New Quality Concepts in Pharmaceuticals

Aksu, Buket; Yeğen, Gizem

doi:10.5455/musbed.20140327095117

Cited by 2 publications

(2 citation statements)

References 12 publications

(26 reference statements)

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“…Generating a design space that includes all parameters that could affect the quality of a product can be long acting and exhausting. Because of this, risk analysis instruments can be used to point at parameters really affecting a CQA in the finished product [14]. Once the CQAs, CPPs, and CMAs are associated with inputs to the process, experiments can be efficiently designed to develop predictive models and confirm causal relationships, through a risk assessment [12].…”

Section: Design Of Experimentsmentioning

confidence: 99%

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QbD Implementation in Biotechnological Product Development Studies

Aksu¹,

Sezer²,

Yeğen³

et al. 2016

Special Topics in Drug Discovery

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Biotechnological drug development is an extensive area still growing and coming into prominence day by day. Since biotechnological product manufacturing is irreversible, highly expensive, and contains so many critical parameters throughout the process, quality control tests applied to the finished product become inefficacious; therefore, maintaining predefined quality is crucial. Quality by Design (QbD), a systematic approach, is designing and optimizing of formulation and production processes in order to provide a predefined product quality by following a risk and scientific-based path. Determining the critical variables for biotechnological products and their manufacturing via risk assessment is the first and most vital stage of QbD approach, before exploring the multivariate relations among the independent and dependent critical variables by mathematical modeling with the assistive technologies. Response Surface Method (RSM), Artificial Neural Network (ANN), and Genetic Algorithm (GA) are some of the assistive technologies used to perform mathematical modeling. After modeling, additional knowledge is vested and this provides the chance to find a range in which the product quality is always ensured, called as "Design space". So, product quality is procured all along the process by keeping the critical variables under control with less effort, money, and mistakes.

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Section: Design Of Experimentsmentioning

confidence: 99%

“…In order to reduce the experiment load necessary for model verification or experiment design, usually it is also possible to divide parameter sets into logical groups. Working on parameters related to single unit operations can allow small groupings be made in product development [14].…”

Section: Design Of Experimentsmentioning

confidence: 99%

QbD Implementation in Biotechnological Product Development Studies

Aksu¹,

Sezer²,

Yeğen³

et al. 2016

Special Topics in Drug Discovery

Self Cite

View full text Add to dashboard Cite

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Applications of Mathematical Modelling in Pharmaceutical Formulation and Process Development

YEĞEN,

SENEL,

PABUCCUOĞLU

et al. 2023

A-JESA

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Pharmaceutical development and authorization stages have high requirements that increase labour and costs. Risks for product quality and process robustness also increase in parallel with complex practices existing in the pharmaceutical industry and emerging as a result of developments. Although it is challenging to eliminate parameters leading to increased risks, there is need to appropriately manage the risks likewise arrange decision making processes. Designing and optimizing formulation and production processes to deliver the predetermined product quality is known as Quality by Design (QbD) in pharmaceutical development. In terms of data and knowledge, QbD can be carried out using a variety of technologies in this process. Mathematical modelling is one of these tools allows for the quick formation of subject knowledge, which may subsequently be used in an independent or integrated manner and to produce Design of Experiments (DOE). Response surface method (RSM), Artificial Neural Network (ANN), Genetic Algorithm (GA) are some of the assistive technologies used in mathematical modelling that enables to enlighten the effect of formulation and process variables on product quality attributes. The use of advanced mathematical modelling techniques in formulation and process development has become widespread and it appears to be beneficial in different areas of pharmaceutical development.

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New Quality Concepts in Pharmaceuticals

Cited by 2 publications

References 12 publications

QbD Implementation in Biotechnological Product Development Studies

QbD Implementation in Biotechnological Product Development Studies

Applications of Mathematical Modelling in Pharmaceutical Formulation and Process Development

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