machine-learning-models-for-excipient-selection
Formulation development for biologics is a complex challenge, with traditional excipient selection methods consuming valuable time and drug substance. Learn how machine learning models are transforming this process, enabling faster and more efficient identification of optimal excipients. Discover how to accelerate your drug product to market.
Menu
Taming Complexity: Using Machine Learning to Select the Right Excipients
FAQ
1. Current Situation
2. Typical Market Trends
3. Current Challenges and How They Are Solved
4. How Leukocare Can Support These Challenges
5. Value Provided to Customers
Taming Complexity: Using Machine Learning to Select the Right Excipients
Formulation development for biologics is a tough job. As drug product leaders, you know the pressure to move quickly from candidate selection to a stable, effective, and manufacturable product. Finding the right combination of excipients is a big piece of that puzzle.
1. Current Situation
Traditionally, selecting excipients has been a mix of experience, established platform approaches, and iterative lab work. We rely on a standard toolkit of buffers, sugars, salts, and surfactants that have worked in the past.[1, 2, 3] Design of Experiments (DoE) helps us map the formulation space, but it’s a process that uses up a lot of time and, more importantly, a lot of your valuable drug substance.
This conventional method has served the industry well, especially for standard monoclonal antibodies. It often involves a lot of trial and error.[20, 4] It can feel like you’re navigating a complex space with a limited map, especially when a molecule proves to be particularly sensitive or challenging.
2. Typical Market Trends
The landscape is changing. The biopharmaceutical market is growing, with projections showing a rise to over USD 740 billion by 2030.[5] This growth is fueled by molecules that are more complex than ever: bispecific antibodies, fusion proteins, viral vectors, and RNA-based therapies. These new modalities don't always fit neatly into existing formulation platforms.
At the same time, the pressure to shorten development timelines has never been greater.[6] The industry has seen CMC timelines shrink dramatically, driven by urgent health needs and intense competition. Everyone is looking for ways to get to the clinic faster without compromising on a robust, well-characterized product.[7, 8] This has led to a big increase in using computational tools and artificial intelligence across drug discovery and development, a market expected to grow significantly in the coming years.
3. Current Challenges and How They Are Solved
The main challenge in excipient selection is navigating the sheer number of possibilities. You have a limited amount of early-stage material, and you can’t possibly test every combination of excipients at every concentration. Key problems include:
Stability: The main goal is to prevent aggregation and degradation, which are constant risks for biologics. A poorly chosen excipient can fail to protect the molecule or even contribute to its instability.[12, 13]
Viscosity: For high-concentration formulations needed for subcutaneous delivery, viscosity can become a major hurdle, making the product difficult to manufacture and administer.
Material Constraints: In early development, every milligram of the drug substance is precious. Extensive screening studies can quickly deplete your supply, delaying other critical activities.[14]
Timeline Pressure: Traditional, iterative screening is slow. Each cycle of testing and analysis adds weeks or months to the timeline, which can be the difference in a competitive race.
To fix these problems, the industry is turning to in silico methods and machine learning (ML). These approaches use data from past formulations, combined with the specific properties of a new molecule, to predict which excipients are most likely to work.[15, 16] By analyzing protein structures and sequences, ML models can identify potential liabilities and suggest stabilizers that counteract them.[11, 17] This data-driven approach lets formulation scientists run narrower, more focused experimental studies, saving both time and material.[18, 19]
4. How Leukocare Can Support These Challenges
This is where our approach comes in. We’ve built our formulation development services around a special platform that combines artificial intelligence with our deep understanding of protein chemistry. Instead of starting with broad, conventional screening, we start with data.
Our AI-powered platform analyzes the structural and physicochemical properties of your molecule to predict its behavior and stability challenges. It then models how different excipients will interact with your specific protein. This helps us find a small, rationally selected set of candidate excipients for experimental testing.
This isn’t about replacing lab work; it’s about making it more intelligent and efficient. We can quickly rule out excipients that are unlikely to be effective and focus on those with the highest probability of success. For a company with a fast-track program, this means getting to a lead formulation faster. For a small biotech with limited resources, it means preserving precious material for other essential studies.
5. Value Provided to Customers
By using predictive modeling at the start of the formulation process, we provide clear, real benefits that align with the pressures you face.
Accelerated Timelines: Our data-first approach cuts down the time spent on experimental screening. By focusing on the most promising candidates, we help you reach a stable, manufacturable formulation faster, making the path to IND filing shorter.
Reduced Material Consumption: Because we run fewer, more targeted experiments, we use much less drug substance. This is especially valuable in early development when material is scarce.
Data-Driven Decisions: Our process provides a solid scientific reason for the chosen formulation. This creates a strong data package that supports regulatory filings and gives confidence to internal stakeholders and investors.
Making Development Less Risky: By spotting possible formulation problems early, we help de-risk the entire development program. This is particularly important for novel or difficult-to-formulate molecules where platform approaches may fall short.
A Collaborative Partner: We see ourselves as a part of your team. We provide not just data, but a smart partnership. We work with you to understand your molecule's unique needs and your program's goals, making sure the formulation strategy fits with your overall CMC plan.
Our goal is to provide a formulation built on science and data-driven, giving you a faster, more reliable path to the clinic and beyond.
FAQ
1. How reliable are machine learning predictions for excipient selection?
Predictive models are great for narrowing down options, but they are not a replacement for experimental verification. The reliability of the models really depends on the quality and breadth of the training data. Our approach uses predictions to design smarter, more efficient experiments.[21, 22] We confirm the model's suggestions with focused lab work to ensure we arrive at a strong and stable formulation.
2. Does this modeling approach work for new modalities with limited historical data?
Yes, because our models are built on basic principles of protein structure and chemistry, not just historical data for a specific molecule type. The system analyzes the unique sequence and structural features of any protein, allowing it to make useful predictions even for novel formats like bispecific antibodies or fusion proteins where what we usually know might not be enough.[23, 24]
3. What kind of data do you need to start the modeling process?
To begin, we typically need the amino acid sequence of the molecule. If a 3D structure or homology model is available, it can make the predictions better, but it is not absolutely necessary. This low data requirement allows us to start formulation assessment very early in the development process, often in parallel with cell line development.[11]
4. How does this approach fit with Design of Experiments (DoE)?
Our modeling work comes before DoE. The AI-driven predictions help us select the most important excipients to include in a DoE study. This makes the next experimental design more focused and powerful. Instead of using a DoE to screen a wide range of components, you use it to find the best concentrations and interactions of a smaller, more promising set of excipients.
