to have a good understanding of the influence of process conditions on the modification. The substance is crystallized in an anti-solvent crystallization at 50 °C, followed by a cooling step down to 0°C. The initial solvent composition (solvent 1 and 2) and the charging rate of the anti-solvent were identified as critical process parameters.

The Use of Factorial Design

Three factors were varied on two levels which gave a 23 set of experiments (full factorial design, without centre points). In addition, two more intermediate levels of addition time (120 and 300) were added. The crystallization was observed and the identity of the isolated product was analyzed with XRPD. The outcome of the factorially designed experiments is illustrated by the parameter cube in Figure 5. It shows that the unwanted Form A (red circles) or a mixture of Form A and B (orange and yellow circles) is obtained at slow charging rates of the anti-solvent (i.e low supersaturation). Form B (green circles) is predominantly obtained at fast charging (high supersaturation levels). Further, the residual solvents in the solution have a strong influence on the crystallization: a high level of solvent 1 and 2 at fast charging rate increases the risk of initial oil formation (squares) before nucleation starts, and also the risk of obtaining form A or mixtures of A and B (yellow circle). The region where Form B is obtained consistently is at low level of solvent 1 and fast addition rates of the anti-solvent (upper left part of the cube).


Control over the final product quality is essential in the pharmaceutical industry. This requires the design of robust and flexible crystallization processes that deliver consistent material with the expected solid state and chemical properties. The use of the quality-by-design approach (Qbd) throughout the life-cycle of a drug product is an efficient tool for faster and more focused process development. It results in greater regulatory flexibility as well as more robust processes. This paper describes three important tools for a successful process design:

  • The use of phase diagrams for a complete understanding of the thermodynamics of the system,
  • The use of process analytical techniques (PAT) to increase the understanding of the process kinetics,
  • The application of factorial experimental design to understand the effect of process variations



  1. ICH Harmonized Tripartite Guideline. Pharmaceutical Development Q8(R1), Step 4 Version, November 2008
  2. The Gold Sheet, Pharmaceutical &Biotechnology Quality Control, vol 39, no 12 (2005)
  3. Black, Simon N; Quigley, Kathryn & Parker, Alexander (2006). A Well-Behaved Crystallisation of a Pharmaceutical Compound. Organic Process Research & Development, 10(2). 241-244
  4. Jacques, J; Collet, A & Wilen, S.H. (1994). Enantiomers. Racemates & Resolutions, Re-issue, Krieger, Florida ISBN 0-89464-876-4.
  5. Lorenz, L; Sheehan, P & Seidel-Morgenstern (2001). Coupling of simulated moving bed chromatography and fractional crystallisation for efficient enantioseparation. J. Chrom. A, 908, 201-214.
  6. Jarring, K; Larsson, T; Stensland, B. & Ymen, I. (2006). Thermodynamic Stablity and Crystal Structures for Polymorphs and Solvates of Formeterol Fumarate. J. Pharm. Sci. 95 (5) 1144-1161
  7. Chiarella, R.A.; Davey, R.J. & Peterson, M.L. (2007). Making Co-Crystals-The Utility of Ternary Phase Diagrams, Crystal Growth & Design, 7 (7) 1223-1226 DOI: 10.1021/cg070218y
  8. Simon N. Black, Edwin A. Collier, Roger J. Davey, Ron J. Roberts (2007). Structure, solubility, screening and synthesis of molecular salts. J. Pharm. Sci. 96 (5) pp1053-1068, DOI: 10.1002/jps.20927
  9. Simon N. Black, Andrew Phillips & Claire Scott (2009). Crystallization Design Space: Avoiding a Hydrate in a Water- Based Process. Org. Process Res. Dev. 13(1) 78-83 DOI: 10.1021/op800249r
  10. Yu, Lawrence X.; Lionberger, Robert A.; Raw, Andre S.; D’Costa, Rosario; Wu, Huiquan; Hussain, Ajaz S. (2004). Applications of process analytical technology to crystallization processes. Advanced Drug Delivery Reviews, 56, 349-369


Critical Process Parameters And Critical Quality Attributes: Why Does The Selection Process Take So Long?

Quick, can you name the top 10 Critical Process Parameters (CPPs) and Critical Quality Attributes (CQAs) of your bio-pharmaceutical manufacturing process? Well if you hesitated don’t feel bad. Most pharmaceutical process validation and engineering professionals could probably only list three or four of each for products that they should be intimately familiar with. The awareness, understanding, and integration of these central concepts by process validation professionals is only now taking root.  Are these new terms or concepts that most of us are not familiar with? Have you read the 2011 Food and Drug Administration (FDA) Guidance for Industry on Process Validation? How can the early (or retrospective) development of your process control strategy help?

New Terms And Old Concepts

It is not difficult to intuit what the process parameters are for most units of a manufacturing process. Generally they involve temperature, time, flow rates, pressures, and numerous other discreet input settings or output readings on the process equipment that are employed in a manufacturing process. But what is the criticality of each of those process parameters?  Certainly, do not expect a regulatory agency to answer that question for you.  Biopharmaceutical firms that have developed their manufacturing process are expected to have conducted the necessary experimentation, whether employing a design of experiments methodology or some other strategy, to have documented the parameters which are critical, key or non-key to drive their process.  Similarly, the attributes that the active pharmaceutical ingredient (API), bulk drug substance, or final formulated drug product is purported to possess in terms of biological activity, composition, identity, purity, and performance are quality attributes that must be firmly understood, tested for, and documented by the manufacturing firm.  These attributes are not to be simply in-process or release tests used during routine operations but rather parameters that pose higher risk and are indicative of process and product variations.

The FDA Guidance On Process Validation

The FDA Guidance for Industry – Process Validation: General Principles and Practices— although published in January of 2011, has only been read by less than 5% of process engineering and validation professionals (informal survey conducted by this author).  The19-page document co-written by the Center for Drug Evaluation and Research (CDER), Center for Biologics Evaluation and Research (CBER), and the Center for Veterinary Medicine (CVM) is a well-crafted, quick read that is a must for all industry professionals.  The guide very effectively lays out the statutory and regulatory requirements for process validation but better yet offers recommendations and general considerations for the same.  Three Stages: Process Design, Process Qualification, and Continued Process Validation are delineated.  The establishment of the Process Parameters and Quality Attributes, whether they are critical, key, or nonkey, should be established at the Process Design stage or before.

“Developing” Robustness Early

The process parameters that a firm should investigate and document are many, and a design-of- experiments approach may prove helpful to determine their criticality and boundaries to which the manufacturing process can be acceptably run.  In reality, the product development scientist first developing clinical entities may be the first source of parameter and variable information.  Through numerous refining experimental iterations, the range of CPPs and CQAs may be honed to those that require robust testing at the Process Qualification Stage.  The proper documentation of these process parameters and quality attributes may not exist for some products that have long been in the commercial manufacturing stage.  For those firms, a concerted reverse engineering effort may immediately be required in order to determine and document all process parameters and attributes reported to regulatory agencies as being possessed by the product.  Subsequently, historical data mining and statistical analysis of the same should render evidence of those parameters and quality attributes demonstrating a critical level of control to the process and safety and efficacy to the product.  The sooner a manufacturing firm has this process control strategy data available and agreed upon by their cross functional team of subject matter experts, the sooner the firm can conduct a gap assessment and subsequent process re-qualification where deemed necessary.

In sum, the strong knowledge of your manufacturing process’ CPPs and product’s CQA will provide immediate and critical measures of the control or variability of the process and product.  Moreover, a firm and its process technical staff, as well as the quality assurance professionals, should educate themselves on the details of those CPPs and CQAs in order to minimize their variation.  The new FDA guidance on these principles, although issued over two years ago, is still a mystery to those whose professional career is to implement the same in their manufacturing facilities.  The need for well established, existing manufacturing processes to possess a comprehensive process control strategy clearly identify the critical process parameters and critical quality attributes is as necessary and expected as for those beginning the process today.  Investment in process knowledge is critical for both patient and corporate good health.





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