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The essential digital capabilities for CGT manufacturing to succeed at scale

Cell and gene therapies (CGTs) is an emerging, high-growth area, but their manufacture is different from established small molecule and biologics platforms in many ways. These range from starter cell variability and traceability for patient safety, to the need for fast turnarounds, very dynamic scheduling and rapid deviation management. All of these, and more, profoundly affect the IT system requirements for CGT. As more CGTs are approved for...

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IT for CGT: Digital capabilities for cell and gene therapy

There are many differences in manufacturing cell and gene therapies (CGTs) compared to established small molecule and biologics platforms and this profoundly affects the IT systems requirements. Some products are personalized so the process includes personal screening and sequencing data, with traceability and data privacy throughout. Starter cell variability adds complexity to a manufacturing process that must have a rapid turnaround, very dynamic scheduling and rapid deviation management. Outcomes must be tracked for the long term to improve patient outcomes as well as to support novel reimbursement models.Industrialization of CGTs therefore needs the support of advanced systems for manufacturing execution, orchestration, traceability, scheduling, patient data and outcome tracking. Some processes will be encapsulated in closed systems, and there may be analytical requirements for continuous process verification and dynamic adjustment. Operators distributed across the globe will be supported remotely by augmented and virtual reality technologies. This paper helps executives and IT professionals to understand the IT needed to support CGT manufacture, and stimulates collaboration across the industry to meet these challenges.

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Lab of the Future: Manifesto: digital technology-based capabilities for the quality control (QC) lab of the future

Most quality control labs in biomanufacturing have not yet achieved digital transformation. Lab processes are often manual which is slow and leads to errors and variability as well as long lead times.

Now those traditional ways of working are further challenged by the drive towards inline monitoring and real time release testing, and by new cell and gene therapies with tiny batch sizes and short shelf-lives.

The lab of the future is digital and requires much stronger IT for demand management and process automation, increasingly informed by data analytics and connected to manufacturing operations. This will be enabled by stronger IT security and operations, systems interoperability and governance, and data aggregation using common models, analytics and visualization.

Huge changes in lab personnel skills and culture are needed to work with the systems and the data in these new ways.

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Connected data, cobots and smart scientists: how to create the lab of the future

Many will recognize that the biopharmaceutical quality control (QC) laboratories need much more digital enablement to align with the rest of the manufacturing environment. Issues range from a lack of automation and data not being captured automatically or consistently for advanced analytics. Change and industry collaboration is clearly needed – and a vision of the future has been delivered by BioPhorum through a new paper, Manifesto: Digital...

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Is your digital manufacturing plant cyber resilient?

As the maturity of digital manufacturing plants grows, so does the risk of a cybersecurity or other digital incident. A successful phishing attack or a data center cooling failure, as examples, could adversely impact manufacturing operations and potentially take a facility offline for hours, days or even longer. So the ability of a plant to minimize the risk of a digital disaster, and quickly restore operations if one occurs, is a vital...

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Digital plant maturity model (DPMM): assessment tool

What does the concept of ‘digital plant’ mean in biopharmaceutical manufacturing? How can it be defined, measured and transformed? What is needed to move up the maturity curve. All are questions that a business needs to answer to establish a practical strategy and realize the opportunities that digital offers.

The biomanufacturing Digital Plant Maturity Model (DPMM) describes the stages of maturity from simple paper-based plants through to the fully automated and integrated ‘adaptive plant’ of the future. The maturity assessment tool can be used alongside the model. Using the characteristics provided for each dimension of the model, an assessment can be made of a plant or a network of plants against the five digital maturity levels against eight dimensions. The maturity model and provides the language and mechanism for having the right conversations with the right stakeholders and the Assessment Tool ensures a neutral assessment of the current state, and facilitates agreement on the future state.

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