The chirality of enantiomers can be the sole distinguishing factor between beneficial and harmful medications, and between active and non-active enantiomers that may comprise up to half of a product. However, chiral resolution methods are challenging.

Crystallization, for example, generally results in racemic crystals that aren’t easily resolved. Likewise, popular quantification methods, such as chiral HPLC and polarimetry, which are required to understand chiral systems, often are limited by the need for multiple separation methods for multicomponent samples.

A new chiral quantification method developed by a team of European researchers offers a more streamlined method to separate these chemically-identical but molecularly-different enantiomers. It uses ultraviolet-circular dichroism spectroscopy and multivariate partial least squares calibration models to accurately measure the full quaternary phase diagram. That diagram is considered vital to optimize the chiral resolution process.

Unique spectra generated

“With chiral spectroscopy, we generate unique spectra that are characteristic of the sample composition,” Maxime Charpentier, PhD, a researcher at the EPSRC Centre for Innovative Manufacturing in Continuous Manufacturing and Crystallization, University of Strathclyde and first author of a recent paper, tells GEN. This method was tested using the epilepsy medication levetiracetam.

The researchers used chiral spectroscopy to understand the solid-liquid equilibria in the system and to identify the conditions permitting a chiral separation process with crystallization. It assessed the asymmetry between the solid solubilities, and particularly, of the enantiospecific cocrystal that can be crystallized from the racemic composition. Importantly, the solubility information can be extracted from a single measurement step.

“Chiral quantification is relevant at every step of a chiral drug manufacturing process, such as organic synthesis, solid state analysis and drug formulation. It also is necessary for the development of crystallization processes,” Charpentier points out.

This new method can be applied to commercial systems as long as it fits the validation criteria, he says. Although the researchers used UV circular dichroism to generate spectra, Charpentier says vibrational circular dichroism or Raman optical activity also are options. “The key is to associate the spectral information with the sample compositions through a calibration,” he explains. “For this, multivariate analysis is particularly relevant, as it permits the spectra evolution to be linked accurately with the varying composition of each component in mixtures.”

Charpentier wants to further explore this method using different molecular systems and applications to identify new pros and cons. “There are great prospects for progress in chiral spectra generation with new devices, novel techniques, and optimized analytical conditions,” he says.

The growth of data analytics will only expand, and will reach ever deeper as new tools are developed, and artificial intelligence and machine learning mature.

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