(507g) The Role of Software Tools in Quality By Design: A Case Study on Monoclonal Antibody Production

The
role of software tools in Quality by Design

A
case study on monoclonal antibody production

Maria M. Papathanasiou^a,
Nilay Shah^a, Efstratios N. Pistikopoulos^b

^aDept. of
Chemical Engineering, Centre for Process Systems Engineering (CPSE), Imperial
College London SW7 2AZ, London, U.K

^bArtie McFerrin Department of Chemical
Engineering, Texas A&M University, College Station TX, 77843

Over the last few years,
the U.S Food and Drug Administration (FDA) has highlighted issues with drug
shortages arising mostly from deficiencies in the pharmaceutical quality and
manufacturing. Moreover, regulatory bodies raise concerns that the process
currently in use are not state-of-the-art, while little emphasis is given to the
manufacturing process which can constitute 25% of the total product cost. Along
those lines, FDA urges the pharmaceutical industry to move towards novel
process setups that promise: (i) risk-free scale up, (ii) seamless monitoring
and (iii) high product quality [1]. In the case of
monoclonal antibodies, the aforementioned challenges come along with
competition arising due to patent expiration. Based on recent reports, the
global pharmaceutical industry is threatened with $194 billion loss on sales,
as biosimilars are expected to take 4-10% of the market by 2020 [2]. Therefore, the
necessity for the use of novel, eco-efficient processes with higher product titers
becomes eminent.

In this work we present
an in-silico software platform to assist computational experimentation in
monoclonal antibody manufacturing. The platform comprises two main parts,
namely the upstream and the downstream processing. The latter is looking into
both the initial capture step, as well as the chromatographic separation unit
used for the purification of the antibody. The platform is designed and
executed following the systematic PAROC framework and software platform that
comprises four main steps: (i) model development and validation, (ii) model
reduction/approximation, (iii) controller development and (iv) controller
validation under in an in-silico, ‘closed-loop’ fashion [3]. For the controller
design we employ multi-parametric Model Predictive Control (mp-MPC) strategies
that allow control laws to be easily tested and implemented on embedded device.
The optimal control laws are retrievable immediately through simple function
evaluations, enabling fast response without the computational requirements of a
computer in the background [4].

For the upstream system,
we consider a GS-NS0 cell culture system operated in fed-batch mode [5], while the downstream
configuration is focusing on the simulation of the capture step as presented in
[6] and the Multicolumn
Countercurrent Solvent Gradient Purification (MCSGP) process [7]. Following their
development, the models are used in order to evaluate the performance of the
studied units under continuous operation and their performance is assessed [8]. The latter falls under
the umbrella of process intensification that promises shorter production times,
decreased costs, tighter product quality and significantly lower energy
consumption. The presented, in-silico results indicate that continuous
operation could lead to prolonged culture times and cell viability in the
upstream. In the case of the downstream, the MCSGP unit is simulated under the
operation of the mp-MPC controller that returns periodic input profiles and
successfully handles disturbances resulting from variations in the composition
of the upstream mixture. Lastly, this work focuses on the key challenges faced
during the design of the aforementioned tools and how they can be addressed.
Moreover, the in-silico integration of the aforementioned processes is discussed,
along with suggestions to overcome issues with unavailable measurements during
their experimental application.

References

[1]       U.S.
Food and Drug Administration, “Guidance for Industry Quality Systems Approach
to Pharmaceutical CGMP Regulations,” 2006.

[2]       EvaluatePharma, “World Preview 2016, Outlook
to 2022,” 2016.

[3]       E. N. Pistikopoulos, N. A. Diangelakis, R.
Oberdieck, M. M. Papathanasiou, I. Nascu, and M. Sun, “PAROC - An integrated
framework and software platform for the optimisation and advanced model-based
control of process systems,” Chem. Eng. Sci., vol. 136, 2015.

[4]       P. Dua, K. Kouramas, V. Dua, and E. N.
Pistikopoulos, “MPC on a chip—Recent advances on the application of
multi-parametric model-based control,” Comput. Chem. Eng., vol. 32, no.
4–5, pp. 754–765, Apr. 2008.

[5]       M. M. Papathanasiou, A. L. Quiroga-Campano,
F. Steinebach, M. Elviro, A. Mantalaris, and E. N. Pistikopoulos, “Advanced
model-based control strategies for the intensification of upstream and
downstream processing in mAb production,” Biotechnol. Prog., vol. 33,
no. 4, 2017.

[6]       F. Steinebach, M. Angarita, D. J. Karst, T.
Müller-Späth, and M. Morbidelli, “Model based adaptive control of a continuous
capture process for monoclonal antibodies production,” J. Chromatogr. A,
vol. 1444, pp. 50–56, Apr. 2016.

[7]       T. Müller-Späth et al., “Two step
capture and purification of IgG2 using multicolumn countercurrent solvent
gradient purification (MCSGP),” Biotechnol. Bioeng., vol. 107, no. 6,
pp. 974–984, Dec. 2010.

[8]       K. B. Konstantinov and C. L. Cooney, “White
paper on continuous bioprocessing May 20-21, 2014 continuous manufacturing
symposium,” J. Pharm. Sci., vol. 104, no. 3, pp. 813–820, 2015.