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When we founded amofor more than six years ago, most of what we did lived in equations, theories, and model ideas. We believed that physical modeling could change how drug formulations are developed, making them faster and better, but in the early days it still felt like a promise. 2025 was the first year where […]

Pharmaceutical formulation is quietly changing. For the last 50 years, drug formulation relied on long iteration cycles, and trial-and-error: mixing excipients -> testing -> seeing what happens -> improve and repeat. For new highly complex molecules, this approach is no longer viable. This is why the industry is shifting to a predict-first, science-driven strategy. Quality-by-Design

“It’s a job you can’t find anywhere else.” That’s how Marius Rother, not without enthusiasm, describes his highly specialized role at amofor. It’s a position at the intersection of science, software development, and tailored problem-solving in drug formulation. With these simulations, Marius helps pharmaceutical companies tackle their toughest formulation challenges using physics-based simulation tools. But

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

Every pharmaceutical formulator knows this painful reality: your drug powder comes out of the spray dryer, but it still needs secondary drying to remove residual solvents. It slows down your workflow and can compromise product stability. Two recent studies by Kerkhoff et al. (2025) and Kerkhoff et al. (2025) show how amofor uses PC-SAFT-based predictive

Most machine learning models fail in formulation for one simple reason: The data they need doesn’t exist.You can’t generate shelf life data across a ten-year horizon within a development timeline or measure what happens inside a droplet during spray drying in real time. At amofor, we approach the problem differently. Instead of training models on

How can formulation scientists make more thoughtful decisions without becoming thermodynamics experts? That’s the question that led to SOLCALC. SOLCALC is an Excel-based add-in designed specifically for drug formulation applications. With the proper training, this software transforms PC-SAFT modeling from a powerful theory into a formulation tool that scientists can use confidently. Below, we explain

Can predictive modeling replace years of drug stability testing? This case study explores how researchers of Janssen Pharmaceuticals, TU Dortmund University, and amofor teamed up to tackle one of the most persistent challenges in ASD formulation: accurately predicting shelf life without waiting years for empirical data. The team developed a physics-based approach that combines long-term

Creating effective and stable drug formulations shouldn’t be guesswork. It’s deeply rooted in science. Yet, many formulators still rely on labor-intensive experimental trial-and-error approaches to probe solubilities and mitigate crystallization risks. Worse, without a deep understanding of why a formulation works, scientists can’t predict how changes might affect its bioavailability, shelf life, or manufacturability. So,

Spray drying is a leading technique for manufacturing amorphous solid dispersions (ASDs). At its core lies atomization: you dissolve an active pharmaceutical ingredient (API) and polymers in a solvent mixture, feed this solution into a spray dryer, and collect the resulting powder. But anyone who’s run a spray drying campaign knows that it’s an extremely