Process systems engineering tools in the pharmaceutical industry
Highlights
► Process systems engineering tools in the pharmaceutical industry are summarized. ► A futuristic view of process systems engineering tools usage is presented. ► Traditional approaches for control spaces definition result in passive control. ► A novel data driven modeling approach Dynamic Design of Experiments is presented. ► This novel approach enables batch modeling with time-varying process conditions.
Introduction
Almost a decade has elapsed since the FDA publication “Pharmaceutical cGMPs for the 21st Century: A Risk-Based Approach” and almost eight years since “PAT – A Framework for innovative Pharmaceutical Manufacturing and Quality Assurance” were issued. Much progress and innovation in pharmaceutical manufacturing has occurred since the publication of these landmark documents. For example, pharmaceutical companies have readily adopted in-process measurements systems, such as near infrared spectroscopy for concentration, and focused beam reflectance measurements for estimation of particle size distribution. The application of multivariate process monitoring for real time fault detection and isolation has also found its way into pharmaceutical manufacturing. The industry has moved away from quality control strategies based on uni-variate parameters specifications, and towards the multivariate design space approach. While, tremendous progress has been achieved in the decade, there is work to be done to realize the full potential of the process systems engineering (PSE) toolbox.
The purpose of the paper is to describe the current state of the art of the application of PSE tools in the pharmaceutical industry. The sub areas of PSE discussed in this work are process analytical technology (PAT) measurement systems, process monitoring, plant wide information technology systems, process control, modeling, and optimization methodologies. This paper focuses on PSE applications primarily related to active pharmaceutical ingredient (API), and solid oral dosage manufacturing. Details on the application of PAT measurement systems, and process control in biologics are out of scope of this work, for readers interested in biologics PSE applications we are listing a few relevant review papers (Junker and Wang, 2006, Schugerl, 2001).
To augment information available in the open literature, we conducted an industrial benchmarking survey on the above-mentioned PSE sub areas that contained twenty-one questions in total1. The survey was submitted to current pharmaceutical industry professionals in all areas of the industry: active pharmaceutical ingredient, solid oral dosage, and biologics, in both development and manufacturing. The companies that submitted responses to the survey are listed in Table 1.
The paper is organized as follows, for each of the sub areas of PSE covered; we provide a brief background on how PSE tools are currently used in the pharmaceutical industry. Where possible, literature references have been provided, with a preference towards papers published by pharmaceutical industry professionals. The questionnaire results pertaining to each PSE area are presented at the end of each section. We then discuss the impact of increased out-sourcing of product development and manufacturing and the prospect of continuous processes on the future utilization of PSE tools. Lastly, we present the application of a novel method for batch process optimization called dynamic design of experiments. A simulated API synthesis reaction process is used to explain the method.
Section snippets
The current state of PSE tools in pharma
We describe the current state of the utilization PSE tools in the pharmaceutical industry. The sub areas of PSE discussed are measurement systems, multivariate process monitoring, plant wide information systems, and process control and optimization methodologies. The results of this section are a combination of work documented in the literature by authors in the pharmaceutical industry, and the results of the industrial benchmarking survey.
The future of pharmaceutical manufacturing and PSE tools
In this section, we briefly discuss the future trends of contract manufacturing and continuous processing in the pharmaceutical industry and their impact on the utilization and advancement of PSE tools.
The need for data-driven models
The inner workings of the majority of batch pharmaceutical processes are not well understood for a fundamental or knowledge-driven (KD) model to be developed. An additional roadblock in the development of such models is the small production rates of the majority of pharmaceutical products compared to the production rate of bulk chemical and petrochemicals for which a plethora of knowledge-driven models has found extensive use over the last four to six decades. Because, such KD models provide a
A new approach: Design of Dynamic Experiments
In an effort to develop a data-driven approach for the optimization of the end-result of a batch process unit with respect a time-evolving decision variable, Georgakis (2009) generalized the classical Design of Experiments (DoE) with respect to time-varying decision variables. Examples of such time-varying decision variables are the temperature of a batch reactor, the cooling rate of a crystallizer, or the feeding rate of the nutrient in a fed-batch fermentation unit. A set of experiments is
Conclusions
In this paper, we summarized the state of the art of the utilization of PSE tools in the pharmaceutical industry and tried to glance a bit into the future. We have presented the results of an industrial benchmarking survey, and discussed the projected impacts of out-sourcing and the rise of continuous manufacturing on PSE tool advancement. We hope to have motivated the audience for the greater need of data-driven rather than knowledge-driven models, suitable for quick deployment in process
Acknowledgements
The authors would like the thank the following individuals for providing completed questionnaires: Hector Guzman, Daniel Patience, Partha Mudipalli, Roger Bakale, Steve Mehrman, Becky Taillon, Trevor Wigle, Dan Dobry, Dafni Bika, Sze Wing Wong, Gert Thurau, Koji Muteki, and Martin Warman.
References (90)
- et al.
Optimal seed recipe design for crystal size distribution control for batch cooling crystallisation processes
Chemical Engineering Science
(2010) - et al.
Multivariate statistical monitoring of batch processes: An industrial case study of fermentation supervision
Trends in Biotechnology
(2001) - et al.
Evaluation of in-line spatial filter velocimetry as PAT monitoring tool for particle growth during fluid bed granulation
European Journal of Pharmaceutics and Biopharmaceutics
(2010) - et al.
Batch statistical process control of a fluid bed granulation process using in-line spatial filter velocimetry and product temperature measurements
European Journal of Pharmaceutical Sciences
(2011) - et al.
Understanding variation in roller compaction through finite element-based process modeling
Computers & Chemical Engineering
(2010) - et al.
Real-time monitoring of nitrile biotransformations by mid-infrared spectroscopy
Journal of Microbiological Methods
(2000) - et al.
Near infrared and Raman spectroscopy for the in-process monitoring of pharmaceutical production processes
International Journal of Pharmaceutics
(2011) - et al.
In-line particle sizing for real-time process control by fibre-optical spatial filtering technique (SFT)
Advanced Powder Technology
(2011) New perspectives for the on-line monitoring of pharmaceutical crystallization processes using in situ infrared spectroscopy
International Journal of Pharmaceutics
(2002)In situ Raman spectroscopy for in-line control of pharmaceutical crystallization and solids elaboration processes: A review
Chemical Engineering Research & Design
(2007)
Applications of NIR spectroscopy to monitoring and analyzing the solid state during industrial crystallization processes
International Journal of Pharmaceutics
Process control and end-point determination of a fluid bed granulation by application of near infra-red spectroscopy
International Journal of Pharmaceutics
Real-time alignment of batch process data using COW for on-line process monitoring
Chemometrics and Intelligent Laboratory Systems
Handling uncertainty in the establishment of a design space for the manufacture of a pharmaceutical product
Computers & Chemical Engineering
Experiences in batch trajectory alignment for pharmaceutical process improvement through multivariate latent variable modeling
Journal of Process Control
On the operability of continuous processes
Control Engineering Practice
Latent variable model predictive control (LV-MPC) for trajectory tracking in batch processes
Journal of Process Control
Nondestructive measurements of the compact strength and the particle-size distribution after milling of roller compacted powders by near-infrared spectroscopy
Journal of Pharmaceutical Sciences
Prediction of roller compacted ribbon solid fraction fro quality by design development
Powder Technology
Study growth kinetics in fluidized bed granulation with at-line FBRM
International Journal of Pharmaceutics
Disturbance detection and isolation by dynamic principal component analysis
Chemometrics and Intelligent Laboratory Systems
Application of a fluorescence sensor for miniscale on-line monitoring of powder mixing kinetics
Journal of Pharmaceutical Sciences
Manufacturing pharmaceutical granules: Is the granulation end-point a myth?
Powder Technology
On-line ATR FTIR measurement of supersaturation during solution crystallization processes. Calibration and applications on three solute/solvent systems
Chemical Engineering Science
Assessment of the critical factors affecting the porosity of roller compacted ribbons and the feasibility of using NIR chemical imaging to evaluate the porosity distribution
International Journal of Pharmaceutics
Design of output constraints for model-based non-square controllers using interval operability
Journal of Process Control
Input–output operability of control systems: The steady-state case
Journal of Process Control
Analysis of the constraint characteristics of a sheet forming control problem using interval operability concepts
Froth-based modeling and control of flotation processes
Minerals Engineering
Real-time assessment of granule and tablet properties using in-line data from a high-shear granulation process
Journal of Pharmaceutical Sciences
Prospects for multivariate classification of a pharmaceutical intermediate with near-infrared spectroscopy as a process analytical technology (PAT) production control supplement
European Journal of Pharmaceutics and Biopharmaceutics
Monitoring the wetting phase of fluidized bed granulation process using multi-way methods: The separation of successful from unsuccessful batches
Chemometrics and Intelligent Laboratory Systems
Comparative performance of concentration and temperature controlled batch crystallizations
Journal of Process Control
Modelling and control of combined cooling and antisolvent crystallization processes
Journal of Process Control
Development, validation and transfer of a near infrared method to determine in-line the end point of a fluidised drying process for commercial production batches of an approved oral solid dose pharmaceutical product
Journal of Pharmaceutical and Biomedical Analysis
Simulation of a single- and multiproduct batch chemical plants for optimal design and operation
Computers & Chemical Engineering
Raman spectroscopy for the in-line polymer-drug quantification and solid state characterization during a pharmaceutical hot-melt extrusion process
European Journal of Pharmaceutics and Biopharmaceutics
Progress in monitoring, modeling and control of bioprocesses during the last 20 years
Journal of Biotechnology
Process characterization of powder blending by near-infrared spectroscopy: Blend end-points and beyond
Journal of Pharmaceutical and Biomedical Analysis
Examples of NIR based real time release in tablet manufacturing
Journal of Pharmaceutical and Biomedical Analysis
Near-infrared multivariate calibration updating using placebo: A content uniformity determination of pharmaceutical tablets
Vibrational Spectroscopy
Reaction monitoring using Raman spectroscopy and chemometrics
Chemometrics and Intelligent Laboratory Systems
Cross-directional control of sheet and film processes
Automatica
A new measure of process output controllability
Journal of Process Control
In-linemonitoring of the thermaldegradation of poly(l-lactic acid) during melt extrusion by UV–vis spectroscopy
Polymer
Cited by (79)
In-line particle size measurement during granule fluidization using convolutional neural network-aided process imaging
2023, European Journal of Pharmaceutical SciencesQuality evaluation of white sugar crystals using the friability test and their non-destructive prediction using near-infrared spectroscopy
2023, Journal of Drug Delivery Science and TechnologyLinked experimental and modelling approaches for tablet property predictions
2022, International Journal of PharmaceuticsA review on the modernization of pharmaceutical development and manufacturing – Trends, perspectives, and the role of mathematical modeling
2022, International Journal of PharmaceuticsCitation Excerpt :The ultimate aim of QbD is to achieve the implementation of Level 1 strategies, which are capable of guaranteeing quality assurance. The benefits of implementing a modern Level 1 control strategy are for both companies and patients, as stressed by regulators, practitioners and academics (Collins, 2018; Fisher et al., 2019; Troup and Georgakis, 2013; Yu et al., 2014). McKinsey (2021) estimates that the adoption of a smart quality control system for pharmaceutical process development and manufacturing could have a tangible impact on profit, reduce the product launch time by more than 30%, increase manufacturing and supply chain capacity and responsiveness by 20 to 30%, and prevent major compliance issues by reducing manual errors and variability.
Maximizing information from chemical engineering data sets: Applications to machine learning
2022, Chemical Engineering Science