PMID- 34112369
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20210611
IS  - 1873-4863 (Electronic)
IS  - 0168-1656 (Linking)
VI  - 306S
DP  - 2019
TI  - Accurate definition of control strategies using cross validated stepwise 
      regression and Monte Carlo simulation.
PG  - 100006
LID - S2590-1559(19)30002-2 [pii]
LID - 10.1016/j.btecx.2019.100006 [doi]
AB  - Drug manufacturing processes must consistently deliver safe and effective 
      product. A key part of achieving this is process validation utilizing Quality by 
      Design (QbD) principles. To meet process validation requirements, process 
      characterization (PC) studies are often performed to expand process understanding 
      and establish an appropriate control strategy that enables the manufacturing 
      process to consistently deliver a target product profile. Two key elements of the 
      control strategy resulting from PC work are a list of critical process parameters 
      (CPPs) and defined operating ranges (ORs). These are frequently derived based on 
      mathematical models describing the relationship between process parameters and 
      critical quality attributes (CQAs). Risk assessment and design of experiments 
      (DOE) techniques are effectively deployed in the industry to identify parameters 
      to study and build process understanding. However, traditional data analysis 
      techniques do not fully utilize the data produced by these studies. In 
      particular, stepwise regression algorithms based on p-values are prone to 
      generate false positives and overfit data, potentially leading to unnecessarily 
      complex control strategies. Many of the deficiencies of traditional stepwise 
      regression can be alleviated by applying cross validation to stepwise regression 
      algorithms, as well as Monte Carlo simulations to estimate model accuracy and 
      predict CQA distributions. These methods can greatly enhance process 
      understanding and assist in the selection of CPPs. A series of PC studies were 
      performed in bioreactors to evaluate a process to produce a recombinant 
      monoclonal antibody. The studies examined process parameters such as dissolved 
      oxygen, pH, temperature, inoculation density, as well as cell density at two key 
      process steps. The resulting data were analyzed using several Monte Carlo based 
      methods. First, cross validation was used to determine model size and select 
      parameters to be included in the model. Next, Monte Carlo cross validation was 
      used to compare the accuracy of different models. Finally, simulated CQA profiles 
      were generated to validate proposed ORs. This workflow provides greater process 
      understanding based on a given PC data set and provides higher statistical 
      confidence in both CPP selection and establishment of a control strategy.
CI  - Copyright (c) 2019 Gilead Sciences, Inc. Published by Elsevier B.V. All rights 
      reserved.
FAU - Yang, Patrick Y
AU  - Yang PY
AD  - Biologics Development, Gilead Sciences, 4010 Ocean Ranch Blvd, Oceanside, CA 
      92056, United States. Electronic address: pyang94@g.ucla.edu.
FAU - Hui, Cerintha J
AU  - Hui CJ
AD  - Biologics Development, Gilead Sciences, 4010 Ocean Ranch Blvd, Oceanside, CA 
      92056, United States. Electronic address: cerintha.hui@gilead.com.
FAU - Tien, Daniel J
AU  - Tien DJ
AD  - Biologics Development, Gilead Sciences, 4010 Ocean Ranch Blvd, Oceanside, CA 
      92056, United States. Electronic address: djt233@cornell.edu.
FAU - Snowden, Andrew W
AU  - Snowden AW
AD  - Biologics Development, Gilead Sciences, 4010 Ocean Ranch Blvd, Oceanside, CA 
      92056, United States. Electronic address: andy.snowden@gilead.com.
FAU - Derfus, Gayle E
AU  - Derfus GE
AD  - Biologics Development, Gilead Sciences, 4010 Ocean Ranch Blvd, Oceanside, CA 
      92056, United States. Electronic address: gayle.derfus@gilead.com.
FAU - Opel, Cary F
AU  - Opel CF
AD  - Biologics Development, Gilead Sciences, 4010 Ocean Ranch Blvd, Oceanside, CA 
      92056, United States. Electronic address: cary@cfopel.com.
LA  - eng
PT  - Journal Article
DEP - 20190428
PL  - Netherlands
TA  - J Biotechnol
JT  - Journal of biotechnology
JID - 8411927
SB  - IM
OTO - NOTNLM
OT  - Cross validation
OT  - DOE
OT  - Monte Carlo
OT  - Multivariate data analysis
OT  - Process validation
OT  - QbD
EDAT- 2019/01/01 00:00
MHDA- 2019/01/01 00:01
CRDT- 2021/06/11 05:47
PHST- 2018/11/27 00:00 [received]
PHST- 2019/03/22 00:00 [revised]
PHST- 2019/03/22 00:00 [accepted]
PHST- 2021/06/11 05:47 [entrez]
PHST- 2019/01/01 00:00 [pubmed]
PHST- 2019/01/01 00:01 [medline]
AID - S2590-1559(19)30002-2 [pii]
AID - 10.1016/j.btecx.2019.100006 [doi]
PST - ppublish
SO  - J Biotechnol. 2019;306S:100006. doi: 10.1016/j.btecx.2019.100006. Epub 2019 Apr 
      28.