1. Tools of Quality by Design
1.1 Design of Experiments (DOE)
Design of experiments (DOE) is a structured and organized method to determine the
relationship among factors that influence outputs of a process. It has been suggested that
DOE can offer returns that are four to eight times greater than the cost of running the
experiments in a fraction of the time.
Application of DOE in QbD helps in gaining maximum information from a minimum
number of experiments. When DOE is applied to a pharmaceutical process, factors are
the raw material attributes (e.g., particle size) and process parameters (e.g., speed and
time), while outputs are the critical quality attributes such as blend uniformity, tablet
hardness, thickness, and friability.
As each unit operation has many input and output variables as well as process
parameters, it is impossible to experimentally investigate all of them.
DOE results can help identify optimal conditions, the critical factors that most influence
CQAs and those who do not, as well as details such as the existence of interactions and
synergies between factors.
1.2 Process Analytical Technology
PAT has been defined as “A system for designing, analyzing, and controlling
manufacturing through measurements, during processing of critical quality and
performance attributes of raw and in-process materials and processes, with the goal of
ensuring final product quality”.
The goal of PAT is to “enhance understanding and control the manufacturing process,
which is consistent with our current drug quality system: quality cannot be tested into
products; it should be built-in or should be by design.”
The design space is defined by the key and critical process parameters identified from
process characterization studies and their acceptable ranges. These parameters are the
primary focus of on-, in- or at-line PAT applications. In principle, real-time PAT
assessments could provide the basis for continuous feedback and result in improved
process robustness. NIR act as a tool for PAT and useful in the RTRT (Real Time
Release Testing) as it monitors the particle size, blend uniformity, granulation, content
uniformity, polymorphism, dissolution and monitoring the process online, at the line and
offline, thus it reduces the release testing of the product.
1.3 Risk Management Methodology
Quality Risk Management is defined as “A systematic process for the assessment,
control, communication and review of risks to the quality of the drug (medicinal) product
across the product lifecycle”.
Risk assessment tools can be used to identify and level parameters (e.g., process,
equipment, input materials) with potential to have an impact on product quality, based on
prior knowledge and primary experimental data.
The early list of potential parameters can be fairly broad, but can be modified and
prioritized by additional studies (e.g., through a combination of design of experiments,
mechanistic models).
Once the considerable parameters are identified, they can be further studied (e.g., through
a combination of design of experiments, mathematical models, or studies that lead to
mechanistic understanding) to achieve a higher level of process understanding.
The pharmaceutical industry and regulators can evaluate and manage risks by using wellknown
risk management tools and/ or internal procedures such as,
Basic risk management facilitation methods (flowcharts, check sheets etc.);
Failure Mode Effects Analysis (FMEA);
Failure Mode, Effects and Criticality Analysis (FMECA);
Fault Tree Analysis (FTA);
Hazard Analysis and Critical Control Points (HACCP);
Preliminary Hazard Analysis (PHA);
Risk ranking and filtering;
1.3.1 Failure mode effects analysis (FMEA)
FMEA is one of the most commonly used risk-assessment tools in the
pharmaceutical industry. It is a systematic and proactive method to identify and
mitigate the possible failure in the process. Failure modes represent any errors or
defects in a process, material, design, or equipment. Once failure modes are
established, FMEA tool evaluates the effect of these failures and prioritizes them
accordingly. This tool is further advanced with studying criticality of the
consequences and providing clear indication of situation.
1.3.2 Failure Mode, Effects and Criticality Analysis (FMECA)
It is the extension of earlier said FMEA tool. Extending FEMA to incorporate an
investigation of the degree of severity of consequences, their probabilities of
occurrence and their detect-ability is Failure mode, effects and criticality analysis.
In FMECA, each failure mode of the product is identified and then evaluated for
criticality. This criticality is then translated into a risk, and if this level of risk is
not acceptable, corrective action must be taken. This can be utilized for failure
and risk associated with manufacturing processes. The tool can also be used to
establish and optimize maintenance plans for repairable systems and/or contribute
to control plans and other quality assurance procedures.
1.3.3 Fault tree analysis (FTA)
This tool assumes failure of the functionality of a product or process. The results
are represented pictorially in the form of a tree of fault modes. This can be used to
investigate complaints or deviation in order to fully understand their root cause
and ensure that intended improvement will resolve the issues and not cause any
other different problem.
1.3.4 Hazard analysis and critical control points (HACCP)
HACCP provides detailed documentation to show process or product
understanding through identifying parameters to control and monitor. The
definition of hazard includes both safety and quality concern in a process or
product. It involves hazard analysis, determining critical control point,
establishing critical limit, establishing a system to monitor critical control point
and establishing a record keeping system. This might be used to identify and
manage risk associated with physical, chemical and biological hazards.
The output of a risk assessment may be a combination of quantitative and
qualitative estimation of risk. As part of FMEA, a risk score or Risk Priority
Number (RPN) may be assigned to the deviation or to the stage of the process that
is affected; this helps to categorize the deviation. RPN is calculated by multiplying Probability (P), Detectability (D) and Severity (S), which are
individually categorized and scored. Rating scales usually range from 1 to 5.
RPN = probability score × severity score × detectability score
Where, the score was defined prior to the risk analysis stage. A RPN of < 40 was
considered a low risk; a RPN of 40–99 was identified as an intermediate risk; and
a RPN of ≥ 100 was defined as a high risk
2 . QbD Approach for Analytical Method Development
1. Determine what to measure and where/when to measure it. Develop measurement
requirements based on product QTPP and CQA.
2. Select appropriate analytical technique for desired measurement. Define method
performance criteria.
3. Assess risk of method operating parameters and sample variation. Use risk assessment
tools.
4. Examine potential multivariate interactions (DoE & design space).Understand method
robustness and ruggedness.
5. Define control space and system suitability, meet method performance criteria.
6. Monitor method performance, update as needed as process and analytical technology
evolves.
2.1 Analytical Method Understanding
Understand how variation in input parameters affects analytical results
Examine multivariate relationships Across instrument, laboratory, analyst, sample
and method parameters
Employ mechanistic understanding based on chemical, biochemical and physical
characteristics
Incorporate prior knowledge of techniques and methods
2.2 Analytical Method “Design Space”
A science and risk based and multi-variate approach to evaluate effects of various
factors on method performance.
Typically DoE (Design of Experiment) is used to find ranges for instrument operating
parameters, to understand sample preparation variations and variations of method
precision. Example terminology for design space: MODR (method operable design
range)
Method performance criteria are response factors
Can be conducted together with method validation
2.3 Benefits of Application of QbD Approach to Analytical Methods
Development of a robust method
Applicable throughout the life cycle of the product
Regulatory flexibility movements within “Design Space” are not considered a change
in method.
2.4 Regulatory Considerations
Define intended use of the analytical method (e.g. RTRT (real time release testing) or
endpoint testing)
Not all analytical techniques are inter-changeable.
Example: from HPLC to NIR.
Require additional development and validation efforts.
Submission of comparability protocols is recommended.
Need sufficient statistical power to support analytical “Design Space”
Applicants need to clearly define terminologies
Proposal for regulatory flexibility should consider potential risk to product quality.
2.5 Elements of QbD to analytical method
2.5.1 In determination of impurity
Gavin gives a quality by design approach to impurity method development for atomoxetine
hydrochloride. An ion-pairing HPLC method was developed and associated system suitability
parameters for the analysis of atomoxetine hydrochloride are studied. Statistically designed
experiments were used to optimize conditions and demonstrate life cycle robustness for the
separation of atomoxetine and impurities. Weiyong Li describes a three-step method
development/optimization strategy for HPLC assay/impurity methods for pharmaceuticals i.e.
multiple-column/mobile phase screening, further optimization of separation by using multiple
organic modifiers in the mobile phase, and multiple-factor method optimization using Plackett–
Burman experimental designs. Commercially available chromatography optimization software,
Dry Lab was used to perform computer simulations.
2.5.2 In screening of column used for chromatography
The particulars of the experimental design, evaluation criteria used and some of the most
commonly used analytical columns from reputed column manufacturers. A systematic approach
is used to evaluate seven RP-HPLC columns against predefined performance criteria. This
approach is a fundamental part of a QbD method development.
2.5.3 In development of HPLC method for drug products/substances
A novel approach to applying quality by design (QbD) principles to the development of high
pressure reversed phase liquid chromatography (HPLC) methods. Four common critical
parameters in HPLC – gradient time, temperature, pH of the aqueous eluent, and stationary phase
are evaluated within the quality by design framework by the means of computer modeling
software and a column database.
2.5.4 Instability studies
An application of quality by design (QbD) concepts to the development of a stability indicating
HPLC method for a complex pain management drug product containing drug substance, two
preservatives, and their degradants are described. The initial method lacked any resolution in
drug degradant and preservative oxidative degradant peaks, and peaks for preservative and
another drug degradant. The method optimization was done using Fusion AE™ software that
follows a DOE approach. The QbD based method development enabled in developing a design
space and operating space with particulars of all method performance characteristics and
limitations and method robustness within the operating space.
2.5.5 In UHPLC
Rapid high-performance liquid chromatography with high prediction accuracy, with design
space computer modeling, which demonstrates the accuracy of retention time prediction at high
pressure (enhanced flow-rate) and shows that the computer-assisted simulation can be used with
enough precision for UHPLC applications. The validation and verification experiments
demonstrate that the method is robust across the parameter ranges provided in Table 2. However,
in this particular method example, a method control strategy was enacted that constrained the
organic modifier to 63% (rather than the verification level of 62%) and fixed the flow rate to
1.00 mL/min to ensure acceptable retention of degradation products.
2.6 A Quality by Design approach to analytical methods
Methods are commonly developed using a one-factor-at-a time (OFAT) approach where one
variable is changed sequentially until a suitable method is produced. This type of development
may create an adequate method but provides a limited understanding of method capabilities and
method robustness. Rather, a systematic screening approach that evaluates a number of
stationary phases, pH ranges and organic modifiers provides a more thorough approach to
method development. A quality by Design (QbD) approach to method development uses
statistical design of experiments (DoE) to develop a robust method ‘design space’. The design
space defines the experimental region in which changes to method parameters will not
significantly affect the results.
A potential QbD approach to analytical methods can be exemplified as follows:
Step 1: Define method intent
The goal of LC method development have to be clearly defined, as pharmaceutical QbD is a
systematic, scientific, risk based, holistic and proactive approach that begins with predefined
objectives and emphasizes product and process understanding and control. The ultimate goal of
the analytical method is to separate and quantify the impurities as well as main compound.
Step 2: Perform experimental designs
A systemic experimental design is needed to assist with obtaining in-depth method
understanding and performing optimization. Here an efficient and comprehensive experimental
design based on systemic scouting of key components of the RP-LC (mobile phase composition,
mobile phase delivery system, and column, pH, and flow rate and column temperature) is
presented. The QbD forms a chromatographic database that will assist with method
understanding, optimization and selection. In addition to this, it can be used to evaluate and
implement change of the method and it should be needed in the future, for example should the
chromatographic column used no longer be commercially available, or an impurity is no longer
relevant.
Initially an experimental design comprised of a standard set of 2 columns, 3 pH values and 4
mobile phases (2 compositions, 2 delivery systems) developed, after this a set of 3 column
temperatures, 3 flow rates was developed to further optimize the peak symmetric properties. This
led to a total of 30 (2 column x 3 pH x 4 mobile phases, + 3 column temperatures + 24 x 3 flow
rates) chromatographic conditions. In addition, it enabled the creation of a database that
describes the relationship of the compound retention and possible RP-LC conditions.
Steps 3: Evaluate experimental results and select final method conditions.
These method conditions were evaluated using the three tiered approach. At the first level, the
conditions were evaluated for peaks symmetry, peak fronting and peak tailing. At the second
level, condition was evaluated for the better separation of impurities and drug substances.
Step 4: Perform risk assessment with robustness and ruggedness evaluation.
As the final method is selected against method attributes, it is highly likely that the selected
method is reliable and will remain operational over the lifetime of product. Therefore, the
evaluation of method robustness and ruggedness to be carried out as the fourth step method
development is mainly for the method verification and finalization. A risk-based approach based
on the QbD principles set out in ICH Q8 and Q9 was applied to the evaluation of method
robustness and ruggedness.
Structured methodologies for risk assessment, such as Cube plots or 3D surface plots can be
implemented to identify the potential risk of the method due to a small change of method
parameters or under a variety of conditions such as different laboratories, analysts, instruments,
reagents, days etc.
A) Robustness
To establish the robustness of test method and to demonstrate its reliability for minor changes in
chromatographic condition
B) Ruggedness
The ruggedness of analytical method is the degree of reproducibility of test results obtained by
the analysis of the same samples under a variety of conditions such as different laboratories,
different instruments, different lots of reagents, different assay, temperatures, different days,
different analysts, etc.
Step 5: Define analytical method performance control strategy
As a result of robustness and ruggedness studies, the overall method gives us an understanding
about method performance under various conditions can be improved The analytical method
performance control strategy along with appropriate system suitability criteria can be defined to
manage risk and ensure the method delivers and the desirable method attributes. If the risk is
high and is hard to manage, it is an opportunity for the analyst to go back to the database
described in step 2 to find a more appropriate method and to go through the procedure as
described to ensure method robustness and ruggedness.
2.7 Opportunities of and barriers against a QbD approach to analytical
methods
There are several opportunities of this QbD approach to analytical methods, including:
Methods will be more robust and rugged, resulting in fewer resources spent
investigating out-of-specification results and greater confidence in analysis
testing cycle times.
Resources currently invested in performing traditional technology transfer and
method validation activities will be redirected to ensuring methods are truly
robust and rugged.
The introduction of new analytical methods—from research and development
to quality control laboratories—using a QbD approach will lead to a higher
transfer success rate than with traditional technology-transfer approaches.
A specified process will help the systematic and successful implementation of
the QbD methodology and fosters a team approach.
A true continuous learning process is established through the use of a central
corporate knowledge repository that can be applied to all methods.
By registering only a commitment to ensure method changes meet the
registered method performance criteria, flexibility to continuously improve
methods can be achieved.
The QbD approach to analytical methods also faces several barriers, including the following:
Current expectations of analytical technology transfer and method validation
must change because current validation guidance does not lead to methods
that can always be reliably operated.
Acceptance must be gained for registration of the method performance criteria
rather than the method conditions.
External guidance must be developed in this area; ICH guideline Q2(R1)
requires revision (or removal) and Center for Drug Evaluation and Research
guidance must be created for analytical methods.
A common language for some of the new terms is required, including
analytical method design space, analytical method control strategy, and
method performance criteria.
Analysts must learn new tools and skills.
A consistent worldwide approach is required for this initiative to be effective.
2.8 Application of QbD in various analytical methods
1. Chromatographic techniques like HPLC (For stability studies, method development, and
determination of impurities in pharmaceuticals).
2. A hyphenated technique like LC-MS.
3. Advanced techniques like mass spectroscopy, UHPLC, and capillary electrophoresis.
4. Karl Fischer titration for determination of moisture content.
5. Vibrational spectroscopy for identification and quantification of compounds e.g. UV
method.
6. Analysis of genotoxic impurity.
7. Dissolution studies.