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The US Food and Drug Administration introduced the quality by design approach and process analytical technology guidance to encourage innovation and efficiency in pharmaceutical development, manufacturing, and quality assurance. As part of this renewed emphasis on the improvement of manufacturing, the pharmaceutical industry has begun to develop more efficient production processes with more intensive use of online measurement and sensing, real-time quality control, and process control tools. Here, we present dropwise additive manufacturing of pharmaceutical products (DAMPP) as an alternative to conventional pharmaceutical manufacturing methods. This mini-manufacturing process for the production of pharmaceuticals utilizes drop on demand printing technology for automated and controlled deposition of melt-based formulations onto edible substrates. The advantages of drop-on-demand technology, including reproducible production of small droplets, adjustable drop sizing, high placement accuracy, and flexible use of different formulations, enable production of individualized dosing even for low-dose and high-potency drugs. In this work, DAMPP is used to produce solid oral dosage forms from hot melts of an active pharmaceutical ingredient and a polymer. The dosage forms are analyzed to show the reproducibility of dosing and the dissolution behavior of different formulations.

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Abstract. Three different approaches have been evaluated for monitoring ribbon density through realtime near-infrared spectroscopy measurements. The roll compactor was operated to produce microcrystalline cellulose (MCC) ribbons of varying densities. The first approach used the slope of the spectra which showed a variation through the ribbon that could be attributed to density. A second qualitative approach was also developed with a principal component analysis (PCA) model with spectra taken in-line during the production of ribbons in an ideal roll pressure range. The PCA (i.e., real-time) density scans show that the model was able to qualitatively capture the density responses resulting from variation in process parameters. The third approach involved multivariate partial least squares (PLS) calibration models developed at wavelength regions of 1,120-1,310 and 1,305-2,205 nm. Also, various PLS models were developed using three reference methods: caliper, pycnometer, and in-line laser. The third approach shows a quantitative difference between the model-predicted and the measured densities. Models developed at high-wavelength region showed highest accuracy compared with models at low-wavelength region. All the PLS models showed a high accuracy along the spectra collected throughout the production of the ribbons. The three methods showed applicability to process control monitoring by describing the changes in density during in-line sampling.

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One important aspect of effective real time process management is the implementation of intelligent systems that can assist human operators in making supervisory control decisions. Conventional practice is to simply sound alarms when process variables go out of range leaving it to the operator to interpret the alarm patterns and choose mitigation strategies. Failure of the operator to exercise the appropriate mitigation actions often has an adverse effect on product quality, process safety, and the environment. The difficulties associated with implementing intelligent control and the opportunities for improvements are even greater in the pharmaceutical manufacturing of solid oral dosage products due to specific processing challenges associated with particulate and granular systems. The advent of the Process Analytical Technology (PAT) initiative developed by the FDA has given the pharmaceutical industry an opportunity to apply systems engineering tools that encourage innovation in drug manufacturing. In this work, an intelligent alarm system (IAS) framework has been developed to deal with fault detection, diagnosis, and mitigation of conditions that result from process anomalies. The integrated framework, using wavelet analysis, principal component analysis, signed directed graphs, and qualitative trend analysis, along with ontological based knowledge framework, helps in quick detection and diagnosis of process faults. This reduces the likelihood of abnormal event progression, production disruptions, and productivity losses. The key feature of this framework is that it provides a mitigation strategy to the operator along with rationalized alarm thresholds, which help in reducing the workload and facilitate in taking corrective action.

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