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Hur I, Casas-Orozco D, Laky D, Destro F, Nagy ZK. Digital design of an integrated purification system for continuous pharmaceutical manufacturing. Chem Eng Sci 2024; 285:119534. [PMID: 38975615 PMCID: PMC11225065 DOI: 10.1016/j.ces.2023.119534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2024]
Abstract
In this work dynamic models of the continuous crystallization, filtration, deliquoring, washing, and drying steps are introduced, which are developed in the open-source pharmaceutical modeling tool PharmaPy. These models enable the simulation and digital design of an integrated continuous two-stage crystallization and filtration-drying carousel system. The carousel offers an intensified process that can manufacture products with tailored properties through optimal design and control. Results show that improved crystallization design enhances overall process efficiency by improving critical material attributes of the crystal slurry for downstream filtration and drying operations. The digital design of the integrated process achieves enhanced productivity while satisfying multiple design and product quality constraints. Additionally, the impact of model uncertainty on the optimal operating conditions is investigated. The findings demonstrate the systematic process development potential of PharmaPy, providing improved process understanding, design space identification, and optimized robust operation.
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Affiliation(s)
- Inyoung Hur
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 4797 USA
| | - Daniel Casas-Orozco
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 4797 USA
| | - Daniel Laky
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 4797 USA
| | - Francesco Destro
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 4797 USA
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Zoltan K. Nagy
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 4797 USA
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2
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Ferdoush S, Kzam SB, Martins PHC, Dewanckele J, Gonzalez M. Fast time-resolved micro-CT imaging of pharmaceutical tablets: Insights into water uptake and disintegration. Int J Pharm 2023; 648:123565. [PMID: 37918497 PMCID: PMC10786181 DOI: 10.1016/j.ijpharm.2023.123565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 10/23/2023] [Accepted: 10/29/2023] [Indexed: 11/04/2023]
Abstract
We use dynamic micro-computed tomography (micro-CT) with a high temporal resolution to visualize water penetration through the porous network of immediate-release pharmaceutical solid tablets and characterize dynamic swelling and disintegration mechanisms. We process the micro-CT images using two theoretical scenarios that reflect different paths of pore structure evolution: a scenario where tablet porosity remains constant during the swelling process and a scenario where the tablet porosity progressively diminishes and eventually closes during the swelling process. We calculate the time evolution of the volume of water absorbed by the tablet and, specifically, absorbed by the excipients and the pore structure, as well as the formation and evolution of cracks. In turn, the three-dimensional disintegration pattern of the tablets is reconstructed. Restricting attention to the limiting scenario where tablet porosity is assumed fixed during the swelling process, we couple liquid penetration due to capillary pressure described by the Lucas-Washburn theory with the first-order swelling kinetics of the excipients to provide a physical interpretation of the experimental observations. We estimate model parameters that are in agreement with values reported in the literature, and we demonstrate that water penetration is dominated by intra-particle porosity rather than inter-particle porosity.
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Affiliation(s)
- Shumaiya Ferdoush
- School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Sarah Bu Kzam
- School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Pedro H C Martins
- School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | | | - Marcial Gonzalez
- School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA; Ray W. Herrick Laboratories, Purdue University, West Lafayette, IN 47907, USA.
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3
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Laky DJ, Casas-Orozco D, Abdi M, Feng X, Wood E, Reklaitis GV, Nagy ZK. Using PharmaPy with Jupyter Notebook to teach digital design in pharmaceutical manufacturing. COMPUTER APPLICATIONS IN ENGINEERING EDUCATION 2023; 31:1662-1677. [PMID: 38314247 PMCID: PMC10838379 DOI: 10.1002/cae.22660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 06/03/2023] [Indexed: 02/06/2024]
Abstract
The use of digital tools in pharmaceutical manufacturing has gained traction over the past two decades. Whether supporting regulatory filings or attempting to modernize manufacturing processes to adopt new and quickly evolving Industry 4.0 standards, engineers entering the workforce must exhibit proficiency in modeling, simulation, optimization, data processing, and other digital analysis techniques. In this work, a course that addresses digital tools in pharmaceutical manufacturing for chemical engineers was adjusted to utilize a new tool, PharmaPy, instead of traditional chemical engineering simulation tools. Jupyter Notebook was utilized as an instructional and interactive environment to teach students to use PharmaPy, a new, open-source pharmaceutical manufacturing process simulator. Students were then surveyed to see if PharmaPy was able to meet the learning objectives of the course. During the semester, PharmaPy's model library was used to simulate both individual unit operations as well as multiunit pharmaceutical processes. Through the initial survey results, students indicated that: (i) through Jupyter Notebook, learning Python and PharmaPy was approachable from varied coding experience backgrounds and (ii) PharmaPy strengthened their understanding of pharmaceutical manufacturing through active pharmaceutical ingredient process design and development.
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Affiliation(s)
- Daniel J. Laky
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana, USA
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Daniel Casas-Orozco
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Mesfin Abdi
- Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food & Drug Administration, Silver Spring, Maryland, USA
| | - Xin Feng
- Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food & Drug Administration, Silver Spring, Maryland, USA
| | - Erin Wood
- Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food & Drug Administration, Silver Spring, Maryland, USA
| | - Gintaras V. Reklaitis
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Zoltan K. Nagy
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana, USA
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4
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Casas-Orozco D, Laky D, Mackey J, Reklaitis G, Nagy Z. Reaction kinetics determination and uncertainty analysis for the synthesis of the cancer drug lomustine. Chem Eng Sci 2023; 275:118591. [PMID: 38179266 PMCID: PMC10765472 DOI: 10.1016/j.ces.2023.118591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Fast and reliable model development frameworks are required to support current trends in modernization of pharmaceutical processing, promoting the use of digital platforms to assist process design and operation. In this work, we use a parameter estimation framework built into the PharmaPy library to determine rate parameters and uncertainty regions of different mechanistic and semi-empirical kinetic expressions for the synthesis of the drug lomustine. The parameter estimation procedure was complemented by identifiability analysis, resulting in simplified reaction mechanisms. Comparison of parameters and their uncertainty in process design was demonstrated through design space analysis, showing important differences in model prediction and the extent of their corresponding design spaces. The results of this work can serve to analyze lomustine manufacturing processes that include separation and isolation steps, where parametric sensitivity is expected to propagate along the manufacturing line and impact process feasible operation, and attainment of critical quality attributes of the product.
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Chen Y, Sampat C, Huang YS, Ganesh S, Singh R, Ramachandran R, Reklaitis GV, Ierapetritou M. An integrated data management and informatics framework for continuous drug product manufacturing processes: A case study on two pilot plants. Int J Pharm 2023:123086. [PMID: 37257793 DOI: 10.1016/j.ijpharm.2023.123086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 06/02/2023]
Abstract
The pharmaceutical industry continuously looks for ways to improve its development and manufacturing efficiency. In recent years, such efforts have been driven by the transition from batch to continuous manufacturing and digitalization in process development. To facilitate this transition, integrated data management and informatics tools need to be developed and implemented within the framework of Industry 4.0 technology. In this regard, the work aims to guide the data integration development of continuous pharmaceutical manufacturing processes under the Industry 4.0 framework, improving digital maturity and enabling the development of digital twins. This paper demonstrates two instances where a data integration framework has been successfully employed in academic continuous pharmaceutical manufacturing pilot plants. Details of the integration structure and information flows are comprehensively showcased. Approaches to mitigate concerns in incorporating complex data streams, including integrating multiple process analytical technology tools and legacy equipment, connecting cloud data and simulation models, and safeguarding cyber-physical security, are discussed. Critical challenges and opportunities for practical considerations are highlighted.
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Affiliation(s)
- Yingjie Chen
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, U.S
| | - Chaitanya Sampat
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, U.S
| | - Yan-Shu Huang
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, U.S
| | - Sudarshan Ganesh
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, U.S
| | - Ravendra Singh
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, U.S
| | - Rohit Ramachandran
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, U.S
| | - Gintaras V Reklaitis
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, U.S
| | - Marianthi Ierapetritou
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, U.S.
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Casas-Orozco D, Laky D, Wang V, Abdi M, Feng X, Wood E, Reklaitis GV, Nagy ZK. Techno-economic analysis of dynamic, end-to-end optimal pharmaceutical campaign manufacturing using PharmaPy. AIChE J 2023; 69:e18142. [PMID: 38179085 PMCID: PMC10765457 DOI: 10.1002/aic.18142] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 04/16/2023] [Indexed: 01/06/2024]
Abstract
Increased interest in the pharmaceutical industry to transition from batch to continuouos manufacturing motivates the use of digital frameworks that allow systematic comparison of candidate process configurations. This paper evaluates the technical and economic feasibility of different end-to-end optimal process configurations, viz. batch, hybrid and continuous, for small-scale manufacturing of an active pharmaceutical ingredient. Production campaigns were analyzed for those configurations containing continuous equipment, where significant start-up effects are expected given the relatively short campaign times considered. Hybrid operating mode was found to be the most attractive process configuration at intermediate and large annual production targets, which stems from combining continuous reactors and semi-batch vaporization equipment. Continuous operation was found to be more costly, due to long stabilization times of continuous crystallization, and thermodynamic limitations of flash vaporization. Our work reveals the benefits of systematic digital evaluation of process configurations that operate under feasible conditions and compliant product quality attributes.
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Affiliation(s)
- Daniel Casas-Orozco
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
| | - Daniel Laky
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
| | - Vivian Wang
- Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food & Drug Administration, Silver Spring, MD, USA
| | - Mesfin Abdi
- Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food & Drug Administration, Silver Spring, MD, USA
| | - X Feng
- Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food & Drug Administration, Silver Spring, MD, USA
| | - E Wood
- Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food & Drug Administration, Silver Spring, MD, USA
| | - Gintaras V Reklaitis
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
| | - Zoltan K Nagy
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
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Ferdoush S, Gonzalez M. Semi-mechanistic reduced order model of pharmaceutical tablet dissolution for enabling Industry 4.0 manufacturing systems. Int J Pharm 2023; 631:122502. [PMID: 36529354 PMCID: PMC10759183 DOI: 10.1016/j.ijpharm.2022.122502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 12/10/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022]
Abstract
We propose a generalization of the Weibull dissolution model, referred to as generalized Weibull dissolution model, that seamlessly captures all three fractional dissolution rates experimentally observed in pharmaceutical solid tablets, namely decreasing, increasing, and non-monotonic rates. This is in contrast to traditional reduced order models, which capture at most two fractional dissolution rates and, thus, are not suitable for a wide range of product formulations hindering, for example, the adoption of knowledge management in the context of Industry 4.0. We extend the generalized Weibull dissolution model further to capture the relationship between critical process parameters (CPPs), critical materials attributes (CMAs), and dissolution profile to, in turn, facilitate real-time release testing (RTRT) and quality-by-control (QbC) strategies. Specifically, we endow the model with multivariate rational polynomials that interpolate the mechanistic limiting behavior of tablet dissolution as CPPs and CMAs approach certain values of physical significance (such as the upper and lower bounds of tablet porosity or lubrication conditions), thus the semi-mechanistic nature of the reduced order model. Restricting attention to direct compaction and using various case studies from the literature, we demonstrate the versatility and the capability of the semi-mechanistic ROM to estimate changes in dissolution due to process disturbances in tablet weight, porosity, lubrication conditions (i.e., the total amount of shear strain imparted during blending), and moisture content in the powder blend. In all of the cases considered in this work, the estimations of the model are in remarkable agreement with experimental data.
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Affiliation(s)
- Shumaiya Ferdoush
- School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Marcial Gonzalez
- School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA; Ray W. Herrick Laboratories, Purdue University, West Lafayette, IN 47907, USA.
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Laky D, Casas-Orozco D, Laird CD, Reklaitis GV, Nagy ZK. Simulation-optimization framework for the digital design of pharmaceutical processes using Pyomo and PharmaPy. Ind Eng Chem Res 2022; 61:16128-16140. [PMID: 38179037 PMCID: PMC10765421 DOI: 10.1021/acs.iecr.2c01636] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
The problem of performing model-based process design and optimization in the pharmaceutical industry is an important and challenging one both computationally and in choice of solution implementation. In this work, a framework is presented to directly utilize a process simulator via callbacks during derivative-based optimization. The framework allows users with little experience in translating mechanistic ODEs and PDEs to robust, fully discretized algebraic formulations, required for executing simultaneous equation-oriented optimization, to obtain mathematically guaranteed optima at a competitive solution time when compared with existing derivative-free and derivative-based frameworks. The effectiveness of the framework in accuracy of optimal solution as well as computational efficiency is analyzed on on two case studies: (i) an integrated 2-unit reaction synthesis train used for the synthesis of an anti-cancer active pharmaceutical ingredient, and (ii) a more complex flowsheet representing a common synthesis-purification-isolation train of a pharmaceutical manufacturing processes.
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Affiliation(s)
- Daniel Laky
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
| | - Daniel Casas-Orozco
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
| | - Carl D. Laird
- Chemical Engineering Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Gintaras V. Reklaitis
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
| | - Zoltan K. Nagy
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
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Destro F, Barolo M. A review on the modernization of pharmaceutical development and manufacturing - Trends, perspectives, and the role of mathematical modeling. Int J Pharm 2022; 620:121715. [PMID: 35367580 DOI: 10.1016/j.ijpharm.2022.121715] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 03/23/2022] [Accepted: 03/29/2022] [Indexed: 01/20/2023]
Abstract
Recently, the pharmaceutical industry has been facing several challenges associated to the use of outdated development and manufacturing technologies. The return on investment on research and development has been shrinking, and, at the same time, an alarming number of shortages and recalls for quality concerns has been registered. The pharmaceutical industry has been responding to these issues through a technological modernization of development and manufacturing, under the support of initiatives and activities such as quality-by-design (QbD), process analytical technology, and pharmaceutical emerging technology. In this review, we analyze this modernization trend, with emphasis on the role that mathematical modeling plays within it. We begin by outlining the main socio-economic trends of the pharmaceutical industry, and by highlighting the life-cycle stages of a pharmaceutical product in which technological modernization can help both achieve consistently high product quality and increase return on investment. Then, we review the historical evolution of the pharmaceutical regulatory framework, and we discuss the current state of implementation and future trends of QbD. The pharmaceutical emerging technology is reviewed afterwards, and a discussion on the evolution of QbD into the more effective quality-by-control (QbC) paradigm is presented. Further, we illustrate how mathematical modeling can support the implementation of QbD and QbC across all stages of the pharmaceutical life-cycle. In this respect, we review academic and industrial applications demonstrating the impact of mathematical modeling on three key activities within pharmaceutical development and manufacturing, namely design space description, process monitoring, and active process control. Finally, we discuss some future research opportunities on the use of mathematical modeling in industrial pharmaceutical environments.
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Affiliation(s)
- Francesco Destro
- CAPE-Lab - Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131 Padova PD, Italy
| | - Massimiliano Barolo
- CAPE-Lab - Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131 Padova PD, Italy.
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