Process development landscape is changing and facing new challenges. Nowadays, time to clinic and time to market are becoming more important than ever for developers and manufacturers of biopharmaceuticals.
Although historically, the development timeline for a pharmaceutical drug has been 8 to 10 years, today with the use of expedited programs, such as fast track approval and breakthrough drug designation, has made the development time shrink.
In this article, we will speak about some insights on tools to make process development more efficient. Do you want to know which are?
We are now facing shorter timelines in biopharmaceutical process development and the consequence of this change is that process development and CMC activities have become even more critical factors in the time to market.
However, today’s pipelines have an increasing number of more complex and diversified molecules, adding significant challenges to process development organizations. For some of these new molecules, the platform approach for analysis, manufacturing process, and facility fit does not match and needs to be adapted.
That is why, there is the need to improve facility efficiency by a platform approach, whether it is for development, manufacturing, or analysis.
Today, advanced approaches are emerging to support smarter process development. Such tools include high-throughput development, mechanistic modeling, and fiber-based chromatography.
High-throughput process development
The total number of experiments has increased dramatically, as there are more drug candidates entering in the biopharmaceutical’s pipelines. To facilitate these activities is vital to develop efficiency in process. One approach to doing so is high-throughput process development (HTPD) that enables to perform parallel and automation experimentation.
One example could be the need of HTPD workflows to generate stable cell lines. Some candidates are developed in parallel in a streamlined cell line development (CLD) workflow using high-throughput process. These candidates could be variants of the monoclonal antibody (mAb) construct with, e.g., optimized vs wildtype gene sequences, choice of signal peptide, or heavy chain (HC) to light chain (LC) ratio. In this process, top clones for each construct will need to be assessed for titer, viable cell density (VCD), viability, and product quality assessment.
These studies require high-throughput tools that are representative of bioreactor scales. If we take a look into the downstream process, the HTPD has been successfully applied in many industrial labs for more than a decade.
One of the most common tools in downstream HTPD is the use of 96-well plates filled with resin. This formar is a suitable for screening of binding, elution, and wash conditions. These are operated in static batch mode and can be used in manual operation or with a robotic platform.
Another common tool is the use of mini columns in sizes of 50 to 600 µL for HTPD that operate in dynamic mode.
Both tools are operated in parallel mode and use relatively small amounts of sample, which greatly increases the amount of data. This data enables better decisions and proves that complementation between tools is key.
Another approach to improve the efficiency in the development process is the mechanistic model in chromatography.
While the theory of mechanistic modeling is not new, its adoption in the biopharmaceutical industry certainly is. Its use has been delayed by a lack of user-friendly software and computer power. But now, the software is commercially available, and the capacity of computer power is constantly improving.
This mechanistic model is a mathematical representation of the physiochemical interactions during chromatography. This uses equations that describe how molecules move and interact with ligands. This is represented as differential equations. When solved by using the fitting of data from chromatographic runs you get the actual chromatographic behavior.
With this tool you can simulate chromatographic behavior and experiment in silico.
There are many applications of mechanistic modeling today. For example, it is possible to predict step elution conditions for cation exchangE, the impact of bed height variability on process performance, as well as explain certain deviations in manufacturing.
Mechanistic modeling can be used:
- As a risk assessment tool. To guide and perhaps reduce process characterization efforts, and it can also be used to predict scale-up from lab scale columns to process columns.
- To increase process development efficiency. For example in pre-clinical phases, it is possible to reduce the time needed to develop a process for toxicity runs. In Phase 1 you can reduce the number of chromatographic runs per step. In Phase 2, you can predict scale-up and support tech transfer activity. In Phase 3 to inform the risk assessment of which parameters have the biggest impact on product quality or productivity or process economy. In the commercial stage, mechanistic models can support identification of root causes or the management of deviations.
Combining HTPD and mechanistic modeling
The combination of both tools, mechanistic modeling and HTPD, creates a tool that is even stronger. One example of the benefits of this combinaation is the development of validated scale-up and scale-down models. This is important both in process development as well as for troubleshooting of the manufacturing process.
HTPD tools are typically not validated as scale-down models. This is because they have different kinds of offsets that make them less representative of a larger-scale process. I you use mechanistic modeling, you can explain the offsets and why they do not reflect the same chromatographic behavior. By recalculating the data with a mechanistic model, you get a way to validate the HTPD development format as a scaled-down model. So, you will be able to do more experiments in a validated format, which is smaller and in parallel. This leads to a more robust and optimal process design.
But this combination has some challengers. Building capabilities in HTPD and mechanistic modeling requires investments during all the process, as well as in equipment.
New emerging technologies
Apart from the approaches mentioned before, there are other technologies emerging.
Rapid purification using fiber-based chromatography is one example of such innovations. This technology utilizes the high flow rates and high capacities of cellulose fibers.
Fiber-based chromatography can offer improved protein capture compared to conventional chromatography. It has with residence times measured in seconds rather than minutes and it provides full chromatograms.
If we take a look into the applications, these are some examples:
- Screening situations where speed is key, e.g., extensive process characterization work or multiple feed studies
- Cell line screening from Ambr™ systems or Fibro plus ÄKTA™ connected to an autosampler to run multiple samples in an automated way
- Rapid titer determination
- Sensitive mAb and mAb-conjugates that benefit from quick processing and short elution time
- Low titer situations in research applications
As we have seen, in the biopharmaceutical industry, process developers face new challenges. Shorter timelines, accelerated development programs and new molecular entities have created a necessity: to understand and utilize modern tools and solutions to improve efficiency in process development.
53Biologics has the expertise and the capabilities to improve the efficiency of your biologics development and manufacturing project.
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