accelerating-preformulation-studies-with-ai
Struggling with slow, costly preformulation studies for new biologics? AI offers a powerful solution to tame this complexity. Learn how AI is accelerating preformulation studies, driving efficiency and saving vital resources.
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1. Current Situation
2. Typical Market Trends
3. Current Challenges and How They Are Solved
5. Value Provided to Customers
6. FAQ
Taming Complexity: How AI is Accelerating Preformulation Studies
The journey of a new biologic from a promising molecule to a stable, effective drug product is full of challenges. For CMC and Drug Product Development leaders, the preformulation stage is a key point where fundamental decisions shape the entire development trajectory. Getting through this stage efficiently helps meet tight deadlines and save money.
1. Current Situation
Traditionally, preformulation has been a process that takes a lot of resources, relying heavily on lots of repeated lab experiments. This try-it-and-see approach is often slow and can use up a lot of expensive drug substance. The complex nature of biologic molecules means they can never be fully characterized, needing careful checks to make sure changes don't harm the product. This complexity often causes slowdowns, especially for advanced delivery systems like long-acting injectables, which need precise release profiles over extended periods. The pressure to build a strong CMC story for investors and regulatory bodies makes this early phase even harder. For many teams, especially in smaller or virtual biotech companies, not having enough staff makes this job even tougher. [1][2][3][4]
2. Typical Market Trends
The pharmaceutical industry is changing a lot because new treatments need to get to market faster. The global drug formulation market is expected to grow from $1.7 trillion in 2025 to $2.8 trillion by 2035. This growth is driven by a growing demand for biologics and personalized medicines, which need special kinds of formulas. [6, 7][8]
In response, there's a growing trend to outsource work, with the formulation development outsourcing market expected to reach over $60 billion by 2030. Pharmaceutical and biotech companies are working more with Contract Development and Manufacturing Organizations (CDMOs) to get expert help, control rising R&D costs, and speed up development. The global CDMO market itself was valued at over $225 billion in 2024, with North America holding the largest share. This is especially true for complex biologics like monoclonal antibodies, cell and gene therapies, and mRNA-based therapeutics, where advanced manufacturing technologies are key. [9, 10][11, 12, 13, 14]
3. Current Challenges and How They Are Solved
CMC leaders face specific pressures in early development. For a fast-track biotech leader, the main issue is huge time pressure from the board and no room for mistakes. They're wary of vendors who don't offer deep strategic thinking. They need a partner who thinks ahead and acts as a strategic guide, not just someone who follows orders.
For leaders at a small biotech with no internal drug product team, the challenges are staffing limitations and often, bad experiences with service providers who are too academic. They are under pressure to build a strong CMC story for investors but don't have the internal staff to handle many different vendors. They need an independent team focused on solutions, offering clear communication and reliable results without creating extra work.
Mid-size biotech directors often have partners they usually work with but run into problems with new types of treatments or when they don't have enough internal capacity. Their biggest headaches are slow internal processes and the hassle of bringing on new vendors through purchasing. They need a good, strong reason, like a specific, complex challenge, to justify hiring a new partner and trust that this new addition won't cause problems.
Everyone still relies on old trial-and-error methods, which wastes R&D money and slows things down. The industry is now turning to Artificial Intelligence (AI) and Machine Learning (ML) to solve these problems. AI-driven platforms can look at huge amounts of data to guess formulation stability, how well it dissolves, and other key factors, so fewer actual experiments are needed. By simulating how a drug will act, these models help reduce risks in development and speed up decisions. Molecules discovered using AI are already succeeding more often in early clinical trials compared to traditional methods. [2][15, 16][1]
4. How Leukocare Can Support These Challenges. [18, 19]
Leukocare addresses these specific problems by combining advanced data science with deep knowledge of formulations. Our approach is based on predictive modeling and AI-powered stability predictions, customized for what each customer needs.
For the Fast-Track Biotech Leader, our Smart Formulation Platform offers a data-driven, forward-thinking way to create a strong, market-ready formulation. We act as strategic partners, working with CMC pros to find the quickest, smoothest path to Biologics License Application (BLA).
For the Small Biotech, we offer an organized, proactive partnership. We provide one clear contact person and provide reliable results and paperwork that meets investor and regulatory standards. Our focus is on giving real understanding and solid progress, not just industry buzzwords.
To get started with a Mid-size Biotech, we start by tackling specific, tough problems like lyostability or issues with a new type of treatment. We show our worth through a small project, demonstrating how we can help internal drug product teams by solving a hard problem, which builds trust to grow the partnership.
5. Value Provided to Customers
We offer speed, reduced risk, and a clear path forward, all based on data.
For the Ideal Customer (Fast-Track Biotech Leader): The main benefit is a data-driven formulation designed for tight deadlines. Our promise is simple: "We help you reach BLA faster—with a formulation designed by science, guided by data, and built for regulatory success."
For Small Biotechs: We provide data-informed decision-making and hands-on support that speeds up development for Phase I. Our promise offers clarity and reliability: "We give you structure, speed, and real value—driven by data, and delivered with reliability."
For Mid-size Biotechs: The benefit is reliable, data-driven expertise for extra work or specific challenges without internal issues. Our promise is clear and reliable: "Let us solve one complex problem—using our modeling platform and formulation intelligence to deliver results you can trust."
By going beyond old, trial-and-error methods, we help our partners save expensive drug substance, shorten development times, and create a stronger, data-supported CMC package from the start.
6. FAQ
Q: How does AI actually accelerate preformulation?
A: AI and machine learning models look at existing data about ingredients, buffer conditions, and molecule features to guess which formulations will likely be stable and work well. This allows us to focus lab work on the best candidates, greatly cutting down on experiments and speeding up the process. One study showed a machine learning model could find the best slow-release formulation in just one try, something that usually takes several attempts. [4]
Q: What kind of data is needed to train your predictive models?
A: Our models are based on lots of past formulation project data, along with public info and data from high-throughput screening. Big, high-quality datasets are great, but newer ML methods work well even with less data, allowing for strong predictions even for new molecules.
Q: Is this approach accepted by regulatory agencies?
A: Yes, regulatory bodies like the FDA are actively supporting the use of new technologies in drug development. A data-driven way to formulation gives a stronger, clearer CMC story that shows a deep understanding of the product and process, which regulators like. [1][20]
Q: Can AI completely replace physical lab experiments?
A: Not completely. Our AI approach isn't meant to get rid of lab work, but to make it much more efficient and focused. Predictive models guide experiments, making sure lab time is spent confirming and improving the best formulation candidates, instead of just broad, speculative testing. [21]
Q: How does this data-driven approach help with new or complex modalities like viral vectors or RNA?
A: These new types of treatments have unique stability problems that typical formulation methods don't always handle well. Our platform can spot patterns and connections specific to these complex molecules, letting us create custom formulation plans that reduce development risks and help teams making decisions when exploring new treatment areas.
