Investigating the Trade-Off between Design and Operational Flexibility in Continuous Manufacturing of Pharmaceutical Tablets: A Case Study of the Fluid Bed Dryer

Round 1

Reviewer 1 Report

The paper addresses an important issue, i.e., continuous pharmaceutical manufacturing under the antagonistic demands of optimization and flexibilization. Using PSE and QbD concepts, the topic is nicely 

illustrated with a fluid bed dryer as a case study. The paper should be accepted with minor corrections as follows: 

1. Introduction

The novelty of the contribution should be better elaborated, and the authors should add the present literature on the following aspects:

• Regulatory requirements; e.g., FDA guidance documents:
• Food and Drug Administration. Pharmaceutical cGMPs for the 21st Century—A Risk-Based Approach; Technical Report; U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER): Rockville, MD, USA, 2004.
• Food and Drug Administration. Guidance for Industry Q8 Pharmaceutical Development; Technical Report August; U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER): Rockville, MD, USA, 2009.
• Q13 CONTINUOUS MANUFACTURING OF DRUG SUBSTANCES AND DRUG PRODUCTS, Draft Guidance for Industry
• etc.

As mentioned by the authors, flexibility is typically linked to uncertainties, time-dependent changes (i.e., degradation), and robustification strategies; so the authors should discuss issues related to probabilistic and/or dynamic design space. For instance, see the following manuscripts and references therein: 

• Wang, Z.; Ierapetritou, M. Global sensitivity, feasibility, and flexibility analysis of continuous pharmaceutical manufacturing processes. In Computer Aided Chemical Engineering, 1st ed.; Elsevier B.V.: Amsterdam, The Netherlands, 2018; Volume 41, pp. 189–213.
• Laky, D.; Xu, S.; Rodriguez, J.S.; Vaidyaraman, S.; García Muñoz, S.; Laird, C. An Optimization-Based Framework to Define the Probabilistic Design Space of Pharmaceutical Processes with Model Uncertainty. Processes 2019, 7, 96. https://doi.org/10.3390/pr7020096
• von Stosch, M.; Schenkendorf, R.; Geldhof, G.; Varsakelis, C.; Mariti, M.; Dessoy, S.; Vandercammen, A.; Pysik, A.; Sanders, M. Working within the Design Space: Do Our Static Process Characterization Methods Suffice? Pharmaceutics 2020, 12, 562. https://doi.org/10.3390/pharmaceutics12060562

And putting their contribution into the context of Quality by Control (QbC) countermeasures: 

• Djuris, J.; Djuric, Z. Modeling in the quality by design environment: Regulatory requirements and recommendations for design space and control strategy appointment. Int. J. Pharm. 2017.
• Su, Q.; Ganesh, S.; Moreno, M.; Bommireddy, Y.; Gonzalez, M.; Reklaitis, G.V.; Nagy, Z.K. A perspective on Quality-by-Control (QbC) in pharmaceutical continuous manufacturing. Comput. Chem. Eng. 2019, 125, 216–231.
• Szilágyi, B.; Borsos, Á.; Pal, K.; Nagy, Z.K. Experimental implementation of a Quality-by-Control (QbC) framework using a mechanistic PBM-based non-linear model predictive control involving chord length distribution measurement for the batch cooling crystallization of l-ascorbic acid. Chem. Eng. Sci. 2019, 195, 335–346. 

2. Methodology 

Some general comment; this section would benefit from a more rigorous notation. 

• Please explain what "model parameters have been validated" (p.3 line 121) means. Did you use experimental data, did model fitting/parameter identification, and then test data to validate the parameters/the model? If so, which measure did you use?
• When proposing the operational envelope concept in Sec. 2.2, I guess that the readability would benefit if you explain this concept in terms of critical process parameters (CPP) and critical quality attributes (CQA) - referring only to "parameters" might be ambiguous (model parameters/kinetics, process/design parameters, or KPIs?).
• To me, the arguments (p. 3, lines 139-147) regarding non-convex vs. convex and non-linear vs. linear optimization problems can be improved. The authors want to express that a convex optimization problem is preferred. 
• How are \Delta b^{min} and \Delta b^{max} defined and known a-priori?
• Overall, any difference/improvements compared with Samsatli et al. [24]?

3. Case Study

• p. 7, line 225: Sobol' sampling at least needs some reference.
• Step 3, a) Which optimization problem is solved here? Please refer to the used equation system.
• Step 3, b) How was f=0.325 selected? How does this relate to M3? In terms of flexibility, how to rate the derived result? How important is it to ensure a maximal distance to all feasible boundaries? 
• "Interestingly....not affected by the change in \Delta V but only by the change in temperature" -> should be explained on the physicochemical properties/thermodynamics?
• p. 9, line 275: As stated correctly, Figure 5 looks odd. Please try to validate the irregularities using at least a multi-start optimization approach.
• Figure 6: It looks that more than 325 sample points were used (please compare Figs. 2-4)

References

• please check all given References for correctness/completeness; for instance, Ref. 5, the year (2018) is missing.

Author Response

Our response with the listing of the changes is given in the file attached.

Reviewer 2 Report

See pdf file

Comments for author File: Comments.pdf

Author Response

Our response with the listing of the changes is given in the file attached.