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Purpose: Reliable process monitoring in real-time remains a challenge for the pharmaceutical industry. Dealing with random and gross errors in the process measurements in a systematic way is a potential solution. In this paper, we present a process model-based framework, which for given sensor network and measurement uncertainties will predict the most likely state of the process. Thus, real-time process decisions, whether for process control or exceptional events management, can be based on the most reliable estimate of the process state. Methods: Reliable process monitoring is achieved by using data reconciliation (DR) and gross error detection (GED) to mitigate the effects of random measurement errors and non-random sensor malfunctions. Steady-state data reconciliation (SSDR) is the simplest forms of DR but offers the benefits of short computational times. We also compare and contrast the model-based DR approach (SSDR-M) to the purely data-driven approach (SSDR-D) based on the use of principal component constructions. Results: We report the results of studies on a pilot plant-scale continuous direct compression-based tableting line at steady-state in two subsystems. If the process is linear or mildly nonlinear, SSDR-M and SSDR-D give comparable results for the variables estimation and GED. SSDR-M also complies with mass balances and estimate unmeasured variables. Conclusions: SSDR successfully estimates the true state of the process in presence of gross errors, as long as steady state is maintained and the redundancy requirement is met. Gross errors are also detected while using SSDR-M or SSDR-D. Process monitoring is more reliable while using the SSDR framework.

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The improvements in healthcare systems and the advent of the precision medicine initiative have created the need to develop more innovative manufacturing methods for the delivery and production of individualized dosing and personalized treatments. In accordance with the changes observed in healthcare systems towards more innovative therapies, this paper presents dropwise additive manufacturing of pharmaceutical products (DAMPP) for small scale, distributed manufacturing of individualized dosing as an alternative to conventional manufacturing methods A dropwise additive manufacturing process for amorphous and self-emulsifying drug delivery systems is reported, which utilizes drop-on-demand printing technology for automated and controlled deposition of melt-based formulations onto inert tablets. The advantages of drop on demand technology include reproducible production of droplets with adjustable sizing and high placement accuracy, which enable production of individualized dosing even for low dose and high potency drugs. Flexible use of different formulations, such as lipid-based formulations, allows enhancement of the solubility of poorly water soluble and highly lipophilic drugs with DAMPP. Here, DAMPP is used to produce solid oral dosage forms from melts of an active pharmaceutical ingredient and a surfactant. The dosage forms are analyzed to show the amorphous nature, self-emulsifying drug delivery system characteristics and dissolution behavior of these formulations.

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The features of a drop-on-demand-based system developed for the manufacture of melt-based pharmaceuticals have been previously reported. In this paper, a supervisory control system, which is designed to ensure reproducible production of high quality of melt-based solid oral dosages, is presented. This control system enables the production of individual dosage forms with the desired critical quality attributes: amount of active ingredient and drug morphology by monitoring and controlling critical process parameters, such as drop size and product and process temperatures. The effects of these process parameters on the final product quality are investigated, and the properties of the produced dosage forms characterized using various techniques, such as Raman spectroscopy, optical microscopy, and dissolution testing. A crystallization temperature control strategy, including controlled temperature cycles, is presented to tailor the crystallization behavior of drug deposits and to achieve consistent drug morphology. This control strategy can be used to achieve the desired bioavailability of the drug by mitigating variations in the dissolution profiles. The supervisor control strategy enables the application of the drop-on-demand system to the production of individualized dosage required for personalized drug regimens.

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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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In recent years, the US Food and Drug Administration has encouraged pharmaceutical companies to develop more innovative and efficient manufacturing methods with improved online monitoring and control. Mini-manufacturing of medicine is one such method enabling the creation of individualized product forms for each patient. This work presents dropwise additive manufacturing of pharmaceutical products (DAMPP), an automated, controlled mini-manufacturing method that deposits active pharmaceutical ingredients (APIs) directly onto edible substrates using drop-on-demand (DoD) inkjet printing technology. The use of DoD technology allows for precise control over the material properties, drug solid state form, drop size, and drop dynamics and can be beneficial in the creation of high-potency drug forms, combination drugs with multiple APIs or individualized medicine products tailored to a specific patient. In this work, DAMPP was used to create dosage forms from solvent-based formulations consisting of API, polymer, and solvent carrier. The forms were then analyzed to determine the reproducibility of creating an on-target dosage form, the morphology of the API of the final form and the dissolution behavior of the drug over time. DAMPP is found to be a viable alternative to traditional mass-manufacturing methods for solvent-based oral dosage forms.

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Three different approaches have been evaluated for monitoring ribbon density through real-time 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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