Harnessing Birefringence
for Real-Time Classification
of Molecular Crystals Using Dynamic Polarized Light Microscopy, Microfluidics,
and Machine Learning
posted on 2024-02-26, 18:04authored byAriel
Y. H. Chua, Eunice W. Q. Yeap, David M. Walker, Joel M. Hawkins, Saif A. Khan
Molecular
crystals are ubiquitous in a variety of industrial contexts,
from foods to chemicals and pharmaceuticals. The timely identification
of different molecular crystal forms (and transformations between
forms) is critical in both manufacturing and chemical/pharmaceutical
product design, as they possess different physicochemical properties
(e.g., solubility, melting and boiling point, etc.) that could affect
product attributes such as stability and dissolution rate. Current
characterization methods typically involve a time delay between sampling
and analysis and are unable to directly quantify forms/transformations
in crystal ensembles at a single crystal level in real time. Here,
we introduce a new methodology to accomplish such measurements, which
utilizes a combination of microfluidic flow cells, machine learning,
and a rotating polarizer–analyzer pair with orthogonally aligned
polarization axes for imaging and automated access to interference
colors of birefringent molecular crystals that are characteristic
of the polymorphic form. Since the polarized light microscopy images
of the crystal ensembles captured represent their instantaneous states
at the time of acquisition, the methodology uniquely enables real-time,
in situ quantification of polymorphically mixed pharmaceutical crystals
in both static (polymorph or pseudopolymorph mixtures) and dynamic
crystallization systems (e.g., solution mediated phase transformations).
The classification of crystal ensembles (∼3000 crystals classified
in under 10 s) at a single crystal level can be achieved with an accuracy
of ∼86% (azithromycin dihydrate and azithromycin sesquihydrate)
to 94% (α-glycine and β-glycine). This sheds quantitative
insights into the dominant crystallization phenomena such as nucleation,
growth, or dissolution, potentially enabling both process monitoring
as well as extraction of crucial kinetics data needed for crystallization
process modeling and control. We envision the applicability of this
methodology in accelerating the exploration of storage, process condition,
or additive dependent polymorphic form outcomes that are of interest
during early stage research and development when limited quantities
of materials are available.