Leveraging AI as a Tool for Freeze-Drying Process Development:

A Starting Point, Not the Finish Line

AI tools like ChatGPT, Gemini, and DeepSeek are transforming how pharmaceutical and biotech industries approach freeze-drying, aka lyophilisation, process development. From drafting protocols and regulatory content to suggesting equipment and PAT tools and formulation excipients, AI is readily accessible and offers a powerful first step in lyophilisation process design. However, while these tools can accelerate early-stage work, expert scientific judgement and contextual insight remain irreplaceable.

This application note explores the use of AI as a tool and necessary starting point to assist with both freeze-drying cycle development and formulation design for lyophilised products, while also reviewing its challenges and ultimately explaining why human expertise too, will always be essential in our field.

Tailored Product Understanding:

Every product - whether it be a monoclonal antibody, peptide, or vaccine - possesses a distinct physicochemical profile that requires a tailored approach to freeze-drying. When optimising a lyophilisation cycle recipe or formulation, this requires a combination of advanced analytical techniques and empirical testing, including:

• Freeze-Drying Microscopy (FDM) and Modulated Differential Scanning Calorimetry (mDSC) to determine critical formulation parameters such as collapse temperature, eutectic point or glass transitions. This information is crucial for developing a safe, scalable and reliable lyo process.
• Iterative empirical evaluation to assess key quality attributes including cake structure, residual moisture content, and reconstitution behaviour.

Results from these pre/post-lyophilisation assessments are critical in understanding an individual product’s characteristics and requirements for freeze-drying.  At this stage of its development, AI is not able to predict how a novel product or formulation will react to the freeze-drying process.

Formulation Development:

Whilst AI tools can be a powerful co-pilot in the early stages of freeze-drying process development, they are far less suited to supporting formulation development, where scientific intricacies and product-specific challenges go beyond the platform’s capabilities.

Formulation work requires a deep understanding of molecular behaviour, excipient interactions, and sample stability. These insights can currently only be gained through laboratory data, analytical testing, and expert interpretation. These variables are highly specific and cannot be reliably predicted or generalised by an AI model. When it comes to tailoring a formulation to ensure stability, efficacy, and safety, hands-on scientific expertise remains irreplaceable.

Further to this, it is key to understand how AI platforms access and collate data. These platforms may consider information from untrustworthy sources as fact. In our experience, this leads to inconsistent results when using AI platforms to assist with formulation design suggestions.

Equipment-Specific Calibration:

Not all freeze-dryers perform in the same way particularly when scaling up from laboratory to pilot or GMP compliant/ clinical production environments. These differences introduce critical variables that AI tools cannot fully account for, including:

• Heat transfer dynamics unique to specific shelf designs and configurations.
• Condenser capacity limitations, which can impact process efficiency and product integrity.
• Variability in chamber pressure stability, affecting reproducibility and cycle control.

Regulatory and Quality Expertise:

While AI can provide general guidance, it lacks the nuanced understanding of current regulatory feedback, emerging inspection trends, and the evolving expectations surrounding Chemistry, Manufacturing and Controls (CMC) documentation. In contrast, experienced CDMOs like Biopharma Group integrate these regulatory and quality considerations from the outset, ensuring that:

• All projects, including formulation development and cycle optimisation studies, are conducted in full alignment with recognised standards such as ISO 9001, ISO 13485, and Quality by Design (QbD) principles.
• Analytical validation plans are robust and tailored to support successful regulatory submissions, with different regulatory agencies often making assessments to their own national or regional requirements.
• Technology transfer is executed seamlessly, whether to internal manufacturing teams or external partner sites.

Case Study:

As a direct comparison between expert knowledge and the current capabilities of AI systems, Biopharma Group performed an inhouse lyophilisation cycle development case study to determine which method would produce the most efficient and robust cycle recipe.

ChatGPT 4 was used to simulate an efficient and robust freeze-drying cycle trace based on a 10% Dextran 40K solution in a DIN10R vial using a 2 mL fill volume. The prompt provided to ChatGPT is seen below:

“Simulate a freeze-drying cycle trace for a 10% Dextran solution in a DIN10R Vial with a 2ml fill and present as a graph”

This was then directly compared to a recipe designed by the R&D department at Biopharma to produce material in the same format, with a final use as internal validation samples. Sample concentration, fill volume and container size are identical between each condition.

Figure 1 below shows the simulated graph result from ChatGPT, while Figure 2 shows the actual results of the cycle recipe designed at Biopharma Group’s lyophilisation scientists.

Outcomes:

• Simulated freeze-drying parameters using AI systems provide an excellent starting point for designing a cycle recipe, incorporating viable shelf temperatures and chamber pressures.
• However, certain aspects of the simulated recipe are unreliable, most notably the change in product temperature over time, the length of the secondary drying step, and the overall cycle duration.
• The cycle designed at Biopharma Group produced visually excellent cakes using an efficient process approximately half the length of the simulated cycle.
• It should be noted that these results suggest that more complex scientific tasks, such as formulation design, are outside of the current capabilities of AI systems.

Conclusions:

AI tools represent exciting new advancements in pharmaceutical development, bringing speed, structure, and accessibility to early-stage processes. When it comes to critical operations like freeze-drying, where product integrity, patient safety, and commercial success are on the line however, there is simply no substitute for experienced, data-driven human collaboration and evaluation.

At Biopharma Group, we fully embrace the integration of digital tools but also understand their limitations. AI solutions are easily accessible and act as a reasonable initial step into investigating lyophilisation parameters. However, the data sets and knowledge these systems can access is limited and as yet not able to replace human expertise, hands-on experience and scientific knowledge.  

Want to discuss your process development needs? Contact the team today