Automating your Cell Therapy QC assays

Why you should be automating your ATMP QC assays. 

Somatic ex-vivo gene therapy products are an emerging class of living cell-based therapies that have come to prominence thanks to the approval of two CD19 CAR T products: Kymriah and Yescarta. 

As opposed to an allogeneic cell therapy or even a traditional biologic such as a monoclonal antibody (mAb), these products are made on a per patient basis with patient demand being matched through a scale-out and not a traditional scale-up model. This has resulted in a high cost of goods for the production of these living therapies, a technical risk which compounds the already significant market risk of these products.

Controlling the per batch cost of manufacturing (to enable wider payor adoption) has thus moved areas such as Chemistry, Manufacturing and Controls (CMC) and Quality Control (QC) to the fore. Cell and gene therapy companies have a need to increase production output (without increasing facility footprint), maintain quality and reduce costs. If they are unable to address these issues, the market has already observed the outcomes. Dendreon, the now defunct creator of Provenge, at one point was running an unsustainable Cost of Goods (CoGs) of 77%, compared to a CoGs of approximately 10% for a mAb.

The literature around CoGs in cell therapy production indicates that the cost of producing a single batch of genetically modified patient material will be between $50,000 - $100,000 USD, with approximately 50% of the total cost coming from full time employee (FTE) and QC/QA costs ($25,000-$50,000), with 20% of this cost bracket coming from QC ($5,000-$10,000, 10% of total CoGs).

As autologous products are made with donor material, their manufacturing process must account for high donor variability to deliver the desired target product profile (TPP). The quality of the cell therapy product against its TPP is assessed using predetermined critical quality attributes (CQAs), which in turn are measured via a variety of different analytical methods. The importance of rigorous QC has introduced the aforementioned FTE cost into the manufacturing process and with high competition for the talent required to operate in a regulated cell therapy QC environment anything companies can do to move towards an autonomous 24/7/365 facility operation is going to deliver benefit to patient and payer.

One solution is analytics automation. Using ‘open and static’ or ‘bolt together’ solutions for analytics automation will allow cell therapy QC teams to increase throughput, reduce FTE cost and avoid excessive CapEx (e.g. by having to construct new facilities). These solutions should also be flexible, allowing companies to scale/descale for variances in patient demand.

So what QC assays should you look to automate now?

Despite there being a number of CQAs and a variety of techniques to measure them (Table 1), in our experience, it’s invariably the same 3 release assays that come up each time: qPCR, ELISA and flow cytometry.

 By introducing analytics automation for these 3 methods, throughput can be increased, and QC cost decreased. 

CQA

Assay Method

Example criteria

Potency

Chromium-51 release (scintillation counter) or IFN-γ (ELISA)

----

Viability

Flow cytometry

≥ 70%

Transduction efficiency

qPCR

----

Residual Beads?

Microscopy

----

Purity (CD3+ cells)

Flow cytometry

≥ 80%

Appearance

Visual inspection

----

Cell count

Flow cytometry

Specified dose ± 25% (Calculated from # viable cells)

Identity (NKG2D+ cells)

Flow cytometry

≥ 50%

Microbiological tests

----

----

Sterility

Plate assay (14 days) or qPCR (a few hours)

No growth

Endotoxin

LAL Assay

≤ 8.67 EU/mL

Mycoplasma

28-day culture or RT-PCR

No mycoplasma

Safety Tests

----

----

Vector copy number

qPCR

< 5.0 copies/cell

Replication Competent Virus

qPCR

No detection

Table 1. An example of some CQAs, techniques and acceptance parameters for a gene modified somatic product. Data reproduced from the National Academies of Science, Engineering and Medicine and a Novartis CTL09 presentation. Other assays sometimes used include Secretome analysis (SomaScan), Mass cytometry (CyTOF), TCR deep sequencing, RNAseq and Multiplex gene expression analysis (Nano-string).

Automation can also mean many things – automated sample prep, assay plate preparation, equipment set-up and automated data analysis. What we have found is that often methods have been developed and validated in-house for running the experiment (e.g standard analytical data integration workstreams), and yet the assay sample preparation is still done by hand.

The manual manipulation of arrays of small volumes of incredibly valuable, but often indistinguishable, colorless liquid samples introduces a high chance of error (necessitating expensive additional error checking) and also limits throughput to the human that has to sit and prepare all the plates. It also means that as autologous cell therapy companies scale-out their manufacturing, often to contract development and manufacturing organisations (CDMOs) or different geographical sites, they are introducing a source of human variance into their QC processes. Whilst some early moving groups are now looking at liquid handling robots for sample plate preparation, unintuitive user interfaces and a lack of integration with other pieces of QC equipment has often prevented the widespread adoption of liquid handling robots.

At Synthace, we believe that to build the QC Lab of the Future let’s start by removing that burden of manual assay prep from the lab scientists.

Our partners in cell and gene therapy already use our software to increase their throughput, save time and reduce cost in QC techniques such as qPCR plate preparation. In the lab, this means walk away time, less time programming robots and an end to manual pipetting - freeing up their scientists to focus on analysis, increasing facility throughput and invariably ensuring that more patients get access to breakthrough therapies.

To find out how you can automate your QC - schedule a quick chat with our expert biological automation experts