Automation and digital manufacturing for cell and gene therapies: why paper is the enemy of scale

Automation and digital manufacturing for cell and gene therapies: why paper is the enemy of scale

Current challenges

Recent approvals1 the U.S. Food and Drug Administration (FDA) and the Medicines and Health Products Regulatory Agency (MHRA) for potentially curative therapies in difficult-to-treat blood cancers, such as leukemias, lymphomas, and more recently multiple myeloma, offer some hope to patients seeking to benefit from these life-saving therapies. Yet <2%2 of patients who could benefit from these therapies have gained access, due to the significant scalability challenges faced by the developers who are bringing these treatments to market.

Many of these challenges can be traced to process challenges such as quality assurance / quality control (QA / QC) on paper and the inability to establish the chemistry, manufacturing and controls (CMC) process for medicines early enough. for advanced therapies (ATMP). For those who have managed to reach the market, the costs of manufacturing and quality control are so high that these potentially curative therapies3 they are expensive and therefore often offered only as last-line therapy to refractory patients who have failed all other treatments.

Defining a CMC process for ATMP is significantly more complicated than that for small molecules or biological products, since the production of living cell products is inherently variable in nature and the manufacturing process must be able to adapt to this variability. At its most extreme, we have patient-specific (autologous) ATMPs, where each batch is unique to a single patient. For example, chimeric antigen T receptor (CAR T) cell therapies are produced using the patient’s cells, which are genetically engineered and cultured ex vivo in a thick process of 14-21 days.

Experts4 agree that CMC should begin as soon as potential therapy has been identified in preclinical activity in the laboratory. Detailed records of the chemical and biological processes used to produce the therapy must be recorded. This registration must be able to scale as the therapy moves through the subsequent stages of development, clinical trials and production. With ATMPs, however, this is often a manual process, with the creation and storage of paper documents. There are several ripple effects to this. Manual registration is time-consuming and error-prone, record archiving is expensive, and subsequent QA and QA processes can only be provided by specialists who have access to the physical records that act as a bottleneck for scalability. These paper-based processes also increase the challenges of process characterization, comparability, technology transfer, and obtaining regulatory approval. Iovance, Novartis and Bristol Myers Squibb are all examples of companies that have faced challenges in translating their preclinical / clinical processes on a commercial scale.

The manufacturing requirements of ATMPs are also extremely difficult to scale out of the lab. For autologous ATMPs, each production run creates a single treatment for a single patient. This means that it is currently possible to scale production only horizontally, adding new production units through manual intervention by a highly skilled workforce that requires significant investments in facilities and human resources. The more manual the manufacturing process, the more difficult this scaling becomes and the more expensive the cost of goods (COG) for the resulting treatments. In addition to this, the patient specificity of these treatments means that cross-contamination or misadministration would have very serious consequences on the patient’s health creating a zero-defect requirement in manufacturing. This makes manufacturing, chain of custody (CoC) and chain of identity (CoI) regulations both critical and potentially costly to implement. It also means that, unlike traditional drug treatments, each autologous therapeutic dose must be subject to individual QA and QC controls that act as a bottleneck to scale production.

Finally, the logistical challenges of personalized autologous therapies produced in a central manufacturing facility present significant challenges for the production of ATMP. These challenges question the logic of trying to readjust old paper-based centralized production models and justify us thinking of new digital platforms that could enable closer to the patient production. As we say in Ori, the card is the enemy of the ladder.

View the solution with digital

If we look at other highly regulated industries such as finance, banking, general manufacturing and mass transit systems, a cloud-based digital transformation has already been shown to have a significant positive impact on cost, quality and scale, while remaining compliant with regulatory requirements. .

Many of the scaling costs and problems caused by existing paper records in AMTPs could be alleviated by using integrated, cloud-based digital platforms, including electronic batch production records (eBMR), production execution systems (MES), management systems information laboratory (LIMS), digital CoI / CoC solutions and other relevant digital systems. These systems can capture all operations of the protocol unit in motion, such as analytical and sensor readings, and the mechanical and environmental status of the system during the production cycle. By capturing this data in a fully digital construct, IIOT provides valuable information for process characterization and effective root cause analysis, helping to reduce the time and cost of the process development cycle. Collecting all data acquired during this phase seamlessly feeds the technology transfer and CMC evidence required for regulatory approval. During the manufacturing process, the captured data meets QA and QC needs while allowing for approval by remote QC specialists. Furthermore, thanks to digitization, the quality control process can be further simplified by allowing the detection of process deviations so that manual quality control can only be targeted to these exceptional cases (e.g. release by exception).

Once these data points are collected on a digitally native platform, opportunities open up to link with other systems in the supply chain in a way that was not possible until recently. These digital integrations allow for better management of both upstream and downstream assets and thus help reduce production time and costs.

Creating a fully integrated digital vein in the manufacturing platform and supply chain would greatly increase productivity and the potential for full automation. This would increase both the speed and accuracy of the manufacturing process, reduce the likelihood of CoC and CoI errors, and open up the possibility of distributed manufacturing.

This data also has significant potential to provide insight into optimal biological processes early in the preclinical process discovery phase in order to reduce development time and improve visibility of sources of variability and optimization opportunities. When this is combined with patient characterization data, this information promises to be able to predict the course of protocols for individual patients and to provide recommended on-the-go adjustments to improve the quality of the resulting therapy. These predictions can also help better prepare clinical resources and sites after production, potentially delivering treatments to patients sooner and saving more lives.

Innovative cell therapies, such as CAR T cell therapy, and gene therapies can only have a significant clinical impact if they prove financially viable for manufacturers. Otherwise, the unfortunate scenario would be that so few ATMPs reach the market on a large scale that therapy developers completely abandon this promising treatment modality. These healing treatments are now available, we must not let patients slip through our fingers due to these challenges.


  1. Products approved for cell and gene therapy. FDA. Published March 1, 2022. Accessed April 1, 2022.
  2. Swetlitz I. Hospitals are saving lives with CAR-T. Getting paid is another story. STATISTICS. Published March 12, 2019. Accessed April 1, 2022.
  3. Melenhorst JJ, Chen GM, Wang M, et al. Decennial leukemia remissions with persistence of CAR CD4 + T cells. Nature. 2022; 602 (7897): 503-509. doi: 10.1038 / s41586-021-04390-6
  4. Macdonald G. Dev. COGS crisis: the cell therapy industry must rethink CMC, says expert. Dark Horse Consulting. Published August 6, 2019. Accessed April 1, 2022.

About the author

Matt Todd is a pragmatic technologist with over twenty years of experience as an expert in technology and data space, including ten years as the co-founder of an application development company. He is currently Head of Architecture at Ori Biotech, a cell and gene therapy manufacturing technology company with offices in London and New Jersey that raised $ 100 million Series B in December 2021.

Having worked with a wide range of technologies and methodologies, his focus is always on maximizing value and ROI by pushing for the adoption and enabling of situational solutions best suited to achieve strategic goals.

Matt has a deep understanding of architecture and data at all levels of the company, combined with operational experience of designing, building and managing large-scale mission-critical systems in cloud-native environments. He holds a BA in Artificial Intelligence and Computing from the University of Birmingham and remains an active member of the Birmingham technology scene.

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