By Gunnar Malmquist / Principal Scientist
Process development of biologics is becoming more and more data and simulation driven (Fig 1). An example of this trend is the increased interest in mechanistic modeling. This approach allows you to simulate and predict chromatographic behavior and experiments in silico.
Fig 1. The evolution of process development from one-factor-at-a-time (OFAT), through design of experiments (DoE), high-throughput process development (HTPD), and quality by design (QbD) to mechanistic modeling.
Mechanistic modeling is considered a part of smart process development, which is a collection of approaches to get better process outcomes and speed up process development. Together with statistical models based on multivariate data analysis (MVDA) such as design of experiments (DoE), it can be a powerful tool to save time and create more robust processes.
The approach can be a shortcut to more robust process outcomes, but it is in no way a straight path. This article outlines both the current opportunities and challenges for using mechanistic modeling for process development of chromatography steps.
What is mechanistic modeling in chromatography?
Mechanistic models use computer simulations to decrease the number of experiments needed during process development. The simulations are based on known physiochemical phenomena involved in chromatography.
Mechanistic modeling is a mathematical representation of the physiochemical transport and interactions that occur during chromatography. For example, this methodology uses differential equations that describe how molecules move between resin beads and inside the bead pores. It also uses adsorption isotherms to quantify how molecules compete for ligands when binding.
This is not a new methodology per se, but the use of mechanistic modeling is moving from academic research to implementation in the biopharma industry. The growing interest stems from the need to improve outcomes and speed up process development. Thanks to greater computer power and improved software, mechanistic modeling is now more accessible to process developers in the industry.
Why use mechanistic modeling in downstream process development?
Mechanistic modeling is a complement to other process development approaches, such as HTPD and MVDA. As opposed to MVDA, physiochemical effects are also considered in mechanistic modeling and allow interpretation of the model parameter values for increased process understanding.
With mechanistic models, process developers can get a better understanding of both the entire process and the parameters that can influence the process with a smaller number of experiments. That way, process development can become faster, more scientific, and more reliable. The ability to anticipate and model scale effects is another advantage of mechanistic modeling over MVDA. Mild extrapolations are also covered in the mechanistic modeling framework.
Some examples of applications are: predict step elution conditions for ion exchange chromatography, facilitate tech transfer by predicting scale-up from lab to process chromatography columns, and explain deviations in manufacturing.
What are the current opportunities with mechanistic modeling?
As mentioned, the advantage with mechanistic modeling is that it can speed up process development and provide improved processes. The time invested in calibrating the model results in a smaller number of experiments needed, saving both time and resources such as sample and resin.
The deeper process understanding obtained from this methodology also results in more robust processes and improved performance, for example with respect to yield or throughput. It is also very beneficial for tech transfer as it enables handover of a scalable predictive model instead of just a large set of data. An added advantage is that it is possible to use historical data as well to get even deeper understanding of existing processes.
There are several ways mechanistic modeling can bring value to drug development work. At present, the approach is typically used during late-stage development, but the possibilities to use it in early-stage process development are increasing. Accelerating early process development allows you to shorten the timeline to toxicology and first-in-human studies.
Figure 2 shows an overview of the opportunities throughout the different drug development phases. The methodology is particularly useful for process development and characterization as well as tech transfer.
Fig 2. Examples of how different steps in drug development can benefit from chromatography mechanistic modeling.
What can you get out of mechanistic modeling of chromatography?
In chromatography, mechanistic modeling can be used to simulate elution profiles at different conditions, such as column dimensions, load ratio, flow rate, pH, and conductivity. Simulations can be performed for both major and minor sample components. The output can then be used to optimize the separation.
Figures 3 to 6 show example chromatograms from simulations of mAb elution profiles on Capto S ImpAct resin. The figures show profiles for charge variants, aggregates and fragments, host cell proteins (HCP), and leached protein A.
By inspecting the simulated elution profiles, it is possible to generate potential pooling criteria that can be tested and optimized in silico prior to experimental verification.
Fig 3. Example chromatogram showing simulated elution profiles for charge variants.
Fig 4. Example chromatogram showing simulated elution profiles for aggregates and fragments. The UV curve is plotted on a separate y-axis scale.
Fig 5. Example chromatogram showing simulated elution profiles for HCP. The UV curve is plotted on a separate y-axis scale.
Fig 6. Example chromatogram showing simulated elution profiles for leached protein A (PrA). The UV curve is plotted on a separate y-axis scale.
What are the current challenges with mechanistic modeling?
The possibilities to speed up process development work with mechanistic modeling are indeed many, but applying this approach is in no way a quick fix. In order to reap the benefits of the methodology, you need to make a long-term investment in both time, knowledge, and people. The learning curve is rather steep even with current improved software packages.
In other words, it takes time to get to the point where you have enough knowledge to take maximum advantage of this approach. It can also be challenging to find the right people for the job, as it is a specialized skillset asked for by many organizations.
Another challenge worth mentioning is the increased analytic workload that this approach creates. Using simulations results in less experimental work for process developers. However, it also generates more analytic work, as you need to collect a large number of fractions for product quality and yield analysis.
A recent mechanistic modeling case study, presented at HTPD conference in 2019, clearly demonstrates the shift to increased analytic workload. In the study, the number of chromatographic runs for model determination was reduced by 62% compared to a traditional central composite DoE three-factor design. However, the number of fractions to analyze for product quality went up by 69%.
What about challenging separations?
Today, good models exist for cation exchange separations performed in bind and elute mode. The models for hydrophobic interaction and multimodal chromatography might not be as developed, but substantial effort is being put into application in these modalities. Therefore, it is not currently fully known how useful mechanistic modeling is for more challenging separations.
A sharp tool to add to the smart process development toolbox
Mechanistic modeling is not a quick fix for faster process development for any separation. There is a certain learning curve and the first studies might be rather time consuming. However, together with other smart process development approaches, such as HTPD and MVDA, mechanistic modeling will ultimately help speed up chromatography process development work and provide deeper process understanding.
Are you considering mechanistic modeling? Reach out to us if you’d like to discuss the possibilities and challenges.
Author: Gunnar Malmquist
Dr. Gunnar Malmquist is a Principal Scientist within our BioProcess R&D section and has over four decades of chromatography experience. His current focus is resin design strategies, quality by design, and process analytical technology together with empirical and mechanistic modelling of chromatography data for smarter process development and increased process understanding.