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Abstract

QBD brings forward a visionary approach to perfecting the robustness, responsibility, and nonsupervisory compliance of designs. To this end, the review discusses the imperative generalities and practical operation of QbD in logical system development. First, the review discusses the birth and timing of the preface of QbD and its connection in the period of pharma lores, and also discusses its principles, similar as the part of Analytical Target Profile (ATP), Critical Quality Attributes (CQAs), and Method Operable Design Region (MODR). Through tools similar as Risk Assessment and Design of trials (DoE), it allows factors affecting the performance of the system to be linked and controlled. The review also discusses the concrete returns on investment from enforcing QbD, as well as the reduction in failure cases from designs, and the reduction in the time, trouble, and expenditure of making specialized nonsupervisory dossiers. It also highlights the obstacles to enforcing it, similar as the high original costs and the demand for expert- position input. It quotes exemplifications from nonsupervisory agencies similar as the FDA and EMA, offering unique perspectives from these agencies regarding the crossover from the before, rule- grounded systems towards the newer, wisdom and threat grounded systems. Eventually, the review weighs up unborn trends similar as digitalization, real- time analytics, and transnational adjustment, which are fated to take advantage further to the operation and value- added by QbD. Through this extensive review, the unborn eventuality in revolutionizing logical system development from an exercise in megahit- and- miss to one involving structure, knowledge, and order is brought into focus.

Keywords

Digitalization, Analytical Target Profile (ATP), Critical Quality Attributes (CQAs), and Method Operable Design Region (MODR), Design of trials (DoE)

Introduction

Introduction to Quality by Design (QbD)

Quality by Design (QbD) is an over- to- date, methodical medicinal development methodology grounded on designing into products and processes from the onset, to make quality in. rather of emphasizing end- product testing, QbD involves understanding and controlling the factors that impact quality across the development cycle [1]. QbD has long been explosively promoted by bodies similar as the U.S. Food and Drug Administration( FDA) and the International Council for Harmonisation( ICH). For logical system development, QbD relocates the conventional pass- and- error methodology towards further wisdom- driven, threat, and knowledge- acquainted methodology[2]. Analytical designs are planned around predefined objects for quality, considering all implicit sources of variation. Robust, unremarkable, and reproducible designs that deliver accurate affair routinely are to be designed [3].ICH QbD was codified by the preface of ICH guidelines,  Q8( Pharmaceutical Development), Q9( Quality Risk Management), and Q10( Pharmaceutical Quality System), which, in total, advocate methodical planning for quality[4]. For logical designs, guidance for the operation of QbD principles to methodology and lifecycle operation is available through ICH Q14( Analytical Procedure Development). A major part of QbD is to establish an Analytical Target Profile( ATP), which establishes the objects the logical system needs to deliver[5]. Following that, Critical Quality Attributes( CQAs) and similar parameters of the system as affect the effectiveness of the system are estimated. threat assessment tools and design of trials( DoE) are also used to understand and control variation. This leads to defining the Method Operable Design Region( MODR), where the system truly delivers to the standard. This conception of using QBD can help associations lower the trouble of failure in designs, lower time and the cost of developing, and give nonsupervisory comfort. nonstop enhancement and cycle operation is supported by the methodology, easing confident system adaption as and when demanded without any concession to the quality.

QBD's intention [1.2]

  1. To establish product quality specifications that are directly correlated with clinical performance outcomes.
  2. To enhance process capability and minimize product variability and defects through the optimization of product and process design, understanding, and control.
  3. To enhance the efficiency of product development and manufacturing processes.
  4. To strengthen the root cause analysis process and enhance the management of post-approval changes.

Strengths of QbD

  1. Improved clarity in the understanding of the process dynamics.
  2. Optimized management of process modifications
  3. Eliminate instances of batch process failure
  4. Prevent violations of regulatory requirements.

Prospect of Qbd

  1. Enhance manufacturing throughput, optimize cost-efficiency, and minimize product rejections and material waste.
  2. Create an extensive knowledge base grounded in scientific principles for all products.
  3. Approach centered on risk assessment and recognition.
  4. Enhanced efficiency in transitioning technology to production

Fundamental Principles of QbD in Analytical Method Development

Integration of multiple crucial principles is used in Quality by Design( QbD) to make logical system development more robust, more effective, and further wisdom- driven. Unnaturally, QbD is about controlling and understanding variability, and this leads to designs that regularly satisfy specified conditions for performance. The original introductory principle is defining the Analytical Target Profile( ATP)[3,4]. ATP easily states what's to be measured by the logical system and the performance criteria demanded, similar as delicacy, perfection, perceptivity, and particularity. description of ATP outspoken ensures that the process of developing is thing-specific, avoiding gratuitous trials and crimes[6]. Secondly, defining the Critical Quality Attributes( CQAs) is pivotal. CQAs are the parcels of the logical system that directly affect it to achieve the ATP. They can be parameters similar as resolution, limit of discovery, and robustness. Knowing CQAs allows one to concentrate trouble on crucial factors that affect the quality of the method.Next is the rigorous determination of the system variables and the factors which can affect the CQAs. These variables can be instrument setting, reagent attention, or way in sample medication[7]. QbD ensures thorough knowledge about how each factor affects the system's performance. A central tenet is threat assessment, which ranks system variables by their effect on CQAs. Failure Mode and goods Analysis( FMEA) and Ishikawa plates are employed to pinpoint and classify pitfalls. By exercising this threat- driven methodology, coffers are concentrated on controlling the most poignant variables[6,7]. Another vital aspect is the operation of Design of trials( DoE), which is an systematized statistical method to probe relations among numerous factors and responses. DoE efficiently and totally optimizes methodology conditions by testing factor relations and determining robust operating ranges. Incipiently, QbD entails defining a Method Operable Design Region( MODR), which is an n- dimensional space through which system parameters can change without compromising the capability to achieve ATP[8]. similar latitude enables methodology adaption without the need forre-approval by controllers, easing nonstop enhancement.

Parts of QBD

QBD Consist up 4 major parts. I.e

  1. Analytical Target Profile (ATP) 
  2. Critical Quality Attributes (CQAs)
  3. Risk assessment
  4. analytical method development

Fig.1 Components of QBD

Analytical Target Profile (ATP)

In the paradigm of Quality by Design( QbD), the Analytical Target Profile( ATP) is the foundation used to construct an effective, dependable logical system. ATP designates the thing and anticipated performance of the system by establishing measurable, specific objects, just as a Quality Target Product Profile( QTPP) is used to direct pharmaceutical product design [9]. The ATP explicitly delineates what's to be measured by the system, i.e., whether it's chastity, assay, declination, or other crucial parameters of quality, and provides similar acceptance criteria as delicacy, perfection, particularity, linearity, range, discovery limit, and robustness[10]. Having defined these objects outspoken, ATP keeps the process of logical system development streamlined and coincident to the specified operation, precluding gratuitous reiterations and inapplicable testing.

Critical Quality Attributes (CQAs)

After establishing the ATP, the posterior crucial step is determining the Critical Quality Attributes( CQAs) of the logical test. CQAs are the important measurable characteristics of the test that most directly affect the test's capability to satisfy the ATP conditions. exemplifications are resolution among peaks in chromatography testing designs, signal- to- noise, system felicity factors, and test perfection. Identification of the CQAs is generally carried out through thorough threat assessment and scientific knowledge of the method. Each parameter of the method is estimated to assess their effect on the logical result, which allows prioritization grounded on which attributes are most pivotal to method performance and product assurance. During system development and confirmation, the CQAs are controlled and covered to insure thickness is maintained. For case, in an HPLC method, the retention time, peak harmony, and perceptivity could be regarded as the CQAs. Having these kept within specified ranges ensures that the method yields robust results under changing conditions. In addition, the unequivocal isolation among ATP and CQAs allows for a focused optimization of the system. The ATP establishes the “ what ” and the CQAs establish the “ how ” factors, i.e., the variables controlling the CQAs are acclimated to fulfill the ATP conditions. By establishing ATP and CQAs outspoken in the development program, QbD enables deeper wisdom in understanding the system, enhances sound design of the system, and enables nonsupervisory dexterity. It indeed provides for adaptations to the system in the design space without nonsupervisory overburden, ultimately icing the fitness for purpose of the system throughout the life cycle.

Fig 1: Various parts of QBD

Table 1: Relationship between ATP, CQAs, and Analytical Method Parameters

S. No.

Element

Definition

Example

Role in QbD Approach

1

Analytical Target Profile (ATP)

Desired outcome/performance of analytical method

“Assay of drug X must be ≥ 98% and ≤ 102%”

Guides method development objectives

2

Critical Quality Attributes (CQAs)

Attributes affecting method performance aligned to ATP

Peak resolution, retention time, precision

Key measurable factors controlled during development

3

Specificity

Ability to measure the analyte distinctly without interference

Clear separation of active compound in HPLC

Ensures method is selective and reliable

4

Linearity

Method’s ability to elicit test results that are directly proportional

50–150% concentration range with R² ≥ 0.999

Confirms quantitative capability of method

5

Robustness

Capacity to remain unaffected by small deliberate changes

Minor flow rate or temperature shifts in chromatography

Indicates method reliability under varied conditions

6

Detection Limit

Lowest amount of analyte detected (not necessarily quantified)

LOD = 0.005 µg/mL for impurity detection

Defines method sensitivity, especially for impurities

Risk Assessment and Design of Experiments (DoE) in QbD

Threat assessment and Design of trials( DoE) are crucial motorists of the Quality by Design( QbD) methodology for logical system development. They offer a methodical methodology to explore and manage variability in the logical process to insure the system is constantly able of meeting the Analytical Target Profile( ATP). threat assessment is the first step, designed to assess and identify implicit factors that can affect the performance of the logical procedure. It involves the thorough examination of parameters similar as pH, temperature, mobile phase composition, inflow rate, and discovery wavelength in chromatography ways, or attention of reagents and incubation time in bioassays. The end is to classify these parameters according to their effect on Critical Quality Attributes( CQAs) and to rank order the precedence for their disquisition. Some common tools used to assess pitfalls are Failure Mode and goods Analysis( FMEA), Ishikawa( fishbone) plates, and threat Ranking and Filtering. These tools help in determining the high- threat variables and prioritizing the most serious factors, saving time and expenditure by precluding gratuitous trial. Only after high- threat factors are laid out, Design of trials( DoE) is introduced as the statistical methodology to study the influences of these factors on the logical system's performance totally. DoE is grounded on planned and controlled variations in the parameters of the system to examine their individual and interactive goods on the CQAs. DoE is different from one- factor- at-a-time( OFAT) trials in that it provides further information using smaller trials. Fractional factorial, factorial, response face methodology( RSM), and Box- Behnken are several designs used in DoE to define the geography of the system's performance. By using DoE, it's possible for the critic to pinpoint optimal settings, probe parameter relations, and establish robust operating conditions to insure the trustability of the system. The information generated using DoE allows for the construction of fine models soothsaying system geste , which helps in establishing the Method Operable Design Region( MODR). These are used in system robustness studies and ongoing monitoring programs. Combining threat assessment and DoE, QbD increases the wisdom- driven nature of system development, minimizes trial- and- test designs, and establishes confidence in the robustness of the system. Through it, it's easier to make nonsupervisory forms by icing full understanding and control over the variability of the system

Establishing the Method Operable Design Region (MODR)

The Method Operable Design Region( MODR) is one of the core principles of Quality by Design( QbD), and it describes the multidimensional region under which logical methodologies report stable, reproducible performances that satisfy the Analytical Target Profile( ATP). Having the MODR defined creates room to vary routine methodologies without compromising the methodology's quality. MODR is deduced grounded on data handed by Design of trials( DoE) and threat assessment, whereby pivotal parameter designs and their relations are examined to ascertain the range over which the system can be operated robustly. In comparison to conventional designs grounded on fixed parameters, MODR provides for operating in a defined “ design space, ” which is more dynamic in terms of logical system development. Development of MODR is carried out through multiple way. To begin, crucial system parameters are first defined, and also methodical trial through factorial or response face designs is accepted to measure the effect of similar parameters on the system effectiveness. issues, generally responses to Critical Quality Attributes( CQAs) including delicacy, perfection, particularity, and perceptivity, are statistically modeled. These models are used to collude out a multidimensional region, specifying ranges allowed for each crucial parameter whereby the system is in line with predefined acceptance criteria. This region is the MODR. By operating in this design space, small, deliberate differences or normal variation in the system conditions do not negatively impact logical results. The MODR conception has several advantages. It enhances the robustness of the system by easing variability forbearance and rigidity in system conditions, which minimizes frequent revalidation. This is especially salutary under routine quality control, where there can be slight parameter diversions due to environmental or instrument changes. Regulatory bodies endorse for having MODR as part of a QbD strategy, as it provides evidence of thorough understanding of system geste and control strategy. Regulatory cessions containing well- defined MODR prove that the system is sound from wisdom and able of producing stable, predictable performance, which can simplify blessing procedures and dockpost-approval changes. MODR is also salutary in the a nonstop enhancement of logical designs. As further data is accumulated during the system life cycle, MODR can be revised or extended, which is a living document that's streamlined with changing system performance.

Benefits of Implementing QbD in Analytical Method Development

The operation of Quality by Design( QbD) in logical system development provides numerous advantages to insure logical processes are more effective, secure, and advanced in quality. Through the wisdom- driven and methodical process, there's further understanding of the system's performance, as well as further control over the logical result. Among the foremost advantages is enhanced system robustness. By determining crucial system parameters and defining the Method Operable Design Region( MODR), QbD provides assurance that designs will serve reliably under variable operating conditions. similar robustness decreases the circumstance of unlooked-for failure under normal operation, reducing system time-out and precious problem- working processes. QbD also allows for further effective system reproducibility and transferability. Once logical designs are designed using an in- depth knowledge of their variables and their operating ranges, it's simpler to take the system from the development laboratories to the quality control laboratories or other product spots. This is particularly pivotal in transnational pharma product, where designs must be put into place in multitudinous places having different outfit and labor force. Another major benefit is compliance and nonsupervisory inflexibility. Agencies including the EMA and the FDA welcome QbD methodologies due to their wisdom- and threat- driven paradigm for system design. blessings can be expedited and smallerpost-approval adaptations can be endured through the submission of operations containing similar QbD factors as MODR, including ATP and threat assessments, which show detailed control strategies. QbD also enables nonstop enhancement through the life cycle of the system. Since the system is designed to be supported by considerable data and threat assessment, differences in the design space are admissible without revalidation, to enable adaption and optimization deduced from experience. This is in discrepancy to conventional fixed designs, which tend to bear considerable requalification when changed. Another crucial advantage is cost effectiveness. While further trouble and trial are demanded in the original phase using QbD, smaller system failures, lower revalidation, and easier nonsupervisory relations restate into long- term savings. Advanced system performance translates directly to smaller batch rejections and lower costs related to quality. In addition, QbD is probative of further effective threat operation. Through methodical identification and control of crucial factors, QbD prevents system failure and guarantees that logical data truly represents product quality. A threat- grounded mindset fits moment's quality standard and promotes an earlier, visionary quality culture.

Challenges and Limitations of QbD Implementation

Whereas there are numerous salutary aspects to Quality by Design (QbD) concerning logical methodology, there are likewise numerous difficulties and constraints defying associations when executing this methodology. A major challenge is the outspoken time and resource expenditure. Creating an logical system according to the QbD paradigm involves expansive planning, trial design, data accession, and data interpretation. In discrepancy to conventional methodologies, where trial- and- error can be sufficient, QbD necessitates previous knowledge about variable system factors and crucial quality attributes. All this outspoken work can be resource-empty, using professed staff, state- of- the- art outfit, and robust data systems, which might not be available in every association. Another problem is the demand for technical moxie. perpetration of successful QbD is greatly dependent on multidisciplinary groups having logical chemistry, statistical, threat, and nonsupervisory knowledge. Small companies, or others inexperienced in using QbD, could be incapacitated by the difficulty in structure and training similar groups, which compromises the effectiveness of the approach. Lacking sufficient moxie can affect in incorrect threat assessments or sour trial design, compromising the robustness of the end logical system. Data complexity and running are another limitation. Large quantities of data affect from Design of trials (DoE) and threat assessments performed through QbD, challenging advanced software tools and data interpretation capabilities. Handling this data efficiently to make meaningful conclusions is vital but can be a tailback in the absence of proper informatics structure. Also, uniting QbD data to being quality operation systems can bear major changes. Another limitation is resistance to change within associations. A change from conventional logical development processes to a QbD methodology, in numerous cases, implies a artistic shift. labor force and directors who have come accustomed to certain procedures might be reluctant to change to new processes that employ more strict attestation, statistical input, andcross-functional cooperation. To overcome this resistance, training, open dispatches about the gain, and leadership guidance are demanded. Regulatory misgivings can be an handicap as well. Indeed though nonsupervisory agencies promote QbD, the guidelines are still under development, and there might be difficulty in matching internal company practices to differing nonsupervisory conditions across regions. This can confuse attestation requirements and the operation of nonsupervisory inflexibility. In addition, not all logical procedures will be helped inversely by QbD. Extremely simple, well- established procedures may not warrant the added expenditure and complication of enforcing QbD. In certain situations, the conventional development process can work well, particularly for low- threat analyses.

Regulatory Perspectives and Future Trends in QbD

Quality by Design( QbD) has attracted robust support from transnational nonsupervisory agencies, which are apprehensive of the eventuality it offers to ameliorate product quality, insure the safety of cases, and grease nonsupervisory form. Agencies similar as the U.S. Food and Drug Administration( FDA), European Medicines Agency( EMA), and the International Council for Harmonisation( ICH) have encouraged the principles of QbD, including their operation to their guidelines and fabrics for the development of medicines, including logical procedures. FDA guidance documents, including “Pharmaceutical Quality for the 21st Century” and ICH Q8 (Pharmaceutical Development), Q9(Quality Risk Management), and Q10(Pharmaceutical Quality System), highlight wisdom- and threat- grounded principles in close alignment with QbD. Pharmaceutical companies are encouraged by these guidelines to embrace QbD to gain complete process and product trait understanding to insure further manufacturing and nonsupervisory freedom. For logical procedures, ICH Q14 directly discusses the development and life cycle operation of logical procedures through operation of principles of QbD to insure robust and stable logical strategies. Controllers anticipate that similar QbD cessions should comprise easily defined Analytical Target Biographies (ATPs), threat assessments, Design of trials (DoE) data, and Method Operable Design Region s(MODRs). similar detailed information aids in nonsupervisory review and promotes cooperative understanding among assiduity and controllers and can lead to shorter blessing timeframes as well as smallerpost-approval changes. In the future, arising trends in QbD portend increased integration with digital technologies and data analytics. The arrival of artificial intelligence( AI), machine literacy, and other sophisticated statistical tools is anticipated to make QbD operations more effective and accurate. Prophetic modeling and in-real- time monitoring will grease further adaptive control over logical processes, moving from reactive to visionary quality assurance. also, nonstop manufacturing and Process Analytical Technology( PAT) methodologies are being more and more coupled to QbD to make further nimble and responsive product surroundings. Confluence promotes the notion of “ Quality by Control ” in resemblant to Quality by Design, in which processes can correct themselves using real- time data. Regulatory adjustment across nations is also changing, with enterprise to harmonize QbD prospects encyclopedically. This will make it easier to make affiliated global cessions and enhance transnational trade in medicinals, to the benefit of cases through earlier access to quality medicines. Indeed in the face of similar implicit advances, there are hurdles to be overcome in harmonizing nonsupervisory requirements, training professionals, and enabling small associations to apply QbD. Ongoing collaboration among controllers, the assiduity, and academia is essential to break these issues and make QbD a standard practice.

CONCLUSION

Perpetration of Quality by Design( QbD) into logical system development is a major corner in the elaboration of pharmaceutical quality assurance. By moving the paradigm from reactive issue- fixing to visionary process understanding, QbD improves the robustness, reproducibility, and nonsupervisory compliance of logical designs. Essential rudiments similar as the Analytical Target Profile( ATP), Critical Quality Attributes( CQAs), and Method Operable Design Region( MODR) establish a strong frame for specifying and controlling the performance of the system. threat- driven methodologies similar as Design of trials( DoE) enable controlled assessment of the variables in the system, dwindling failure eventuality and supporting ongoing enhancement over the life cycle of the system. Although it has multitudinous strengths, QbD perpetration is brazened by certain issues similar as the demand for specialized professionals, further resource commitment, and the integration of sophisticated statistical tools. All these, however, are being precipitously addressed by enhanced assiduity mindfulness, favorable nonsupervisory programs, and the arrival of digital results that grease data analysis and decision timber. Global nonsupervisory agencies are promoting the perpetration of QbD through harmonized fabrics that reduce the burden of submission and grease invention. In the future, QbD is likely to come more bedded in pharmaceutical development, especially as the assiduity adopts nonstop manufacturing, real- time analytics, and machine literacy- informed decision timber. All these put further focus on knowledge operation, threat reduction, and the lifecycle strategy, which is well in line with the changing requirements for high- quality, case- concentrated medicines. In summary, QbD offers a structured, scientific, and threat- grounded approach to logical system development that not only enhances quality but also facilitates nonsupervisory compliance and invention. Its uninterrupted elaboration and relinquishment will be critical to advancing pharmaceutical lores and icing harmonious delivery of safe and effective rectifiers to cases worldwide.

REFERENCES

  1. Chavan A. V, Gandhimathi R. Quality by Design Approach: Progress in Pharmaceutical Method Development and Validation. Biomed Pharmacol J 2023;16(3).
  2. R. Phadke, A. Gosar, R. Mali and D. Patil,A Review on Quality by Design Approaches to Analytical Method Development. Indo Am. J. Pharm. 2019, 9, 2044 .
  3. Das V, Bhairav B, Saudagar RB. Quality by design approaches to analytical method development. Res J PharmTechnol. 2017;10(9):3188–3194.
  4. Kumar VP, Gupta VN. A review on quality by design approach (QBD) for pharmaceuticals. Int J Drug Dev Res.2015;7(1):52–60
  5. Kishor S. Arote,Darshan A. Salade, Nilesh V. Patil, Dr. Vikas V. Patil, Amol R.
  6. Pawar,A Review on: Analytical Method Development and Validation and It’s QbD Approach,International Journal of Novel Research and Development,2022,7(9),365-378.
  7. Swapnali Dhananjay Patil  & O P Agrawal,Quality by Design – A Review,International Journal of Pharmaceutical and Biological Science Archive,Volume 11 Issue 5; 2023; Page No. 79-84
  8. International Council for Harmonisation (ICH). ICH Harmonised Tripartite Guideline Q8(R2): Pharmaceutical Development. 2009.
  9. Bajaj, M., and S. Nanda. “Analytical Quality by Design (AQbD): New Paradigm for Analytical Method Development.” International Journal of Development Research,  2015,vol. 5, no. 2,  pp. 3589–3599.
  10. Beg, Sarwar, et al. Handbook of Analytical Quality by Design. Elsevier, 2011.
  11. Bhutani, H., et al. “Quality by Design (QbD) in Analytical Sciences: An Overview.” Pharma Times, 2014,vol. 46, no. 8,, pp. 71–75.
  12. Chavan, S. D., et al. “Quality by Design.” Research and Reviews: Journal of Pharmaceutical Quality Assurance, 2015,vol. 1, no. 2,  pp. 18–24.
  13. Das, V., et al. “Quality by Design Approaches to Analytical Method Development.” Research Journal of Pharmacy and Technology, 2017,vol. 10, no. 9,  pp. 3188–3194.
  14. Dewi, M. K., et al. “Quality by Design: Approach to Analytical Method Validation.” Sciences of Pharmacy,  2022 vol. 1, no. 1,  pp. 33–40.
  15. Gandhi, A., and C. Roy. “Quality by Design (QbD) in Pharmaceutical Industry: Tools, Perspectives and Challenges.” International Journal of Pharmaceutical Sciences and Research, 2016,vol. 4, no. 11,  pp. 12–20.
  16. Gaykar, D., and S. C. Khadse. “A Review on Analytical Quality by Design.” International Journal of Pharmaceutical Sciences Review and Research, 2017,vol. 44, no. 2,  pp. 96–102.
  17. George, L., and W. Howard. “Process Analytical Technology (PAT) in Pharmaceutical Development.” American Pharmaceutical Review, 2012, pp. 24–28.
  18. International Council for Harmonisation (ICH). ICH Harmonised Tripartite Guideline Q9: Quality Risk Management. 2005.
  19. International Council for Harmonisation (ICH). ICH Harmonised Tripartite Guideline Q10: Pharmaceutical Quality System. 2008.
  20. International Council for Harmonisation (ICH). ICH Harmonised Tripartite Guideline Q14: Analytical Procedure Development. 2022.
  21. Jadhav, J. B., et al. “Quality by Design (QbD) Approach Used in Development of Pharmaceuticals.” International Journal of Pure and Applied Bioscience,2014 vol. 2, no. 5, pp. 214–223.
  22. Kumari, N., et al. “Quality by Design: A Systematic Approach for the Analytical Method Development.” Journal of Drug Delivery and Therapeutics, 2019,vol. 9, no. 3-s, pp. 858–862.
  23. Nadpara, N. P., et al. “Quality by Design (QbD): A Complete Review.” International Journal of Pharmaceutical Sciences Review and Research, 2012vol. 17, no. 2, pp. 20–28.
  24. Patil, A. S., and A. M. Pethe. “Quality by Design (QbD): A New Concept for the Development of Quality Pharmaceuticals.” International Journal of Pharmaceutical Quality Assurance,2013, vol. 4, no. 2,  pp. 13–19.
  25. Raman, N. V. V. S. S., et al. “Analytical Quality by Design Approach to Test Method Development and Validation in Drug Substance Manufacturing.” Journal of Chemistry, 2015, pp. 1–8.
  26. Rathore, A. S., and A. M. Kapoor. “Analytical Quality by Design: A Tool for Regulatory Flexibility and Robust Analytics.” BioPharm International, 2015,vol. 28, no. 9,  pp. 34–39.
  27. Rathore, A. S., and R. Winkle. “Quality by Design for Biopharmaceuticals.” Nature Biotechnology, 2009, vol. 27, no. 1, pp. 26–34.
  28. Sangshetti, J. N., et al. “Quality by Design Approach: Regulatory Need.” Arabian Journal of Chemistry ,2017, vol. 10, pp. S3412–S3425.
  29. Schweitzer, M. G., et al. “Implications and Opportunities of Applying QbD Principles to Analytical Measurements.” Pharmaceutical Technology, 2010, vol. 34, pp. 52–59.
  30. Sharma, R., et al. “A Review on Quality by Design Approach in Analytical Methods.” Journal of Drug Delivery and Therapeutics, 2021,vol. 11, no. 5,  pp. 1–8.
  31. Valentine, E. N. “Quality by Design (QbD): Manufacturing and Product Quality of Generics Drugs Perspective.” Journal of Global Trends in Pharmaceutical Sciences, 2013,vol. 4, no. 4,  pp. 1257–1262.
  32. Woodcock, Janet. “The Concept of Pharmaceutical Quality.” American Pharmaceutical Review, vol. 7, no. 6, 2004, pp. 10–15.

Reference

  1. Chavan A. V, Gandhimathi R. Quality by Design Approach: Progress in Pharmaceutical Method Development and Validation. Biomed Pharmacol J 2023;16(3).
  2. R. Phadke, A. Gosar, R. Mali and D. Patil,A Review on Quality by Design Approaches to Analytical Method Development. Indo Am. J. Pharm. 2019, 9, 2044 .
  3. Das V, Bhairav B, Saudagar RB. Quality by design approaches to analytical method development. Res J PharmTechnol. 2017;10(9):3188–3194.
  4. Kumar VP, Gupta VN. A review on quality by design approach (QBD) for pharmaceuticals. Int J Drug Dev Res.2015;7(1):52–60
  5. Kishor S. Arote,Darshan A. Salade, Nilesh V. Patil, Dr. Vikas V. Patil, Amol R.
  6. Pawar,A Review on: Analytical Method Development and Validation and It’s QbD Approach,International Journal of Novel Research and Development,2022,7(9),365-378.
  7. Swapnali Dhananjay Patil  & O P Agrawal,Quality by Design – A Review,International Journal of Pharmaceutical and Biological Science Archive,Volume 11 Issue 5; 2023; Page No. 79-84
  8. International Council for Harmonisation (ICH). ICH Harmonised Tripartite Guideline Q8(R2): Pharmaceutical Development. 2009.
  9. Bajaj, M., and S. Nanda. “Analytical Quality by Design (AQbD): New Paradigm for Analytical Method Development.” International Journal of Development Research,  2015,vol. 5, no. 2,  pp. 3589–3599.
  10. Beg, Sarwar, et al. Handbook of Analytical Quality by Design. Elsevier, 2011.
  11. Bhutani, H., et al. “Quality by Design (QbD) in Analytical Sciences: An Overview.” Pharma Times, 2014,vol. 46, no. 8,, pp. 71–75.
  12. Chavan, S. D., et al. “Quality by Design.” Research and Reviews: Journal of Pharmaceutical Quality Assurance, 2015,vol. 1, no. 2,  pp. 18–24.
  13. Das, V., et al. “Quality by Design Approaches to Analytical Method Development.” Research Journal of Pharmacy and Technology, 2017,vol. 10, no. 9,  pp. 3188–3194.
  14. Dewi, M. K., et al. “Quality by Design: Approach to Analytical Method Validation.” Sciences of Pharmacy,  2022 vol. 1, no. 1,  pp. 33–40.
  15. Gandhi, A., and C. Roy. “Quality by Design (QbD) in Pharmaceutical Industry: Tools, Perspectives and Challenges.” International Journal of Pharmaceutical Sciences and Research, 2016,vol. 4, no. 11,  pp. 12–20.
  16. Gaykar, D., and S. C. Khadse. “A Review on Analytical Quality by Design.” International Journal of Pharmaceutical Sciences Review and Research, 2017,vol. 44, no. 2,  pp. 96–102.
  17. George, L., and W. Howard. “Process Analytical Technology (PAT) in Pharmaceutical Development.” American Pharmaceutical Review, 2012, pp. 24–28.
  18. International Council for Harmonisation (ICH). ICH Harmonised Tripartite Guideline Q9: Quality Risk Management. 2005.
  19. International Council for Harmonisation (ICH). ICH Harmonised Tripartite Guideline Q10: Pharmaceutical Quality System. 2008.
  20. International Council for Harmonisation (ICH). ICH Harmonised Tripartite Guideline Q14: Analytical Procedure Development. 2022.
  21. Jadhav, J. B., et al. “Quality by Design (QbD) Approach Used in Development of Pharmaceuticals.” International Journal of Pure and Applied Bioscience,2014 vol. 2, no. 5, pp. 214–223.
  22. Kumari, N., et al. “Quality by Design: A Systematic Approach for the Analytical Method Development.” Journal of Drug Delivery and Therapeutics, 2019,vol. 9, no. 3-s, pp. 858–862.
  23. Nadpara, N. P., et al. “Quality by Design (QbD): A Complete Review.” International Journal of Pharmaceutical Sciences Review and Research, 2012vol. 17, no. 2, pp. 20–28.
  24. Patil, A. S., and A. M. Pethe. “Quality by Design (QbD): A New Concept for the Development of Quality Pharmaceuticals.” International Journal of Pharmaceutical Quality Assurance,2013, vol. 4, no. 2,  pp. 13–19.
  25. Raman, N. V. V. S. S., et al. “Analytical Quality by Design Approach to Test Method Development and Validation in Drug Substance Manufacturing.” Journal of Chemistry, 2015, pp. 1–8.
  26. Rathore, A. S., and A. M. Kapoor. “Analytical Quality by Design: A Tool for Regulatory Flexibility and Robust Analytics.” BioPharm International, 2015,vol. 28, no. 9,  pp. 34–39.
  27. Rathore, A. S., and R. Winkle. “Quality by Design for Biopharmaceuticals.” Nature Biotechnology, 2009, vol. 27, no. 1, pp. 26–34.
  28. Sangshetti, J. N., et al. “Quality by Design Approach: Regulatory Need.” Arabian Journal of Chemistry ,2017, vol. 10, pp. S3412–S3425.
  29. Schweitzer, M. G., et al. “Implications and Opportunities of Applying QbD Principles to Analytical Measurements.” Pharmaceutical Technology, 2010, vol. 34, pp. 52–59.
  30. Sharma, R., et al. “A Review on Quality by Design Approach in Analytical Methods.” Journal of Drug Delivery and Therapeutics, 2021,vol. 11, no. 5,  pp. 1–8.
  31. Valentine, E. N. “Quality by Design (QbD): Manufacturing and Product Quality of Generics Drugs Perspective.” Journal of Global Trends in Pharmaceutical Sciences, 2013,vol. 4, no. 4,  pp. 1257–1262.
  32. Woodcock, Janet. “The Concept of Pharmaceutical Quality.” American Pharmaceutical Review, vol. 7, no. 6, 2004, pp. 10–15.

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Anup Kumar Patra
Corresponding author

College Of Pharmaceutical Sciences, Puri.

Photo
Basant Kumar Behera
Co-author

College Of Pharmaceutical Sciences, Puri.

Photo
Pragyna Paramita Priyadarshini
Co-author

College Of Pharmaceutical Sciences, Puri.

Photo
Bidusmita Pradhan
Co-author

College Of Pharmaceutical Sciences, Puri.

Anup Kumar Patra*, Pragyna Paramita Priyadarshini, Bidusmita Pradhan, Basant Kumar Behera, Amiya Kanta Mishra, Quality by Design (QBD): An approach in Analytical Method Development, Int. J. of Pharm. Sci., 2025, Vol 3, Issue 10, 773-784 https://doi.org/10.5281/zenodo.17301340

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