QbD implementation and Post Approval Lifecycle Management (PALM)

Ohage, Ettore; Iverson, Raquel; Krummen, Lynne A.; Taticek, Ron; Vega, María Florencia

doi:10.1016/j.biologicals.2016.06.007

Cited by 28 publications

(11 citation statements)

References 2 publications

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“…In this section, case studies will highlight recent examples of charge variants as CQAs. Broader perspectives of QbD risk assessment and CQA determination and their control strategies have been discussed extensively in the following publications (Alt et al, ; Finkler & Krummen, ; Goetze, Schenauer, & Flynn, ; Hakemeyer et al, ; Kelley, , ; Kelley, Cromwell, & Jerkins, ; Kepert et al, ; Nowak et al, ; Ohage, Iverson, Krummen, Taticek, & Vega, ; Rosenberg, Verthelyi, & Cherney, ). In addition, many reports of other PQAs as CQAs are outside the scope of this review, for example, aggregation (Singh, Kumar, & Rathore, ; Zurdo et al, ), cysteinylation (Zurdo et al, ), and glycosylation (Reusch & Tejada, ).…”

Section: Commonly Occurring Charge Variants and Their Cqa Managementmentioning

confidence: 99%

“…Commonly occurring PTMs that produce charge variants. Schematic showing the major charge variant producing PTMs and locations of the modified amino acid residues on an IgG1 molecule 2016; Finkler & Krummen, 2016;Goetze, Schenauer, & Flynn, 2010;Hakemeyer et al, 2016;Kelley, 2016aKelley, , 2016bKelley, Cromwell, & Jerkins, 2016;Kepert et al, 2016;Nowak et al, 2017;Ohage, Iverson, Krummen, Taticek, & Vega, 2016;Rosenberg, Verthelyi, & Cherney, 2012). In addition, many reports of other PQAs as CQAs are outside the scope of this review, for example, aggregation (Singh, Kumar, & Rathore, 2017;Zurdo et al, 2015), cysteinylation (Zurdo et al, 2015), and glycosylation (Reusch & Tejada, 2015).…”

Section: Figurementioning

confidence: 99%

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Industrial bioprocessing perspectives on managing therapeutic protein charge variant profiles

Chung

Tian

Tan

et al. 2018

Biotech & Bioengineering

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Controlling the charge profile of therapeutic protein is a critical challenge in the current quality-by-design (QbD) paradigm, throughout all phases of biologics process development (PD): cell line development, upstream cell culture, recovery process, downstream purification, and analytical characterization. Charge variant profiles may influence efficacy and/or lead to unintended side-effects. Thus, maintaining a consistent charge profile is of tremendous importance, and increasingly, researchers have focused efforts toward developing strategies to mitigate variability during cell culture and to improve separation and detection of charge variants. Current understanding of factors affecting charge variant formation during manufacturing remains inadequate, and sometimes, even substantial commitment of resources may still not fully achieve the desired or consistent profiles. As such, this review attempts to provide a comprehensive resource for the biologics community by summarizing the impact of charge variants and CQA management, analytical methods for charge variant detection, as well as strategies in downstream and upstream PD for controlling charge variant profiles.

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Section: Commonly Occurring Charge Variants and Their Cqa Managementmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Industrial bioprocessing perspectives on managing therapeutic protein charge variant profiles

Chung

Tian

Tan

et al. 2018

Biotech & Bioengineering

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“…In summary, QbD enables a control strategy tailored to the product and the process throughout a product's lifecycle. In general, the design space is described and licensed independently from a specific manufacturing site . Therefore, the design space was theoretically calculated from the modeling equation by inputting the specification of each quality attribute (Figure ).…”

Section: Resultsmentioning

confidence: 99%

“…In general, the design space is described and licensed independently from a specific manufacturing site. [14][15][16][17][18][19] Therefore, the design space was theoretically calculated from the modeling equation by inputting the specification of each quality attribute ( Figure 5). As described in the design space, the set point was within the design space (Table 4), which means that a product produced under these conditions theoretically meets the specifications of target quality attributes.…”

Section: Resultsmentioning

confidence: 99%

Application of the Quality‐by‐Design (QbD) Approach for Erythropoietin Alpha Purification

Kim

Seo

Kwon

et al. 2019

Bulletin Korean Chem Soc

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This study was aimed at process characterization and improving quality of purification of erythropoietin α, a biopharmaceutical agent. In biopharmaceutical manufacturing, quality should always be targeted to ensure safety and efficacy. Design‐of‐experiments–based approaches have been explored to rapidly and efficiently achieve an optimized yield and an increased understanding of a product and process variables affecting the product's critical quality attributes in the biopharmaceutical industry; this system is known as the quality‐by‐design approach. Changes in three critical process parameters—buffer pH, flow rate, and loading amount—were evaluated. Process characterization was conducted on a scaled‐down model previously validated by comparison with data from a large‐scale production facility. Seven critical quality attributes—relative aggregate content, host cell protein, host cell deoxynucleotides, endotoxin, Z‐value (N‐glycan score), relative content of charge isomers, and step yield—were analyzed. Multivariate regression analysis was performed to establish statistical prediction models for performance indicators and quality attributes; accordingly, we constructed contour plots and conducted a Monte Carlo simulation to clarify the design space. As a result of the optimization analysis of the purification process, it was confirmed that proven acceptance ranges were optimized as follows: loading amount (mg/mL) 0.4–4.0, buffer pH 7.0–8.0, and flow rate (mL/min) 0.5–1.6.

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“…Continuous improvement is the final step of applying QbD, which involves constant monitoring of the CQAs, inputs, and product quality with the purpose of fine-tuning process performance to further improve quality assurance of the product, and it is continuously applied during the product lifecycle [20].…”

Section: Introductionmentioning

confidence: 99%

Quality by Design Micro-Engineering Optimisation of NSAID-Loaded Electrospun Fibrous Patches

et al. 2019

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The purpose of this study was to apply the Quality by Design (QbD) approach to the electrospinning of fibres loaded with the nonsteroidal anti-inflammatory drugs (NSAIDs) indomethacin (INDO) and diclofenac sodium (DICLO). A Quality Target Product Profile (QTPP) was made, and risk assessments (preliminary hazard analysis) were conducted to identify the impact of material attributes and process parameters on the critical quality attributes (CQAs) of the fibres. A full factorial design of experiments (DoE) of 20 runs was built, which was used to carry out experiments. The following factors were assessed: Drugs, voltage, flow rate, and the distance between the processing needle and collector. Release studies exhibited INDO fibres had greater total release of active drug compared to DICLO fibres. Voltage and distance were found to be the most significant factors of the experiment. Multivariate statistical analytical software helped to build six feasible design spaces and two flexible, universal design spaces for both drugs, at distances of 5 cm and 12.5 cm, along with a flexible control strategy. The current findings and their analysis confirm that QbD is a viable and invaluable tool to enhance product and process understanding of electrospinning for the assurance of high-quality fibres.

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QbD implementation and Post Approval Lifecycle Management (PALM)

Cited by 28 publications

References 2 publications

Industrial bioprocessing perspectives on managing therapeutic protein charge variant profiles

Industrial bioprocessing perspectives on managing therapeutic protein charge variant profiles

Application of the Quality‐by‐Design (QbD) Approach for Erythropoietin Alpha Purification

Quality by Design Micro-Engineering Optimisation of NSAID-Loaded Electrospun Fibrous Patches

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