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.

A machine learning framework for computationally expensive transient models

Transient simulations of dynamic systems, using physics-based scientific computing tools, are practically limited by availability of computational resources and power. While the promise of machine learning has been explored in a variety of scientific disciplines, its application in creation of a framework for computationally expensive transient models has not been fully explored. Here, we present an ensemble approach where one such computationally expensive tool, discrete element method, is combined with time-series forecasting via auto regressive integrated moving average and machine learning methods to simulate a complex pharmaceutical problem: development of an agitation protocol in an agitated filter dryer to ensure uniform solid bed mixing. This ensemble approach leads to a significant reduction in the computational burden, while retaining model accuracy and performance, practically rendering simulations possible. The developed machine-learning model shows good predictability and agreement with the literature, demonstrating its tremendous potential in scientific computing.

Robust state estimation of feeding-blending systems in continuous pharmaceutical manufacturing

State estimation is a fundamental part of monitoring, control, and real-time optimization in continuous pharmaceutical manufacturing. For nonlinear dynamic systems with hard constraints, moving horizon estimation (MHE) can estimate the current state by solving a well-defined optimization problem where process complexities are explicitly considered as constraints. Traditional MHE techniques assume random measurement noise governed by some normal distributions. However, state estimates can be unreliable if noise is not normally distributed or measurements are contaminated with gross or systematic errors. To improve the accuracy and robustness of state estimation, we incorporate robust estimators within the standard MHE skeleton, leading to an extended MHE framework. The proposed MHE approach is implemented on two pharmaceutical continuous feeding-blending system (FBS) configurations which include loss-in-weight (LIW) feeders and continuous blenders. Numerical results show that our MHE approach is robust to gross errors and can provide reliable state estimates when measurements are contaminated with outliers and drifts. Moreover, the efficient solution of the MHE realized in this work, suggests feasible application of on-line state estimation on more complex continuous pharmaceutical processes.

Model-based analysis of high shear wet granulation from batch to continuous processes in pharmaceutical production--a critical review

The manufacturing of pharmaceutical dosage forms, which has traditionally been a batch-wise process, is now also transformed into a series of continuous operations. Some operations such as tabletting and milling are already performed in continuous mode, while the adaptation towards a complete continuous production line is still hampered by complex steps such as granulation and drying which are considered to be too inflexible to handle potential product change-overs. Granulation is necessary in order to achieve good flowability properties and better control of drug content uniformity. This paper reviews modelling and supporting measurement tools for the high shear wet granulation (HSWG) process, which is an important granulation technique due to the inherent benefits and the suitability of this unit operation for the desired switch to continuous mode. For gaining improved insight into the complete system, particle-level mechanisms are required to be better understood, and linked with an appropriate meso- or macro-scale model. A brief review has been provided to understand the mechanisms of the granulation process at micro- or particle-level such as those involving wetting and nucleation, aggregation, breakage and consolidation. Further, population balance modelling (PBM) and the discrete element method (DEM), which are the current state-of-the-art methods for granulation modelling at micro- to meso-scale, are discussed. The DEM approach has a major role to play in future research as it bridges the gap between micro- and meso-scales. Furthermore, interesting developments in the measurement technologies are discussed with a focus towards inline measurements of the granulation process to obtain experimental data which are required for developing good models. Based on the current state of the developments, the review focuses on the twin-screw granulator as a device for continuous HSWG and attempts to critically evaluate the current process. As a result, a set of open research questions are identified. These questions need to be answered in the future in order to fill the knowledge gap that currently exists both at micro- and macro-scale, and which is currently limiting the further development of the process to its full potential in pharmaceutical applications.