Applying Systems Thinking to Regenerative Medicine Proceedings of a Workshop (2021) / Chapter Skim
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6 Addressing Regenerative Medicine Manufacturing and Supply Chain Challenges with Systems-Level Approaches
6 Addressing Regenerative Medicine Manufacturing and Supply Chain Challenges with Systems-Level Approaches
Pages 85-108
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From page 85... ...
(Wang) • A systems approach to digital modeling and simulation in manufacturing can be applied to support capacity planning, inform strategies to manage supply chain disruptions and demand surges, and model costs of production and automation.
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From page 86... ...
The session featured presentations on using artificial intelligence (AI) in cell and gene therapies, in modeling manufacturing processes in regenerative medicine, and in modeling the supply chain and other processes involved in cell therapy manufacturing and distribution.
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From page 87... ...
Harnessing Core Artificial Intelligence Capabilities Khalil said that her group is working on harnessing core AI capabilities that involve deep learning and that extend to approaches involving causal learning. Data are harnessed from a variety of sources, including text and files analyzed using natural language processing (NLP)
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. Applying Artificial Intelligence to Learn Cause and Effect from Data Another fundamental capability harnessed specifically in cell and gene therapy is the ability to learn cause and effect from data, Khalil said.
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She acknowledged that cell and gene therapy is a nascent field with many challenges, such as process variability, donorto-donor variability, availability of raw materials, a shortage of analytical assays, a need for analytical development, limited choice of vendors, logistics and supply chain issues, a shortage of trained personnel, and rapid timelines. Thus, she said, she and her colleagues are always looking for ways to learn more about the patient journey so that processes can be expedited and barriers removed.
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From page 90... ...
how to achieve a better understanding of the cell and gene therapy manufacturing process by identifying the factors that are most important, (2) whether those factors and their associated data can be used
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The next step in the process involves grouping data, applying machine learning models, and learning from random forest analysis to build a system of intelligence. The system of intelligence is composed of a user interface, a data ingestion pipeline of clinical and other datasets, and a knowledge ingestion pipeline of medical and biological knowledge.
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CMaT is strategically positioned to address challenges in the emerging cell manufacturing industry, particularly those related to quality, cost, speed, and agility, she added. She focused on CMaT's work with multivariate critical quality attributes (CQAs)
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The project aims first to understand the variance and then to establish CQAs and CPPs that are predictive of potency, safety, and consistency. The first phase of the project focused on examining the experimental process parameters by optimizing the microscaffold's input process parameters and looking at the responses of viability and CD4/CD8 naïve-memory attributes, Kotanchek said.
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From page 94... ...
In cases where the driving variables are known and the model structure is nonlinear, then a nonlinear regression parameter may be appropriate. If the driving variables are known but the model structure is not, then a variety of powerful machine learning tools can be applied (e.g., neural networks, support vector machine model, random forests, and symbolic regression)
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They can be used to guide decision making and to enable active learning and active design of experiments, Kotanchek added. The diversity of the accurate-fit models in an ensemble is used to generate trust metrics, Kotanchek explained.
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CMaT has been able to merge the multi-omics dataset to run comparisons of accuracy and prediction across various machine learning methods (e.g., conditional inference forest, random forest, gradient boosted trees, and symbolic regression)
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She highlighted the importance of identifying the objective of the analysis at each phase of discovery, development, design, validation, optimization, process control, supply chain, commercialization, or strategy. This involves considering questions about variable selection and relationships, prediction, optimization and deployment, risk management, and insight and understanding (see Box 6-2)
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From page 98... ...
He presented several case studies of projects that have adopted a multidisciplinary approach to addressing the systems issue by building a team across different universities and disciplines. He also outlined two systems-based modeling projects and reviewed simulation case studies related to capacity planning, supply chain disruptions, demand surges and priority queue, and the cost of goods and automation.
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From page 99... ...
SOURCE: Ben Wang workshop presentation, October 23, 2020.
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From page 100... ...
The third challenge is that uncertainties abound, including uncertainties related to demand fluctuations, inevitable machine breakdowns, quality control and process failures, and supply chain disruptions. Process Modeling to Address Supply Chain Challenges Digital modeling has become an efficient and cost-effective approach to exploring the dynamics of the supply chain and the process models, Wang said.
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From page 101... ...
Analytical and simulation tools can help decision makers and plant managers design the best configuration, he said. Use Case: Supply Disruption Simulations Wang presented another use case involving simulations on supply chain disruptions (e.g., a disruption in which a natural or humanmade disaster causes a major shortage in the production system of a key reagent)
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a manual operation biosafety cabinet, (2) an automatic operation biosafety cabinet, (3)
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A sizable challenge in employing sophisticated machine learning approaches, Khalil added, is creating a method of aggregating and learning from data and feedback loops. This is particularly challenging for the manufacturing process in the context of the compliance required, she said.
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The Multiple Myeloma Research Foundation has a registry on multiple myeloma patients followed longitudinally over time and, at this point, may even include data on novel cell therapies.2 That registry has enabled advances in predicting disease progression and identifying patients' risk levels. These types of algorithms have benefits across the entire ecosystem, she concluded.
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From page 105... ...
The lack of patient data in their clinical trials is a major challenge, Tyagarajan said. Her group uses the clinical trial dataset to train registry data, she said, but AI and machine learning require larger amounts of data.
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The second set is less tangible and involves labor, skilled workforce, regulations, investment, infrastructure, and transportation. Roy asked how the supply chain fits into these variables.
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From page 107... ...
This includes aspects such as cell viability, flow data from gene expression and omics, and process measures. She said that they take a probabilistic machine learning approach to the whole system.
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