Using residence time distribution in pharmaceutical solid dose manufacturing - A critical review

A modern strategy for digital real-time release testing in continuous tablet manufacturing

The pharmaceutical industry is currently moving away from traditional approaches to quality control with off-line quality tests, limited in-line process monitoring and minimal control strategies towards more sophisticated methods. This transition addresses several critical aspects, including the reduction of ecological and economic footprints and ensuring the safety for patients and personnel. In that context, the initial step is the application of process monitoring tools, such as process analytical technology (PAT) and soft sensors, for real-time product quality assessment. This will enable real-time release testing (RTRT), which redefines conventional approaches by relying solely on the process data reported by equipment or collected from sensors to predict the product quality. However, the implementation of RTRT requires reliable material tracking algorithms, which align the process data with the product's characteristics. This study proposes a modern digital RTRT strategy that aligns process data collected from a state-of-the-art manufacturing line with a sophisticated process monitoring strategy for specific product quantities, i.e., single dosage units (tablets). To trace the material through the production line and align it to the collected process data, residence time distribution (RTD) models and material tracking algorithms were developed. The digital RTRT strategy was designed and demonstrated using the industrial manufacturing line ConsiGmaTM-25. The developed strategy makes full product quality information digitally available, including critical quality attributes (CQAs) and processing conditions experienced during the production. The obtained results were validated using traditionally established off-line methods.

Real-Time Total Content Uniformity Assessment of Solid Dosage Forms Using Near-Infrared Transmission Spectroscopy

Real-time monitoring of solid dosage forms is crucial in continuous manufacturing processes for detecting product variability and bias and minimizing defective products. Traditional process analytical technologies often struggle to maintain high-throughput production. This study presents a novel approach using near-infrared (NIR) transmission spectroscopy for rapidly and comprehensively assessing the quality of solid dosage forms. A chemometric model was developed, based on NIR spectra obtained from tablets produced through wet granulation, to accurately predict active pharmaceutical ingredient (API). Performance was validated against the conventional high-performance liquid chromatography (HPLC) method, achieving a high correlation coefficient (R2 = 0.9979) and a low root-mean-square error of prediction (RMSEP = 1.09%). Critically, even at a high inspection speed of 250,000 tablets/h, the method maintained high analytical accuracy (RMSEP = 1.19%) and met content uniformity requirements (±15%). With negligible bias compared with HPLC across all formulations, the proposed NIR transmission spectroscopy method offers a powerful alternative for the rapid and precise quantification of API content and real-time quality control, thereby substantially contributing to quality assurance in continuous manufacturing processes.

Statistical data treatment for residence time distribution studies in pharmaceutical manufacturing

Residence time distribution (RTD) method has been widely used in the pharmaceutical manufacturing for understanding powder dynamics within unit operations and continuous integrated manufacturing lines. The dynamics thus captured is then used to develop predictive models for unit operations and important RTD-based applications ensuring product quality assurance. Despite thorough efforts in tracer selection, data acquisition, and calibration model development to obtain tracer concentration profiles for RTD studies, there can exist significant noise in these profiles. This noise can make it challenging to identify the underlying signal and get a representative RTD of the system under study. Such concerns have previously indicated the importance of noise handling for RTD measurements in literature. However, the literature does not provide sufficient information on noise handling or data treatment strategies for RTD studies. To this end, we investigate the impact of varying levels of noise using different tracers on measurement of RTD profile and its applications. We quantify the impact of different denoising methods (time and frequency averaging methods). Through this investigation, we see that Savitsky Golay filtering turns out to a good method for denoising RTD profiles despite varying noise levels. The investigation is performed such that the key features of the RTD profile (which are important for RTD based applications) are preserved. Subsequently, we also investigate the impact of denoising on RTD-based applications such as out-of-specification (OOS) analysis and RTD modeling. The results show that the degree of noise levels considered in this work do not significantly impact the RTD-based applications.

Sample Size Requirements of a Pharmaceutical Material Library: A Case in Predicting Direct Compression Tablet Tensile Strength by Latent Variable Modeling

The material library is an emerging, new data-driven approach for developing pharmaceutical process models. How many materials or samples should be involved in a particular application scenario is unclear, and the impact of sample size on process modeling is worth discussing. In this work, the direct compression process was taken as the research object, and the effects of different sample sizes of material libraries on partial least squares (PLS) modeling in the prediction of tablet tensile strength were investigated. A primary material library comprising 45 materials was built. Then, material subsets containing 5 × i (i = 1, 2, 3, …, 8) materials were sampled from the primary material library. Each subset underwent sampling 1000 times to analyze variations in model fitting performance. Both hierarchical sampling and random sampling were employed and compared, with hierarchical sampling implemented with the help of the tabletability classification index d. For each subset, modeling data were organized, incorporating 18 physical properties and tableting pressure as the independent variables and tablet tensile strength as the dependent variable. A series of chemometric indicators was used to assess model performance and find important materials for model training. It was found that the minimum R2 and RMSE values reached their maximum, and the corresponding values were kept almost unchanged when the sample sizes varied from 20 to 45. When the sample size was smaller than 15, the hierarchical sampling method was more reliable in avoiding low-quality few-shot PLS models than the random sampling method. Two important materials were identified as useful for building an initial material library. Overall, this work demonstrated that as the number of materials increased, the model's reliability improved. It also highlighted the potential for effective few-shot modeling on a small material library by controlling its information richness.

Steady-state modeling of a new continuous API dryer: Reduced-order model and investigation of dryer performance

In the present study, a reduced-order model is proposed to analyze a novel continuous dryer with an application in the pharmaceutical industry. The model was validated using process data from ibuprofen drying test runs, and the results were in good agreement with the experimental data. The test substance was an ibuprofen paste with an initial LOD of up to 30 w%. The simulations showed that the contact heat transfer coefficient can be correlated with the degree of wetness. Furthermore, a set of simulations was performed to analyze the influence of input parameters on the dryer's performance: i) the inlet air flow rate and ii) the inlet air temperature. The simulation results demonstrated that a variation in the inlet air temperature significantly affects the air temperature profile, while the inlet air flow rate has a minor effect. Besides, it was also established that the inlet solid LoD has the most considerable effect on the product quality (e.g., final solid moisture content). The results showed a deviation of less than 10% for the product LoD and the product temperature in most cases.

Soft sensor for content prediction in an integrated continuous pharmaceutical formulation line based on the residence time distribution of unit operations

In this study, a concentration predicting soft sensor was achieved based on the Residence Time Distribution (RTD) of an integrated, three-step pharmaceutical formulation line. The RTD was investigated with color-based tracer experiments using image analysis. Twin-screw wet granulation (TSWG) was directly coupled with a horizontal fluid bed dryer and an oscillating mill. Based on integrated measurement, we proved that it is also possible to couple the unit operations in silico. Three surrogate tracers were produced with a coloring agent to investigate the separated unit operations and the solid and liquid inputs of the TSWG. The soft sensor's prediction was compared to validating experiments of a 0.05 mg/g (15% of the nominal) concentration change with High-Performance Liquid Chromatography (HPLC) reference measurements of the active ingredient proving the adequacy of the soft sensor (RMSE < 4%).