(586y) Near-Infrared Spectroscopy for Predicting Dry Granulated Ribbon Density: Effect of Modeling Approach, Chemical Composition, and Spectroscopic Signature On Calibration Model's Performance | AIChE

(586y) Near-Infrared Spectroscopy for Predicting Dry Granulated Ribbon Density: Effect of Modeling Approach, Chemical Composition, and Spectroscopic Signature On Calibration Model's Performance


Talwar, S. - Presenter, Duquesne University
Nunes, C., Bristol Myers Squibb
Stevens, T., Bristol-Myers Squibb Company
Nesarikar, V., Bristol Myers Squibb
Timmins, P., Bristol Myers Squibb
Anderson, C. A., Duquesne University
Drennen, J. K. III, Duquesne University

The powder properties of pharmaceutical materials play a critical
role in determining the manufacturability and performance of solid dosage
forms.  For a product manufactured by dry granulation, a commonly
utilized unit operation in pharmaceutical industry; the properties of the
resultant granules can have a direct impact on the downstream operations
(milling, tabletting) as well as on product performance (disintegration,
dissolution and friability).   Dry granulation is often
accomplished by roller compaction process; wherein the density of the output
ribbons is often a critical quality attribute (CQA) and can impact the particle
size of milled granules, blend flowability and
tabletability.  Defining and maintaining optimal ribbon density is
therefore one of the key objectives during drug product process development and

Our research objective was to develop an objective NIR
based spectroscopic method for on/at-line determination of ribbon density
.  The
raw NIR spectrum of a sample displays information pertaining to its chemical
composition, as well as the three-dimensional physical
structure.  Thus, it was pertinent to investigate the impact of
chemical variability on the spectral representation of a physical attribute,
i.e. density.  Additionally, with respect to the NIR calibration
model, the choice of modeling algorithm can influence model's specificity to
the property of interest, and its accuracy.  Thus, the specific aims
were to evaluate the impact of (i) chemical composition on
spectroscopic signatures, and (ii) mathematical algorithms on
the NIR model performance.

The calibration model was developed utilizing standardized
compacts of excipient based formulations comprising of diluents/binders
(microcrystalline cellulose + lactose), disintegrant (0-3%), glidant (0-1.5%)
and lubricant (0-1.5%).  The model was then tested on roller
compacted ribbons and compacts containing an active pharmaceutical ingredient
(API) at various loads (0-20 % w/w).  The standard compacts were
prepared using a compaction emulator (StylcamTM) and characterized
using AOTF-NIR spectrometer.  Key formulation and process variables
evaluated were: grade of excipients, ratio of diluents, levels of lubricant and
glidant, and compression pressure.  The densities of standard
compacts were also determined using volumetric techniques (GeoPyc, AcuPyc and
dimensional measurements).  Two modeling approaches were evaluated
used for density calibration: 1) a univariate model between density and
spectral slope, and 2) multiple linear regression (MLR) approach which modeled
density as a function of spectral slope augmented with formulation information.

Near-infrared (NIR) spectroscopy is a powerful technique for
characterization of the physical attributes of pharmaceutical samples, in
addition to its utility in chemical analysis.  The physical integrity
of a sample affects its absorption of NIR radiations.  An increase in
sample density produces a lesser proportion of air-material interfaces, in
comparison to material-material interfaces.  This suggests a lower
probability of NIR photons encountering a change in refractive index, which
causes radiation scattering.  This results in a net increase in
absorption, depicted as a positive baseline shift in a sample's NIR
spectrum.  Consequently, the slope of the baseline referred to as ?spectral
? can be used to model for sample density via an univariate modeling
approach.  NIR analysis of the compact and ribbon density samples revealed
that the main effect of chemical design variability on spectroscopic signature
of density was a decrease in spectral slope values.  An increase in
API concentration in the ribbons and compacts produced smaller slopes even at
higher compression forces.  The reasons were attributed to the
appearance of new shapes/chemical peaks in the raw
spectrum.  Additionally, due to the poor deformation behavior of the
API, an increase in drug loads produced compacts of lower densities with
smaller spectral slopes.

As a next step, the NIR data in conjunction with the formulation
and process related variables was used to build a model based on a MLR approach.  The
data analysis indicated that the MLR model (error <1%) statistically
outperformed the spectral slope based univariate model.  Further, the
model also enabled prediction of ribbon density in presence of API, with
>95% accuracy for 1-10% drug load. Addition of formulation information in
the MLR model provided a better description of ribbon density; which led to an
improved model performance compared to spectral slope approach.

The use of NIR for ribbon density measurement has several
benefits, e.g. non-destructive, rapid, improved statistics, potential for
at/in-line monitoring, over traditional volumetric methods.  The NIR
method showed capability for prediction of ribbon density at pilot-scale, using
calibrations developed on compacts created at small scale.  The study
also demonstrated that a calibration model based on standard compacts can
effectively be utilized to predict density of ribbons samples obtained from
product manufacturing operations.  The MLR model approach has a
wide-ranging applicability during development of low dose formulations, as the
model is largely unaffected by the variations in API or excipients encountered
during formulation robustness and process development stages.