Hello, my name is Damon Ho, and I am a Scientific Associate III at Pharmaron Biologics UK, based in Liverpool. Thank you for tuning into this talk. I'm really proud to have completed this work during my student placement via Liverpool John Moores University at Pharmaron Biologics. And even happier to announce that JMP was a huge part of the success of the optimization of our capture chromatography step.

Moving on to the first section of the poster. To introduce Pharmaron to individuals who may not have heard of us before, we are a leading pharmaceutical research and development services provider with worldwide operations. In Liverpool, we currently work on viral vector-based gene therapy development and clinical manufacture, currently focusing on adeno-associated viruses or AAVs.

At Pharmaron, we have an impressive platform process that can manufacture multiple AAV serotypes and products, which is illustrated in Figure 1.

This process utilizes depth filtration, followed by capture chromatography, and then intermediate polishing chromatography, followed by polishing chromatography, onto our formulation steps, and finally, sterile filtration.

Now, I'll introduce to you to our robot and the use of JMP. We are immensely proud to share that we have a state-of-the-art high throughput process development robot called the Beckman Biomek i7, as you can see in Figure 2 and Figure 3, that can rapidly assess a product's fit with our platform and finally tune parameters for its seamless integration into our process.

Utilizing this robot for HTPD alongside the use of JMP for DoE, we can generate a high-yielding, high-purity drug product in a fraction of the time compared to conventional methods.

This poster focuses on optimizing the initial capture chromatography step in our platform process for an AAV product.

A DoE was designed using JMP software to determine the optimal loading conditions for processing. Three factors were chosen to be optimized, which were the residence time, loading density, and loading pH, with viral titre and viral aggregation to be measured as outputs.

Now, for some quick information on how the DoE was designed in JMP. A central composite design, or CCD was chosen to create the DoE for a number of reasons. Firstly, that a minimal number of factor combinations are required to estimate main effects, and it is able to analyze two-factor interactions and quadratic effects.

The CCD also has a good lack of fit detection, which can easily show which factors affect the chosen outcome, and graphical analysis is possible through various tools available in JMP, as you can see throughout the poster.

The CCD was created in JMP by first selecting a response surface design, entering the parameter names of loading pH, loading density, and residence time, and then inputting the predefined high and low ranges. The goal was to maximize the monomeric virus content and viral capsid recovery, so these are input in the responses section.

The software is very flexible, and you can always add more responses in the finished model. In this instance, as this was a conventional CCD, on face was selected. Triplicate measurements were selected for the center point. The CCD was then ready to be generated. In total, 17 different experimental conditions were generated via the CCD model with varying low, medium, and high parameters. Center points are defined as all medium parameters that form the foundation of the CCD model, with the low and high parameters acting as probes to test how the factors interact and influence each other.

This forms a 3D response surface that can quite accurately predict the interactions of factors. Using JMP, ultimate conditions can then be hypothesized and tested in a subsequent confirmation run.

Our HTPD robot was utilized to perform the capture chromatography at a very small scale, allowing multiple conditions to be run at the same time, which would have taken considerably longer at lab scale. This system allows for a highly-accurate and reproducible process due to its automatic nature.

Once all of the conditions were run on the Biomek i7 platform, we employed the use of our world-class analytics to measure AAV capsid titre by an enzyme-linked immunosorbent assay, shortened dualizer, and multi angle dynamic light scattering, also known as MADLS.

Monomeric virus content was measured by conventional dynamic light scattering or DLS. Not only is JMP using the design of an experimental study, but also in the analysis of results.

Once analytical data was available, this was entered into JMP with the data able to be visualized in a number of different ways, including counterplots displayed in Figure 4, Figure 4a, 4b, and 4c. A simulation was run to generate tens of thousands of virtual results that helps build an optimized model and also to compare against real-world experimental data to build a confidence level in the model. This is shown in Figure 5.

The contour plots highlight the impact of a higher loading density, which increases viral monomer content shown in Figure 4a as the brown. However, upon analyzing capsid ELISA recovery, which is highlighted in Figure 4b, it showed that a lower loading density, also shown in brown here, returns the highest capsid yield. This was challenged by MADLS data shown in Figure 4c, which provided the evidence to support the impact of a higher load density to increase viral monomer content, shown as the brown and the red and orange here in this bottom right corner.

An important consideration is that MADLS does not measure aggregates, thus confirming DLS findings. Ph and residence time did not have a significant impact on capsid titre, MADLS or DLS results, so the contour plots are not included in this poster.

Now, we can move on to the interpretation of the findings. After the three learning conditions that were analyzed, learning density had the most significant impact on capsid recovery and the monomeric nature of the AAV, which is visualized by the steep gradient of the line on the left graft of the predictive profiler shown in figure 6.

Residence time shown in the central graph did not have any notable impact on even monomeric virus or capsid ELISA recovery, which is indicated by the near horizontal line of the prediction profiler.

Loading pH shown in the right graph also did not significantly impact the capsid recovery or monomeric virus, which also has a near horizontal line.

The optimum condition suggested by the DoE is a higher loading density with a shorter residence time and loading at pH condition A. This is an ideal outcome of the model as with a higher loading density, less capture chromatography media is used to achieve the same target loading density.

A shorter residence time also results in a faster process, which streamlines the AAV loading stage.

Loading a pH condition A is also ideal as it does not require pH adjustment of the load material, thus saving time during load material preparation.

Using these optimized loading conditions determined by the DoE, a confirmation run can then be to verify the DoE output. Once confirmed, these optimum conditions can then be used in a scaled up run, which is specifically designed to achieve a high quality, high purity, and low impurity AAV product.

This approach led to a rapid low-resource demand and low-cost assessment to optimize our capture chromatography step for our Pharmaron platform process.

In total, the optimization experiments took place over only three days. This would have taken many, many weeks to complete if it were a lab scale, along with using much more AAV material and much higher cost associated when increasing the scale as only one condition can be run at the time.

The use of JMP software in tandem with our HTPD capabilities can allow us to perform robust optimization of our platform process to suit specific gene therapy product needs. This greatly speeds up the feasibility and optimization stages, which are often the most time-consuming phases of the process development pathway. Ultimately, this drastically reduces the time taken from product development to the successful delivery of a gene therapy product to a patient in the clinic.

I would like to end by thanking you for listening to my poster presentation, and I hope you have learned a thing or two about what we can do at Pharmaron, plus the importance of JMP in our product development pathway. Thank you very much.