The Future of Digital Method Development: An Interview with Anne Marie Smith

Digital method development is the process of using digital tools to collect and analyze data sets and is the norm in most laboratories. But the introduction of new tools like artificial intelligence (AI) and machine learning (ML), could shake up the space.

The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) is a collaborative effort between regulatory bodies and pharmaceutical companies to discuss and provide guidelines for pharmaceutical product development (1). Recently, the ICH put forth new guidelines, titled “Analytical Procedure Development Q14,” which focused on analytical procedures for drug substances (1).

Anne Marie Smith, a Product Manager at ACD/Labs, presented a poster at the 2024 HPLC conference in Denver, Colorado titled “Practical Implementation of ICH Q14 Guidelines in Digital Method Development,” where she discussed these new guidelines in depth and what it means for digital method development (2).

Smith sat down with LCGC International to discuss the future of digital method development and data analysis.

Please briefly describe the new ICH Q14 guidelines are and how it impacts method development.

The guideline came into effect in June 2024, and it provides approaches for developing and maintaining analytical procedures for drug substances and products. It focuses on the use of science and risk-based approaches. It is complimentary to other guidelines like ICH Q2 (validation), and it uses some approaches and tools referenced in ICH Q8–12. The guidelines work to create robust methods with reduced effort by leveraging prior knowledge and using a more strategic approach. Two methodologies are outlined that can be used to meet the guidelines—minimal and enhanced approaches.

What are the most important takeaways that analytical scientists should be aware of from these new guidelines?

It’s important to recognize that these are guidelines and not requirements, so some interpretation will be required. Both minimal and enhanced approaches are acceptable for method development. However, the enhanced approach can give better certainty of procedure performance, serve as a foundation for the analytical procedure control strategy, and the additional development data/knowledge can improve efficiency in regulatory post approval changes. One of the best ways to get started is to build your knowledge base now. Getting data into a database can help as we learn more about how to utilize artificial intelligence (AI) and machine learning (ML) and give you a better starting point for your method development.

What role does software play in developing robust analytical procedures?

Software can make it easier to develop a robust analytical procedure, reducing the number of runs a user needs to obtain and reducing solvents, making the process greener. It can give you a better starting point by calculating PhysChem properties like pKa, logP, and logD, which helps determine a suitable pH for method development based on your compounds and column and help determine the most suitable or varied columns. It can also be used to help transfer methods between laboratories or types of instruments (high-performance liquid chromatography [HPLC] to ultrahigh-pressure liquid chromatography [UHPLC]) potentially leading to shorter, greener methods. Software can help you make data-driven decisions as you design your experiments for screening, optimization, and robustness leading to better separations with fewer experiments. By understanding the design space, you can understand the impact of slight changes and ensure the robustness of your method. Furthermore, software makes it easier to keep track of everything you have done, leading to accurate and complete documentation with the ability to leverage that knowledge in the future.

What are the biggest challenges or roadblocks you see laboratories facing today when it comes to software for research and development? How can they address these challenges?

There has been a move to software-as-a-service (SaaS) and cloud-based services. Applications can be delivered through a browser, but that does not address where the data is stored or how you get it to the cloud. Addressing this involves various infrastructure strategies like determining what data is required on demand and edge computing.

What are the biggest trends that you think we’ll see in analytical chemistry informatics over the next few years?

There is a strong push and interest in AI and ML, though everyone is still learning how to do this.

What role will artificial intelligence and machine learning play in this space moving forward?

Companies gather a plethora of data daily. Being able to learn from this data can lead to quicker development of more robust methods. It removes the low-hanging fruit, enabling the scientist to have a better starting point and focus on the science. It can also reduce the number of true experiments needed, leading to many environmental benefits.

Is there anything else I’m not asking you about this topic?

In what ways does use of prior knowledge help with method development? How does one get started with setting up a knowledge base? Using prior knowledge helps users reduce work by finding an existing method that would work for their compound, or something similar enough that they only need to make minor tweaks to it. This reduces time for experiments to be completed and turns the method development process into a greener process. Setting up the knowledge base is easy and can often be automated. Structures, data and metadata are linked together so that the information can easily be extracted and found when needed.

This interview was lightly edited for clarity.

References

(1) International Council of Harmonisation, Welcome to the ICH Official Website. ICH.org. Available at: https://www.ich.org/ (accessed 2024-08-05).

(2) HPLC 2024, HPLC 2024 - 52nd International Symposium on High Performance Liquid Phase Separations and Related Techniques. HPLC2024.org. Available at: https://hplc2024-symposium.org/ (accessed 2024-08-05).

About the Interviewee

Anne Marie Smith is currently a Product Manager at ACD/Labs.