(668c) On-Line Estimation of Diastereomer Composition Using Raman: Differentiation in High and Low Slurry Density Pls Models

Raman Spectroscopy is capable of differentiating
similar molecules with different crystal lattice structures given the lattice
vibrations of the entire molecule within the lattice structure of the crystal
is different.  In addition, the use of
fiber optics to collect data through an immersion probe allows analysis of
solid phase composition in real-time. 
However, the Raman intensity of the solids depends on the amount of
inelastic scattering of the solids detected by the analyzer within the
detection zone.  As a result, the
relative Raman intensity corresponding to the diastereomers in a slurry will be
impacted by a number of solid-state factors. 
It has been suggested that Raman intensity with respect to different
polymorphs may be a function of particle size and shape.  This is based on the assumption that Raman
signals primarily come from the surface of the crystals.  Additionally, slurry density may be another
solid-state factor since the number of crystals inside the detection zone will
influence the Raman intensity of the solids. 
In theory, the Raman spectrum will be affected by the amount of solvent
and solids detected.  Thus, slurry
density should impact the Raman signal intensity of the solid phase.

In the present work, the first objective was to examine
whether information provided by Raman spectroscopy is sufficient or whether it
needs to be complemented by additional process measurements in order to provide
an accurate estimation, through a Partial Least Square (PLS) model, of the
solid composition of one of the two diastereomers involved in the production of
an active pharmaceutical ingredient, denoted here as compound A.  The selection of process variables was based
on the cooling crystallization procedure of compound A.  Since the changing temperature, slurry
density, and percent composition of the diastereomers would affect peak
position and intensity, those were the variables selected in our modeling
task.  Partial Least Square regression
(PLS) was used to quantify the composition of the diastereomers mixture.  The second objective was to examine whether
additional subsets of the calibration data needed to build models that better
represent data variations.  Principle
Component Analysis (PCA) was used to examine the data and separate data into two
different subsets according to slurry density. 

This presentation addresses the
estimation of fractional composition of two diastereomers during
crystallization.  The estimation is
obtained through a Partial Least Square (PLS) model that utilizes on-line Raman
spectroscopy and additional process information such as temperature and slurry
density.  12 PLS models were constructed
with the same 95 calibration standards using Raman spectra, temperature and/or
slurry density data.  They differ from
each other on whether they model all the data or one of two subsets and on
whether they involve temperature and/or slurry density along with the spectral
data.  The models were further tested
and compared against data from four 250mL scaled crystallization experiments.  It was shown that in-situ Raman spectroscopy
is capable of differentiating diastereomers in a crystallization slurry,
provided the changing process parameters of temperature and slurry density are
included in the model.  Models developed
for the high or low slurry density subsets of the data were more accurate than
the corresponding models developed for the whole data.  PCA analysis was used for the effective
separation of the training data into two subgroups.  The sample-to-model distance also proved useful in selecting the
PLS model to be used with a new data point to estimate the diastereomer
composition.