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Quality by Design and Control Strategies for Parenteral Dosage Forms
Published in Sandeep Nema, John D. Ludwig, Parenteral Medications, 2019
Vinay Radhakrishnan, Amit Banerjee, Mansoor A. Khan
In adopting a QbD approach and applying the science and risk-based principles to assess quality attributes and process parameters, design space can be created to describe the boundaries within which unit operations of a manufacturing process may operate. In essence, design space can demonstrate control of variables that may impact a CQA and a control strategy can be established to accommodate design space. In fact, a combination of well-defined design space boundaries and RTRT can effectively demonstrate and confirm control and serve as the basis for release of the product without the need for specific end-product testing. In fact, where the risk is understood, and the severity and probability of impact are controllable, the demonstration of process control through the creation of design space could conceivably reduce the need to perform in-process testing as well. Continuous formal verification to demonstrate process capability in accordance with well-grounded design space criteria could serve as the basis for product release to a specification derived largely from CQAs.
Multiple Response Optimization Hits the Spot
Published in Mark J. Anderson, Patrick J. Whitcomb, Martin A. Bezener, Formulation Simplified, 2018
Mark J. Anderson, Patrick J. Whitcomb, Martin A. Bezener
The overlay plot provides a window into regions where experimenters can meet specifications for multiple responses. Thus, it facilitates the development of a QbD “design space,” which is defined by the Food and Drug Administration (FDA) as the “multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have demonstrated to provide assurance of quality” (Guidance for Industry, 2009). However, operating at the outer edges of the overlay window, that is, its frame, cannot assure quality due to it being only a 50/50 chance of that response going out of bounds. To improve these odds, we advise you impose a confidence interval (CI) or, better yet, a tolerance interval (TI). The following case study illustrates the application of the CI and, ultimately the TI, to achieve a QbD design space.
Hybrid Modeling of Pharmaceutical Processes and Process Analytical Technologies
Published in Jarka Glassey, Moritz von Stosch, Hybrid Modeling in Process Industries, 2018
QbD can be seen as a comprehensive approach to product development that includes designing and developing processes and identifying critical quality attributes, critical process parameters, and sources of variability (see Figure 8.1 for a graphical illustration of a QbD workflow). This approach aims to improve the understanding of how critical quality attributes (CQAs) are influenced by critical process parameters (CPPs) and the interactions between them (Kelley 2009). As part of the FDA’s initiative “Pharmaceutical cGMPs for the Twenty-First Century: A Risk-Based Approach,” it also produced a further document, “Guidance for Industry: PAT—A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance” (FDA 2004), in order to promote the introduction of new technologies to improve the efficiency and effectiveness of manufacturing process design, quality control, and assurance. For example, Wu et al. (2015) argue that the FDA has been working in three specific areas to support new approaches for the improvement of product quality and manufacturing: Continuous manufacturing with constant flow of materials in and out of a processThe use of PAT to monitor and control processesDevelopment of new statistical approaches to detect changes in process and product quality (Wu et al. 2015)
Phytosomal gel of Manjistha extract (MJE) formulated and optimized with central composite design of Quality by Design (QbD)
Published in Journal of Dispersion Science and Technology, 2023
Mohamad Taleuzzaman, Ali Sartaj, Dipak Kumar Gupta, Sadaf Jamal Gilani, Mohd. Aamir Mirza
The optimization of conventional drug delivery involves one variable at a one time (OVAT).[19] The formulations optimization is used to establish the "cause and effects" variable that changed simultaneously to achieve by OVAT. The partial achievement can be turned to full achievement by adopting Quality by Design (QbD) that gives a complete perception of the product and process.[20] “QbD is a systematic approach of development that begins with predefined objectives and emphasizes product, process understanding, and process control, based on sound science and quality risk management,” as described in ICH Q8 (R2). According to ICH Q8 rule, the first step for applying QbD is the firm a relationship between the QTPP, critical quality attributes, critical process parameters, and critical material attributes.[21,22]
Development of triblock polymersomes for catalase delivery based on quality by design environment
Published in Journal of Dispersion Science and Technology, 2021
Camila Areias Oliveira, Camila Forster, Patrícia Léo, Carlota Rangel-Yagui
Food and Drug Administration (FDA) and other regulatory agencies stimulate the use of strategies based on quality concerns throughout the development and optimization of products. Quality by Design (QbD) includes a set of tools to define the desired safety and efficacy of a product based on the association between performance and critical quality attributes (CQAs). Within the QbD context, Design of Experiments (DoE) is an efficient tool to evaluate the conditions that enable better performance of pharmaceutical products using only a few experimental conditions, requiring low investment and allowing maximum return regarding information about the interaction among multiple factors.[1–4] However, few works focus on early development stages of biological topical active ingredients, as catalase, an endogenous heme-containing enzyme found in many biological systems that catalyzes hydrogen peroxide conversion to water.[5–7]