SUMMARY AND GENERAL CONCLUSIONS
SUMMARY AND GENERAL CONCLUSIONS
Traditionally, extruders are equipped with standard, univariate process monitoring sensors such as
probes monitoring temperature and pressure, which are used to control the hot-melt extrusion
process in an indirect manner by correlating the extrudate properties to these measurements. The
data generated by these sensors do not suffice to assess the critical quality attributes of the
extrudates, will not account for process-induced variations in material properties, cannot guarantee
stable processing and are not able to ensure the production of an end product with predefined and
consistent quality characteristics. Therefore, the application of spectroscopic process monitoring
tools, in particular Raman spectroscopy and NIR spectroscopy, was evaluated in this thesis, to
monitor, visualize and predict the drug concentration and the material behaviour of polymer-drug
mixtures during hot-melt extrusion, and to improve overall process understanding.
In Chapter 2, the principles of hot-melt extrusion and its advantages and possible shortcomings as a
non-conventional pharmaceutical manufacturing technique are discussed. Twin-screw extrusion is
preferred over single-screw extrusion, since it provides a better mixing capability and allows the
dispersion of an API into the polymer. Traditional monitoring and control of hot-melt extrusion
processes includes the determination of barrel and product temperature, the measurement of
pressure in the extrusion die, the continuous logging of torque values and the monitoring of screw
speed and throughput.
The pharmaceutical industry is encouraged by several regulatory authorities to implement more
advanced and innovative process analytical techniques into manufacturing processes to improve
both product quality and process performance. An overview and critical evaluation of more
advanced process analytical techniques (compared to the traditional monitoring sensors) for
monitoring and visualizing material behaviour during processing and improving the process
understanding of hot-melt extrusion is provided in Chapter 3. Various sensors such as spectroscopic
tools, ultrasonic techniques and rheological applications are available for observation of critical
process and product parameters during extrusion. An appropriate combination of the different
process analytical techniques evaluated in this chapter will lead to a thorough process understanding
and will allow monitoring of the critical process and product parameters. This monitoring will
SUMMARY AND GENERAL CONCLUSIONS
eventually lead to optimization of the HME process and the formulations, and provide the possibility
for improved process control in routine manufacturing.
In Chapter 4, the feasibility of Raman spectroscopy as a process analytical tool for the in-line
monitoring and prediction of the API concentration and for the monitoring of solid state of the
extrudates and possible interactions between polymers and drugs during hot-melt extrusion was
assessed. A Raman probe was implemented in the die of a twin-screw extruder, and spectra were
collected every 5 seconds during extrusion. For the in-line API quantification, a PLS model regressing
the collected Raman spectra versus the drug concentrations was developed. The predictive ability of
this model was evaluated by the correlation between the predicted and real drug concentration
values (R² = 0.997) and the prediction error for new measurements (RMSEP = 0.59% w/w), which
both demonstrated the suitability of Raman spectroscopy for the prediction of the API concentration
during hot-melt extrusion. A comparison between the in-line collected Raman spectra and the offline obtained DSC thermograms and ATR FT-IR spectra of the extrudate samples collected during
processing demonstrated that information concerning the solid state of a polymer-drug melt can be
obtained from the Raman spectra. Broadening of peaks in the spectra indicated that the drug was
present in its amorphous state, and the manifestation of peak shifts for both peaks corresponding to
the API and to the polymer demonstrated that interactions between both components occurred
during hot-melt extrusion.
Once the suitability of Raman spectroscopy to monitor the API concentration during hot-melt
extrusion was evaluated, an advanced validation strategy for the Raman spectroscopic drug
quantification method was applied in Chapter 5. Different models for the prediction of the API
content were compared, based on the use of single spectra or averaged spectra, and using partial
least squares regression or multivariate curve resolution. The predictive models were developed by
extruding and monitoring five calibration mixtures, each with a different API concentration and were
validated using five other validation mixtures, each extruded on three different days by two different
operators. For the validation of the different models, the accuracy profiles of the analytical
procedures were calculated, based on the β-expectation tolerance intervals and the concept of total
error (bias + standard deviation). For each of the validated API concentrations, the β-expectation
tolerance intervals were calculated. These intervals allowed the determination of the proportion of
future measurements (set at 95%) that will be found within preset acceptance limits, which were set
at 10% of the relative bias. The model developed using averaged spectra and multivariate curve
resolution was the only model which guaranteed that 95% of future concentration measurements
will not deviate more than 10% from the reference value. The model showed good precision,
SUMMARY AND GENERAL CONCLUSIONS
trueness, linearity, specificity and accuracy over the applied concentration range. The robustness of
the developed predictive model was evaluated via an experimental design varying throughput, screw
speed and barrel temperature. No significant influence of any of these process settings on the
predicted concentration values was found. The uncertainty of the measurements was assessed via
the uncertainty of bias and the expanded uncertainty at each concentration level. The uncertainty
measurements for this model showed that the unknown true value can be found at a maximum of ±
7.00% around the measured result, with a confidence level of 95%.
In Chapter 6, the Raman probe was implemented in each section of the extrusion barrel, to enhance
the understanding of material behaviour during hot-melt extrusion. The influence of variations in
drug concentration, barrel temperature and screw speed on the polymer-drug solid state and
interactions was examined. Since hot-melt extrusion is a starve fed process, the barrel will not be
completely filled with material in every segment. This causes a background signal in the in-line
collected Raman spectra, which could not be corrected for. Principal component analysis was applied
to separate this source of variation in the spectra from the relevant information. When a formulation
with a high drug load was extruded, melting of the drug occurred in the first kneading zone, located
between barrel sections 2 and 3. This was the same for a low drug load. However, an additional
spectral difference was found when the melt passed through a third kneading zone, where the drug
dissolved into the polymer and a solid glassy suspension was produced due to the high shear forces
applied on the mixture. In the Raman spectra, this was indicated by the presence of peak shifts, and
it was confirmed by FT-IR spectra collected from samples gathered from the extrusion barrel. When
extruding mixtures with a low drug load, increasing the processing temperature did not influence the
solid state of the final product or the location in the barrel where the final product is attained. At a
high drug load however, the solid state of the end product is reached further down the barrel when
the temperature is decreased. Doubling the screw speed when processing a formulation with a low
drug load did not affect the solid state of the product or the location in the barrel where it is
obtained. In contrast, at a high drug load, the section where the final product is produced, is situated
earlier in the barrel when applying a higher rotational speed. The performed in-barrel measurements
provide real-time information about polymer/drug behaviour throughout the barrel, hence allowing
the optimization of process settings and barrel and screw design required to obtain extrudates with
predefined, constant solid state characteristics.
The aim of the experimental work discussed in Chapter 7, was to classify extrudates in-line according
to their solid state. A Raman probe was implemented in the die head of an extruder, and collected
spectra every 20 seconds. The influence of variations in drug concentration, changes in barrel
SUMMARY AND GENERAL CONCLUSIONS
temperature and three different screw configurations on the solid state of the extrudates was
assessed. Soft independent modelling of class analogy was performed to develop a model which
allowed distinction between glassy solid solutions and crystalline dispersions of a poorly soluble API
in a polymer, based on the in-line collected Raman spectra. Off-line analysis with DSC and XRD
appeared not to be sensitive enough to detect small fractions of crystalline drug, which can later on
induce recrystallization of all of the API during storage. These small crystalline fractions were
detected in the Raman spectra, where the solid state characterization was based on the appearance
of peak broadening and peak shifts. Modifications in screw configuration did not affect the solid state
of the final products.
The influence of die pressure on the Raman spectra was also examined in Chapter 7. The applied
drug concentration, processing temperature and feeder performance all influence the die pressure,
and this effect can be found in the background signal of the Raman spectra before pre-processing.
Disturbances in the feeding process can thus be observed and identified in the Raman
measurements.
In Chapter 8, the suitability of NIR spectroscopy for the in-line determination of the drug
concentration, the polymer-drug solid state and molecular interactions during extrusion was
evaluated. The NIR probe was mounted in the extrusion die, and NIR spectra of the melt were
collected every 30 seconds. A PLS model was developed for in-line API concentration monitoring. A
good correlation between predicted and real drug concentrations was found (R² = 0.97), and the
prediction error for new measurements was 1.54% (w/w). The in-line collected NIR spectra showed
peak broadening during extrusion, caused by the presence of amorphous API, and a new peak was
found, which was not present in the spectra from the physical mixtures, indicating the formation of
hydrogen bonds during processing.
In Chapter 9, the aim was to evaluate the suitability of ultrasonic techniques as process analytical
tools for the in-line monitoring of the API concentration. Ultrasonic transit time and peak height
were measured by the implementation of two ultrasound transducers in a custom made die adapter.
First, a PLS model for quantification was developed based solely on the monitored die pressure and
torque. It was demonstrated that adding the transit time and peak height monitored using ultrasonic
techniques improved the predictive ability of this PLS model.