Hello, and welcome to my presentation about JMP in qualification and validation of biological assays. I've divided this presentation into five parts. At first, I want to give you a small introduction about my person and the company I'm working for, VelaLabs. The second part is a general introduction about method qualification and method validation like we perform it at VelaLabs often. The third part is how we collect and summarize the data. Then I will continue with the JMP data table where I've created some scripts to evaluate the data generated during qualification and validation. The last part, I will talk about some additional robustness parameters where different functions of JMP are used.
My name is Alexander Gill, and I'm at VelaLabs since 2019. I'm a laboratory expert in the l igand binding assay group . I'm mostly responsible for method development, qualification, and validation for Biacore assays and ELI assays. VelaLabs is a contract laboratory for quality control u nder GMP conditions. We have four operational departments: the ligand binding assay group, the physico- chemical group, and the cell- based assay group, and the microbiological group.
Method qualification and validation is important in the life cycle of pharmaceuticals and biologicals. Here, the life cycle of such drugs is shown from the pre- clinical phase over the clinical phases and the application. During the pre- clinical phase, developed methods are suitable which are on the scientifically sound. Afterwards, for the clinical trials phase 1 and phase 2, we use mostly qualified methods.
For method qualification, we show with some suitable parameters the performance of the assay. If the assay is then validated, derived from the data generated during qualification, we create limits which must be reached during method validation. The validated method afterwards is used for clinical trials phase 3, new drug application, and also for batch release in post-marketing afterwards.
Here, I've shown some examples for the performance parameters. The accuracy shows if the method has any bias or shift, or especially it lacks bias or shift/ the intermediate precision is the variability between runs where we show that different operators and different devices on different days do not influence the result. The repeatability is the variability within one run where we try to keep the differences between the reported values as small as possible.
The linearity shows the dose response of the assay over the whole assay range. During the robustness, we show that different parameters can or cannot influence the result. For example, different ligand lots or different models of devices. Then the sensitivity to detect stability- indicating changes, there we use mostly stress samples to show that they can be easily distinguished to non- stress samples. Specificity is, for example, a blank subtraction or positive or negative controls.
The data collection is mostly performed in Microsoft Excel because it's more accessible within our company. I will come later to this. We also collect the reported value, which is the final outcome of the assay. The reported value is calculated using a validated software like PLA, SoftMax Pro, or the Biacore Software. This is to ensure the data integrity. Every step where a human is involved in the evaluation has to be checked by a second operator. As I use a relative potency assay as example for this presentation, I've also shown here what's the reported value for this assay. It's the relative potency with the 95 % confidence interval as a quality parameter.
Here are the reasons why we use Microsoft Excel for the data collection because it's available on every PC within our company and every employee has basic knowledge about it. The raw data from the validated softwares are also often exported in Excel. What is really important that the data in Excel are organized in datasets, so they can be transferred to JMP more easily.
Here is a basic experimental design for a method qualification or validation. The first six runs are basically designed around the intermediate precision where we use 50%, 100%, and 200 % sample mimics in each of these six runs. These runs are spread above two devices, two operators, and performed on three different days. We report the mean relative potency for each of these dosage points, the standard deviation, the coefficient of variation, and the 95 % confidence interval. For accuracy, we use the same dataset as for intermediate precision, but we calculate the mean recovery, and therefore standard deviation, C V, and 95 % confidence interval both for all 18 datasets together and also for each dosage point separate.
The seventh run is for the determination of repeatability where we use six 100 % sample mimics within one run and also report the mean relative potency, standard deviations, CV, and the 95 % confidence interval. Then for linearity, which is here in run 1, we use the sample mimics for intermediate precision and additionally use 75 % and 150 % sample mimic within this one run to show that the results are linear over the whole assay range. Therefore, we report the correlation coefficient, the slope, Y-intercept, and residual sum of squares. For robustness, in this case, we show a lower and a higher immobilization level and also use two different lots of the ligand.
Then now, I'll show you the Excel table where we can see here in the first few columns the metadata for each data set, then the reported value with the 95 % confidence interval, the slope ratio, which is additional quality parameter and shows afterwards if the analyte is comparable to the reference. The column for recovery is empty because the recovery will be calculated in the JMP software. Here, the matrix where it's defined which datasets are used for which parameters.
Then there are two different possibilities to transfer this data into the JMP software. One is with this function where a data table can directly be created out of this table. But in this case, I won't use this function because I have already created a JMP table with all the scripts I need. I just copy all the data. But for this procedure, it's important to show all available digits of the reported values because only the shown digits are pasted afterwards into the JMP software.
I now copy with CTRL+ C all this data and then go to the JMP data table where I can paste all this data. Then we get here an alert because in the column Recovery, I created a formula to calculate the recovery. I don't want to paste the data in here, but the Excel table does not contain data in this column. We click Okay, and everything is pasted as we wanted.
For what purposes JMP can be used under GMP conditions? We use it during the method development phase for design of experiments, for example, to investigate more different parameters of the method within one set of experiments. Then use it for the statistical data analysis and also for comparability studies. For example , if a customer wants to compare a biosimilar with the originator.
During qualification and validation, JMP can also be used for the design of experiments. For example, for the intermediate precision parameters or to spread the robustness parameters over the qualification runs. Then I will show afterwards for the determination of assay performance in qualification and for the check of the assay performance during validation. But for this, an additional QC check is required afterwards if all the calculations are performed in the right way. This is very important that JMP is not really usable for the determination of reported values. Therefore, as I mentioned before, we used mostly validated softwares.
Now we go to the JMP data table where I will first show you how I create most of the script. Therefore, I use distribution. For example, if I create the accuracy at 50 %, I select the Recovery and choose it for the Y columns. Then I click Okay. Then we have here all available datasets. To limit these datas ets, I create a local data filter and use Accuracy and edit. If it then choose all the columns indicated with an X, we have reduced the data sets to 18.
To reduce it further for only the 50 % sample mimics, I add with the AND function an additional filter for the nominal potency, which I then limit to the sample mimics with about 50 % nominal potency. Then you see we have only six datasets left with mean recovery of 99 % and a coefficient of variation of about 6 % and the confidence interval. To save this script, I go again to the red triangle here and save the script to data table.
For example, as accuracy 50 % 2, because I've already created a similar script here. The difference for the intermediate position, if we open, for example, here the intermediate position at 100 % is only that we not use the recovery here, but the relative potency and have also again the same parameters reported.
For repeatability, w e choose only one run with the six 100 % sample mimics. We report also the same data like the mean relative potency, the standard deviation, the 95 % confidence interval, and also the coefficient of variation.
What's also very interesting here is the linearity where we use a different function. I created this using a Y by X plot and plotted the relative potency by the nominal potency and created a linear fit through all these data points. Then we report the Y-intercept, the slope of the linear fit, the RS quare or coefficient of correlation, and also the sum of squares error or residual sum of squares. Then we go back to the presentation.
For additional robustness parameters, we, for example, show the performance of the assay using different material lots. For them, we show if they have equal variances. If the variances are equal, we use the T-t est. If not, we use the Welch- test. For example, for ELISA methods, w e also measure sometimes the plates on two different models of plate readers to show if both models can be used. This is then analyzed using a paired T- test.
At the end, I want to thank you for your attention. If you have any further questions, you can type it into the Q&A or contact me directly.