As molecules grow in complexity, traditional drug formulation workflows are hitting their limits. Design of Experiments (DoE) show which formulations work, but not why. It’s the difference between observing correlation and understanding causality. This limitation is a particular hurdle for the development of Amorphous Solid Dispersions (ASDs).
If drug formulators can explain why an ASD works, they can design it faster, better, and with greater reliability for scale-up production. amofor provides the framework for this shift: physical modeling rooted in thermodynamics that delivers true mechanistic understanding.
The Limits of Current Drug Formulation Practices
For decades, formulators have been in reactive mode. Even sophisticated DoE begin with testing a wide array of combinations: drug load, polymer type, solvent system. This process, while more structured than pure trial-and-error, is resource-intensive and fundamentally retrospective. It can identify a functional formulation but provides no insight into the root cause of success. Teams remain vulnerable during scale-up.
This approach is especially fragile when applied to today’s complex APIs. These molecules, with high molecular weights, high lipophilicity, and complex hydrogen bonding networks, often exist outside the boundaries of traditional rules like Lipinski’s Rule of Five. Without understanding, each new compound resets the learning curve to zero.
Regulatory bodies now encourage developers to apply QbD principles, defining target product profiles and designing formulations based on scientific, risk-based workflows,” says Dr. Christian Lübbert, CEO of amofor.
Only mechanistic understanding can close this gap. Therefore, we bet that the next decade will be defined as much on digital formulation workflows as on experiments.
What Mechanistic Understanding Means in Drug Formulation
A mechanism is a system of causally interacting parts and processes that produce one or more effects (Mechanisms of Science). In drug formulation, this means tracing the molecular forces behind solubility, stability, and drug release. Why does one polymer work while another fails? Why does drug release fall off a cliff at 15% loading? Why does crystallization only appear in some batches? These are not questions DoE can answer.
At amofor, we build this understanding using physical modeling and equations grounded in thermodynamics and molecular mobility. Our models, developed and validated across hundreds of systems in collaboration with big pharma, are designed to explain a formulation’s behavior, not just predict it.
Only a Systems View Can Make ASDs Predictable
Formulation success, especially for ASDs, depends on the complex interplay of all components: API, polymer, solvent, and water. Most tools look at APIs in isolation or binary interactions. We model the entire network simultaneously:
- API-polymer miscibility
- Polymer-water interactions and plasticization effects
- API-API self-association and aggregation risks
- The dynamic evolution of these interactions during processing
These networks are complex. A small shift in solvent ratio or drying temperature can flip the balance, leading to phase separation, crystallization, or failed release. By quantifying these interaction energies using physical modeling, we decode the molecular behaviors that determine real-world performance. And we show you why it will work and under what precise conditions.
A High-Fidelity Predictive Workflow, Powered by Physics-Based Modeling
This systemic view is the foundation of a predictive workflow that unites all the critical models needed for an ASD formulation. At its foundation lies PC-SAFT thermodynamics, which describes solubility and miscibility with molecular precision. On top of this, we layer models for molecular mobility and diffusion to capture shelf life and release, glass transition and water uptake to describe metastability, crystallization kinetics to quantify stability risks, and drying and evaporation physics to optimize processing. Together, these models form a digital twin of your formulation that forecasts behavior under specific conditions with remarkable accuracy.
This predictive power fundamentally transforms the role of experimentation. By simulating hundreds of virtual formulations, we dramatically narrow the experimental workload. You can eliminate thermodynamically unstable options and focus your experiments only on the most promising candidates, saving precious lab time and material resources.
Formulators can:
- Confidently select the optimal polymer and excipients.
- Identify the exact drug load limit before phase separation.
- Predict shelf life under ICH storage conditions.
- Optimize secondary drying times with precision.
- Simulate release profiles in biorelevant media.
Currently, real-world adoption is growing rapidly. More and more pharmaceutical companies are building their formulation processes around QbD workflows1 powered by physical modeling. amofor’s SOLCALC softwarehas become core infrastructure for major formulation teams.
This shift is transforming how companies design, decide, and de-risk, bringing mechanistic clarity to every stage of development.
Real-World Formulation Problems Demand Mechanistic Solutions
We are often called in once a formulation is already defined, but a stubborn issue remains. Trace crystallinity. Drying that takes days. Release failures that suddenly emerge during scale-up.
For these specific problems we reverse engineer the formulation and process, tracing the molecular interactions step by step until the root cause is revealed. Solutions are often small: a minor solvent-ratio change, a shift in drying temperature, a tweak in process conditions. These “tiny” adjustments can eliminate crystallinity, cut drying times by half, or restore batch consistency. But they are only visible if you understand why problems occur.
While we often solve late-stage crises, the greatest value comes when we are engaged from the start. Involving us already in preclinical stages means formulations can be designed right the first time, avoiding costly troubleshooting downstream.
In applied projects, this approach has delivered concrete results:
- Our shelf life models have been benchmarked against more than 150 long-term stability studies, predicting real-world crystallization with an accuracy of ±20%.
- In collaboration with Janssen, we showed how two identical ASDs, same drug, same polymer, same ratio, can differ in stability by a factor of 1,000 (6 months vs. 6,000 years) simply due to different cooling rates.
- Our manufacturing models have helped partners cut secondary drying times by 50–75%, directly accelerating scale-up and reducing production bottlenecks.
These are concrete recipes and process recommendations that give our clients control over the most difficult parts of formulation from the very start.
amofor Focuses Where Complexity Lives
We do not specialize in simple generics like acetaminophen or ibuprofen. We focus on the frontier of drug development: the complex compounds that define the industry’s future and too often block pipelines. Beyond the rule of five2 is our playground and daily business.
Our SOLCALC software and in-silico services are designed to meet this complexity head-on. With minimal targeted lab data, we simulate behavior across the full formulation lifecycle, from candidate selection to process optimization and shelf-life prediction.
To provide these precise answers, we maintain a sharp focus on the molecular fundamentals. This means we deliberately don’t simulate nozzle geometry, fluid flow, or tablet compression. Our strength lies in understanding intermolecular interactions, the root cause of formulation performance.
This is the foundation of true formulation-by-design.
Ready to move beyond correlation?
Book a consulting session with Dr. Christian Lübbert. We discuss your highly specific problems and can walk you through a case study based on your molecule to deliver a tailored mechanistic recommendations with actionable insights.
- https://www.fda.gov/regulatory-information/search-fda-guidance-documents/q8r2-pharmaceutical-development
- Degoey et al.: Beyond the Rule of 5: Lessons Learned from AbbVie’s Drugs and Compound Collection. J. Med. Chem. 2018, 61, 7, 2636–2651. https://pubs.acs.org/doi/10.1021/acs.jmedchem.7b00717