Effect of material properties on the residence time distribution (RTD) characterization of powder blending unit operations. Part II of II: Application of models

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.

Reviewing the Impact of Powder Cohesion on Continuous Direct Compression (CDC) Performance

The pharmaceutical industry is undergoing a paradigm shift towards continuous processing from batch, where continuous direct compression (CDC) is considered to offer the most straightforward implementation amongst powder processes due to the relatively low number of unit operations or handling steps. Due to the nature of continuous processing, the bulk properties of the formulation will require sufficient flowability and tabletability in order to be processed and transported effectively to and from each unit operation. Powder cohesion presents one of the greatest obstacles to the CDC process as it inhibits powder flow. As a result, there have been many studies investigating potential manners in which to overcome the effects of cohesion with, to date, little consideration of how these controls may affect downstream unit operations. The aim of this literature review is to explore and consolidate this literature, considering the impact of powder cohesion and cohesion control measures on the three-unit operations of the CDC process (feeding, mixing, and tabletting). This review will also cover the consequences of implementing such control measures whilst highlighting subject matter which could be of value for future research to better understand how to manage cohesive powders for CDC manufacture.

Flowsheet modelling of a powder continuous feeder-mixer system

In this study, an integrated flowsheet model of the continuous feeder-mixer system was calibrated, simulated and compared against experimental data. The feeding process was first investigated using two major components (ibuprofen and microcrystalline cellulose (MCC)), in a formulation comprised of: 30 wt% of ibuprofen, 67.5 wt% MCC, 2 wt% of sodium starch glycolate and 0.5 wt% of magnesium stearate. The impact of a refill on feeder performance was experimentally evaluated for different operating conditions. Results showed that it had no influence on feeder performance. While simulations with the feeder model fairly reproduced the material behaviour observed in the feeder, unintended disturbances were underpredicted due to the model's low complexity. Experimentally, mixer's efficiency was assessed based on ibuprofen residence time distribution. Mean residence time pointed to a higher mixer's efficiency at lower flow rates. Blend homogeneity results showed that for the entire set of experiments, ibuprofen RSD <5%, irrespective of process variables. A feeder-mixer flowsheet model was calibrated, after regressing the axial model coefficients. The regression curves exhibited a R² above 0.96, whereas the RMSE varied from 1.58x10^-4 to 1.06x10^-3 s^-1 across all fitted curves. Simulations confirmed that flowsheet model captured the powder dynamics inside the mixer and qualitatively predicted the mixer's filtering ability against feeding composition fluctuations, as well as ibuprofen RSD in blend, in line with real experiments.

Optimal quantification of residence time distribution profiles from a quality assurance perspective

Residence time distribution (RTD) has been widely applied across various fields of chemical engineering, including pharmaceutical manufacturing, for applications such as material traceability, quality assurance, system health monitoring, and fault detection. Determination of a representative RTD, in principle, requires an accurate process analytical technology (PAT) procedure capturing the entire range of tracer concentrations from zero to maximum. Such a wide concentration range creates at least two problems: i) decreased accuracy of the model across the entire range of concentrations, relating to limit of quantification, and ii) ambiguity associated with the detection of the tracer for low concentration levels, relating to limit of detection (LOD). These problems affect not only the RTD profile itself, but also RTD-based applications, which can potentially lead to erroneous conclusions. This article seeks to minimize the impact of these problems by understanding the relative importance of different features of RTD on the detection of out-of-specification (OOS) products. In this work, the RTD obtained experimentally was truncated at different levels, to investigate the impact of the truncation of RTD on funnel plots for OOS detection. The main finding is that the tail of the RTD can be truncated with no loss of accuracy in the determination of exclusion intervals. This enables the manufacturing scientist to focus entirely on the peak region, maximizing the accuracy of chemometric models.

Different Stochastic Resonances Induced by Multiplicative Polynomial Trichotomous Noise in a Fractional Order Oscillator with Time Delay and Fractional Gaussian Noise

A general investigation on the mechanism of stochastic resonance is reported in a time-delay fractional Langevin system, which endues a nonlinear form multiplicative colored noise and fractional Gaussian noise. In terms of theoretical analysis, both the expressions of output steady-state amplitude and that of the first moment of system response are obtained by utilizing stochastic averaging method, fractional Shapiro and Laplace methods. Due to the presence of trichotomous colored noise, the excitation frequency can induce fresh multimodal Bona fide stochastic resonance, exhibiting much more novel dynamical behaviors than the non-disturbance case. It is verified that multimodal pattern only appears with small noise switching rate and memory damping order. The explicit expressions of critical noise intensity corresponding to the generalized stochastic resonance are given for the first time, by which it is determined that nonlinear form colored noise induces much more of a comprehensive resonant phenomena than the linear form. In the case of slow transfer rate noise, a newfangled phenomenon of double hypersensitive response induced by a variation in noise intensity is discovered and verified for the first time, with the necessary range of parameters for this phenomenon given. In terms of numerical scheme, an efficient and feasible algorithm for generating trichotomous noise is proposed, by which an algorithm based on the Caputo fractional derivative are applied. The numerical results match well with the analytical ones.

Development and Use of a Residence Time Distribution (RTD) Model Control Strategy for a Continuous Manufacturing Drug Product Pharmaceutical Process

Residence-time-distribution (RTD)-based models are key to understanding the mixing dynamics of continuous manufacturing systems. Such models can allow for material traceability throughout the process and can provide the ability for removal of non-conforming material from the finished product. These models have been implemented in continuous pharmaceutical manufacturing mainly for monitoring purposes, not as an integral part of the control strategy and in-process specifications. This paper discusses the steps taken to develop an RTD model design space and how the model was statistically incorporated into the product’s control strategy. To develop the model, experiments were conducted at a range of blender impeller speeds and total system mass flow rates. RTD parameters were optimized for each condition tested using a tank-in-series-type model with a delay. Using the experimental RTD parameters, an equation was derived relating the mean residence time to the operating conditions (i.e., blender impeller speed and mass flow rate). The RTD parameters were used in combination with real-time upstream process data to predict downstream API concentration, where these predictions allowed validation across the entire operating range of the process by comparison to measured tablet assay. The standard in-process control limits for the product were statistically tightened using the validation acceptance criteria. Ultimately, this model and strategy were accepted by regulatory authorities.

Tracking raw material flow through a continuous direct compression line Part I of II: Residence time distribution modeling and sensitivity analysis enabling increased process yield

Continuous manufacturing (CM) offers advantages in quality and space-time yield compared to common batch manufacturing. However, higher yield losses due to the start-up procedure make a broad application uneconomical. This work discusses the possibility of reducing yield losses by adjusting the degree of back-mixing. Back-mixing of nonconforming material from disturbances or start-up will result in the contamination of subsequent material. Therefore, higher degrees of back-mixing cause the discharge of additional material. Choosing an advantageous setting of operational parameters may be a simple way to change the degree of back-mixing. Based on direct compression, this work demonstrates the identification of promising parameters. Therefore, step-change experiments using color-marked material in the feeder, blender, and tablet press quantify the impact of three operational parameters per device. Models for the devices and the entire process result from those measurements. Subsequently, a global variance-based sensitivity analysis identifies the most influential parameters. As a result, adjusting the minimal filling level of the feeder and the rotational feed frame speed of the tablet press reduces back-mixing by more than 30%. At high costs of the raw materials, the resulting savings can significantly improve the economic performance of CM compared to batch manufacturing.

Residence time distribution as a traceability method for lot changes in a pharmaceutical continuous manufacturing system

Residence time distribution (RTD) models were developed to track raw material lots and investigate batch transitions in a continuous manufacturing system. Two raw materials with similar physical properties (granular metformin and lactose) were identified via Principal Component Analysis (PCA) from a library of bulk material properties and used to simulate the switching of lots during production. In-line near-infrared (NIR) spectra were collected with the powder flowing through a chute in a continuous manufacturing system to monitor metformin and lactose concentration in step-change experiments with Partial Least Squares (PLS) models. RTD provided an understanding of raw material propagation through the continuous manufacturing system. Transition times between raw material changes were identified using the results of two multivariate approaches PLS and PCA. The methodology was implemented to discriminate the transition zone in a raw material change, contributing to design control strategies for acceptance and diverting mechanisms.

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

While continuous manufacturing (CM) of pharmaceutical solid-based drug products has been shown to be advantageous for improving the product quality and process efficiency in alignment with FDA's support of the quality-by-design paradigm (Lee, 2015; Ierapetritou et al., 2016; Plumb, 2005; Schaber, 2011), it is critical to enable full utilization of CM technology for robust production and commercialization (Schaber, 2011; Byrn, 2015). To do so, an important prerequisite is to obtain a detailed understanding of overall process characteristics to develop cost-effective and accurate predictive models for unit operations and process flowsheets. These models are utilized to predict product quality and maintain desired manufacturing efficiency (Ierapetritou et al., 2016). Residence time distribution (RTD) has been a widely used tool to characterize the extent of mixing in pharmaceutical unit operations (Vanhoorne, 2020; Rogers and Ierapetritou, 2015; Teżyk et al., 2015) and manufacturing lines and develop computationally cheap predictive models. These models developed using RTD have been demonstrated to be crucial for various flowsheet applications (Kruisz, 2017; Martinetz, 2018; Tian, 2021). Though extensively used in the literature (Gao et al., 2012), the implementation, execution, evaluation, and assessment of RTD studies has not been standardized by regulatory agencies and can thus lead to ambiguity regarding their accurate implementation. To address this issue and subsequently prevent unforeseen errors in RTD implementation, the presented article aims to aid in developing standardized guidelines through a detailed review and critical discussion of RTD studies in the pharmaceutical manufacturing literature. The review article is divided into two main sections - 1) determination of RTD including different steps for RTD evaluation including experimental approach, data acquisition and pre-treatment, RTD modeling, and RTD metrics and, 2) applications of RTD for solid dose manufacturing. Critical considerations, pertaining to the limitations of RTDs for solid dose manufacturing, are also examined along with a perspective discussion of future avenues of improvement.

Review: Continuous Manufacturing of Small Molecule Solid Oral Dosage Forms

Continuous manufacturing (CM) is defined as a process in which the input material(s) are continuously fed into and transformed, and the processed output materials are continuously removed from the system. CM can be considered as matching the FDA's so-called 'Desired State' of pharmaceutical manufacturing in the twenty-first century as discussed in their 2004 publication on 'Innovation and Continuous Improvement in Pharmaceutical Manufacturing'. Yet, focused attention on CM did not really start until 2014, and the first product manufactured by CM was only approved in 2015. This review describes some of the benefits and challenges of introducing a CM process with a particular focus on small molecule solid oral dosage forms. The review is a useful introduction for individuals wishing to learn more about CM.

Soft sensor for real-time estimation of tablet potency in continuous direct compression manufacturing operation

One of the critical quality attributes of the solid oral dosage forms produced in continuous direct compression operations is the tablet potency. A novel soft sensor comprising of a combination of first principle-based and empirical models has been developed to enable real-time monitoring of blend and tablet potency, and concentrations of other excipients at various stream levels along the direct compression line. The soft sensor model has only three adjustable parameters, primarily associated with the equipment design and operation, so the model is product agnostic which is key to enable flexible manufacturing. The estimation accuracy of the soft sensor is demonstrated through a series of real time experiments which include steady state and dynamic transitions of potency during the runs, compared with offline analytically tested tablet cores. The results indicate that the proposed soft sensor can be utilized as a robust tool for real-time monitoring of potency, suggesting an extension of its utilization to higher levels of control. Two potential applications of the soft sensor are: 1. An element of a control strategy for product diversion; 2. A predictive model for advanced process control strategy to minimize the variability in tablet composition.

Investigation of Preprocessing and Validation Methodologies for PAT: Case Study of the Granulation and Coating Steps for the Manufacturing of Ethenzamide Tablets

After the Food and Drug Association in the USA published guidelines on the enhanced use of process analytical technology (PAT) and continuous manufacturing, many studies regarding PAT and continuous manufacturing have been published. This paper describes a case study involving granulation and coating steps with ethenzamide to investigate interference for PAT model construction and model management. We investigated what factors should be considered and addressed when PAT is implemented for continuous manufacturing and how predictive models should be constructed. The product qualities that were monitored were moisture content and particle size in the granulation step and tablet weight and moisture content in the coating step. We have constructed models for the granulation step and validated the predictive capability of the models against an external dataset. A partial least squares (PLS) model with manual wavelength selection had the best predictive accuracy for loss on drying against the external validation set. We found that the prediction of loss on drying was accurate, but the prediction of particle size was not sufficiently accurate. In the coating step, because of the small amount of data, we performed three-fold cross-validation and y-scrambling 10 times, to select the optimal hyper-parameters and to check if the models were fitted to chance correlations. We confirmed that the coating agent weights, tablet weights, and water content could be accurately predicted based on the mean of the R² score for cross-validation. Addition of other variables, as well as the absorbance, slightly improved the predictive accuracy.

Determination of Residence Time Distribution in a Continuous Powder Mixing Process With Supervised and Unsupervised Modeling of In-line Near Infrared (NIR) Spectroscopic Data

Successful implementation of continuous manufacturing processes requires robust methods to assess and control product quality in a real-time mode. In this study, the residence time distribution of a continuous powder mixing process was investigated via pulse tracer experiments using near infrared spectroscopy for tracer detection in an in-line mode. The residence time distribution was modeled by applying the continuous stirred tank reactor in series model for achieving the tracer (paracetamol) concentration profiles. Partial least squares discriminant analysis and principal component analysis of the near infrared spectroscopy data were applied to investigate both supervised and unsupervised chemometric modeling approaches. Additionally, the mean residence time for three powder systems was measured with different process settings. It was found that a significant change in the mean residence time occurred when comparing powder systems with different flowability and mixing process settings. This study also confirmed that the partial least squares discriminant analysis applied as a supervised chemometric model enabled an efficient and fast estimate of the mean residence time based on pulse tracer experiments.

Residence time distribution modelling and in line monitoring of drug concentration in a tablet press feed frame containing dead zones

The presence of a 'significant dead zone' in any continuous manufacturing equipment may affect the product quality and need to be investigated systematically. Dead zone will affect the residence time distribution (RTD) of continuous manufacturing and thus the mixing and product quality. Tablet press (feed frame) is one of unit operations that directly influence the critical quality attributes (CQA's). However, currently no systematic methods and tools are available to characterize and model the feed frame dead zone. In this manuscript, the RTD of the tablet press feed frame containing dead zone is investigated. Step-change experiments revealed that the feed frame could be expressed as a traditional continuous stirred tank model. The volume fractions of the dead zones are determined experimentally as well as using RTD model. In addition, an in-line NIR method for drug concentration monitoring inside the feed frame is also developed. The developed NIR calibration model enables to monitor the drug concentration precisely and detect the variation immediately with the probe positioned right above the left paddle. It is also found that the feed frame paddle speed slightly affects the predictive accuracy of NIR, while the die disc speed has no significant effect.

Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature Review

The development and application of emerging technologies of Industry 4.0 enable the realization of digital twins (DT), which facilitates the transformation of the manufacturing sector to a more agile and intelligent one. DTs are virtual constructs of physical systems that mirror the behavior and dynamics of such physical systems. A fully developed DT consists of physical components, virtual components, and information communications between the two. Integrated DTs are being applied in various processes and product industries. Although the pharmaceutical industry has evolved recently to adopt Quality-by-Design (QbD) initiatives and is undergoing a paradigm shift of digitalization to embrace Industry 4.0, there has not been a full DT application in pharmaceutical manufacturing. Therefore, there is a critical need to examine the progress of the pharmaceutical industry towards implementing DT solutions. The aim of this narrative literature review is to give an overview of the current status of DT development and its application in pharmaceutical and biopharmaceutical manufacturing. State-of-the-art Process Analytical Technology (PAT) developments, process modeling approaches, and data integration studies are reviewed. Challenges and opportunities for future research in this field are also discussed.

Mixing Cell: a Device to Mimic Extent of Lubrication and Shear in Continuous Tubular Blenders

Continuous manufacturing (CM) has clear potential for manufacturing solid oral dosages. It provides several advantages that may aid the manufacturing and quality of drug products. However, one of the main challenges of this technology is the relatively large amount of knowledge required and the amounts of material needed to develop the process during the early stages of development. Early process development evaluations of continuous manufacturing equipment often require larger amounts of material compared with batch, which hinder CM prospect for drugs during the early stages of process development. In this work, a small-scale evaluation of the mixing process occurring in a continuous mixing system was performed. The evaluation involved the use of a small-scale "mixing cell" which was able to replicate the lubrication process of a continuous mixer. It is worth mentioning that we designed the mixing cell by reconfiguration of an existing continuous tubular blender. The extent of lubrication evaluation was performed for three example formulations and was done by mimicking the amount of shear provided to a formulation by means of matching the number of paddle-passes that a formulation experiences within a continuous blending process in the batch mixing cell. The evaluation showed that the small-scale mixing cell was able to replicate the extent of lubrication-evaluated by measuring the tensile strength of compacts being made with both the continuous and mixing cell experiments-occurring in the continuous mixer using a fraction of the amount of materials needed to perform the same evaluation in the continuous blending process.