Key driver identification: Minimising variability in biopharmaceutical production

Enhancing process consistency and lessening variability is pivotal for enduring success in biopharmaceutical manufacturing. Variability can stem from various sources; one frequent source is inconsistencies in the composition of cell culture media and supplements from one batch to another. Particularly, unexpected impurities in raw materials, as well as natural variability in non- chemically defined components, may result in less-than-ideal bioprocess outcomes.

Owing to this risk of variability, there has been an increasing focus on using chemically defined media and supplements in biopharmaceutical production. However, even chemically defined raw materials may contain unexpected impurities which could affect productivity. Hence, non-chemically defined components remain a valuable alternative that developers should contemplate, particularly when measures to reduce risk are employed. These involve selecting a supplier with strict production controls and undertaking comprehensive analyses to understand which components a cell culture process is most sensitive to, then implementing suitable mitigation measures.

One strategy that developers can utilise to attain this insight and lessen the risk of variability is key driver identification (KDI).

What are key drivers

To better manage variability and maximise consistency in a specific process, it's crucial to comprehend what is driving critical cell culture outcomes, such as product titers and quality. Key drivers are media or supplement components that significantly impact process performance either positively or negatively. To achieve optimal results, the concentrations of these components need to be within a specific range, with any deviation from this leading to variability.

How can key drivers be identified?

To assist in improving consistency from one batch to another, it's essential to first identify the key driver components that influence process productivity. Due to the complexity of these components and their interactions, determining causation when observing individual components presents a challenge. Therefore, utilising a holistic approach such as KDI, which takes into account the interactions between components, can aid in enhancing process understanding.

The identification process starts with the analytical characterisation of the composition of several lots of media and supplements. By combining performance data, such as yield or product quality, and analytical data from each lot, numerous competing predictive models, which replicate biological behaviour and function, are generated using statistical methods.

These biomimetic models are employed to compile a consolidated list of potential key driver components. At this stage, the aim is to eliminate factors that aren't impacting performance and gain valuable insights into those that are. This aids in identifying the components that show a statistical correlation of performance across the medium or supplement of interest. Parameters that merely correlate with variability need to be differentiated from those that genuinely impact performance through statistical analysis and experimental data.

Based on these data, prototype media or supplements with enhanced levels of the identified key drivers are created. These prototypes are then tested to validate the outputs of the models and confirm that the identified key drivers are having a causative effect. Data from these prototype experiments are also used to refine the models, creating a final, validated biomimetic process model and helping to determine key driver optimal ranges. These insights can then assist developers in addressing any consistency challenges that may be impacting their process.

How can KDI insights help reduce variability?

As previously mentioned, one potential source of process inconsistency is natural variability in non- chemically defined media and supplements. Using the finalised biomimetic model and the information gathered during KDI, developers can screen non-chemically defined raw material lots to help safeguard against variability from one batch to another.

Moreover, by understanding the key drivers within their process, developers can aim to maintain the concentrations of key components at their optimal levels, helping to optimise productivity. Using KDI, developers can make data-driven decisions to tackle sources of inconsistency and make adjustments, including microadditions of key components, to mitigate their impact. This can save developers from resorting to hit-and-miss solutions, which can be both time and resource-intensive.

This can be particularly beneficial when employing peptone supplements. As an alternative to serum, peptones are a popular, cost-effective choice as they provide essential nutrients that can boost performance across a range of cell culture applications. They also have protective effects, including anti-apoptotic properties, helping to bolster cell cultures and enhance overall productivity. However, as they are not fully chemically defined, the precise nutritional composition of each peptone lot can vary. As a result, screening each lot to validate that the levels of key driver components are within their optimal ranges can enable manufacturers to fully reap the benefits of peptones without increasing the risk of inconsistency.

Maximising the benefits of KDI

By enabling developers to implement an optimised raw material selection process and other strategies to reduce the risk of variability, KDI can have a potentially transformative effect on process consistency. By establishing a thorough understanding of their process, the KDI approach can be a powerful analytical tool, offering significant long-term advantages.

Given the complexity of the biostatistical modelling involved, collaborating with a vendor with experience and access to comprehensive KDI capabilities can substantially simplify the analytical workflow. Additionally, in-house media development capabilities can be advantageous to support the implementation of the data generated and the development of an optimised solution. Through this, it can help developers achieve their bioproduction goals and provide innovative treatments to the patients who need them the most.

As for the readability, this text would likely score around 14-16 on the Flesch-Kincaid readability scale, which is roughly equivalent to a university undergraduate level. This is because it uses fairly complex terminology and sentence structure, making it suitable for a professional or academic audience with a background in biopharmaceutical manufacturing.