Rethinking Drug Formulation Approaches

Drug formulation can feel like a game of chess. Each move is critical. The outcome is uncertain, reflecting the unpredictability of formulation experiments. This dynamic leads to a constant cycle of trial and error. At amofor, we develop a so-called “digital twin” of the formulation. It works like a grandmaster-level chess advisor. It provides clarity and guides you to plan every move with precision from the outset.

Understanding the Digital Twin

What is a Digital Twin? A digital twin is an advanced in-silico model that accompanies the drug formulation process. Key features:

  • It can analyze intermolecular interaction profiles and predicts how a drug behaves under different conditions. For example, it can simulate how an Active Pharmaceutical Ingredient (API) might interact with other molecules. This could be in solution or tablet form.
  • It’s constantly updated with real-time experimental data and integrated with new functions. This allows accurate simulations.
  • It helps you to foresee pitfalls and even solve them before they’re put into practice.

The Proactive Approach

“The best way,” as Dr. Christian Lübbert often says to partners, “is to create a digital twin of a formulation from the start.”  This approach ensures a more straightforward and insightful development process from the beginning of the development.

An effective collaboration ideally begins with preclinical toxicology studies and continues with precise formulation selection at subsequent development stages. Early integration is key, starting with an understanding of the API’s interactions with for example polymers (in case of ASD formulations). From this base, we guide partners through key decisions and processes. These include formulation platform selection, polymer and drug load selection, estimating shelf-life, selecting the right solvents, and choosing a manufacturing method (e.g. spray-drying). This proactive strategy streamlines the path from a few lead candidates to the final drug. It makes it faster and more cost-effective.

Yet, today’s reality is in contrast to this ideal drug formulation process. “Normally, it works the other way around,” explains Christian Lübbert. Drug formulators come to us locked into the complexity of their formulations, seeking solutions for deeply ingrained problems (e.g. fast crystallizing compounds). This reactive approach, which is common but far from optimal, underscores the urgent need for a paradigm shift.

Case study

One of our recent partnerships was remarkably fulfilling. Our goal was to analyze formulations properties such as physical stability and manufacturing routes, all happening at a late clinical phase. Several months after successful completion of the project, the client encountered stability issues within a specific batch of the formulation. The client reached out to us for expert advice. Thanks to the thoroughness of the designed modeling approach, and by leveraging the insights and data from our prior engagement, we could promptly narrow down and mitigate the potential sources of error. This swift resolution underscored the value of our initial comprehensive analysis, enabling the client to address the batch-related issues effectively and with minimal additional investigation.

Practical Advice for Leveraging Digital Twins in Experimental Formulation

If you’re struggling with the complexity of experimental drug formulation, consider the digital twin as your innovative ally. This tool helps to simplify complex concepts and dives deep into data, guiding you clearly through the massive data landscape.

Here’s how you can apply it practically:

  1. Start with Predictive Modeling: Initiate your formulation journey by creating a digital twin. Use it to analyze initial data from a few drug candidates for preclinical studies. It allows you to predict outcomes early on and sets a solid foundation for further development.
  2. Embrace a Continuous Feedback Loop: Keep refining your model as new data comes in. Integrate additional experimental findings – like solubility data, sorption data, or DSC measurements – back into your digital model. It makes both the model and your formulation experiments more effective. The aim is to promote a cycle of continuous adaptation that grows with every new piece of information.
  3. Harness Deep Scientific Expertise: Our approach goes beyond scratching at the surface and seeks to understand the “why” behind each outcome. Our experts explore the molecular interactions that drive formulation and provide insights that enable informed decision-making. By contextualizing data within a larger context, we provide clear guidance that distinguishes us from traditional CDMOs. We transform information into actionable insights for successful formulation development.

Adopting this comprehensive strategy, which combines structured modeling, iterative feedback, and deep understanding, will help you navigate the complexities of drug formulation effectively. It’s about making informed decisions every step of the way, significantly improving your chances for success.

Invitation to Connect

We’re inviting anyone who’s developing new drugs to go this innovative new way with us. Try it before conducting too many expensive experiments. It’s not about using complicated processes; it’s about understanding what we’re doing and why it works.

Contact us to start the discussion, even if you face no formulation problems yet.