Hi, my name is Ildiko. I'm representing Alvotech Germany on this meeting together with my colleague, Thomas. We are both members of the Process Innovation Team at Alvotech, and we are working mostly on media development projects. We are active JMP users, mostly using different DOEs, complicated DOEs, but this time, I would like to present a small smart tool for data visualization, which is for REBEL users. REBEL is an ad-line spend media analyzer machine, which was manufactured by 908 Devices company. Last year, they released a JMP add-in tool which facilitates the direct analysis and trending of amino acids and vitamin concentrations.

Together with this measurement, really large data sets are generated which can be immediately visualized in a customized view with this JMP add-in. We would like to present the workflows to compare, to show trending in time, and different results, different media formulations, different bioreactor runs, to show how this Smart Ramping tool can lead to the improved process understanding, and how this can help us in our everyday work.

A few words about the REBEL. What is REBEL? It's a spend media analyzer machine, which is a capillary electrophoresis-based small mass spectrometry instrument working with kits, and it has the capacity to analyze 33 different metabolites in one sample, approximately 7, 10 minutes, so it's really fast.

We use this platform, as I mentioned, in the media development for different purposes. We compare vendors in formulation of a specific big media. We compare them, and we define which vendor could perform the best or media. As I mentioned, we are doing complex deals, and we need to follow the analyte trends in these different media conditions. We have to do fast decisions day-by-day to define feeding strategy, or we just need to see these conditions, the analyte trending across a time course. We also use this tool to compare different conditions in bench scale bioreactor runs. If there is one product lifecycle management, and we would like to implement a new media, for example, and we compare it with the other setup.

When we generate the large data sets, we have this JMP add-in tool for really fast data processing and visualization. I would like to note here that it works only with JMP 16 or above versions. If you go back to the full design and the methods, as I mentioned, I would like to present three small comparison workflow on this poster through three case studies. I would like to show some short demo videos about how these workflows are showed through data import until the customized dashboard view, which could be then further processed, saved, sent, exported, whatever according to our needs. The analysis just takes really 1, 2 minutes for large data set with the prepared data filters. This is a really useful tool for REBEL users.

How it works? First, after installation of the add-in, which is really easy, you just have to install the add-in file. We at Alvotech have a remote version of JMP because this is a GMP-validated environment with so many restrictions. Here, after the add-in is installed, we see here the three workflows, the sample comparison workflow, what I would like to show in the study one, and the both time course workflows for anomaly trending and condition trending in the study two, and I forgot… Here, the study 3 shortly.

The result files, what the machine generates are CSV files coming from a so-called REBEL batch, which is based on the batch run sheet. This is a template, an Excel template, when the sample labels are well-defined. When the result sheet is generated, it can be directly imported to JMP. There is an optional file, which is the so-called sample label file, which is very useful because it has the option to correlate samples to custom metadata, which means it is important in the trending workflows, then we would like to correspond, for example, a bioreactor name to a sample label name to see which conditions are in which bioreactor. The JMP recognizes this sample label and these components. I will show that later in the demo videos, it will be more clear.

Finally, when the results were imported into JMP, it generates a default report dashboard first, which can be customized later with data filters. This is the workflow at REBEL. We go to the first comparison workflow. I would like to show how this customized view were generated. I would like to show that in a short video. First we open the sample comparison workflow. We select the folder where this file is, which is a little bit complicated for us as we are working with a remote JMP.

We find the folder. It's empty first, when we select the folder. We have to select the sample label file and open it. We adjust the header, which starts the row number one. This is the imported file, which JMP generates data table from that. It takes a little time. Directly, we go to the sample comparison dashboard view. We can see here all the analytes in different bioreactors with different media. The tabulate shows the mean values of the analytes because we are doing duplicates or triplicates.

On the left side on the Filter panel, we can select sample label because in this case, he would like to show only the medium types. It also takes some time to open it. This is medium A composition. All the other medium should be appeared one after each other.

Other workflows are much faster. Somehow, the media, the comparison workflow is really, really slow. These are media, same media prepared by different vendors. You can see the differences. Also, we can see the concentration pattern of the analytes, and we can decide if the dilutions of the samples were good or not. Also in the control panel, we can select Confidence Intervals. We can select the standard deviation for the values to figure out if these are in the defined range that we would still accept.

Once we have the final customized dashboard, we can save this dashboard. After saving, it should appear when we click on the data table. Yes. It's in the control panel on the top left. The modified dashboard, which is there, we just click on that and it appears again. Of course, if we need to save the data table to make it appear again next time.

We also can export the data in any format, of course. This was the first workflow. The other workflows, what we are using the most in the media development, the analyte trending workflow and the condition trending workflow. If you go to the analyte trending, I also have a video for that. But before telling, I would like to tell here about the very important action which is needed to take, define the time course component, which works in a way that in the original results table, Excel table, we have the first entry of the sample label column.

In this case, it's the culture station one, vessel number two on the day three. JMP should know which is the time component, first time component, which is number three. It works with delimiters to define the time course component. Delimiter, as it is shown up here, is the character that separates the string of the text. In this case, it's a double space based on this naming convention. The time course component I want to show as first time, which is the number three. This is the third component of the sample label. We can just update the preview here, and it appears that time three will be the first time component, and the sample label is that one. Which is the same, what I defined in the optional sample label file, which is corresponding to the batch record position, what I would like to see in the analyte trending data set.

Once we have this, the JMP import shows the dashboard. It's better to show that on the video as well, go step by step, which makes more sense to see. We open the second Analyte trending workflow. We pass the information from a sample label as I mentioned before, because I created a sample label optional file.

The time course sample label file is there. JMP recognizes immediately and import that. This is how I define the time course component with the double space and the number 3, which shows the time starts with 3, which is day 3. Now the data table appears and also the analyte trending dashboard view, which shows all the analytes. It's important to say, this analyte trending, it shows the mean of everything in a time course.

All the days which we have samples are defined here. We can set the standard error range, and we can also have the filters to show only one culture station because otherwise, it won't be the good trending. This analyte trending shows only one condition. All analytes, we can see the outliers here. We can see the non-usual behavior of an analyte. We can select analytes as just showing up one in a customized view.

We also can see if values were not measured. We can put the confidence intervals, we can put the standard deviations, if we need that. Check the different analytes one by one if we would like to do that. And saving the dashboard to the data table. See that we can, we can save any number of modified dashboards that we would like to export that later.

Here, there is an unusual thing. Actually, a bug was identified in this data set. I'm showing that in the next slide with the condition trending workflow, which is generated from the same data set, at least the first one. Here, I'm not showing the video this time because the data import would be the same then last time.

As I mentioned, this is a DOE for media conditions. It's an AMBR15 small bioreactor setup. Here, this is my favorite workflow, the condition trending workflow, when we select one analyte, throughout time course in all conditions we have. Here, there is this bioreactor position with question marks, which was not in the sample label file. This is a bug in the system. In this case, when checking the data table, the time course components and the name of the sample label, it was not correctly separated. This was identified as a bug.

We can see here that values are missing at specific days, like here and here, day 11 and day 6. We also can see that in the data table missing values. This means a bug which can be reported to the 908 Devices. It's still ongoing to investigate what happened. The reason why I find it really good and really useful, this time course condition trend workflow, because here we can see, for example, different conditions. How unbalanced are the different vessels, different conditions, because it's a DOE with any kind of conditions, and we have no idea about the outcome. Media is not balanced.

Amino acids really show different trending. The alanine, for example, here, it shows up completely different pattern in how the cells are growing and dying after a specific day. It shows really different trends, which is very, very valuable and important information for us when concluding the study. Also, if we would like to make an emergency feeding strategy change, day by day, the REBEL can measure every day all the analytes in all conditions, and we can see that really fast with this workflow.

This is also the condition trending, but in the last study I would like to show, this is the life cycle management of a mAb product. When we would like to show how a new media formulation performs compared to the original media formulation. We just follow the cells growing and the product produced throughout this 14 days time course. We followed, of course, the analyte trends. A large data set were generated, and I created the sample label file which means that after the time course component was defined, same way than last time, then the batch accord positions were added with the same name to make the JMP able to identify the batch accords.

I'm showing you this last video. These are concatenated files. Two different batches under the same folder could be visualized by JMP and imported to JMP. This is also a really good feature in this adding tool. That different result files for the same study can be opened at the same time and data are concatenated. The sample label file is defined and read by JMP immediately.

Here the definition is a little bit different because the first sample label column is also different. I identify as day zero here. Now we see that as a default, all analytes under all conditions appears. That's why we need the local data filter to apply here. Let's see arginine, for example, which is on the slide as a screenshot. We can see clearly how balanced is the media, it's defined condition already. The trending is the same in between two different media types, at least for this analyte. The [inaudible 00:22:57] positions are perfectly here. No bug in this case. Once it's done, we can save the dashboard as before.

Of course, it can be further customized with the Graph Builder options for more advanced JMP users. This is like how you can export that by selecting an export folder, and it can be exported as a picture file, or it can be exported into Excel, the result table. There are many exports here already.

If we can go back and conclude this really useful tool. We can tell that these complex data sets could be visualized really in few clicks by adding some time course component and identification of some component, which is really easy. It is really useful for non-advanced JMP users because I don't believe that JMP knowledge is needed at all to use this add-in tool. The benefit is great because in a very short time frame, it allows to really, really fast data-driven decisions, which is really important.

I have a notification from 908 Devices that the upgraded statistical analysis tool, the version 2.6, is under launching. This is the improved version. When the separated sample label file is not needed, it can be as a separate column in the original result file, which is the sample label file, and there is no need to add an optional sample label Excel file when defining the time course and opening the trending workflows.

That's all. I hope you like that. Thank you so much for the attention.