The future of chemical development won’t be defined by single answers - but by probability distributions
The future of chemical development won’t be defined by single answers - but by probability distributions
Bayesian optimization using Gaussian process models already explore and map parameter spaces with remarkable precision.
They don’t just identify optimal conditions - they quantify uncertainty, confidence, and robustness along the way.
Yet when it comes to regulatory submission and design space justification, these probabilistic insights are still left out.
Regulators typically require traditional DoEs to demonstrate robustness - even if the relevant part of the parameter space has already been systematically explored during the optimization process.
This can create additional effort, unnecessary experiments, and an artificial divide between modern optimization and regulatory compliance.
Now is the moment for regulatory bodies to begin considering probabilistic models for design space definition - to move from only accepting discrete results to probabilistic outputs that reflect how processes truly behave.
Right now, there’s a lack of clear guidance, validation protocols, and benchmark frameworks that would allow probabilistic models to be formally accepted for regulatory justification.
We see this as a critical enabler to bridge data-driven optimization in R&D with robustness evaluation and process validation in commercial manufacturing.
The next step for process science isn’t just automation - it’s regulatory readiness for probabilistic understanding.
