Hello, everyone. Thank you for joining my talk. I am Giulia Lambiase, I'm a Senior Scientist at AstraZeneca. I work in biopharmaceutical development in the analytical science team. Today, I want to talk to you about the use of DoE for the development of analytical characterization methods, most especially chromatography methods.
In today's talk, I'm going to talk about therapeutic proteins, what they are, and why they are challenging for analytical testing, and introduce you to the use of design of experiment for analytical method development and the application of DoE for the development of charge variance method, specifically cation exchange chromatography method.
To start off, protein therapeutics are inherently very complex due to their larger size and the presence of the post- translational modification, and also chemical modifications that can the protein undergo through during the processes of expression in cells, purification, and storage.
Monoclonal antibodies dominate the biopharmaceutical market, representing about 70 % of the total sales of biopharmaceutical products. However, recently, there is a push for new products, next generation biopharmaceuticals, which are bispecific antibodies, antibody fragment, fusion proteins, and many other formats. All of them come with unique challenges due to their complex structure and presence of higher order structure, glyco forms, charge variants, the sulfate bonds, oxidized deamidated species, isomerization, aggregation, fragmentation.
A ll of these modification, chemical process [inaudible 00:02:37] modification, can impact on potency, safety, quality of the final drug product. This is why thorough analytical characterization and analytical testing throughout all the stages of product life cycle is key to meet regulatory standards, to be enabled to deliver a product that meets regulatory quality profile.
We use a plethora of analytical techniques for analyzing proteins, and these are based mostly on chromatography methods, electrophoretic methods, and [inaudible 00:03:25] . Due to the inherent structural complexity of proteins, analytical method development can be quite challenging.
In today's talk, I'm going to specifically talk about chromatography methods and the use of design of experiment to help the development of chromatography separation. Chromatography method can be quite complex, especially if you have a complex analyte like a protein. This is because the separation depends on the interplay of several variables such as mobile phase composition, buffer pH, flow rate, column chemistry, temperature, the type of detector that you decide to use for the analysis. All of these parameters need to be fine- tuned and controlled during the separation process in order to achieve the desired separation. DoE can be very useful versus one factor at a time approach.
One factor at a time approach involved the variation of one parameter at a time, maintaining the other constant. This may lead to a large experimental run, lack of information because there's lack of investigation on factors interactions. Lack of information also leads to additional experiments during method validation, which may lengthen even more the method development process and finally retard the overall product development.
DoE, in comparison to one factor at a time approaches, DoE enables the variation of multiple parameters at a time. This allow, with a reduced number of experiments, to investigate a large number of factors, including the interactions between them. Also the development of mathematical models that allow the assessment of relevance and the statistical significance to facilitate all the steps required during method validation. DoE enables really to investigate a wide design space with less resources, so in a more efficient way. In fact, I like saying DoE enables faster, cheaper, and smarter experiments to deliver stronger and better analytical methods.
In today's talk, I'm going to talk you through a split DoE approach for the development of a cation exchange chromatography method. Cation exchange chromatography is used for the analysis of charge variants. Specifically, if you see here on the left hand side of this slide, you can see a chromatogram of a protein where you can see some acidic species here on the left, [inaudible 00:07:17] on the left of a main species peak, and some basic species peak.
All these acidic basic species can be formed due to the presence of chemical modification that can lead to superficial charge distribution variation in the protein. Cation exchange chromatography methods are quite complex chromatography methods because the separation efficiency is affected by a number of factors and quite sensitive to small changes of these factors such as column chemistry, mobile phase pH, temperature, flow rate, content of salt, time of the separation.
In this approach, I'm going to talk you through an efficient way to develop cation exchange chromatography method using DoE. If you are familiar with DoE, you may know that often requires a sequential approach. In this experiment, I performed a main effects screening design for enabling the selection of the best column chemistry and the mobile phase pH for the charge variance separation of this specific mAb molecule.
During the second DoE, I use response surface methodology, particularly a central composite design DoE, to optimize the chromatography separation by changing the flow rate and [inaudible 00:09:36] . Let's take into more detail in the first DoE experiment. This was a main effects screening design where I screened four column chemistry bought by four different providers, Agilent, Sepax, Phenomenex, and Waters, and I screened a range of pH from 5.5 to 6.5.
My response was the experimental peak capacity, which is a parameter that tells you the efficiency of a chromatographic separation, precisely the number of peaks that can be separated within the chromatogram, the chromatography time that you set. Other parameters such as concentration of buffer, concentration of salt at the start of the chromatography gradient, flow rate, gradient time, shape, temperature, injection volume, concentration, and the UV absorbance were kept constant.
These are the results for the first DoE. On the left hand side, you can see the four different column results. You can see how the experimental peak capacity changes versus the pH change in the mobile phase in all the four different columns. You can see that we aim to have high experimental peak capacity values. You can see that the Phenomenex column performed best.
In all of these three columns, we can see that pH of 6.5 enables greater experimental peak capacities. But the Phenomenex column allowed for better separation results. It is also visible on the right hand side of this slide in the panel A. You can see at pH 6.5, how the separation differs when using different chromatography columns.
We have Agilent , Waters , Phenomenex , and Sepax . Definitely, the separation of the charge variants using the Phenomenex column is much better than in the others because these acidic peaks are very well separated as well as these basic species here from the main product peak.
Panel B, we have isolated only the results of the Phenomenex column. How the chromatography separation was that with the mobile phase or with pH 5.5, 6.0, and 6.5. We can see how the separation improves with the increase in pH. Obviously, the mobile phase pH is dictated by the intrinsic molecule pI. We could only investigate this range. Otherwise, the molecule would have struggled to find its own column.
Based on our fundamental knowledge of chromatography separation with cation exchange columns, we decided that this parameter, so this Phenomenex column and pH of 6.5, were optimal to carry on development. We carried on with the second DoE using a central composite design.
Central composite design is a type of DoE falling within the umbrella of response surface methodology, which is used for optimized conditions for investigating the presence of curvature, for instance, and extrapolate optimal values. In this case, we use our Phenomenex column and mobile phase pH of 6.5, and started to play with other parameters such as buffer concentration, concentration of the salt at the start of the gradient, and flow rate to investigate optimal conditions.
Central composite design enabled to very efficiently, with a few number of runs, to identify optimal separation conditions, optimal method conditions. In fact, at the very end of the split DoE approach, we could say that with the investigation of four column, mobile phase, pH range, salt composition, gradient flow rate. With only 27 experimental runs, we could optimize a method for a monoclonal antibody. This method is very useful because it is now used as a quick, high throughput screening experiment. In a quick, high throughput analytical method for screening differences in the charge variance profile of these specific molecules expressed in different conditions and compare it to a standard.
You can see here that the blue line is our reference standard and the red line is a stress material of the same molecule. You can see how the charge variance profile changed as a consequence of the stress condition applied to this molecule. This was achieved thanks to this analytical method which was developed and optimized with a DoE approach.
We also decided to implement this DoE approach as a platform workflow for analytical method development for new products, new bio pharmaceuticals, and we screened a number of products. For all of them, we applied first the first main effect screen design, and we identified the best column and mobile phase pH to use. Secondly, we applied the central composite design to optimize the separation.
Now, we have identified a platform column and a mobile phase composition for this class of therapeutics. When new molecules comes into the pipeline, we can very quickly, just by using a central composite design, which involves actually just 12 runs, optimize the chromatography profile and deliver an optimal cation exchange method for a specific product.
The key take- home messages from my talk today is that DoE system method development followed by appropriate statistical analysis enables to plan experiment based on time, cost, and analytical resources available very efficiently, and schedule the execution of experiments with adequate sample type and size to extrapolate the maximum amount of information from our chemical data and efficiently address the challenges and goals of the intended research.
It definitely saves time and cost for experiment execution in comparison to one factor at a time approaches. Most especially, it allows the complexity of analytical method development, but still interrogating several factor at a time and studying the effect of both individual method parameters and the interaction on the dependent variable.
With today's talk, I hope I inspired you to apply more DoE in your experiments. Thank you very much, everyone, for your attention. If you have any questions, feel free to reach out to me. Thank you.