Development of Quality-By-Design Analytical Methods

Vogt, Frederick G.; Kord, Alireza S.

doi:10.1002/jps.22325

Cited by 231 publications

(135 citation statements)

References 24 publications

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“…The design was provided the following empirical second order equation (Full Model). Where Y was represented response, b 0 was intercept, b 1 and b 2 are the coefficient of main effects, b 12 was the coefficient for the interaction term and b 11 and b 22 were the coefficients for the second order quadratic terms. Non-significant estimated coefficients were dropped from the full model by adopting a significance test for the regression coefficient.…”

Section: Fig3: In-vitro Drug Release Profile Curve For Batch F1 To F9mentioning

confidence: 99%

“…Additionally, use of quality risk management can improve the decision making if a quality problem arises. Effective quality risk management can facilitate better and more informed decisions, can provide regulators with greater assurance of a company's ability to deal with potential risks and can beneficially affect the extent and level of direct regulatory oversight 12,13 .…”

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confidence: 99%

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2016

IJPSR

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This study posed a challenge for the risk management to research and development projects in a highly regulated and volatile pharmaceutical industry, in which projects are extremely long, complex, costly and prone to failure. Project management in new product development, must be efficient and effective. This emphasizes the importance of risk management. Quality by design (QbD) refers to an advanced approach toward drug development. QbD is a vital part of the modern approach to pharmaceutical quality. There is much confusion among pharmaceutical scientists in generic drug industry about the appropriate element and terminology of QbD. The purpose of this research was to discuss the pharmaceutical QbD for formulation development with a case study of Orally Disintegrating Tablet (ODT) of Donepezil Hydrochloride (DPH). The study describes elements of the QbD for DPH ODT, include: Defining quality target product profile, identifying critical quality attributes, establishing design space, control strategy. ODT of DPH was prepared by direct compression method using Crospovidone, MCC and level of polymer was optimized, factorial design was used as part of risk analysis to optimize the level of other excipients. Thus, the work facilitates the adoption and implementation of QbD for formulation development using QbD and could increase efficiencies, provide regulatory support.

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Section: Fig3: In-vitro Drug Release Profile Curve For Batch F1 To F9mentioning

confidence: 99%

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confidence: 99%

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2016

IJPSR

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“…Finally, and as the definition of PAT states, once the manufacturing process is selected, appropriate analytical technologies are necessary to monitor it is necessary to measure process parameters in a timely fashion (ideally online) so that the generated data can be used for process control. From this, it is clear that analytical methods constitute a core element throughout process development under the QbD scope [79].…”

Section: Definition Of a Control Strategymentioning

confidence: 99%

Application of Quality by Design Paradigm to the Manufacture of Protein Therapeutics

Val¹,

Jedrzejewski²,

Exley³

et al. 2012

Glycosylation

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“…2,3 Efforts for applying concepts of quality by design (QbD) principles to analytical method development have increased in recent years in order to achieve more accurate, robust and rigged analytical methods which are used for better control strategy of production processes. [4][5][6] While quality by design principles are well known and adopted for the development of pharmaceutical products, 7,8 QbD concept has not yet been fully adopted for analytical method development and optimization.…”

Section: Introductionmentioning

confidence: 99%

Optimization of UPLC Method for Simultaneous Determination of Rosuvastatin and Rosuvastatin Degradation Products

Zakrajšek¹,

Bevc-Černilec²,

Bohanec³

et al. 2017

ACSi

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An ultra-performance liquid chromatographic method for simultaneous determination of rosuvastatin and rosuvastatin degradation products was developed and optimized by using fractional factorial experimental design. Optimized method is capable to accurately determine all potential degradation products of rosuvastatin. During the optimization the effect of four chosen chromatographic factors was evaluated. The analytical method operational design region was modeled using Umetrics MODDE software and optimal chromatographic conditions were predicted. The results of the model show that the most important factors to reach good separation between the peaks of rosuvastatin impurities are the pH of buffer solution and the amount of ACN and THF in the mobile phase. The final optimized method using QbD approach was validated for linearity, accuracy and precision for determination of rosuvastatin and rosuvastatin degradation products in rosuvastatin pharmaceutical dosage forms. Limit of detection and quantification were determined for two known specified impurities. The use of experimental designs enabled us to obtain the maximum amount of information about the analytical method design region. Optimization of the method was done without additional experiments, only weighing the responses and rebuilding the statistical model. This approach is very cost-effective when evaluating a variety of different factors and their interactions.

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Development of Quality-By-Design Analytical Methods

Cited by 231 publications

References 24 publications

Untitled

Untitled

Application of Quality by Design Paradigm to the Manufacture of Protein Therapeutics

Optimization of UPLC Method for Simultaneous Determination of Rosuvastatin and Rosuvastatin Degradation Products

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