Review Article

Nov - Dec 2017  |  Vol: 3  |  Issue: 6
Quality by Design (QbD) Approach used in Development of Pharmaceutical Formulations

Sagar Kishor Savale

Department of Pharmaceutics, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur 425-405, Maharashtra, India


Corresponding author,

Mr. Sagar Kishor Savale,

 Department of Pharmaceutics,

R. C. Patel Institute of Pharmaceutical

 Education & Research, Shirpur, 425405,

Maharashtra, India.

Mobile No: 9960885333,

Email ID:


Objective:  In this era of competition, quality is a prime factor of importance. The principles of quality have been described by the ICH guidelines: Q8 Pharmaceutical development, Q9 Pharmaceutical quality risk management and Q10 Pharmaceutical quality system. Quality-by-design is a recent concept which has been added as an annex to ICH Q8. It is a scientific approach that helps to build in quality into the product rather than mere testing of the final product. For the implementation of QbD various tools are needed to be used which have been described briefly.

Method: Various reports were taken from review or research articles published in journals, data from various books and other online available literature. The basic principles of these three ICH guidelines with regard to quality of pharmaceutical products have been briefly discussed.

Conclusion: This is a systemic approach to design and development of the pharmaceutical formulations and manufacturing processes that ensures the predefined product quality. Pharmaceutical industry is moving towards quality. A process DOE was used to evaluate effects of the design factors on manufacture ability and final product CQA, and establish design space to ensure desired CQA. Critical material and process parameters are linked to the critical quality attributes of the product.

Keywords: QbD, design and development, ICH Q8, Pharmaceutical risk management, DOE.




In the 1990, harmonisation around the world got going when the ICH proved to be effective in bridging many of the gaps that existed in almost all parts of the documentation required for new drug applications. The optimism fuelled by successful introduction of the first round of harmonized documentation helped to overcome the inertia that had so far beset the international scene.  



All the major objectives with regard to quality issues are being addressed by the ICH guidelines. The three ICH guidelines which throw light upon quality-by-design and related aspects include Q8 Pharmaceutical development, Q9 Pharmaceutical risk management and Q10 Pharmaceutical Quality systems. In fact, the ICH guideline Q8 is sub-divided into two parts: part one deals with pharmaceutical development and Part II is the annex to the guideline which states the principles for Quality-by-Design (QbD). The aim of







Pharmaceutical development is to design a quality product and its manufacturing process to consistently deliver the intended performance of the product. The information and knowledge gained from pharmaceutical development studies and manufacturing experience provide scientific understanding to support the establishment of the design space, specifications, and manufacturing controls. Information from pharmaceutical development studies can be a basis for quality risk management. It is important to recognize that quality cannot be tested into products; i.e., quality should be built in by design. In all cases, the product should be designed to meet patient’s needs and the intended product performance. Strategies for product development vary from company to company and from product to product. An applicant might choose either an empirical approach or a more systematic approach to product development, or a combination of both. A more systematic approach to development (also defined as quality by design) can include, for example, incorporation of prior knowledge, results of studies using design of experiments, use of quality risk management, and use of knowledge management (ICH Q10) throughout the lifecycle of the product. Such a systematic approach can enhance achieving the desired quality of the product and help the regulators to understand a company’s strategy better. Product and process understanding can be updated with the knowledge gained over the product lifecycle. During early days, the quality of the product was measured by end product testing (commonly referred to as quality by testing). However, this would be inefficient. In July 2003, the experts from the three regional grouping (USA, EU, and Japan) working on the Quality Topics within ICH (International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use) created a vision for the future pharmaceutical quality system. This vision recognizes that regulatory agencies will also benefit from this initiative as it will enable them to prioritize and allocate resources more efficiently, and in turn the patients too will be benefitted from improved access to medicines with enhanced assurance of quality (Purohit et al., 2013).

Historical Background

In 2007, the FDA received a total of 5000 proposals for new drug applications (NDA) and biological license applications and abbreviated new drug applications (ANDA). Pharmaceutical cGMPs for the 21st Century: A Risk-Based Approach was launched by the FDA in August 2002. A further guidance on process analytical technology (PAT) was released as part of the cGMPs for the 21st Century initiative, which hoped to encourage the adoption of more modern and flexible manufacturing technology in the pharmaceutical industry. In March 2004, the FDA launched The Critical Path Initiative (CPI) to address the steep decline in the number of innovative pharmaceutical products submitted for approval. The national strategy was to modernize the pharmaceutical sciences through which FDA-regulated products are developed, evaluated, manufactured and used. This prompted to the publishing of a guideline to aid manufacturers implementing modern quality systems and risk management approaches to meet the requirements of the Agency’s current thinking for cGMPs regulations. The impetus is to have quality in-built. Quality by design, in conjunction with a quality system, provides a sound framework for the transfer of product knowledge and process understanding from drug development to the commercial manufacturing processes and for post-development changes and optimization. Good manufacturing practices for the 21st century have been continually evolving as the ICH quality initiatives have been adopted. The move from empirical assessment based on performance to the concept of building quality in based on critical attributes has gained traction as new guidance documents have been published. The ICH published a series of guidance documents supporting QbD approaches (Chaudhary et al., 2014).

ICH Q8 Pharmaceutical Development

It provides information on how to present knowledge gained when applying scientific approaches and quality risk management for developing and manufacturing a product. The annex ICH Q8 (R2) further clarifies the key concepts of QbD. It is in the ICH Q8 annex that QbD is clearly defined as, a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management. Two other important terms for discussing QbD were also defined in ICH Q8; Design Space and PAT. Design space is defined as the multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality. According to ICH Q8, working within the design space is not considered as a change as it has been demonstrated to have no impact on quality. Movement out of the design space would be considered to be a change and would normally initiate a regulatory post-approval change process. Based on this guideline, design space was to be proposed by the applicant and would be subject to regulatory assessment and approval. PAT was also defined in ICH Q8 as a system for designing, analyzing, and controlling manufacturing through timely measurements (i.e., during processing) of critical quality and performance attributes of raw and in-process materials and processes with the goal of ensuring final product quality. It should be emphasized that the ICH Q8 guideline provides guidance on the suggested contents of the Pharmaceutical Development section of the Common Technical Document. This section of regulatory submissions relates to the manufacturing of the drug product which is a very specific term relating to the product that will actually be administered to the patient. This is in contrast to drug substance or bulk material which are the terms usually given to the active pharmaceutical ingredient (API) that is subsequently formulated with excipients to produce the drug product (formulation). This difference between drug product and drug substance is important when considering how and to what extent the original guidance was intended to apply QbD concepts and controls to pharmaceutical and biopharmaceutical manufacturing. This original guideline is not related to the manufacturing of drug substance-the active pharmaceutical ingredient (API) before it is formulated for administration to the patient. The complexity of unit operations for drug product is generally less than that for drug substance and it is appropriate that more control should be demonstrated for the drug product which will actually be administered to humans. The ICH Q8 guideline indicates areas where the demonstration of greater understanding of pharmaceutical and manufacturing sciences could create a basis for flexible regulatory approaches. The guideline lays emphasis on more flexible regulatory approaches which could be achieved if the applicant could demonstrate an enhanced knowledge of product performance over a range of material attributes, manufacturing process options and process parameters. The methods suggested to achieve this enhanced knowledge were formal experimental designs or Design of experiments (DOE) studies, PAT and prior knowledge. It was also recommended to use Quality Risk Management principles to carry out additional studies to acquire knowledge. ICH Q8 stresses that it is the level of knowledge gained and not the volume of data generated that would lead to more favourable consideration by the regulatory bodies. It was further suggested that applicant companies could assess the robustness of the manufacturing process, the ability of the process to reliably to produce a product of the intended quality. The guideline suggests that changes during development should be looked upon as opportunities to gain additional knowledge and further support establishment of the design space. The example of a risk-assessment tool. In this, a cross-functional team of experts work together to develop an Ishikawa (fish-bone) diagram that identifies potential variables which can have an impact on the desired quality attributes. Then, the variables are ranked on probability, severity, and detectability using FMEA analysis or similar tools. Design of experiments or other tools are then used to evaluate the impact of the higher ranked variables, to gain greater understanding of the process, and to develop a proper control strategy. Given below, is a fish-bone diagram for the manufacturing of tablets (Figure 1) (Bhatt et al., 2011).


Figure 1: Fish-bone diagram for Manufacturing of Tablets



ICH Q9 Quality Risk Management

It provides general guidance and references for some of the primary tools used in risk assessment. Examples are provided for industry and regulators to evaluate the risk to quality based on scientific knowledge and risk to patient. This guideline was released at approximately the same time as ICH Q8 and ICH Q10, and needs to be considered as part of the overarching QbD guidance released by regulatory agencies. The purpose of ICH Q9 was to offer a systematic approach to quality risk management. Importantly, it is noted that use of quality risk management can facilitate, but does not obviate, industry’s obligation to comply with regulatory requirements and does not replace appropriate communications between industry and regulators. Two important principles were highlighted in this document for the use of Quality Risk Management, one is the evaluation of the risk to quality should be based on scientific knowledge and ultimately link to the protection of the patient and second is the level of effort, formality and documentation of the quality risk management process should be commensurate with the level of risk (Rathore et al., 2009).


Figure 2: Quality risk management process



These are important caveats that should be remembered as risk assessment. It is a process that can easily be overused and lead to large amounts of unnecessary documentation. In Annex 1 to ICH Q9 tools are Flow charts, Check sheets, Process mapping, Cause and effect diagrams, Failure mode effects analysis (FMEA), Failure mode effects and criticality analysis, Fault tree analysis, Hazard analysis and critical control points, Hazard operability analysis, Preliminary hazard analysis, Risk ranking and filtering, Various statistical tools, Acceptance control charts, DOE (Histograms, Pareto charts, Process capability analysis) suggested for risk management in the pharmaceutical industry. While acknowledging that the selection of quality risk management tools is dependent on specific facts and circumstances, Annex 2 to ICH Q9 suggested areas to which quality risk management tools could be applied by pharmaceutical companies, ranging across all operational areas from quality management to facilities maintenance and even final packaging and labelling. Of particular relevance to this review were the potential applications to the development phase of pharmaceuticals suggested by the ICH. Specifically, application of Quality Risk Management techniques was suggested to assess the critical attributes of raw materials, APIs, excipients and packaging materials, as well as to determine the critical process parameters for a manufacturing process. Other areas suggested for development were to assess the need for additional studies (e.g., bioequivalence and stability) in technology transfer and scale-up and to reduce the variability in quality attributes (Roy et al., 2012).

ICH Q10 Pharmaceutical Quality System

It describes a comprehensive model for an effective pharmaceutical quality system that is based on International Organization for Standardization (ISO) quality concepts, includes applicable cGMP regulations, and complements ICH Q8 and ICH Q9. The Pharmaceutical Quality System had described four key elements such as, a process performance and product quality monitoring system, a corrective action and preventive action system, a change management system, Management review of process performance and product quality. Importantly, the guideline emphasized that these elements should be applied in a manner proportionate and appropriate for each of the stages of product life cycle. That is, the same level of rigor is not appropriate for products in the development stage as in the commercial or discontinuation phases of a products life cycle. It was the regulators hope that adoption of ICH Q10 should facilitate innovation and continual improvement and strengthen the link between pharmaceutical development and manufacturing activities. Knowledge Management and Quality Risk Management were projected as enablers of this innovation. While movement within a registered design space would not require regulatory approval, the change should still be evaluated and documented by the company’s change management system (Shajeeya et al., 2013).



Figure 3: ICH Q10 Pharmaceutical Quality System Model





Elements of Quality by Design

QbD development process includes the following elements that accomplish the following steps as per Figure 4.

Figure 4: Elements of Quality by Design




Quality by Design for Formulation and Development

In design and develop for pharmaceutical product that has the desirable TPQP, a product development must give serious consideration to the biopharmaceutical properties of the drug substance. The availability of drug substance may influence the number of studies and therefore, product understanding. The investigation of physical property, chemical property and biological property is termed as the Preformulation in pharmaceutical science. Critical quality attributes (CQA) are physical, chemical, biological, or microbiological property or characteristic that must be controlled directly or indirectly to ensure the quality of the product. Critical process parameters (CPP) are process inputs that have a direct and significant influence on critical quality required when they vary within the operating range. Design of experiments (DOE) is a structured and organized method to determine the relationship among factors that influence outputs of that process variable (Dalmaso et al., 2008).

Design of experiment and design space

Design of experiment: The applicant can choose to conduct pharmaceutical development studies that can lead to an enhanced knowledge of product performance over a wider range of material attributes, processing options and process parameters. Inclusion of this additional information in this section provides an opportunity to demonstrate a higher degree of understanding of manufacturing processes and process controls. This scientific understanding
establishes the design space. In these situations, opportunities exist to develop more flexible regulatory approaches, for example, to facilitate: risk based regulatory decisions (reviews and inspections); manufacturing process improvements, within the approved design space described in the dossier, without further regulatory review; real time quality control, leading to a reduction of end-product release testing. To realize this flexibility, the applicant should
demonstrate an enhanced knowledge of product performance over a range of material attributes (e.g. particle size distribution, moisture content, and flow properties), processing options and process parameters. This knowledge can be gained by, for example, application of formal experimental designs or PAT. Appropriate use of risk management principles can be helpful in prioritizing the additional pharmaceutical development studies to collect such knowledge. The below given comparison is chart current conventional approach and quality based design which is scientific approach encouraged by US food and drug administration. The design and conduct of the pharmaceutical development studies should be consistent with their intended scientific purpose and the stage of the development of the product. It should be recognized that the level of knowledge gained, and not the volume of data, provides the basic for science-based submissions and their regulatory evaluation (Gawade et al., 2013).

Design Space

The design space is the established range of process parameters that has been demonstrated to provide assurance of quality. In some cases design space can also be applicable to formulation attributes. Working within the design space is not generally considered as a change of the approved ranges for process parameters and formulation attributes. Movement out of the design space is considered to be a change and would normally initiate a regulatory post approval change process. The design space is the established range of process parameters and formulation attributes that have been demonstrated to provide assurance of quality. It forms the linkage between development and manufacturing design (Yu et al., 2008).

Establishment of Design Space through product and process design

Making changes to the formulation and manufacturing process during development generates valuable data that supports establishment of the design space. It is implied that both positive and negative results are important to understanding the design space. Minimum requirements are to provide data to support the proposed formulation and manufacturing process Reports should identify properties of the active ingredient, excipients and manufacturing process that are critical and that present significant risk to product quality and therefore should be monitored or otherwise controlled. Applicants can choose to perform additional development studies that enhance knowledge of product performance over a wider range of attributes, processing options and process parameters. Sharing such information with the regulatory bodies in the development report provides an opportunity to demonstrate a higher degree of understanding of manufacturing processes and process controls this effectively establishes the design space. This sharing of knowledge of the design space with the regulatory bodies will open the door to: True risk based reviews and inspections manufacturing process improvements within the approved design space without further regulatory oversight Real time quality control leading to a reduction in end product release testing (Garud et al., 2013).

Regulatory and Business Advantages of using Design Space

Working within the design space is not generally considered as a change of the approved ranges for process parameters and product attributes. Result will clearly be less supplemental regulatory filings. Movement out of the design space is considered to be a change and would normally initiate a regulatory post approval change process. Deemphasize end product testing and may eliminate certain release tests. Process knowledge can eliminate redundant testing for those attributes that are demonstrated to be controlled in-process Diminish the burden for validating systems by providing more options for justifying and qualifying systems intended to control critical attributes of materials and processes (Woodcock et al., 2004).

Challenges and Barriers to Implementation of Design Space

Fear of punishment resulting from sharing of full spectrum of knowledge and data generated to implement the concepts Industry is well experienced in the current state of design and needs better guidance on risk management and quality systems. Potentially higher upfront costs and expanded development timeline.

Certain Key Aspects of QbD

The Target Product Quality Profile (TPQP): (TPQP) is a term that is a natural extension of TPP for product quality. It is the quality characteristics that drug product should possess in order to reproducibly deliver the therapeutic benefit. For example, TPQP of an immediate release solid oral dosage form includes, Tablet Characteristics, Identity, Assay and Uniformity, Purity/Impurity, Stability, Dissolution time and Disintegration time. Target product quality profile (TPQP) is a quantitative surrogate for dissolution safety and efficacy product to optimize a formulation and manufacturing process. International Society of Pharmaceutical Engineers (ISPE) Product Quality Lifecycle Implementation (PQLI) calls this the Pharmaceutical Target Product Profile. It includes quantitative, stability and release profile for safety products. Associations and Regulatory Authorities on the presentation of enhanced product and process understanding in regulatory dossiers (Gawade et al., 2013).

Drug Substance and Excipients Properties

It is well recognized that excipients could be a major source of variability. Characterization and understanding of excipients pharmaceutical properties depends on the function and characteristics of excipients. The characteristics of excipients are physical, chemical. Biological such as stability, solubility, particle size. Drug-excipients compatibility knowledge information is valuable in the design of formulation and manufacturing processes. Such information may be gained through theoretical investigation and experimental studies (Trivedi et al., 2012).

Formulation Design and Development

In order to design and develop a robust generic product that has the desirable TPQP, a product development scientist must give serious consideration to the biopharmaceutical properties of the drug substance. These biopharmaceutical properties include physical, chemical, and biological properties. Physical properties include physical description (particle size, shape, and distribution) polymorphism. Chemical property like partition co-efficient and chemical stability in solid or solution state. The goal of Preformulation studies is to determine the appropriate salt and polymorphic form of drug substance evaluate understand its critical properties, and generate a thorough understanding of the material’s stability under various processing and in vivo conditions, leading to an optimal drug delivery system (Savale et al., 2017).

Manufacturing Process Design and Development

It is important that the process and product design and development cannot be separated since a formulation cannot become a product without a process. Depending the product developed process knowledge, type of process, the scientist may be necessary to study before the completely the process design and development. The pharmaceutical industry has traditionally put emphasis on new drug discovery and development, and the complexity of drug product manufacturing operations is not well recognized. With the emphasis of QbD by the FDA an industry and drug product cost pressures; this trend is expected to change (Bhasin et al., 2012).

Design of Experiments (DOE)

Design of experiments (DOE) is an efficient procedure for planning experiments so that the data obtained can be analyzed to yield valid and objective conclusions. A structured, organized method for determine the relationship between factors affecting a process and the output of that process is known as Design of experiment. In experiments, we deliberately change one or more process variables (or factors) in order to observe the effects the change will have on one more response variables. The (Statistical) design of experiments (DOE) is an efficient procedure for planning experiments so that the data obtained can be analyzed to yield valid & objective conclusions. DOE begins with determining the objective of an experiments & selecting the process factors for the study. An experiments design is the laying out of a detailed experiments plan in advance of doing the experiment will chose experimental designs Maximize the amount of Information that can be obtained for a given amount of experimental effect (Nadpara et al., 2012).

Benefits of Design of Experiments

Experimental design involves manipulating the independent variable to observe the effect on the dependent variable. This makes it possible to determine a cause and effect relationship. As well as controlling the independent variable the experimenter attempts to eliminate unwanted extraneous variables. Control over extraneous variables is usually greater than in other research methods. Because of strict conditions and control the experimenter can set up the experiment again and repeat or check their results. Replication is very important as when similar results are obtained this gives greater confidence in the results (Huang et al., 2009).

Use of Design of experiment

Design of experiments is used to determine the causes of variation in the response, the find conditions under which the optimal (maximum or minimum) response is achieved, to compare responses at different levels of controlled variables & to develop a Model for predicting response (Nadpara et al., 2012).

Key steps for Design of experiments

Obtaining good results from a Design of experiments involves those seven steps. Set objective
Select process variables. Select an experimental design. Execute the design. Check that the data are consistent with the experimental assumptions. Analyze and interpret the results (Khinast et al., 2011).

Related definitions

Some of the related definitions are stated below

Treatment: Different combinations of conditions for rest.

Treatment levels: The relative intensities at which a treatment will be set during the experiments.
Treatment factors (variables): One of the controlled conditions of the experiments.
Experimental unit: Subject on which a treatment will be applied & from which a response will be elicited also called measurement or response units.

Responses: Outcomes that will be elicited from experimental units after treatments have been applied eq. hardness, friability (release of drug from a formulation).

Experimental design: Rule for assigning treatment levels to experimental units.
Analysis variance (ANOVA): Principal statically means for evaluating potential sources of variation in the responses (Savale et al., 2017).

Replication: Observing individual response of multiple experimental units under identical experimental conditions. It is use to detect Noise.

Randomization: Non-systematic assignment of experimental units to treatments.
Confounding: Design situation in which the effect of one factor or treatment can’t be distinguished from another factor or treatment.

Characterization of a Good Experimental design

The following are the characteristics of a good experimental design. Avoidance of systematic error: Systematic errors lead to bias when estimating difference in response between treatments. Precise estimation: Achieved a relatively small random error, which in turn depends on: Random error in the responses, the number of experimental units and the experimental design employed, Proper estimation of error (Khinast et al., 2011).

Limitations of Conventional Method

The classical method of experimentation is costlier and is restricted to one factor at a time and-
other factor being kept constants .This fails to show interaction effect that may exists between some of the factors consequent on which optimum concentration are difficult to be determined.

Advantages of Design of Experiments over conventional method

A single integrated design, which permits variations of more than one factor at a time & allows determination of interaction effects of well as provide more information on the man effects. Advantages of well-planned experiments are more information per experiments, reduced lead time, improve efficacy, Organized approved, Information reliability, Capability to the interactions & more reliable prediction (Eziokwu et al., 2013).

Some reasons to model a process

once we know the primary variables (factors) that affect the responses of interact, a number of additional objective may be pursued those include, Hitting a target, Maximizing & minimizing a response, Reducing a variation, Making a process robust, Seeking multiple goals.

Key steps for Design of experiments (DOE)

Obtaining good results from a Design of experiment (DOE) involves these steps A) Set objective, B) Select process variables, C) Select an experimental design, D) Execute the design, E) Analyze & interpret the results (Lionberger et al., 2008).

Selections of variables & their level: Process variables include both inputs & outputs i.e. factors & response. The most popular experimental design are tune level design because it is ideal for screening design, simple & economical ; it is also gives most of the information required to get to a multilevel response surface experiments if needed. Selection of experimental design: The choice of an experimental design depends on the objectives of the experiments & the number of factors to be investigated (Eziokwu et al., 2013).

Experimental design objective

Comparative objective, Screening objective, Response surface (method) objective, Optimizing response when factors are proportions of a mixture objective. Optimal fitting of a regression model objective. This primary purpose of the experiments is to select or screen out the few important main effects from the many less important ones. These screening designs are those termed main effects designs (Siegfried et al., 2011).





Table 1: Types of Design of Experiments commonly used

Name of Design


Screening Design (S.D)

Screening designs are effective way to identified significant main effects. The term Screening design refers to an experimental plan i.e. indented to find a few significant factors from a list of many potential ones.

Response Screening Design (RSM)

Response screening design involves just the main effects & interactions or they may also have quadratic & possibly cubic terms to account for curvature model which may be appropriate to described a response

Fractional Factorial Design

Full factorial experiments can requires may runs. The solution to this problem is to use only a fraction of the runs specified by the full factorial design. In general, we pick a fraction such ½, ¼ etc. of the runs called for by the full factorial.

Placket-Burmam Design

These designs have run numbers that are in multiple of 4.placket Burmam (PB) designs are used for screening experiments because in PB designs, main effects are, heavenly confounded with two -factor interactions.

Box-Behnken Design

The Box-Behnken Design is an independent quadratic design which does not contain an embedded factorial or fractional factorial design. These designs are rotatable (or near rotatable) & requires 3 levels of each factors.






The major objectives with regard to quality issues are addressed by the ICH guidelines. These are Q8 Pharmaceutical development, Q9 Pharmaceutical risk management and Q10 Pharmaceutical quality systems. Quality-by-design (QbD) is a new concept which has been added in the annex to guideline Q8. The QbD approach leads to enhanced understanding, well-defined system and regulatory flexibility. Well adoption of QbD tools is the key to achieve long-term benefits. Not to be disorientated among all aspects of QbD, appropriate risk assessment tools such as flow-down maps and Ishikawa diagrams can be considered in the beginning. It will be instructive to keep the processes in perspective. Tools of DoE and PAT should be determined based on the specific intentions and to make their outcome assessment capably, well-trained staff are necessary as well as for establishing a design space which requires mathematical and statistical knowledge. As shown with mentioned case studies, implementation area of QbD is extremely wide. QbD can provide extended knowledge about all phases in any drugs lifecycle. In product development studies, combination of several material attributes and unit operation parameters are evaluated. But, it is also possible to focus on only one unit operation such as fluidised bed granulation, roll compaction and tablet coating. With the contribution of different bodies of the pharmaceutical area, all of the case studies exemplify to encourage the implementation of QbD. As long as pharmaceuticals get more complex in the meaning of advanced manufacturing techniques and the new areas such as personalised medicine, the importance of a well-constructed quality system will gradually increase. Nowadays, much of the scientific basis is already in place for the implementation of QbD. So, the Statistical optimization for pharmaceutical scientist is to define the formulation with optimum characteristics. Statistical optimization can also provide solutions to larger-scale manufacturing problems, which occasionally arise. Importantly, statistical optimization experimentation and analysis provides strong assurances to Regulatory Agencies regarding superior product quality.


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