In traditional pharmaceutical manufacturing, the process steps are carried out in the batch mode and the quality is assured via in-process controls and end-product testing. Samples are drawn mainly at the end of each process step and analysed off-line in the quality control laboratory. After the approval, the batch is released to the next process step or, at the end of the line, for packaging and delivery to the patients. This is the quality by testing (QbT) approach, with no information acquired and used during processing. The implementation of the quality by design (QbD) principles offers more sophisticated options for quality and process control. Under QbD, pharmaceutical quality is assured by understanding and controlling the material attributes (MAs) and the process parameters (PPs) [31] to achieve critical quality attributes (CQAs) that are within specification. Especially, the process analytical technology (PAT) initiative [30] has led to a paradigm change. Data acquired by in-line monitoring devices allows to make decisions about the material quality or control actions in real-time. These are essential especially for continuous manufacturing, which requires enhanced control strategies rather than traditional off-line end product testing [23].

In-line data acquisition is an enabler of real-time release testing (RTRT), which is the ultimate option for quality control and product release. It evaluates and ensures the product quality based on the process data [15]. Real-time measurements and control of relevant in-process MAs and PPs are used to predict the corresponding finished product’s attributes. For common unit operations in solid dosage manufacturing, PAT tools have been successfully implemented [9], [28]. RTRT strategies have been developed for specific CQAs in commercial manufacturing, e.g., the tablet content and content uniformity obtained via near-infrared spectroscopy (NIRS) at-line measurements [13].

Since testing the dissolution performance is time-consuming and laborious, it would be a major step forward to implement RTRT for this CQA. Attempts have been made to predict dissolution via at-line NIR [25]. Dissolution profiles have been predicted via machine-learning approaches using NIR data, the compression force and the particle size distribution of the main excipient [12].

In a few cases, RTRT strategies have been developed for a process chain and several CQAs. For a wet-granulation tableting process in the batch mode, NIRS has been used for concentration-related CQAs and the design of experiments (DoE) approach for predicting the dissolution [19]. However, in continuous manufacturing lines, in addition to the PAT sensor data, a huge amount of in-process data is available, which originates from univariate measurements and from equipment sensors, which can be used directly or as input for soft sensors. Matching the information to the material requires time-aligned data recording and knowing the residence of the material in the equipment. The probability of how long the material remains in a certain process step can be computed via residence time distribution (RTD) models. A substantial effort has been invested in the RTD modeling of pharmaceutical manufacturing equipment [7], [1]. One major application of RTD models in control concepts has been to coordinate the discharge actions [17], [2], [20], [34], [14]. The out-of-specification (OOS) material is detected either via content monitoring by NIRS or via soft sensors, while the RTD model is used to trigger the discharge actions. Most studies, however, have involved simulations. The second application of RTD models has been to track the material on integrated manufacturing lines. Batch transitions in a feeding-blending system have been investigated [29]. Disturbances and raw material changes due to feeder refill have been tracked to the tablet in a direct compaction (DC) line [7], [3]. [16] have determined the RTDs of three continuous tableting routes via wet granulation (WG), via dry granulation (DG) and via hot melt extrusion (HME) combined with pelletization to track the material and coordinate the discharge actions.

In this work, a novel method is introduced that connects process data generated from a state-of-the-art continuous manufacturing line with a specific portion of intermediate material or final product. A sophisticated process monitoring strategy was developed for the line, and the data acquired from the process equipment, PAT tools and soft sensors was collected in a data management platform. This data was then matched to individual material portions using the RTD models of the continuous unit operations and a material tracking algorithm for semi-continuous (mini-batch) unit operations. The proposed method made the complete history of process conditions and product quality attributes digitally available at the end of the manufacturing cycle for a single dosage unit. Specifically, it was possible to assess, which process conditions and quality attributes were experienced by each unit dose of the final product (i.e., tablets manufactured at specific timestamps) at various stages of production (e.g., during granulation, drying, tableting, etc.). The approach was implemented and demonstrated using the ConsigmaTM-25 tableting line.