Development of novel rapid design methods for separation of enantiomers by crystallization: a process systems engineering approach

Pharmaceutical industries face continuous challenges in terms of process development for new drug substances and product.

Considerable amount of time and resources, both human and material, are spent on the engineering research, including the process, scale-up and control development for the promising molecules synthesized often in micro-gram scale firstly.

Crystallization is a particularly important in the pharmaceutical industry as it links the drug substance (active pharmaceutical ingredient) and drug product (e.g. tablets, injectables etc.) manufacturing.

Good operation of crystallization is not only important to achieve the desired yield, but the purity, polymorphism, chirality and also the particle size and shape strongly impact the performance of the drug product, e.g. the bioavailability, dissolution rate and, not rarely, the toxicity.

Crystallization processes are currently being designed in pure experimental fashion, relying on the pharmaceutical Quality by Design (QbD), which undoubtedly moved and moves the pharmaceutical industry forward, but it has tremendous experimental burden. The digital era paws the way for competitive and often complementary computer aided process design techniques.

The goal of this project is the development of novel process systems engineering methods that are suitable for rapid and robust-optimal crystallization process design.

For experimental and simulation demonstration, to keep the simplicity without loss of generality, a challenging but frequently occurring pharmaceutical crystallization process will be considered: the preferential crystallization of enantiomers.

For rapid experimental design, process analytical technology based feedback and feedforward control techniques will be developed as a direct alternative for QbD strategies.

Multi-population balance models will be developed and applied for optimizing digital design as well as for scale-up and potential batch-to-continuous process transition analyses.