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.