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Model-Based Tools for Pharmaceutical Manufacturing Processes

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A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Pharmaceutical Processes".

Deadline for manuscript submissions: closed (30 September 2019) | Viewed by 86433

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Special Issue Editors

Prof. Dr. Krist V. Gernaey

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Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 229, 2800 Kgs. Lyngby, Denmark
Interests: industrial fermentation technology; scale-up/scale-down; resource recovery; continuous production processes; mathematical modeling; process analytical technology (PAT)
Special Issues, Collections and Topics in MDPI journals

Dr. René Schenkendorf

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Guest Editor

Automation & Computer Sciences Department, Harz University of Applied Sciences, 38855 Wernigerode, Germany
Interests: optimal experimental design; process systems engineering; sensitivity and uncertainty analysis
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Dr. Dimitrios I. Gerogiorgis

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Institute for Materials and Processes (IMP), School of Engineering, University of Edinburgh, Edinburgh EH9 3FB, UK
Interests: bio/pharmaceutical manufacturing; food and beverage manufacturing; oil and gas systems engineering; process modeling, design and optimization

Dr. Seyed Soheil Mansouri

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Guest Editor

Department of Chemical and Biochemical Engineering, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
Interests: process system engineering; process control and optimization; downstream process development; chemical and biochemical process intensification
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Special Issue Information

Dear Colleagues,

Active pharmaceutical ingredients (APIs) are highly valuable, highly-sensitive products with strict quality control specifications and regulatory affairs in place. To ensure a profitable and growing pharmaceutical industry, model-based tools are fundamental to advancing the basic understanding, design and optimization of pharmaceutical manufacturing processes. Process analysis principles, for instance, provide a better understanding of underlying pharmaceutical manufacturing mechanisms. Model-based process design concepts facilitate identification of optimal production and purification pathways and configurations. Ideas of process monitoring ensure low life cycle cost and provide new insights into critical failure modes and drug quality control issues. These model-based concepts and combinations thereof are key to explore the whole potential of innovative highly-effective pharmaceutical manufacturing processes and are relevant ingredients of Quality by Design (QbD) policies.

This Special Issue on “Model-Based Tools for Pharmaceutical Manufacturing Processes” intends to curate novel advances in the development and application of model-based tools to address ever-present challenges of the traditional pharmaceutical manufacturing practice. Topics include, but are not limited to:

Development of new modeling concepts for pharmaceutical manufacturing at different levels of aggregation (phenomena, unit operation, plant-wide);
Design and optimization of pharmaceutical processes based on the derived models;
Process intensification, robustification, and flexibilization of multipurpose pharmaceutical manufacturing platforms;
Hybrid modeling combining classical first-principles models with (big) data-driven concepts
Process control, monitoring and fault detection in pharmaceutical industry

Prof. Dr. Krist V. Gernaey
Dr. Rene Schenkendorf
Dr. Dimitrios I. Gerogiorgis
Dr. Seyed Soheil Mansouri
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

upstream and downstream processes
formulation
bio-based and traditional organic synthesis based processes
Quality by Design (QbD)
continuous pharmaceutical manufacturing (CPM)
big data
hybrid models

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Published Papers (10 papers)

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Editorial

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4 pages, 163 KiB

Open AccessEditorial

Model-Based Tools for Pharmaceutical Manufacturing Processes

by René Schenkendorf, Dimitrios I. Gerogiorgis, Seyed Soheil Mansouri and Krist V. Gernaey

Processes 2020, 8(1), 49; https://doi.org/10.3390/pr8010049 - 1 Jan 2020

Cited by 6 | Viewed by 3430

Abstract

Active pharmaceutical ingredients (APIs) are highly valuable, highly sensitive products resulting from production processes with strict quality control specifications and regulations that are required for the safety of patients [...] Full article

(This article belongs to the Special Issue Model-Based Tools for Pharmaceutical Manufacturing Processes)

Research

Jump to: Editorial, Other

13 pages, 2102 KiB

Open AccessFeature PaperArticle

Explicit Residence Time Distribution of a Generalised Cascade of Continuous Stirred Tank Reactors for a Description of Short Recirculation Time (Bypassing)

by Peter Toson, Pankaj Doshi and Dalibor Jajcevic

Processes 2019, 7(9), 615; https://doi.org/10.3390/pr7090615 - 10 Sep 2019

Cited by 41 | Viewed by 15455

Abstract

The tanks-in-series model (TIS) is a popular model to describe the residence time distribution (RTD) of non-ideal continuously stirred tank reactors (CSTRs) with limited back-mixing. In this work, the TIS model was generalised to a cascade of n CSTRs with non-integer non-negative n. The resulting model describes non-ideal back-mixing with n > 1. However, the most interesting feature of the n-CSTR model is the ability to describe short recirculation times (bypassing) with n < 1 without the need of complex reactor networks. The n-CSTR model is the only model that connects the three fundamental RTDs occurring in reactor modelling by variation of a single shape parameter n: The unit impulse at n→0, the exponential RTD of an ideal CSTR at n = 1, and the delayed impulse of an ideal plug flow reactor at n→∞. The n-CSTR model can be used as a stand-alone model or as part of a reactor network. The bypassing material fraction for the regime n < 1 was analysed. Finally, a Fourier analysis of the n-CSTR was performed to predict the ability of a unit operation to filter out upstream fluctuations and to model the response to upstream set point changes. Full article

(This article belongs to the Special Issue Model-Based Tools for Pharmaceutical Manufacturing Processes)

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23 pages, 19232 KiB

Open AccessFeature PaperArticle

Global Sensitivity Analysis of a Spray Drying Process

by Satyajeet Bhonsale, Carlos André Muñoz López and Jan Van Impe

Processes 2019, 7(9), 562; https://doi.org/10.3390/pr7090562 - 23 Aug 2019

Cited by 11 | Viewed by 8976

Abstract

Spray drying is a key unit operation used to achieve particulate products of required properties. Despite its widespread use, the product and process design, as well as the process control remain highly empirical and depend on trial and error experiments. Studying the effect of operational parameters experimentally is tedious, time consuming, and expensive. In this paper, we carry out a model-based global sensitivity analysis (GSA) of the process. Such an exercise allows us to quantify the impact of different process parameters, many of which interact with each other, on the product properties and conditions that have an impact on the functionality of the final drug product. Moreover, classical sensitivity analysis using the Sobol-based sensitivity indices was supplemented by a polynomial chaos-based sensitivity analysis, which proved to be an efficient method to reduce the computational cost of the GSA. The results obtained demonstrate the different response dependencies of the studied variables, which helps to identify possible control strategies that can result in major robustness for the spray drying process. Full article

(This article belongs to the Special Issue Model-Based Tools for Pharmaceutical Manufacturing Processes)

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14 pages, 803 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Robust Process Design in Pharmaceutical Manufacturing under Batch-to-Batch Variation

by Xiangzhong Xie and René Schenkendorf

Processes 2019, 7(8), 509; https://doi.org/10.3390/pr7080509 - 3 Aug 2019

Cited by 20 | Viewed by 7031

Abstract

Model-based concepts have been proven to be beneficial in pharmaceutical manufacturing, thus contributing to low costs and high quality standards. However, model parameters are derived from imperfect, noisy measurement data, which result in uncertain parameter estimates and sub-optimal process design concepts. In the last two decades, various methods have been proposed for dealing with parameter uncertainties in model-based process design. Most concepts for robustification, however, ignore the batch-to-batch variations that are common in pharmaceutical manufacturing processes. In this work, a probability-box robust process design concept is proposed. Batch-to-batch variations were considered to be imprecise parameter uncertainties, and modeled as probability-boxes accordingly. The point estimate method was combined with the back-off approach for efficient uncertainty propagation and robust process design. The novel robustification concept was applied to a freeze-drying process. Optimal shelf temperature and chamber pressure profiles are presented for the robust process design under batch-to-batch variation. Full article

(This article belongs to the Special Issue Model-Based Tools for Pharmaceutical Manufacturing Processes)

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19 pages, 3838 KiB

Open AccessFeature PaperArticle

Online Decision-Support Tool “TECHoice” for the Equipment Technology Choice in Sterile Filling Processes of Biopharmaceuticals

by Haruku Shirahata, Sara Badr, Yuki Shinno, Shuta Hagimori and Hirokazu Sugiyama

Processes 2019, 7(7), 448; https://doi.org/10.3390/pr7070448 - 15 Jul 2019

Cited by 4 | Viewed by 5611

Abstract

In biopharmaceutical manufacturing, a new single-use technology using disposable equipment is available for reducing the work of change-over operations compared to conventional multi-use technology that use stainless steel equipment. The choice of equipment technologies has been researched and evaluation models have been developed, however, software that can extend model exposure to reach industrial users is yet to be developed. In this work, we develop and demonstrate a prototype of an online decision-support tool for the multi-objective evaluation of equipment technologies in sterile filling of biopharmaceutical manufacturing processes. Multi-objective evaluation models of equipment technologies and equipment technology alternative generation algorithms are implemented in the tool to support users in choosing their preferred technology according to their input of specific production scenarios. The use of the tool for analysis and decision-support was demonstrated using four production scenarios in drug product manufacturing. The online feature of the tool allows users easy access within academic and industrial settings to explore different production scenarios especially at early design phases. The tool allows users to investigate the certainty of the decision by providing a sensitivity analysis function. Further enrichment of the functionalities and enhancement of the user interface could be implemented in future developments. Full article

(This article belongs to the Special Issue Model-Based Tools for Pharmaceutical Manufacturing Processes)

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14 pages, 3075 KiB

Open AccessFeature PaperArticle

Dynamic Modelling of Phosphorolytic Cleavage Catalyzed by Pyrimidine-Nucleoside Phosphorylase

by Robert T. Giessmann, Niels Krausch, Felix Kaspar, Mariano Nicolas Cruz Bournazou, Anke Wagner, Peter Neubauer and Matthias Gimpel

Processes 2019, 7(6), 380; https://doi.org/10.3390/pr7060380 - 19 Jun 2019

Cited by 12 | Viewed by 4801

Abstract

Pyrimidine-nucleoside phosphorylases (Py-NPases) have a significant potential to contribute to the economic and ecological production of modified nucleosides. These can be produced via pentose-1-phosphates, an interesting but mostly labile and expensive precursor. Thus far, no dynamic model exists for the production process of pentose-1-phosphates, which involves the equilibrium state of the Py-NPase catalyzed reversible reaction. Previously developed enzymological models are based on the understanding of the structural principles of the enzyme and focus on the description of initial rates only. The model generation is further complicated, as Py-NPases accept two substrates which they convert to two products. To create a well-balanced model from accurate experimental data, we utilized an improved high-throughput spectroscopic assay to monitor reactions over the whole time course until equilibrium was reached. We examined the conversion of deoxythymidine and phosphate to deoxyribose-1-phosphate and thymine by a thermophilic Py-NPase from Geobacillus thermoglucosidasius. The developed process model described the reactant concentrations in excellent agreement with the experimental data. Our model is built from ordinary differential equations and structured in such a way that integration with other models is possible in the future. These could be the kinetics of other enzymes for enzymatic cascade reactions or reactor descriptions to generate integrated process models. Full article

(This article belongs to the Special Issue Model-Based Tools for Pharmaceutical Manufacturing Processes)

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17 pages, 4234 KiB

Open AccessFeature PaperArticle

Dynamic Modelling and Optimisation of the Batch Enzymatic Synthesis of Amoxicillin

by Andrew B. Cuthbertson, Alistair D. Rodman, Samir Diab and Dimitrios I. Gerogiorgis

Processes 2019, 7(6), 318; https://doi.org/10.3390/pr7060318 - 28 May 2019

Cited by 11 | Viewed by 13752

Abstract

Amoxicillin belongs to the β-lactam family of antibiotics, a class of highly consumed pharmaceutical products used for the treatment of respiratory and urinary tract infections, and is listed as a World Health Organisation (WHO) “Essential Medicine”. The demonstrated batch enzymatic synthesis of amoxicillin is composed of a desired synthesis and two undesired hydrolysis reactions of the main substrate (6-aminopenicillanic acid (6-APA)) and amoxicillin. Dynamic simulation and optimisation can be used to establish optimal control policies to attain target product specification objectives for bioprocesses. This work performed dynamic modelling, simulation and optimisation of the batch enzymatic synthesis of amoxicillin. First, kinetic parameter regression at different operating temperatures was performed, followed by Arrhenius parameter estimation to allow for non-isothermal modelling of the reaction network. Dynamic simulations were implemented to understand the behaviour of the design space, followed by the formulation and solution of a dynamic non-isothermal optimisation problem subject to various product specification constraints. Optimal reactor temperature (control) and species concentration (state) trajectories are presented for batch enzymatic amoxicillin synthesis. Full article

(This article belongs to the Special Issue Model-Based Tools for Pharmaceutical Manufacturing Processes)

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35 pages, 7927 KiB

Open AccessFeature PaperArticle

Dynamic Flowsheet Model Development and Sensitivity Analysis of a Continuous Pharmaceutical Tablet Manufacturing Process Using the Wet Granulation Route

by Nirupaplava Metta, Michael Ghijs, Elisabeth Schäfer, Ashish Kumar, Philippe Cappuyns, Ivo Van Assche, Ravendra Singh, Rohit Ramachandran, Thomas De Beer, Marianthi Ierapetritou and Ingmar Nopens

Processes 2019, 7(4), 234; https://doi.org/10.3390/pr7040234 - 24 Apr 2019

Cited by 46 | Viewed by 13721

Abstract

In view of growing interest and investment in continuous manufacturing, the development and utilization of mathematical model(s) of the manufacturing line is of prime importance. These models are essential for understanding the complex interplay between process-wide critical process parameters (CPPs) and critical quality [...] Read more.

^{TM}

-25 line for continuous tablet manufacturing, including wet granulation, is developed. The manufacturing line involves various unit operations, i.e., feeders, blenders, a twin-screw wet granulator, a fluidized bed dryer, a mill, and a tablet press. The unit operations are simulated using various modeling approaches such as data-driven models, semi-empirical models, population balance models, and mechanistic models. Intermediate feeders, blenders, and transfer lines between the units are also simulated. The continuous process is simulated using the flowsheet model thus developed and case studies are provided to demonstrate its application for dynamic simulation. Finally, the flowsheet model is used to systematically identify critical process parameters (CPPs) that affect process responses of interest using global sensitivity analysis methods. Liquid feed rate to the granulator, and air temperature and drying time in the dryer are identified as CPPs affecting the tablet properties. Full article

(This article belongs to the Special Issue Model-Based Tools for Pharmaceutical Manufacturing Processes)

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20 pages, 3024 KiB

Open AccessArticle

An Optimization-Based Framework to Define the Probabilistic Design Space of Pharmaceutical Processes with Model Uncertainty

by Daniel Laky, Shu Xu, Jose S. Rodriguez, Shankar Vaidyaraman, Salvador García Muñoz and Carl Laird

Processes 2019, 7(2), 96; https://doi.org/10.3390/pr7020096 - 14 Feb 2019

Cited by 37 | Viewed by 5624

Abstract

To increase manufacturing flexibility and system understanding in pharmaceutical development, the FDA launched the quality by design (QbD) initiative. Within QbD, the design space is the multidimensional region (of the input variables and process parameters) where product quality is assured. Given the high cost of extensive experimentation, there is a need for computational methods to estimate the probabilistic design space that considers interactions between critical process parameters and critical quality attributes, as well as model uncertainty. In this paper we propose two algorithms that extend the flexibility test and flexibility index formulations to replace simulation-based analysis and identify the probabilistic design space more efficiently. The effectiveness and computational efficiency of these approaches is shown on a small example and an industrial case study. Full article

(This article belongs to the Special Issue Model-Based Tools for Pharmaceutical Manufacturing Processes)

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Other

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13 pages, 1288 KiB

Open AccessFeature PaperConcept Paper

Show Me the Money! Process Modeling in Pharma from the Investor’s Point of View

by Christos Varsakelis, Sandrine Dessoy, Moritz von Stosch and Alexander Pysik

Processes 2019, 7(9), 596; https://doi.org/10.3390/pr7090596 - 4 Sep 2019

Cited by 6 | Viewed by 4916

Abstract

Process modeling in pharma is gradually gaining momentum in process development but budget restrictions are growing. We first examine whether and how current practices rationalize within a decision process framework with a fictitious investor facing a decision problem subject to incomplete information. We then develop an algorithmic procedure for investment evaluation on both monetary and diffusion-of-innovation fronts. Our methodology builds upon discounted cash flow analysis and Bayesian inference and utilizes the Rogers diffusion of innovation paradigm for computing lower expected returns. We also introduce a set of intangible metrics for quantifying the level of diffusion of process modeling within an organization. Full article

(This article belongs to the Special Issue Model-Based Tools for Pharmaceutical Manufacturing Processes)

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