Risk assessment and integrated process modeling–an improved QbD approach for the development of the bioprocess control strategy

Daniel Borchert; Diego A. Suarez-Zuluaga; Yvonne E. Thomassen; Christoph Herwig; Daniel Borchert; Diego A. Suarez-Zuluaga; Yvonne E. Thomassen; Christoph Herwig

doi:10.3934/bioeng.2020022

AIMS Bioengineering

2020, Volume 7, Issue 4: 254-271. doi: 10.3934/bioeng.2020022

Previous Article Next Article

Research article

Risk assessment and integrated process modeling–an improved QbD approach for the development of the bioprocess control strategy

1.
Exputec GmbH, Mariahilferstraße 88a/1/9, 1070 Vienna, Austria
2.
Intravacc, Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, The Netherlands
3.
Vienna University of Technology, Research Area Biochemical Engineering, Gumpendorferstraße 1a, 1060 Vienna, Austria

Received: 31 May 2020 Accepted: 03 August 2020 Published: 10 August 2020

A Process characterization is a regulatory imperative for process validation within the biopharmaceutical industry. Several individual steps must be conducted to achieve the final control strategy. For that purpose, tools from the Quality by Design (QbD) toolbox are often considered. These tools require process knowledge to conduct the associated data analysis. They include cause and effect analysis, multivariate data analysis, risk assessment and design space evaluation. However, this approach is limited to the evaluation of single unit operations. This is risky as the interactions of the operations may render the control strategy invalid. Hence, a holistic process evaluation is required. Here, we present a novel workflow that shows how simple data analysis tools can be used to investigate the process holistically. This results in a significant reduction of the experimental effort and in the development of an integrated process control strategy. This novel QbD workflow is based on a novel combination of risk assessment and integrated process modeling. We demonstrate this workflow in a case study and show that the herein presented approach can be applied to any biopharmaceutical process. We demonstrate a workflow that can reduce the number of factors and increase the amount of responses within a Design of Experiments (DoE). Consequently, this result demonstrates that experimental costs and time can be reduced by investing more time in thoughtful data analysis.
- Quality by Design,
- improved QbD,
- cause and effect analysis,
- quantitative risk assessment,
- design space,
- integrated process model,
- control strategy
Citation: Daniel Borchert, Diego A. Suarez-Zuluaga, Yvonne E. Thomassen, Christoph Herwig. Risk assessment and integrated process modeling–an improved QbD approach for the development of the bioprocess control strategy[J]. AIMS Bioengineering, 2020, 7(4): 254-271. doi: 10.3934/bioeng.2020022

Related Papers:

Abstract

A Process characterization is a regulatory imperative for process validation within the biopharmaceutical industry. Several individual steps must be conducted to achieve the final control strategy. For that purpose, tools from the Quality by Design (QbD) toolbox are often considered. These tools require process knowledge to conduct the associated data analysis. They include cause and effect analysis, multivariate data analysis, risk assessment and design space evaluation. However, this approach is limited to the evaluation of single unit operations. This is risky as the interactions of the operations may render the control strategy invalid. Hence, a holistic process evaluation is required. Here, we present a novel workflow that shows how simple data analysis tools can be used to investigate the process holistically. This results in a significant reduction of the experimental effort and in the development of an integrated process control strategy. This novel QbD workflow is based on a novel combination of risk assessment and integrated process modeling. We demonstrate this workflow in a case study and show that the herein presented approach can be applied to any biopharmaceutical process. We demonstrate a workflow that can reduce the number of factors and increase the amount of responses within a Design of Experiments (DoE). Consequently, this result demonstrates that experimental costs and time can be reduced by investing more time in thoughtful data analysis.

Acknowledgments

This project has received funding from the Ministry of Economic Affairs under PPP-Allowance under the TKI-Programme Life Sciences and Health.

Conflict of interest

The authors declare no conflict of interest.

Author contributions

DBO and CHE had the idea for the improved QbD approach. DBO wrote the manuscript and did the case study. DSU and YTH assisted in writing a reviewed the manuscript. CHE assisted in writing the manuscript.

References

[1]	FDA, Guidance for Industry, 2011. Available from: https://www.fda.gov/downloads/drugs/guidances/ucm070336.pdf.
[2]	Katz P, Campbell C (2012) FDA 2011 process validation guidance: process validation revisited. J GXP Compliance 16: 18-29.
[3]	Guideline IHT (2005) Quality risk management Q9.
[4]	Politis NS, Colombo P, Colombo G, et al. (2017) Design of experiments (DoE) in pharmaceutical development. Drug Dev Ind Pharm 43: 889-901. doi: 10.1080/03639045.2017.1291672
[5]	Burdick RK, LeBlond DJ, Pfahler LB, et al. (2017) Process Design: Stage 1 of the FDA process validation guidance. Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry Cham: Springer International Publishing, 115-154. doi: 10.1007/978-3-319-50186-4_3
[6]	Doran PM (2013) Bioprocess Engineering Principles Boston: Elsevier.
[7]	Bunnak P, Allmendinger R, Ramasamy SV, et al. (2016) Life-cycle and cost of goods assessment of fed-batch and perfusion-based manufacturing processes for mAbs. Biotechnol Prog 32: 1324-1335. doi: 10.1002/btpr.2323
[8]	Diab S, Gerogiorgis DI (2018) Process modelling, simulation and technoeconomic evaluation of crystallisation antisolvents for the continuous pharmaceutical manufacturing of rufinamide. Comput Chem Eng 111: 102-114. doi: 10.1016/j.compchemeng.2017.12.014
[9]	Langer ES, Rader RA (2014) Continuous bioprocessing and perfusion: wider adoption coming as bioprocessing matures. BioProcessing J 13: 43-49. doi: 10.12665/J131.Langer
[10]	Fisher AC, Kamga MH, Agarabi C, et al. (2019) The current scientific and regulatory landscape in advancing integrated continuous biopharmaceutical manufacturing. Trends Biotechnol 37: 253-267. doi: 10.1016/j.tibtech.2018.08.008
[11]	Herwig C, Glassey J, Kockmann N, et al. (2017) Better by Design: Qualtiy by design must be viewed as an opportunity not as a regulatory burden. The Chemical Engineer 915: 41-43.
[12]	Herwig C, Garcia-Aponte OF, Golabgir A, et al. (2015) Knowledge management in the QbD paradigm: manufacturing of biotech therapeutics. Trends Biotechnol 33: 381-387. doi: 10.1016/j.tibtech.2015.04.004
[13]	CMC Biotech Working Group (2009) A-Mab: a Case Study in Bioprocess Development.
[14]	Welcome to Python.org. Available from: https://www.python.org/.
[15]	Steinwandter V, Borchert D, Herwig C (2019) Data science tools and applications on the way to Pharma 4.0. Drug discov today 24: 1795-1805. doi: 10.1016/j.drudis.2019.06.005
[16]	Guideline IHT (2009) Pharmaceutical Development Q8 (R2).
[17]	Thomassen YE, Van Sprang ENM, Van der Pol LA, et al. (2010) Multivariate data analysis on historical IPV production data for better process understanding and future improvements. Biotechnol Bioeng 107: 96-104. doi: 10.1002/bit.22788
[18]	Borchert D, Suarez-Zuluaga DA, Sagmeister P, et al. (2019) Comparison of data science workflows for root cause analysis of bioprocesses. Bioproc Biosyst Eng 42: 245-256. doi: 10.1007/s00449-018-2029-6
[19]	Suarez-Zuluaga DA, Borchert D, Driessen NN, et al. (2019) Accelerating bioprocess development by analysis of all available data: A USP case study. Vaccine 37: 7081-7089. doi: 10.1016/j.vaccine.2019.07.026
[20]	Kirdar AO, Green KD, Rathore AS (2008) Application of multivariate data analysis for identification and successful resolution of a root cause for a bioprocessing application. Biotechnol Prog 24: 720-726. doi: 10.1021/bp0704384
[21]	Sagmeister P, Wechselberger P, Herwig C (2012) Information processing: rate-based investigation of cell physiological changes along design space development. PDA J Pharm Sci Technol 66: 526-541. doi: 10.5731/pdajpst.2012.00889
[22]	Golabgir A, Gutierrez JM, Hefzi H, et al. (2016) Quantitative feature extraction from the Chinese hamster ovary bioprocess bibliome using a novel meta-analysis workflow. Biotechnol Adv 34: 621-633. doi: 10.1016/j.biotechadv.2016.02.011
[23]	Bowles JB (2003) An assessment of RPN prioritization in a failure modes effects and criticality analysis. The 2003 Proceedings Annual Reliability and Maintainability Symposium IEEE, 380-386. doi: 10.1109/RAMS.2003.1182019
[24]	Sharma KD, Srivastava S (2018) Failure mode and effect analysis (FMEA) implementation: a literature review. J Adv Res Aeronaut Space Sci 5: 1-17.
[25]	Sharma RK, Kumar D, Kumar P (2007) Modeling system behavior for risk and reliability analysis using KBARM. Qual Reliab Eng Int 23: 973-998. doi: 10.1002/qre.849
[26]	Braglia M, Frosolini M, Montanari R (2003) Fuzzy TOPSIS approach for failure mode, effects and criticality analysis. Qual Reliab Eng Int 19: 425-443. doi: 10.1002/qre.528
[27]	Borchert D, Zahel T, Thomassen YE, et al. (2019) Quantitative CPP evaluation from risk assessment using integrated process modeling. Bioengineering 6: 114. doi: 10.3390/bioengineering6040114
[28]	Zahel T, Hauer S, Mueller EM, et al. (2017) Integrated process modeling—a process validation life cycle companion. Bioengineering 4: 86. doi: 10.3390/bioengineering4040086
[29]	Bonate PL (2001) A brief introduction to Monte Carlo simulation. Clin Pharmacokinet 40: 15-22. doi: 10.2165/00003088-200140010-00002
[30]	Wechselberger P, Sagmeister P, Engelking H, et al. (2012) Efficient feeding profile optimization for recombinant protein production using physiological information. Bioprocess Biosyst Eng 35: 1637-1649. doi: 10.1007/s00449-012-0754-9
[31]	Abu-Absi SF, Yang LY, Thompson P, et al. (2010) Defining process design space for monoclonal antibody cell culture. Biotechnol Bioeng 106: 894-905. doi: 10.1002/bit.22764
[32]	Liu H, Ricart B, Stanton C, et al. (2019) Design space determination and process optimization in at-scale continuous twin screw wet granulation. Comput Chem Eng 125: 271-286. doi: 10.1016/j.compchemeng.2019.03.026
[33]	Guideline IHT (2011) Development and manufacture of drug substances (chemical entities and biotechnological/biological entities) Q11.

Reader Comments

Your name:*

Email:*
© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)