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Dorsey PJ, Lau CL, Chang TC, Doerschuk PC, D'Addio SM. Review of Machine Learning for Lipid Nanoparticle Formulation and Process Development. J Pharm Sci 2024:S0022-3549(24)00422-2. [PMID: 39341497 DOI: 10.1016/j.xphs.2024.09.015] [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: 06/08/2024] [Revised: 09/10/2024] [Accepted: 09/11/2024] [Indexed: 10/01/2024]
Abstract
Lipid nanoparticles (LNPs) are a subset of pharmaceutical nanoparticulate formulations designed to encapsulate, stabilize, and deliver nucleic acid cargoes in vivo. Applications for lipid nanoparticles include new interventions for genetic disorders, novel classes of vaccines, and alternate modes of intracellular delivery for therapeutic proteins. In the pharmaceutical industry, establishing a robust formulation and process to achieve target product performance is a critical component of drug development. Fundamental understanding of the processes for making LNPs and their interactions with biological systems have advanced considerably in the wake of the COVID-19 pandemic. Nevertheless, LNP formulation research remains largely empirical and resource intensive due to the multitude of input parameters and the complex physical phenomena that govern the processes of nanoparticle precipitation, self-assembly, structure evolution, and stability. Increasingly, artificial intelligence and machine learning (AI/ML) are being applied to improve the efficiency of research activities through in silico models and predictions, and to drive deeper fundamental understanding of experimental inputs to functional outputs. This review will identify current challenges and opportunities in the development of robust LNP formulations of nucleic acids, review studies that apply machine learning methods to experimental datasets, and provide discussion on associated data science challenges to facilitate collaboration between formulation and data scientists, aiming to accelerate the advancement of AI/ML applied to LNP formulation and process optimization.
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Affiliation(s)
- Phillip J Dorsey
- Pharmaceutical Sciences & Clinical Supply, Merck Research Labs, Merck & Co. Inc., Rahway, NJ 07065, USA; University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Christina L Lau
- Cornell University, School of Electrical and Computer Engineering, Ithaca, NY 14853, USA
| | - Ti-Chiun Chang
- Pharmaceutical Sciences & Clinical Supply, Merck Research Labs, Merck & Co. Inc., Rahway, NJ 07065, USA
| | - Peter C Doerschuk
- Cornell University, School of Electrical and Computer Engineering, Ithaca, NY 14853, USA
| | - Suzanne M D'Addio
- Pharmaceutical Sciences & Clinical Supply, Merck Research Labs, Merck & Co. Inc., Rahway, NJ 07065, USA.
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2
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Dürauer A, Jungbauer A, Scharl T. Sensors and chemometrics in downstream processing. Biotechnol Bioeng 2024; 121:2347-2364. [PMID: 37470278 DOI: 10.1002/bit.28499] [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: 03/15/2023] [Revised: 06/14/2023] [Accepted: 07/07/2023] [Indexed: 07/21/2023]
Abstract
The biopharmaceutical industry is still running in batch mode, mostly because it is highly regulated. In the past, sensors were not readily available and in-process control was mainly executed offline. The most important product parameters are quantity, purity, and potency, in addition to adventitious agents and bioburden. New concepts using disposable single-use technologies and integrated bioprocessing for manufacturing will dominate the future of bioprocessing. To ensure the quality of pharmaceuticals, initiatives such as Process Analytical Technologies, Quality by Design, and Continuous Integrated Manufacturing have been established. The aim is that these initiatives, together with technology development, will pave the way for process automation and autonomous bioprocessing without any human intervention. Then, real-time release would be realized, leading to a highly predictive and robust biomanufacturing system. The steps toward such automated and autonomous bioprocessing are reviewed in the context of monitoring and control. It is possible to integrate real-time monitoring gradually, and it should be considered from a soft sensor perspective. This concept has already been successfully implemented in other industries and requires relatively simple model training and the use of established statistical tools, such as multivariate statistics or neural networks. This review describes a scenario for integrating soft sensors and predictive chemometrics into modern process control. This is exemplified by selective downstream processing steps, such as chromatography and membrane filtration, the most common unit operations for separation of biopharmaceuticals.
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Affiliation(s)
- Astrid Dürauer
- Institute of Bioprocessing Science and Engineering, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Alois Jungbauer
- Institute of Bioprocessing Science and Engineering, University of Natural Resources and Life Sciences, Vienna, Austria
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Theresa Scharl
- Institute of Statistics, University of Natural Resources and Life Sciences, Vienna, Austria
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Jungbauer A, Ferreira G, Butler M, D'Costa S, Brower K, Rayat A, Willson R. Status and future developments for downstream processing of biological products: Perspectives from the Recovery XIX yield roundtable discussions. Biotechnol Bioeng 2024; 121:2524-2541. [PMID: 38795025 DOI: 10.1002/bit.28738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 05/07/2024] [Accepted: 05/10/2024] [Indexed: 05/27/2024]
Abstract
Governments and biopharmaceutical organizations aggressively leveraged expeditious communication capabilities, decision models, and global strategies to make a COVID-19 vaccine happen within a period of 12 months. This was an unusual effort and cannot be transferred to normal times. However, this focus on a single vaccine has also led to other treatments and drug developments being sidelined. Society expects the pharmaceutical industry to provide an uninterrupted supply of medicines. However, it is often overlooked how complex the manufacture of these compounds is and what logistics are required, not to mention the time needed to develop new drugs. The overarching theme, therefore, is patient access and how we can help ensure access and extend it to low- and middle-income countries. Despite unceasing efforts to make medications available to all patient populations, this must never be done at the expense of patient safety. A major fraction of the costs in biopharmaceutical manufacturing are for drug discovery, process development, and clinical studies. Infrastructure costs are very difficult to quantify because they often depend on whether a greenfield facility or an existing, depreciated facility is used or adapted for a new product. To accelerate process development concepts of platform process and prior knowledge are increasingly leveraged. While more traditional protein therapeutics continue to dominate the field, we are also experiencing the exciting emergence and evolution of other therapeutic formats (bispecifics, tetravalent mAbs, antibody-drug conjugates, enzymes, peptides, etc.) that offer unique treatment options for patients. Protein modalities are still dominant, but new modalities are being developed that can be learned from including advanced therapeutics-like cell and gene therapies. The industry must develop a model-based strategy for process development and technologies such as continuous integrated biomanufacturing must be adopted. The overall conclusion is that the pandemic pace was unsustainable, focused on vaccine delivery at the expense of other modalities/disease targets, and had implications for professional and personal life (work-life balance). Routinely reducing development time from 10 years to 1 year is nearly impossible to achieve. Environmental aspects of sustainable downstream processing are also described.
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Affiliation(s)
- Alois Jungbauer
- Department of Biotechnology, Institute of Bioprocess Science and Engineering, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Gisela Ferreira
- Process Development, AstraZeneca, Gaithersburg, Maryland, USA
| | - Michelle Butler
- Pharmaceutical Technical Development, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Susan D'Costa
- Technology Development and Manufacturing, Genezen Laboratories, Indianapolis, Indiana, USA
| | - Kevin Brower
- Mammalian Platform, Sanofi, Framingham, Massachusetts, USA
| | - Andrea Rayat
- Department of Biochemical Engineering, University College London, London, UK
| | - Richard Willson
- Department of Chemical and Biomolecular Engineering, University of Houston, Houston, Texas, USA
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4
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Drobnjakovic M, Hart R, Kulvatunyou BS, Ivezic N, Srinivasan V. Current challenges and recent advances on the path towards continuous biomanufacturing. Biotechnol Prog 2023; 39:e3378. [PMID: 37493037 DOI: 10.1002/btpr.3378] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 05/13/2023] [Accepted: 06/21/2023] [Indexed: 07/27/2023]
Abstract
Continuous biopharmaceutical manufacturing is currently a field of intense research due to its potential to make the entire production process more optimal for the modern, ever-evolving biopharmaceutical market. Compared to traditional batch manufacturing, continuous bioprocessing is more efficient, adjustable, and sustainable and has reduced capital costs. However, despite its clear advantages, continuous bioprocessing is yet to be widely adopted in commercial manufacturing. This article provides an overview of the technological roadblocks for extensive adoptions and points out the recent advances that could help overcome them. In total, three key areas for improvement are identified: Quality by Design (QbD) implementation, integration of upstream and downstream technologies, and data and knowledge management. First, the challenges to QbD implementation are explored. Specifically, process control, process analytical technology (PAT), critical process parameter (CPP) identification, and mathematical models for bioprocess control and design are recognized as crucial for successful QbD realizations. Next, the difficulties of end-to-end process integration are examined, with a particular emphasis on downstream processing. Finally, the problem of data and knowledge management and its potential solutions are outlined where ontologies and data standards are pointed out as key drivers of progress.
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Affiliation(s)
- Milos Drobnjakovic
- Systems Integration Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - Roger Hart
- National Institute for Innovation in Manufacturing Biopharmaceuticals, Newark, New Jersey, USA
| | - Boonserm Serm Kulvatunyou
- Systems Integration Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - Nenad Ivezic
- Systems Integration Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - Vijay Srinivasan
- Systems Integration Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
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Fuchs M, Bhawnani R, Sripada SA, Molek J, Ghodbane M. Predictive modeling of single pass tangential flow filtration for continuous biomanufacturing. Biotechnol Prog 2023; 39:e3353. [PMID: 37155963 DOI: 10.1002/btpr.3353] [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: 02/16/2023] [Revised: 04/17/2023] [Accepted: 04/25/2023] [Indexed: 05/10/2023]
Abstract
Opportunities for process intensification have made continuous biomanufacturing an area of active research. While tangential flow filtration (TFF) is typically employed within the biologics purification train to increase drug substance concentration, single-pass TFF (SPTFF) modifies its format by enabling continuity of this process and achieving a multifold concentration factor through a single-pass over the filtration membranes. In continuous processes feed concentration and flow rate are determined by the preceding unit operations. Therefore, tight control of SPTFF output concentration must be achieved through precise design of the membrane configuration, unlike TFF. However, predictive modeling can be utilized to identify configurations that achieve a desired target concentration across ranges of possible feed conditions with minimal experimental data, hence enabling accelerated process development and design flexibility. We hereby describe the development of a mechanistic model predicting SPTFF performance across a wide design space using the well-established stagnant film model, which we demonstrate is more accurate at higher feed flow rates. The flux excursion dataset was generated within time constraints and with minimal material consumption, showing the method's ability to be quickly adapted. While this approach eliminates characterizing complex physicochemical model variables or the need for users with specialized training, the model and its assumptions become inaccurate at low flow rates, below 25 L/m2 /h, and high conversions, above 0.9. As this low flow rate, high conversion operating regime is relevant for continuous biomanufacturing, we explore the assumptions and challenges involved in predicting and modeling SPTFF processes, while suggesting added characterization to gain further process insight.
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Affiliation(s)
- Madeline Fuchs
- Biopharm Drug Substance Development, GlaxoSmithKline, King of Prussia, Pennsylvania, USA
| | - Rajan Bhawnani
- Biopharm Drug Substance Development, GlaxoSmithKline, King of Prussia, Pennsylvania, USA
| | - Sobhana A Sripada
- Biopharm Drug Substance Development, GlaxoSmithKline, King of Prussia, Pennsylvania, USA
| | - Jessica Molek
- Biopharm Drug Substance Development, GlaxoSmithKline, King of Prussia, Pennsylvania, USA
- MSAT Specialty Large Molecule, GlaxoSmithKline, King of Prussia, Pennsylvania, USA
| | - Mehdi Ghodbane
- Biopharm Drug Substance Development, GlaxoSmithKline, King of Prussia, Pennsylvania, USA
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Bayer B, Duerkop M, Pörtner R, Möller J. Comparison of mechanistic and hybrid modeling approaches for characterization of a CHO cultivation process: Requirements, pitfalls and solution paths. Biotechnol J 2023; 18:e2200381. [PMID: 36382343 DOI: 10.1002/biot.202200381] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 11/17/2022]
Abstract
Despite the advantages of mathematical bioprocess modeling, successful model implementation already starts with experimental planning and accordingly can fail at this early stage. For this study, two different modeling approaches (mechanistic and hybrid) based on a four-dimensional antibody-producing CHO fed-batch process are compared. Overall, 33 experiments are performed in the fractional factorial four-dimensional design space and separated into four different complex data partitions subsequently used for model comparison and evaluation. The mechanistic model demonstrates the advantage of prior knowledge (i.e., known equations) to get informative value relatively independently of the utilized data partition. The hybrid approach displayes a higher data dependency but simultaneously yielded a higher accuracy on all data partitions. Furthermore, our results demonstrate that independent of the chosen modeling framework, a smart selection of only four initial experiments can already yield a very good representation of a full design space independent of the chosen modeling structure. Academic and industry researchers are recommended to pay more attention to experimental planning to maximize the process understanding obtained from mathematical modeling.
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Affiliation(s)
| | | | - Ralf Pörtner
- Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg, Germany
| | - Johannes Möller
- Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg, Germany
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Schwaminger SP, Zimmermann I, Berensmeier S. Current research approaches in downstream processing of pharmaceutically relevant proteins. Curr Opin Biotechnol 2022; 77:102768. [PMID: 35930843 DOI: 10.1016/j.copbio.2022.102768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/04/2022] [Accepted: 07/12/2022] [Indexed: 11/03/2022]
Abstract
Biopharmaceuticals and their production are on the rise. They are needed to treat and to prevent multiple diseases. Therefore, an urgent need for process intensification in downstream processing (DSP) has been identified to produce biopharmaceuticals more efficiently. The DSP currently accounts for the majority of production costs of pharmaceutically relevant proteins. This short review gathers essential research over the past 3 years that addresses novel solutions to overcome this bottleneck. The overview includes promising studies in the fields of chromatography, aqueous two-phase systems, precipitation, crystallization, magnetic separation, and filtration for the purification of pharmaceutically relevant proteins.
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Affiliation(s)
- Sebastian P Schwaminger
- Division of Medicinal Chemistry, Otto Loewi Research Center, Medical University of Graz, Graz, Austria; Bioseparation Engineering Group, School of Engineering and Design, Technical University of Munich, Garching, Germany.
| | - Ines Zimmermann
- Bioseparation Engineering Group, School of Engineering and Design, Technical University of Munich, Garching, Germany
| | - Sonja Berensmeier
- Bioseparation Engineering Group, School of Engineering and Design, Technical University of Munich, Garching, Germany.
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Single pass tangential flow filtration: Critical operational variables, fouling, and main current applications. Sep Purif Technol 2022. [DOI: 10.1016/j.seppur.2022.120949] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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9
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Defining the optimal operating conditions and configuration of a single-pass tangential flow filtration (SPTFF) system via CFD modelling. Sep Purif Technol 2022. [DOI: 10.1016/j.seppur.2022.120776] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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10
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Design and optimization of Single Pass Tangential Flow Filtration for inline concentration of monoclonal antibodies. J Memb Sci 2022. [DOI: 10.1016/j.memsci.2021.120047] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Brunner V, Siegl M, Geier D, Becker T. Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review. Front Bioeng Biotechnol 2021; 9:722202. [PMID: 34490228 PMCID: PMC8417948 DOI: 10.3389/fbioe.2021.722202] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 08/03/2021] [Indexed: 01/10/2023] Open
Abstract
Among the greatest challenges in soft sensor development for bioprocesses are variable process lengths, multiple process phases, and erroneous model inputs due to sensor faults. This review article describes these three challenges and critically discusses the corresponding solution approaches from a data scientist’s perspective. This main part of the article is preceded by an overview of the status quo in the development and application of soft sensors. The scope of this article is mainly the upstream part of bioprocesses, although the solution approaches are in most cases also applicable to the downstream part. Variable process lengths are accounted for by data synchronization techniques such as indicator variables, curve registration, and dynamic time warping. Multiple process phases are partitioned by trajectory or correlation-based phase detection, enabling phase-adaptive modeling. Sensor faults are detected by symptom signals, pattern recognition, or by changing contributions of the corresponding sensor to a process model. According to the current state of the literature, tolerance to sensor faults remains the greatest challenge in soft sensor development, especially in the presence of variable process lengths and multiple process phases.
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Affiliation(s)
- Vincent Brunner
- Chair of Brewing and Beverage Technology, Technical University of Munich, Freising, Germany
| | - Manuel Siegl
- Chair of Brewing and Beverage Technology, Technical University of Munich, Freising, Germany
| | - Dominik Geier
- Chair of Brewing and Beverage Technology, Technical University of Munich, Freising, Germany
| | - Thomas Becker
- Chair of Brewing and Beverage Technology, Technical University of Munich, Freising, Germany
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