Incorporating random effects in biopharmaceutical control strategies
Abstract 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 (P...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-02-01
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| Series: | AAPS Open |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41120-022-00070-5 |
| _version_ | 1811171606717792256 |
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| author | Thomas Oberleitner Thomas Zahel Marco Kunzelmann Judith Thoma Christoph Herwig |
| author_facet | Thomas Oberleitner Thomas Zahel Marco Kunzelmann Judith Thoma Christoph Herwig |
| author_sort | Thomas Oberleitner |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-10T17:16:51Z |
| format | Article |
| id | doaj.art-133383de0df840e58d52ec27c56e5a02 |
| institution | Directory Open Access Journal |
| issn | 2364-9534 |
| language | English |
| last_indexed | 2024-04-10T17:16:51Z |
| publishDate | 2023-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | AAPS Open |
| spelling | doaj.art-133383de0df840e58d52ec27c56e5a022023-02-05T12:19:05ZengSpringerOpenAAPS Open2364-95342023-02-019111310.1186/s41120-022-00070-5Incorporating random effects in biopharmaceutical control strategiesThomas Oberleitner0Thomas Zahel1Marco Kunzelmann2Judith Thoma3Christoph Herwig4Competence Center CHASE GmbHKörber Pharma Austria GmbH, PAS-X SavvyBoehringer Ingelheim Pharma GmbH & Co. KG, Analytical Dev. BiologicalsBoehringer Ingelheim Pharma GmbH & Co. KG, Analytical Dev. BiologicalsTU WIEN Research Area Biochemical EngineeringAbstract 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.https://doi.org/10.1186/s41120-022-00070-5Biopharmaceutical manufacturingProcess validationProcess characterization studyRandom effectsMixed-effects modelLikelihood model |
| spellingShingle | Thomas Oberleitner Thomas Zahel Marco Kunzelmann Judith Thoma Christoph Herwig Incorporating random effects in biopharmaceutical control strategies AAPS Open Biopharmaceutical manufacturing Process validation Process characterization study Random effects Mixed-effects model Likelihood model |
| title | Incorporating random effects in biopharmaceutical control strategies |
| title_full | Incorporating random effects in biopharmaceutical control strategies |
| title_fullStr | Incorporating random effects in biopharmaceutical control strategies |
| title_full_unstemmed | Incorporating random effects in biopharmaceutical control strategies |
| title_short | Incorporating random effects in biopharmaceutical control strategies |
| title_sort | incorporating random effects in biopharmaceutical control strategies |
| topic | Biopharmaceutical manufacturing Process validation Process characterization study Random effects Mixed-effects model Likelihood model |
| url | https://doi.org/10.1186/s41120-022-00070-5 |
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