Automated Sampling and Online Monitoring of mammalian Cell Culture Processes -Needs & Possibilities with respect to Sample Frequencies-

Paul Kroll1,2, Alexandra Hofer1 and Christoph Herwig1,2

1 Institute for Chemical, Environmental and Biological Engineering, Technische Universität Wien, Austria2 CD Laboratory on Mechanistic and Physiological Methods for Improved Bioprocesses, Technische Universität Wien, Austria

Motivation & GoalOnline monitoring of bioprocesses, especially biopharmaceutical production processes, is of utmost importance to ensure constantproduct quality and availability. Moreover, modern bioprocess development can only be performed in an economically sound way, ifdevelopment times are kept as short as possible, which in turn requires an in-depth understanding of the process and the criticalparameters. In order to achieve this in the context of QbD, a huge number of experiments have to be performed. To handle those andsave time and money, online monitoring and parallel data processing are a must [1]. Therefore, the following is needed:

• robust process monitoring based on redundancy and data treatment

• real-time calculation of relevant process variables & parameters

Which sampling rate is necessary to monitor a process adequately?

Theoretical Background

ConclusionOnline monitoring systems such as the Numera® system enables:

• the automated usage of HPLC methods for bioprocess monitoring

• the higher measurement frequency allows the application of data processing methods like outlier detectionand smoothing algorithms

increased robustness and precision

• The combination of automated liquid handling systems and model-based experimental design could lead to a significant increase of the information per experiment (reduced amount of experiments reduced time-to-market)

• The miniaturization in process development leads to the issue of information per milliliter!

• The higher monitoring frequency in the production will lead to higher process robustness and new possibilitieswith respect to control

Figure 1: A goal of measurements, with respect to the lifecycle of bioprocesses, is the availability ofnecessary knowledge for monitoring and control issues.

Monitoring & Control

Figure 2: High measuring frequencies enable theapplication of data processing methods such asoutlier detection or filter algorithms. In theexample the Niacinamide (vitamin) concentrationwas measured every hour. The time course of theautomated processed and measured samples isclearly smoother and shows an easy to interpretcourse. In addition the used Hample filter (n=12)allows the detection of outliers. This is typical notpossible with less frequent offline data andincrease the robustness of the monitoringsignificantly. The high measurement frequenciesenable the application of smoothing algorithmssuch as the Savitzky-Golay filter, which leads tohigher precision.

up-stream down-streamcells

raw materials

fermentation separation purification formulation sterile filtration

ResultsImpact on data processing:

Enables data processing methods Increase monitoring robustness Higher precision

Extraction of physiological information

BioTech 2017 Sensor Technology and Online Analytics to Enhance (Bio)Process Understanding 07.-08.09.2017 Wädenswil/ Swiss

more information:

www.vt.tuwien.ac.at/biochemical_engineering/

Process Development Production

ResultsGeneration of Information:• Calculation of a information criteria via the FIM (fisher information matrix)

AccuracyFrequency

raw data

rates

physiological parameters/

variables

vol. rates

Hypotheses

Process Control

CQA

Process Understanding

Process Model

product/biomass characterisation

Figure 3: The measuring frequencies have a strong impact on the observed effectstrength and therefore on the precision of specific rates. The balancing betweenpermissible errors and the desired temporal resolution is an optimization problem.

1

2

3

4

𝐻𝐻𝜃𝜃 𝜃𝜃,𝜑𝜑 = 𝐻𝐻𝜃𝜃0 + �𝑘𝑘=1

𝑛𝑛𝑠𝑠𝑠𝑠�𝑖𝑖=1

𝑁𝑁𝑦𝑦

�𝑗𝑗=1

𝑁𝑁𝑦𝑦

𝑠𝑠𝑖𝑖,𝑗𝑗𝜕𝜕 �𝑦𝑦𝑖𝑖(𝑡𝑡𝑘𝑘)𝜕𝜕𝜃𝜃𝑙𝑙

𝜕𝜕 �𝑦𝑦𝑗𝑗(𝑡𝑡𝑘𝑘)𝜕𝜕𝜃𝜃𝑚𝑚 𝑙𝑙,𝑚𝑚 = 1…𝑁𝑁𝜃𝜃

Goal:• Generation of process knowledge

• Definition: An experiment is carried out in order to support, refute orvalidate a hypothesis

• For this purpose, data are collected which should correspond to thehypothesis information

Goal:• Robust and productive process

• Monitoring and control of the process

• Timely measurements of critical and key process parameters (CPP’s &kPP’s)

necessary path

potential pathAdequate Model

Definition of a Model Goal

Information

KNOWLEDGE

Definition of:• Model Structure• Parameters

Model Setup

Validation of:• Model Structure• Parameters

Model Validation

Data

Model Based Experimental

Design

Execution

Experiment

Knowledge Management

System

Database

𝐻𝐻𝜃𝜃0 Initial information �𝑦𝑦 Model prediction

𝜃𝜃 Parameter set 𝒕𝒕 Sample time

𝜑𝜑 Design vector 𝑁𝑁𝑦𝑦 Number of model states

𝒔𝒔 Measurement error matrix

𝒏𝒏𝒔𝒔𝒔𝒔Number of samplepoints

The error of specific rates depends strongly on the signal tonoise ratio. Especially for metabolic flux analysis and thequantification of cell physiology this is quite important.

“Signal to Noise Ratio”

∆𝒕𝒕 =𝑺𝑺𝑺𝑺𝑺𝑺 � 𝒆𝒆𝑩𝑩𝑩𝑩𝟔𝟔𝟔𝟔 � µ

Figure 4: Importance of experimental designwith respect to data generation and thevalidation of hypotheses. Data generation is anecessity for knowledge generation and hence,for process development.

Figure 5: Based on model-based experimentaldesign optimal sampling time points can beevaluated in order to generate maximalinformation. Figure A, B and C show theoptimal distribution of sample effort withvarying amounts od samples taken. It can beseen, that the samples must not be takenequidistant in order generate the desiredinformation.Reducing the amount of sample and neverthelessgaining maximal information, is especially animportant issue for process development wheresmall reactor volumes are typical. The goal is toincrease the information per mililiter.

A

C

B