DoE- the efficient way of bioprocess optimization

Transcription

1 DoE- the efficient way of bioprocess optimization Upstream Day - New Technology Platforms for Cell & Microbial Cultures turning science into solutions

2 Chemometrics Chemometric Software Solutions powered by UMETRICS Design of Experiments (DoE) #1 Multivariate Data Analysis (MVDA) #2 MVDA-online #3 #3 May 2013 Page 2

3 Parameter Optimization Ways of Experimentation Random Approach Intuitive Approach (COST) Efficient Approach (DoE) Changing all factors arbitrarily High number of experiments Pure luck to find optimum Changing One Single factor at a Time High number of experiments Only quasi -optimum can be found Changing all factors at the same time according to a well designed plan Perform least number of experiments Optimum can be found May 2013 Page 3

4 Design of Experiments Design of Experiments Regression Coefficient Plot 3-D Response Surface Plot 2-D Contour Plot Probability Plot (Design Space) A structured, organized method for determining the relationship between factors affecting a process and the output of that process. [FDA, Q8(R2)] Typical objective is to identify optimal process conditions with a reduced number of experiments to increase product yield / productivity. May 2013 Page 4

5 Design of Experiments Three Typical DoE-Applications in Biotechnology Upstream Processing Medium Optimization #1 Parameter Screening #2 Parameter Optimization #3 #2 #3 Optimization of the composition of growth and production culture media Typical objective is to identify a better selection & quantitative composition of medium factors Screening of control parameters and basic process state variables Typical objective is to identify a subset of a few important factors which can be used in a new optimization design Optimization of control parameters including feeding strategy Typical objective is to define a better physical environment for cell growth and protein production May 2013 Page 5

6 Design of Experiments Parallel Systems: the Ideal Equipment for DoE Although a DoE investigation can be carried out in one vessel, systems designed for parallel operation provide the optimal basis to implement a series of experiments in an economical way. Medium Screening with Parameter Screening with Parameter Optimization with 15 parameters = 32 experiments 7 parameters = experiments 3 parameters = experiments CERTOMAT CTplus Designed for Cultivation of Mammalian Cells with up to 96 x 50 ml Flasks BIOSTAT Qplus Fast Track Screening in Early Process Development with up to 12 Culture Vessels BIOSTAT B-DCU II Fully Flexible for Advanced Process Development with up to 6 Culture Vessels May 2013 Page 6

7 DoE Case Study Malaria Vaccine Production with P. pastoris Joint project of the HAW Hamburg (Germany) and the Biomedical Primate Research Center (BPRC, Netherlands) Goal: identify optimal protein expression conditions for high yields of potential malaria vaccines in the methylotrophic yeast Pichia pastoris. May 2013 Page 7

8 DoE Case Study Strategy of Experimentation 3 Primary DoE Objectives Screening #1 Optimization #2 Robustness Testing #3 Which factors significantly influence the response? Which factor settings result in optimal operation conditions? How sensitive is the response to small changes in the optimal factors settings? May 2013 Page 8

9 DoE Case Study Identification of Significant Factors (CPPs) During Screening Regression Coefficient Plot for productivity before (left) and after (right) model pruning before model pruning after deletion interaction terms X X X Model clearly shows that linear model terms dominate over two-factor-interaction terms Interaction-terms are not significant and can be deleted from the model May 2013 Page 9

10 DoE Case Study Identification of Optimal Parameter Settings 3-D Response Surface Plots for Productivity (PRD) Optimal expression conditions can be identified Productivity increased compared to normal operating conditions Start Point for Design Space Estimation (DSE) May 2013 Page 10

11 Design Space Design Space according to ICH Q8(R2) Regardless of how a design space is developed, it is expected that operation within the design space will result in a product meeting the defined quality. [ICH Q8(R2)] May 2013 Page 11

12 Design Space Design Space Estimation with Specification: PRD > 6 Design Space Classical Contour Plot Sweet Spot Plot Contour Plot shows point prediction response surface visualizing average PRD Sweet Spot Plot shows Design Space - the region where we meet our specifications May 2013 Page 12

13 Design Space Risk Based Design Space Estimation with Specification: PRD > 6 Design Space Probability Contour Plot Sweet Spot Plot = DS Monte Carlo simulation used for probability estimation of being in or out of specification Monte Carlo simulation considers uncertainties in process factors and in the model Probability plot illustrates risk of not meeting the specifications (DS = region with low risk) May 2013 Page 13

14 Design Space Real QbD and Good Estimation of Design Space / Risk Kauffmann et al. (2012): Evaluation of Process Models with Monte Carlo Methods, FDA May 2013 Page 14 Peterson, J.J. (2010): What Your ICH Q8 Design Space Needs: A Multivariate Predictive Distribution, GSK

15 Design of Experiments Integrated DoE for Efficient Setup & Execution of Designed Experiments DoE SCADA #2 #1 #2 Quantitative controlled factors Design setup using a simple wizard Automatic S88 recipe configuration Parallel start of multiple experiments Automatic data & design transfer May 2013 Page 15

16 Design of Experiments Motivation for Design of Experiments Key technology in Quality by Design (QbD) discussions Speed up development of new therapeutics with less experiments Increase manufacturing efficiency and lower production costs Improve process understanding with a clear project scope Statistically verified statements with graphical representation... and many more REGULATORY TIME MONEY KNOWLEDGE COMMUNICATION May 2013 Page 16