Process Design: Stage 1 of the FDA Process Validation Guidance

Burdick, Richard K.; LeBlond, David; Pfahler, Lori B.; Quiróz, Jorge A.; Sidor, Leslie; Vukovinsky, Kimberly; Zhang, Lanju

doi:10.1007/978-3-319-50186-4_3

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…This experimentation, in terms of lab work, pales in comparison with the robustness studies briefly highlighted above, i.e. , the DOE experimentation to determine the proven acceptable ranges (PARs) for all critical variables, tasks to which process chemists typically submit without objections.…”

Section: Discussionmentioning

confidence: 99%

How to Develop Organometallic Catalytic Reactions in the Pharmaceutical Industry

Farina¹

2023

Org. Process Res. Dev.

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From a careful analysis of the recent literature, it seems clear that many transition-metal-catalyzed processes used in API synthesis are developed without resorting to any kind of kinetic analysis. Even acknowledging that some of these processes were developed for early development projects and did not need to be thoroughly optimized, the lack of kinetic work is surprising. This Perspective suggests that, in addition to the utilization of modern technologies such as high-throughput experimentation (HTE) and classical optimization tools such as Design of Experiments (DOE), a modest level of kinetic analysis is necessary to develop truly effective catalytic reactions. We offer some basic theoretical considerations and suggest some rather simple tools which can be employed to integrate kinetic analysis of catalytic steps into API development.

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Section: Discussionmentioning

confidence: 99%

How to Develop Organometallic Catalytic Reactions in the Pharmaceutical Industry

Farina¹

2023

Org. Process Res. Dev.

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“…Workflow B proposed in the "Workflow B: modelling random effects using linear mixed models" section puts the method for considering random effects in process design (stage 1) proposed by Burdick et al (Burdick et al, 2017) into the context of a workflow that includes variable selection. This aligns with the ICH8 recommendations for including all potential sources of variation into the computation of the control strategy (ICH, 2017).…”

Section: Implications For the Biopharmaceutical Industrymentioning

confidence: 99%

“…In particular, the variation of random blocks can be computed separately and added to the overall variation of the model's prediction. Burdick et al briefly illustrated the statistical methods behind LMMs and how they could be applied in process validation stage 1 (process design) in general (Burdick et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Incorporating random effects in biopharmaceutical control strategies

Oberleitner¹,

Zahel²,

Kunzelmann

et al. 2023

AAPS Open

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Objective Random effects are often neglected when defining the control strategy for a biopharmaceutical process. In this article, we present a case study that highlights the importance of considering the variance introduced by random effects in the calculation of proven acceptable ranges (PAR), which form the basis of the control strategy. Methods Linear mixed models were used to model relations between process parameters and critical quality attributes in a set of unit operations that comprises a typical biopharmaceutical manufacturing process. Fitting such models yields estimates of fixed and random effect sizes as well as random and residual variance components. To form PARs, tolerance intervals specific to mixed models were applied that incorporate the random effect contribution to variance. Results We compared standardized fixed and random effect sizes for each unit operation and CQA. The results show that the investigated random effect is not only significant but in some unit operations even larger than the average fixed effect. A comparison between ordinary least squares and mixed models tolerance intervals shows that neglecting the contribution of the random effect can result in PARs that are too optimistic. Conclusions Uncontrollable effects such as week-to-week variability play a major role in process variability and can be modelled as a random effect. Following a workflow such as the one suggested in this article, random effects can be incorporated into a statistically sound control strategy, leading to lowered out of specification results and reduced patient risk.

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“…According to ICH, the QbD approach includes the definition of a quality target product profile (QTPP), from which critical quality attributes (CQAs) are derived [ 7 ]. Based on prior knowledge and risk assessments, potentially critical process parameters (PCPP) are identified, whose effects on the CQAs are studied in more detail using statistical analyses, such as one factor at a time (OFAT) or design of experiments (DoE) methods [ 8 , 9 , 10 ]. Then, based on the results of these statistical analyses, a control strategy can be set for the process using, for example, design spaces or proven acceptable ranges (PAR) [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Improved Time Resolved KPI and Strain Characterization of Multiple Hosts in Shake Flasks Using Advanced Online Analytics and Data Science

et al. 2022

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Shake flasks remain one of the most widely used cultivation systems in biotechnology, especially for process development (cell line and parameter screening). This can be justified by their ease of use as well as their low investment and running costs. A disadvantage, however, is that cultivations in shake flasks are black box processes with reduced possibilities for recording online data, resulting in a lack of control and time-consuming, manual data analysis. Although different measurement methods have been developed for shake flasks, they lack comparability, especially when changing production organisms. In this study, the use of online backscattered light, dissolved oxygen, and pH data for characterization of animal, plant, and microbial cell culture processes in shake flasks are evaluated and compared. The application of these different online measurement techniques allows key performance indicators (KPIs) to be determined based on online data. This paper evaluates a novel data science workflow to automatically determine KPIs using online data from early development stages without human bias. This enables standardized and cost-effective process-oriented cell line characterization of shake flask cultivations to be performed in accordance with the process analytical technology (PAT) initiative. The comparison showed very good agreement between KPIs determined using offline data, manual techniques, and automatic calculations based on multiple signals of varying strengths with respect to the selected measurement signal.

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Process Design: Stage 1 of the FDA Process Validation Guidance

Cited by 3 publications

References 5 publications

How to Develop Organometallic Catalytic Reactions in the Pharmaceutical Industry

How to Develop Organometallic Catalytic Reactions in the Pharmaceutical Industry

Incorporating random effects in biopharmaceutical control strategies

Improved Time Resolved KPI and Strain Characterization of Multiple Hosts in Shake Flasks Using Advanced Online Analytics and Data Science

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