An Example of Utilizing Mechanistic and Empirical Modeling in Quality by Design

Hallow, Daniel M.; Mudryk, Boguslaw; Braem, Alan; Tábora, José E.; Lyngberg, Olav; Bergum, James; Rossano, Lucius T.; Tummala, Srinivas

doi:10.1007/s12247-010-9094-y

Cited by 34 publications

(26 citation statements)

References 5 publications

(6 reference statements)

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“…In such cases, models developed using data collected on a laboratory scale can be used to estimate process performance over a range of conditions and thereby guide the selection of design space. The edges of the corresponding design space can be verified on a laboratory scale [153,154]. For scale-dependent phenomena, acceptable performance on a laboratory scale may not be sufficient to verify the design space.…”

Section: Verification Of Design Spacementioning

confidence: 99%

Mathematical Tools for the Quantitative Definition of a Design Space

Rogers

Ierapetritou

2016

Methods in Pharmacology and Toxicology

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This chapter focuses on the application of process modeling to determination of design space for pharmaceutical manufacturing processes. The first two sections define design space and related terms from a regulatory perspective and discuss how these apply in practice during pharmaceutical process development. In Sect. 3, a variety of mathematical techniques that can be used to guide design space development are introduced. An emphasis is placed on the use of feasibility analysis and the relationship between process feasibility and design space. Statistical techniques, including latent variable and Bayesian methods, are also discussed in detail. For methods that have been reported in the literature for pharmaceutical manufacturing applications, an overview of the relevant case studies is provided. If methods have not yet been applied to pharmaceutical processes, potential applications for these techniques are indicated. To illustrate the applicability of these concepts to pharmaceutical manufacturing processes, a brief case study is presented in Sect. 4. A discussion of design space verification and issues related to scale-up is provided in Sect. 5. Finally a brief summary of the concepts presented and their potential role in design space development for pharmaceutical processes is given in Sect. 6. This chapter is intended to provide readers with an understanding of mathematical techniques that can be used during process development to assist in the determination of process design space. An overview of the problem formulation and solution approaches for each method are presented. More detailed mathematical developments for each technique are available in the references provided. Table 1 provides a list of symbols that are used throughout this chapter, while Table 2 describes acronyms used within this chapter.

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Section: Verification Of Design Spacementioning

confidence: 99%

Mathematical Tools for the Quantitative Definition of a Design Space

Rogers

Ierapetritou

2016

Methods in Pharmacology and Toxicology

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“…8.29. Regarding the validation of the design space, there is no universally recommended procedure for verification of a model, and there is limited literature or regulatory guidance that addresses the extent of verification required to justify a design space (Hallow et al 2010). In the present work, eight additional runs were performed inside the design space and confronted with the predictions of the optimization model.…”

Section: Design Spacementioning

confidence: 99%

Amorphous Solid Dispersions

Shah¹,

Sandhu²,

Choi³

et al. 2014

Advances in Delivery Science and Technology

108

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“…More generally, there are two ways of modeling, called parametric (white box) and non‐parametric (black box) modeling. [ 18 ] Non‐parametric models refer to purely data‐driven approaches, for which no further process knowledge is needed, the model structure is inferred from data, for example, artificial neural networks (ANN). However, once the model is used for predictions out of the characterized space, it lacks the ability to extrapolate.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Modeling Methods for DoE‐Based Holistic Upstream Process Characterization

Bayer

Stosch

Striedner

et al. 2020

Biotechnology Journal

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Upstream bioprocess characterization and optimization are time and resource-intensive tasks. Regularly in the biopharmaceutical industry, statistical design of experiments (DoE) in combination with response surface models (RSMs) are used, neglecting the process trajectories and dynamics. Generating process understanding with time-resolved, dynamic process models allows to understand the impact of temporal deviations, production dynamics, and provides a better understanding of the process variations that stem from the biological subsystem. The authors propose to use DoE studies in combination with hybrid modeling for process characterization. This approach is showcased on Escherichia coli fed-batch cultivations at the 20L scale, evaluating the impact of three critical process parameters. The performance of a hybrid model is compared to a pure data-driven model and the widely adopted RSM of the process endpoints. Further, the performance of the time-resolved models to simultaneously predict biomass and titer is evaluated. The superior behavior of the hybrid model compared to the pure black-box approaches for process characterization is presented. The evaluation considers important criteria, such as the prediction accuracy of the biomass and titer endpoints as well as the time-resolved trajectories. This showcases the high potential of hybrid models for soft-sensing and model predictive control.

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An Example of Utilizing Mechanistic and Empirical Modeling in Quality by Design

Cited by 34 publications

References 5 publications

Mathematical Tools for the Quantitative Definition of a Design Space

Mathematical Tools for the Quantitative Definition of a Design Space

Amorphous Solid Dispersions

Comparison of Modeling Methods for DoE‐Based Holistic Upstream Process Characterization

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