A Review on: Quality by Design (QbD)

 

Vrushali R. Kadam1*, Dr. M. P. Patil1, Vrushali V. Pawar2, Dr. Sanjay Kshirsagar3

1Department of Pharmaceutics, MET’s Institute of Pharmacy, Bhujbal Knowledge City, Adgaon, Nashik , ,423301, Savitribai Phule Pune University, Maharashtra, India.

2Department of  Pharmaceutical Chemistry, MET’s Institute of Pharmacy, Bhujbal Knowledge City, Adgaon, Nashik 423301, Savitribai Phule Pune University, Maharashtra, India.

3Principal of MET’s Institute of Pharmacy, Adgaon, Nashik, 423301, Savitribai Phule Pune University, Maharashtra, India.

*Corresponding Author E-mail: kadamvrushali26@gmail.com

 

ABSTRACT:

Quality by Design is the modern approach for quality of pharmaceuticals. Recent pharmaceutical regulatory documents have stressed the critical importance of applying quality by design (QbD) principles for in-depth process understanding to ensure that product quality is built in by design. The purpose of this paper is to discuss the pharmaceutical Quality by Design and describe how it can be used to ensure pharmaceutical quality. Quality cannot be tested into products but quality should be built in by design. Under this concept of QbD throughout designing and development of a product, it is essential to define desire product performance profile [Target product profile (TPP), Target product Quality profile (TPQP) and identify Critical quality attributed (CQA).On the basis of this we can design the product formulation and the process to meet the product attributes. These leads to recognize the impact of raw material Critical material attributes (CMA), Critical process parameter (CPP), on the CQA’s and identification and source of variability. QbD is necessary in regulatory requirement, and to implement new concepts such as design space, ICH guidelines i.e. Q8 pharmaceutical development, Q9 quality risk management, and FDAs process analytical technology (PAT)

 

KEYWORDS: Quality by Design (QbD); Target product profile (TPP); Target Product Quality Profile (TPQP); Critical Quality Attributes (CQA); Process Analytical Technology (PAT).

 

 


1. INTRODUCTION:

Quality has been given an importance by all regulatory bodies for pharmaceutical products. Quality means customer satisfaction in terms of service, product, and process. Many of these quality related activities reflect need for companies to excel in global competition. Customer demands the perfection in quality, reliability, low cost and timely performance. Customer satisfaction can be achieved by two ways i.e. features and free from deficiencies in goods.

 

The features like performance, trustworthiness, robustness, ease of use, and serviceability have to built in the product and such product should be free from deficiencies. Quality, productivity, cost, cycle time and value are interrelated terms. Quality activities must try to detect quality problems early enough to permit actions without requiring compromise in cost, schedule or quality. The emphasis must be on precaution rather than on just correction of quality problems. Quality can be the driving force to empower results in other parameters. Hence the quality has to be built in the product as well as services through proper planning, so that the forth coming failure can be avoided.(1) ‘‘Quality could be planned and most of quality deficit arises in the way process is planned and developed’’, this thought of well known quality expert Joseph Moses Juran gives foundation to the concept of quality by design (QbD).

 

Late 1990 FDA’s internal discussion began and in the year 2002 the concept paper on 21st century Good Manufacturing Practice was published.(1,2,3)

 

1.1   Definition:

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. (1,5)


Table 1: Pharmaceutical aspects: Traditional versus QbD

Aspects

Traditional

QbD

Pharmaceutical

Empirical

Systematic; Multivariate Experiments.

Manufacturing

Fixed

Adjustable within design space; opportunities for Innovation.

Process Control

In process testing for go/on-go; offline analysis wide or slow response

PAT utilized for feedback and feed forward at real Time.

Product Specification

Primary means of quality control; based on batch data

Part of the overall control strategy, based on the desired product Performance.

Control Strategy

Mainly by intermediate product and end product testing

Risk based; controlled shifted up stream, real Time release.

Lifecycle Management

Reactive time problem and OOS; Post approval changes needed

Continual improvement enabled within design Space.

 


1.2   Pharmaceutical Quality by Design:

ICH Q8 defines quality as “The suitability of either a drug substance or drug product for its intended use. This term includes such attributes as the identity, strength, and purity. “ICH Q8 guideline states that Quality by Design is 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”. ICH Guidelines Q8 for Pharmaceutical Development, Q9 for Quality Risk Management, and Q10 for Quality systems are foundation of QbD “Product testing alone is not sufficient to assure that a process consistently produces a product with predetermined specifications. Adequate process design; knowledge and control of factors that produce, process variability and successful validation studies, in conjunction with product testing, provide assurance that the process will produce a product with the required quality characteristics”(3)

 

Pharmaceutical Quality =f (Drug substance, excipients, manufacturing, and packaging)(3)

 

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. Changes in formulation and manufacturing processes during development and lifecycle management should be looked upon as opportunities to gain additional knowledge and further support establishment of the design space. Similarly, inclusion of relevant knowledge gained from experiments giving unexpected results can also be useful. Design space is proposed by the applicant and is subject to regulatory assessment and approval. Working within the design space is not considered as a change. Movement out of the design space is considered to be a change and would normally initiate a regulatory post approval change process. In all cases, the product should be designed to meet patients’ needs and the intended product performance.

 

Strategies for product development vary from company to company and from product to product. The approach can also vary and should be outlined in the submission. 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 better understand a company’s strategy. Product and process understanding can be updated with the knowledge gained over the product lifecycle. (3)

 

1.3   Regulatory aspects:

Regulatory authorities consider that incremental and unsystematic improvement in unit operations, in isolation, would only have little effect on overall process performance or quality. To assure the quality of the product, a more holistic approach provided by QbD should be adopted. QbD is defined in the ICH Q8 guideline 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” (6). In manufacturing of new or marketed products, QbD can help in pre-determining the risk potential of various operations, assuring that suitable control strategies can be applied on time. Since Qbd is a science-based approach, it provides a basis for optimizing and improving the manufacturing operation without facing additional regulatory filings or scrutiny. Furthermore, for technology transfer, QbD generated process understanding can make the transition more efficient (7).

 

1.4 Advantages of QbD:

·        It provides a higher level of assurance of drug product quality.

·        It offers cost savings and efficiency for the pharmaceutical industry.

·        It increases the transparency of the sponsor understands the control strategy for the drug product to obtain approval and ultimately commercialize.

·        It makes the scale-up, validation and commercialization transparent, rational and predictable

·        It facilitates innovation for unmet medical needs.

·        It increases efficiency of pharmaceutical manufacturing processes and reduces manufacturing costs and product rejects.

·        It minimizes or eliminates potential compliance actions, costly penalties, and drug recalls.

·        It offers opportunities for continual improvement.

·        It provides more efficiency for regulatory oversight:

·        It streamlines post approval manufacturing changes and regulatory processes.

·        It more focused post approval CGMP inspections

·        It enhances opportunities for first cycle approval.

·        It facilitates continuous improvement and reduces the CMC supplement.

·        It enhances the quality of CMC and reduces the CMC review time.(1,2)

 

2. Steps in quality by design:

2.1 Quality target product profile (QTPP)

2.2 Critical quality attributes (CQA)

2.3 Risk assessment

2.4 Design space

2.5 Control strategy

2.6 Product Lifecycle Management

 

2.1 Quality target product profile (QTPP):

QTPP can be defined as a summary of the drug development program, which plays a central role in the entire drug discovery and development process. It relate to quality safety and efficacy “considering and planning with the end in mind” e.g. Route of administration, dosage form, bioavailability, strength, stability. It also helps for effective optimization of drug candidate, decision making within an organization with regulatory authority. Recently QTPP is used in development, planning, clinical and commercial decision making, regulatory agency interaction and risk management.(8)

A summary of the drug development program described in terms of labeling concepts and it mainly focus on the safety and efficacy.

·        Description

·        Clinical Pharmacology

·        Indications and Usage

·        Contraindications

·        Warnings

·        Precautions

·        Adverse Reactions

·        Drug Abuse and Dependence

·        Over dosage

·        Dosage and Administration

·        How Supplied

·        Animal Pharmacology and/or Animal Toxicology

·        Clinical Studies


 

Figure 1: Steps in Quality by design

 


A natural extension of Target Product Profile for product quality – Quality characteristics (attributes) that the drug product should possess in order to reproducibly deliver the therapeutic benefit promised in the label guide to establish formulation strategy and keep the formulation effort focused and efficient. It facilitates identification of what’s needed/critical for the patient/consumer in the Quality Target Product Profile (such as Critical Quality Attributes, CQAs)(8)

·        Identifies risks and best approaches to manage.

·        Uses tools/enablers in an optimized fashion (such as integration of QbD and biopharmaceutics)

·        Generates and enables knowledge sharing.

 

A drug product designed, developed and manufactured according to Quality Target Product Profile with specification (such as dissolution/release acceptance criteria) consistent with the desired in vivo performance of the product. (9)

 

2.2 Critical quality attributes (CQA):

Once QTPP has been identified, the next step is to identify the relevant CQAs. A CQA is defined as “a physical, chemical, biological, or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality” (6).

 

CQA related with materials are termed as critical materials attribute (CMA) and those related with process termed as critical process parameter (CPP). Critical material attribute (CMA) are associated with materials i.e drug and excipients used during manufacturing of pharmaceutical product. In order to achieve desired quality product, a through characterization of drug substance with respect to physical, biological, chemical, and mechanical properties such as solubility, polymorphism, particle size, stability and flow property, compatibility with excipients should be considered along with its characterization. Critical process parameters (CPPS) are process inputs that have a direct and significant influence on CQA when they are varied within regular operation range. A pharmaceutical manufacturing process is usually comprised of series of unit operation to produce the desisted quality product. A unit operation is a discrete quality that involves physical or chemical changes such as mixing, milling, granulation, drying, compaction and coating. Identification of CQA can be performed based on prior knowledge and/or quality risk management (QRM)(9)

 

2.3 Risk assessment:

·        Risk is defined as the combination of the probability of occurrence of harm and the severity of that harm.

·        Risk assessment is evaluation and determination of risk which is related to a  situation and is recognized as a threat.

·        ICH Q9 Quality risk management indicates that manufacturing and use of drug product. entails some degree of risk. Hence the evaluation of risk to quality should be done by using scientific knowledge. This study will help to determine which variable are critical or which are not, which will help for establishment of control strategy for in process, row material and final testing.

 

Risk assessment tools can be used to identify and rank parameters (e.g. process, equipment, input materials) with potential to have an impact on product quality, based on prior knowledge and initial experimental data.  The initial list of potential parameters can be quite extensive, but can be modified and prioritized by further studies (e.g. through a combination of design of experiments, mechanistic models). The list can be refined further through experimentation to determine the significance of individual variables and potential interactions. Once the significant 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.(10,11).

 

2.3.1. Risk Assessment Method:

There is various methods for determination of risk are as follows:

1.      Failure Mode Effect Analysis

2.      Failure Mode Effect And Criticality Analysis

3.      Fault Tree Analysis

4.      Hazards Analysis and Critical Control Point

5.      Hazard Operability Analysis

6.      Preliminary Hazard Analysis

7.      Risk ranking and Filtering .

 

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.(13) Risk control activities can then be performed to avoid such failures modes. Since FMEAs require a good understanding of cause and effects, a thorough process understanding is essential(14).

 

 

 

2. Fault tree analysis (FTA):

The fault tree analysis (FTA) was first introduced by Bell Laboratories and is one of the most widely used methods in system reliability, maintainability and safety analysis(13,14). FTA is a deductive analysis approach for resolving an undesired event into its causes in a top down fashion(13). Typically, assumed failures are listed at the top as main event and all of the associated elements in that system that could cause the event are listed as subsequent branches till the root condition or cause is identified(13, 14). The results are represented pictorially in the form of a tree of fault modes and their relationship are described with logical operators like “AND”,”OR”, etc (13).

 

3. Hazard analysis and critical control points (HACCP):

HACCP provides detailed documentation to show process or product understanding through identifying parameters to control and monitor (9,14). The definition of hazard includes both safety and quality concern in a process or product. Examples of hazards within the pharmaceutical setting include environmental aspects of the facility (environmental conditions, hygiene aspects); material flow; manufacturing steps; personnel hygiene and gowning; and technical aspects relating to process design. HACCP consists of the following seven steps: (i) conduct a hazard analysis and identify preventive measures for each step of the process, (ii) determine the critical control points, (iii) establish critical limits, (iv) establish a system to monitor the critical control points, (v) establish the corrective action to be taken when monitoring indicates that the critical control points are not in a state of control, (vi) establish system to verify that the HACCP system is working effectively, (vii) establish a record-keeping system (13)

 

2.4 Design Space:

ICH Q8 (R2) defines Design space 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. Working within the Design space is not considered as a change; 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.(16)

 

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 for 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.

 

2.5 Control strategy:

A planned set of controls, derived from current product and process understanding that ensures process performance and product quality. The controls can include parameters and attributes related to drug substance and drug-product materials and components, facility and equipment operating conditions, in-process controls, finished-product specifications, and the associated methods and frequency of monitoring and control.

 

Figure 2.Schematic representation of Design Space

 

·        Specifically, the control strategy may include: Control of input material attributes (e.g. drug substance, excipients, primary packaging materials) based on an understanding of their impact on process-ability or product quality.

·        Product specifications

·        Procedural controls

·        Facility controls, such as utilities, environmental systems and operating conditions

·        Controls for unit operations that have an impact on downstream processing or end-product quality (e.g. the impact of drying on degradation, particle size distribution of the granulate on dissolution)

·        A monitoring program (e.g. full product testing at regular intervals) for verifying multivariate prediction models.

 

The Control Strategy should establish the necessary controls - based on patient requirements to be applied throughout the whole product lifecycle from product and process design through to final product, including API and Drug Product manufacture, packaging and distribution. (24)

 

2.6 Product Lifecycle Management:

Life cycle approach differs from that of the traditional approach of method development.

 

 

Figure 3. Product Lifecycle Management

 

According to Elaine More field (Deputy director USFDA) it includes continuous improvement of method performance and the design space allows flexibility for continuous improvement in analytical method can be done without prior regulatory approval because of design space made previously.(1,5, 23)

 

Process changes within the design space will not require review or approval. Therefore, process improvements during the product life cycle with regard to process consistency and throughput could take place with fewer post approval submissions. In addition to regulatory flexibility, the enhanced understanding of the manufacturing process would allow more informed risk assessment as per ICH Q9 regarding the affects of process changes and manufacturing deviations on product quality. This monitoring could include trend analysis of the manufacturing process as additional experience is gained during routine manufacture. For certain design spaces using mathematical models, periodic maintenance could be useful to ensure the model’s performance. The model maintenance is an example of activity that can be managed within a company‘s own internal quality system provided the design space is unchanged It includes all phases in the life of a product from the initial development through marketing until the product’s discontinuation.

 

Knowledge gained from risk assessment and data collected from design of experiment can be used as the repository of knowledge to make justified change whenever required. (25)

 

3. TOOLS OF QUALITY BY DESIGN:

3.1) 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 and objective conclusions. DOE begins with determining the objective of an experiments and selecting the process factors for the study.

 

3.2) 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 and to develop a Model for predicting response.

·        DOE begins with determining the objective of an experiments and selecting the process factors for the study. A experiments design is the laying out of a detailed experiments

·        plan in advance of doing the experiment will chosen experimental designs Maximize the amount of “Information” that can be obtained for a given amount of experimental effect(22)

 

3.3) Advantages of using DOE approach are summarized as following(1,22)

·        Exhaustive information from a minimum number of experiments

·        Study effects individually by simultaneously varying all operating parameters

·        Can account for variability in experiments, process, materials, or operators 

·        Able to provide understanding about the interaction between various variables 

·        Determine acceptable ranges of critical process parameters contributing to identification of a design space.

 

3.4) Three basic principles of statistical experimental designs:

 

Table 2. Three basic principles of statistical experimental designs

 Randomization

 

By properly randomizing the experiments, the effects of uncontrollable factors that may be present can be “averaged out”.

Blocking

 

Blocking reduces known but irrelevant sources of variation between groups. Greater precision in the estimation of the source of variation under study.

Replication

 

It allows the estimation of the pure error associated with the experiment.

 

 

 

Primary Objectives:

1.      Screening: Selection of only vital factors from the factors identified in initial risk assessment.

2.      Characterization: Choice of experimental design which gives potential interactions in selected vital factors.

3.      Robustness testing: Varying the identified critical factors over ranges that are expected to be encountered during routine manufacturing.

 

3.5) Basic steps involved in DoE approach are as follows:

1.      Defining input and output variables and range:

Based on prior knowledge and risk assessment the input variables and their range can be defined. Screening design like full or fractional factorial design can also be utilized to identify the range of various variables. The response variable should be a CQA or closely related to them.

 

2.      Select appropriate experiment design and perform the run:

The choice of experimental design may depend on the purpose of the study (e.g., a screening, optimization, or robustness study), the factors and interactions involved in the studied and available resources (e.g., literature knowledge, time, labor, cost and materials)(1,22).

 

3.      Model diagnostic:

After obtaining the initial model, foremost step is to check whether the model is appropriate or not. Generally, the significance of a parameter is verified using the analysis of variance (ANOVA) method. ANOVA is a statistical method based on the F-test to estimate the significance of model terms (1,22). It involves subdividing the total variation of a data set into variation due to main effects, interaction and residual error. Model terms can be added or eliminated from analysis, depending upon their significance. The new model, with more or fewer model terms, is again forced through this cycle until all terms included in the model satisfy F-test statistics. Once the overall model satisfies an ANOVA check, the next step is to determine what cannot be modeled (i.e. the errors resulting from the model). This is done using a residual analysis technique. Residuals are the difference between the experimental response and the value predicted by the chosen model. A model is considered a “good fit” if its residuals are normally and independently distributed with zero mean and constant variance. Such distribution can be analyzed either by the normal probability plot of residuals, residuals plotted against predicted values and residuals plotted against experiment run order (1,22).

 

 

 

4.      Illustration of design space:

The design space can be tabulated or graphically displayed using various methods. Graphically the design space can be illustration by the following:

 

A] Contour plots:

A contour plot is a graphic representation of the relationships among three numeric variables in two dimensions. Two variables are for X- and Y-axes, and a third variable Z is for contour levels. You can interactively identify, label, color, and move contour levels, and change the resolutions of rectangular grids to get better contouring quality and performance.

 

B] Three-dimensional plots:

These plots are used to illustrate and study the effect of two input variables on an output variable simultaneously. These plots are ideal for showing the process shape; however, contour plots are more useful for determining or displaying acceptable operating ranges for process parameters (22).

 

C] Overlay plots:

When there is more than one quality characteristic in the design space, the use of overlay plots is helpful. The overlay window shows the design space, which indicates the various combinations of the factors that will provide results within the acceptable range.(1,21,22)

 

Table 3: Type of design experiment commonly used

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

Response screening design involves just the main effects and interactions or they may also have quadratic and 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) and requires 3 levels of each factors.

 

4. Process Analytical Technology (PAT):

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.(1,21,22)

 

CONCLUSION:

  QbD offers the opportunity for much greater regulatory flexibility.

  It focuses on building quality into the product and manufacturing processes as well as continuous process improvement, thus leading to reduction of variability.

  Quality by Design (QbD) principles and tools, play an important role in facilitating a higher level of process understanding and create opportunities for investigation and developing control strategies in formulation and process development.

 

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Received on 12.07.2017       Accepted on 10.09.2017     

© Asian Pharma Press All Right Reserved

Asian J. Res. Pharm. Sci. 2017; 7(4):197-204. 

DOI:  10.5958/2231-5659.2017.00030.3