Incorporating random effects in biopharmaceutical control strategies

AAPS Open

Research

Marco Kunzelmann¹  Judith Thoma¹  Thomas Oberleitner²  Thomas Zahel³  Christoph Herwig⁴ 

[1] Boehringer Ingelheim Pharma GmbH & Co. KG, Analytical Dev. Biologicals, Biberach an der Riss, Germany;Competence Center CHASE GmbH, Vienna, Austria;Körber Pharma Austria GmbH, PAS-X Savvy, Vienna, Austria;TU WIEN Research Area Biochemical Engineering, Vienna, Austria;

关键词: Biopharmaceutical manufacturing;    Process validation;    Process characterization study;    Random effects;    Mixed-effects model;    Likelihood model;   

DOI  :  10.1186/s41120-022-00070-5

 received in 2022-05-31, accepted in 2022-12-18,  发布年份 2022

来源: Springer

【 摘 要 】

ObjectiveRandom 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.MethodsLinear 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.ResultsWe 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.ConclusionsUncontrollable 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.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

12302_2023_718_Article_IEq70.gif

Fig. 3

Fig. 5

Fig. 2

Fig. 4

Fig. 4

42004_2023_830_Article_IEq3.gif

Fig. 2

12302_2023_718_Article_IEq56.gif

Fig. 1

Fig. 2

Fig. 1

Fig. 2