Digital design of an integrated purification system for continuous pharmaceutical manufacturing.

In this work dynamic models of the continuous crystallization, filtration, deliquoring, washing, and drying steps are introduced, which are developed in the open-source pharmaceutical modeling tool PharmaPy. These models enable the simulation and digital design of an integrated continuous two-stage crystallization and filtration-drying carousel system. The carousel offers an intensified process that can manufacture products with tailored properties through optimal design and control. Results show that improved crystallization design enhances overall process efficiency by improving critical material attributes of the crystal slurry for downstream filtration and drying operations. The digital design of the integrated process achieves enhanced productivity while satisfying multiple design and product quality constraints. Additionally, the impact of model uncertainty on the optimal operating conditions is investigated. The findings demonstrate the systematic process development potential of PharmaPy, providing improved process understanding, design space identification, and optimized robust operation.

Developing a Virtual Flowability Sensor for Monitoring a Pharmaceutical Dry Granulation Line.

Current technologies to measure granule flowability involve at-line methods that can take hours to perform. This is problematic for a continuous dry granulation tableting line, where the quality assurance and control of the final tablet products depend on real-time monitoring and control of powder flowability. Hence, a real-time alternative is needed for measuring the flowability of the granular products coming out of the roller compactor, which is the unit operation immediately preceding the tablet press. Since particle analyzers have the potential to take inline measurements of the size and shape of granules, they can potentially serve as real-time flowability sensors, given that the size and shape measurements can be used to reliably predict flowability measurements. This paper reports on the use of Partial Least Squares (PLS) regression to utilize distributions of size and shape measurements in predicting the output of three different types of flowability measurements: rotary drum flow, orifice flow, and tapped density analysis. The prediction performance of PLS had a coefficient of determination ranging from 0.80 to 0.97, which is the best reported performance in the literature. This is attributed to the ability of PLS to handle high collinearity in the datasets and the inclusion of multiple shape characteristics-eccentricity, form factor, and elliptical form factor-into the model. The latter calls for a change in industry perspective, which normally dismisses the importance of shape in favor of size; and the former suggests the use of PLS as a better way to reduce the dimensionality of distribution datasets, instead of the widely used practice of pre-selecting distribution percentiles.

Techno-economic analysis of dynamic, end-to-end optimal pharmaceutical campaign manufacturing using PharmaPy.

Increased interest in the pharmaceutical industry to transition from batch to continuouos manufacturing motivates the use of digital frameworks that allow systematic comparison of candidate process configurations. This paper evaluates the technical and economic feasibility of different end-to-end optimal process configurations, viz. batch, hybrid and continuous, for small-scale manufacturing of an active pharmaceutical ingredient. Production campaigns were analyzed for those configurations containing continuous equipment, where significant start-up effects are expected given the relatively short campaign times considered. Hybrid operating mode was found to be the most attractive process configuration at intermediate and large annual production targets, which stems from combining continuous reactors and semi-batch vaporization equipment. Continuous operation was found to be more costly, due to long stabilization times of continuous crystallization, and thermodynamic limitations of flash vaporization. Our work reveals the benefits of systematic digital evaluation of process configurations that operate under feasible conditions and compliant product quality attributes.

Continuous Extraction of 2-Chloroethyl isocyanate for 1-(2-chloroethyl)-3-cyclohexylurea Purification.

This study details the development of simulation-aided design, development, and successful operation of a continuous liquid-liquid extraction platform made with 1.5 mm tubing for the extraction of 2-chloroethyl isocyanate, an important reagent in the synthesis of cancer drugs. Preliminary solvent screening was carried out with partition coefficient calculations to determine solvents of interest. Next, batch and flow extraction experiments of 2-chloroethyl isocyanate in 2-methyl tetrahydrofuran and water were conducted to estimate extraction parameters. Following parameter estimation, experimental and model values for KLa were determined in the range of 1.13×10-3 to 36.0×10-3 s-1. Simulations of the extraction of 2-chloroethyl isocyanate were found to agree with experimental data resulting in a maximum efficiency of 77% and percent extraction of 69% for the continuous platform. Finally, model selection and discrimination was implemented for design space generation with experimental and model determined KLa values to guide lab-scale operation.

Using PharmaPy with Jupyter Notebook to teach digital design in pharmaceutical manufacturing.

The use of digital tools in pharmaceutical manufacturing has gained traction over the past two decades. Whether supporting regulatory filings or attempting to modernize manufacturing processes to adopt new and quickly evolving Industry 4.0 standards, engineers entering the workforce must exhibit proficiency in modeling, simulation, optimization, data processing, and other digital analysis techniques. In this work, a course that addresses digital tools in pharmaceutical manufacturing for chemical engineers was adjusted to utilize a new tool, PharmaPy, instead of traditional chemical engineering simulation tools. Jupyter Notebook was utilized as an instructional and interactive environment to teach students to use PharmaPy, a new, open-source pharmaceutical manufacturing process simulator. Students were then surveyed to see if PharmaPy was able to meet the learning objectives of the course. During the semester, PharmaPy's model library was used to simulate both individual unit operations as well as multiunit pharmaceutical processes. Through the initial survey results, students indicated that: (i) through Jupyter Notebook, learning Python and PharmaPy was approachable from varied coding experience backgrounds and (ii) PharmaPy strengthened their understanding of pharmaceutical manufacturing through active pharmaceutical ingredient process design and development.

Developing a machine learning enabled integrated formulation and process design framework for a pharmaceutical dropwise additive manufacturing printer.

The pharmaceutical manufacturing sector needs to rapidly evolve to absorb the next wave of disruptive industrial innovations - Industry 4.0. This involves incorporating technologies like artificial intelligence, smart factories and 3D printing to automate, miniaturize and personalize the production processes. The goal of this study is to build a formulation and process design (FPD) framework for a pharmaceutical 3D printing technique called drop-on-demand (DoD) printing. FPD can automate the determination of formulation properties and printing conditions (input conditions) for DoD operation that can guarantee production of drug products with desired functional attributes. This study proposes to build the FPD framework in two parts: the first part involves building a machine learning model to simulate the forward problem - predicting DoD operation based on input conditions and the second part seeks to solve and experimentally validate the inverse problem - predicting input conditions that can yield desired DoD operation.

Quality-by-control of intensified continuous filtration-drying of active pharmaceutical ingredients.

Continuous manufacturing and closed-loop quality control are emerging technologies that are pivotal for next-generation pharmaceutical modernization. We develop a process control framework for a continuous carousel for integrated filtration-drying of crystallization slurries. The proposed control system includes model-based monitoring and control routines, such as state estimation and real-time optimization, implemented in a hierarchical, three-layer quality-by-control (QbC) framework. We implement the control system in ContCarSim, a publicly available carousel simulator. We benchmark the proposed control system against simpler methods, comprising a reduced subset of the elements of the overall control system, and against open-loop operation (the current standard in pharmaceutical manufacturing). The proposed control system demonstrates superior performance in terms of higher consistency in product quality and increased productivity, proving the benefits of closed-loop control and of model-based techniques in pharmaceutical manufacturing. This study represents a step forward toward end-to-end continuous pharmaceutical processing, and in the evolution of quality-by-design toward quality-by-control.

Process intensification of atorvastatin calcium crystallization for target polymorph development via continuous combined cooling and antisolvent crystallization using an oscillatory baffled crystallizer.

In this paper, continuous crystallization of Atorvastatin calcium (ASC) using a continuous oscillatory baffled crystallizer (COBC) has been investigated. Like most API manufacturing, ASC is manufactured batchwise and the pure API is recovered via batch combined cooling and antisolvent crystallization (CCAC) process, which has the challenges of low productivity, wide crystal size distribution (CSD) and sometimes polymorphic form contamination. To overcome the limitations of the batch crystallization, continuous crystallization of ASC was studied in a NiTech (United Kingdom) DN15 COBC, manufactured by Alconbury Weston Ltd. (AWL, United Kingdom), with the aim to improve productivity and CSD of the desired polymorph. The COBC has the advantage of high heat transfer rates and improved mixing that significantly reduces the crystallization time. It also has the advantage of spatial temperature distribution and multiple addition ports to control supersaturation and hence the crystallization process. This work uses an array of process analytical technology (PAT) tools to assess key process parameters that affect the polymorphic outcome and CSD. Two parameters were found to have significant impact on the polymorph, they are ratio of solvent to antisolvent at the point of mixing of the two streams and presence of seeds. The splitting of antisolvent into two addition ports in the COBC was found to give the desired form. The CCAC of ASC in COBC was found to be -30-fold more productive than the batch CCAC process. The cycle time for generating 100 g of desired polymorphic form of ASC also significantly reduced from 22 h in batch process to 12 min in the COBC. The crystals obtained using a CCAC process in a COBC had a narrower CSD compared to that from a batch crystallization process.

Small-Scale Continuous Drug Product Manufacturing using Dropwise Additive Manufacturing and Three Phase Settling for Integration with Upstream Drug Substance Production.

The pharmaceutical industry has traditionally relied on mass manufacturing to make its products. This has created multiple problems in the drug supply network, including long production times, inflexible and sluggish manufacturing and lack of personalized dosing. The industry is gradually adapting to these challenges and is developing novel technologies to address them. Continuous manufacturing and 3D printing are two promising techniques that can revolutionize pharmaceutical manufacturing. However, most research studies into these methods tend to treat them separately. This study seeks to develop a new processing route to continuously integrate a 3D printing platform (Drop-on-Demand, DoD, printing) with crystallization that is generally the final step of the active ingredient manufacturing. Accomplishing this integration would enable harnessing the benefits of each method- personalized dosing of 3D printing and flexibility and speed of continuous manufacturing. A novel unit operation, three-phase settling (TPS), is developed to integrate DoD with the upstream crystallizer. To ensure on-spec production of each printed dosage, two process analytical technology tools are incorporated in the printer to monitor drug loading in manufactured drug products in real time. Experimental demonstration of this system is carried out via two case studies: the first study uses an active ingredient celecoxib to test the standalone operation of TPS; the second study demonstrates the operation of the integrated system (crystallizer - TPS - DoD) to continuously make drug products for the active ingredient- lomustine. A dissolution test is also performed on the manufactured and commercial lomustine drug products to compare their dissolution behavior.

Application of MHE-based NMPC on a Rotary Tablet Press under Plant-Model Mismatch.

Active control strategies play a vital role in modern pharmaceutical manufacturing. Automation and digitalization are revolutionizing the pharmaceutical industry and are particularly important in the shift from batch operations to continuous operation. Active control strategies provide real-time corrective actions when departures from quality targets are detected or even predicted. Under the concept of Quality-by-Control (QbC), a three-level hierarchical control structure can be applied to achieve effective setpoint tracking and disturbance rejection in the tablet manufacturing process through the development and implementation of a moving horizon estimation-based nonlinear model predictive control (MHE-NMPC) framework. When MHE is coupled with NMPC, historical data in the past time window together with real-time data from the sensor network enable model parameter updating and control. The adaptive model in the NMPC strategy compensates for process uncertainties, further reducing plant-model mismatch effects. The frequency and constraints of parameter updating in the MHE window should be determined cautiously to maintain control robustness when sensor measurements are degraded or unavailable. The practical applicability of the proposed MHE-NMPC framework is demonstrated via using a commercial scale tablet press, Natoli NP-400, to control tablet properties, where the nonlinear mechanistic models used in the framework can predict the essential powder properties and provide physical interpretations.

A Hierarchical Approach to Monitoring Control Performance and Plant-Model Mismatch.

Controllers are often tuned during plant commissioning, with a fixed process model. However, over time degradation can occur in the process, the process model and the controller, making it necessary to either re-tune the controller or re-identify the process model. Authors have proposed a variety of approaches to identify plant-model mismatch (PMM) and control performance degradation (CPD). While each approach may have its own advantages and disadvantages, they are generally designed to function on different timescales. The differing timescales result in the need for a multi-level hierarchical approach to monitor, detect, and manage PMM and CPD, as illustrated through a continuous pharmaceutical manufacturing application, i.e., a direct compression tablet manufacturing process. This work also highlights the requirement for index-based metrics, that enable the impact of PMM and CPD to be quantified and assessed from a control performance monitoring perspective, to aid fault diagnosis through root cause analysis to guide maintenance decisions for continuous manufacturing applications.

A benchmark simulator for quality-by-design and quality-by-control studies in continuous pharmaceutical manufacturing - Intensified filtration-drying of crystallization slurries.

This article introduces ContCarSim, a benchmark simulator for the development and testing of quality-by-design and quality-by-control strategies in the continuous intensified filtration-drying of paracetamol/ethanol slurries on a novel carousel technology, developed by Alconbury Weston Ltd (United Kingdom). The simulator is based on a detailed mechanistic mathematical modeling framework, and has been validated with filtration and drying experiments on a prototype equipment. A set of design- and control-relevant challenges to be addressed through ContCarSim are proposed. A case study is developed, to demonstrate the features of the simulator and its suitability to design, test and optimize the unit operation. ContCarSim is expected to promote the transition to end-to-end continuous pharmaceutical manufacturing and the adoption of closed-loop quality control by the pharmaceutical industry. The simulator can also be employed as a benchmark for data analytics and process monitoring studies.

Simulation-optimization framework for the digital design of pharmaceutical processes using Pyomo and PharmaPy.

The problem of performing model-based process design and optimization in the pharmaceutical industry is an important and challenging one both computationally and in choice of solution implementation. In this work, a framework is presented to directly utilize a process simulator via callbacks during derivative-based optimization. The framework allows users with little experience in translating mechanistic ODEs and PDEs to robust, fully discretized algebraic formulations, required for executing simultaneous equation-oriented optimization, to obtain mathematically guaranteed optima at a competitive solution time when compared with existing derivative-free and derivative-based frameworks. The effectiveness of the framework in accuracy of optimal solution as well as computational efficiency is analyzed on on two case studies: (i) an integrated 2-unit reaction synthesis train used for the synthesis of an anti-cancer active pharmaceutical ingredient, and (ii) a more complex flowsheet representing a common synthesis-purification-isolation train of a pharmaceutical manufacturing processes.

Amorphous Solid Dispersions Containing Residual Crystallinity: Competition Between Dissolution and Matrix Crystallization.

Crystallinity in an amorphous solid dispersion (ASD) may negatively impact dissolution performance by causing lost solubility advantage and/or seeding crystal growth leading to desupersaturation. The goal of the study was to evaluate underlying dissolution and crystallization mechanisms resulting from residual crystallinity contained within bicalutamide (BCL)/polyvinylpyrrolidone vinyl acetate copolymer (PVPVA) ASDs produced by hot melt extrusion (HME). In-line Raman spectroscopy, polarized light microscopy, and scanning electron microscopy were used to characterize crystallization kinetics and mechanisms. The fully amorphous ASD (0% crystallinity) did not dissolve completely, and underwent crystallization to the metastable polymorph (form 2), initiating in the amorphous matrix at the interface of the amorphous solid with water. Under non-sink conditions, higher extents of supersaturation were achieved because dissolution initially proceeded unhindered prior to nucleation. ASDs containing residual crystallinity had markedly reduced supersaturation. Solid-mediated crystallization (matrix crystallization) consumed the amorphous solid, growing the stable polymorph (form 1). Under sink conditions, both the fully amorphous ASD and crystalline physical mixture achieve faster release than the ASDs containing residual crystallinity. In the latter systems, matrix crystallization leads to highly agglomerated crystals with high relative surface area. Solution-mediated crystallization was not a significant driver of concentration loss, due to slow crystal growth from solution in the presence of PVPVA. The high risk stemming from residual crystallinity in BCL/PVPVA ASDs stems from (1) fast matrix crystallization propagating from crystal seeds, and (2) growth of the stable crystal form. This study has implications for dissolution performance outcomes of ASDs containing residual crystallinity.

Evaluation of a Combined MHE-NMPC Approach to Handle Plant-Model Mismatch in a Rotary Tablet Press.

The transition from batch to continuous processes in the pharmaceutical industry has been driven by the potential improvement in process controllability, product quality homogeneity, and reduction of material inventory. A quality-by-control (QbC) approach has been implemented in a variety of pharmaceutical product manufacturing modalities to increase product quality through a three-level hierarchical control structure. In the implementation of the QbC approach it is common practice to simplify control algorithms by utilizing linearized models with constant model parameters. Nonlinear model predictive control (NMPC) can effectively deliver control functionality for highly sensitive variations and nonlinear multiple-input-multiple-output (MIMO) systems, which is essential for the highly regulated pharmaceutical manufacturing industry. This work focuses on developing and implementing NMPC in continuous manufacturing of solid dosage forms. To mitigate control degradation caused by plant-model mismatch, careful monitoring and continuous improvement strategies are studied. When moving horizon estimation (MHE) is integrated with NMPC, historical data in the past time window together with real-time data from the sensor network enable state estimation and accurate tracking of the highly sensitive model parameters. The adaptive model used in the NMPC strategy can compensate for process uncertainties, further reducing plant-model mismatch effects. The nonlinear mechanistic model used in both MHE and NMPC can predict the essential but complex powder properties and provide physical interpretation of abnormal events. The adaptive NMPC implementation and its real-time control performance analysis and practical applicability are demonstrated through a series of illustrative examples that highlight the effectiveness of the proposed approach for different scenarios of plant-model mismatch, while also incorporating glidant effects.

Application of PharmaPy in the digital design of the manufacturing process of an active pharmaceutical ingredient.

Flowsheet design and optimization constitute one of the key challenges in the chemical engineering and process optimization communities. Software tools for digital design and flowsheet simulation are readily available for traditional chemical processing problems such as distillation and hydrocarbon processing, however tools for pharmaceutical manufacturing are much less widely developed. This paper introduces, PharmaPy, a Python-based modelling platform for pharmaceutical facility design and optimization. The versatility of the platform is demonstrated in simulating both continuous and batch process flowsheets.

Mathematical modeling and digital design of an intensified filtration-washing-drying unit for pharmaceutical continuous manufacturing.

This paper introduces a comprehensive mathematical model of a novel integrated filter-dryer carousel system, designed for continuously filtering, washing and drying a slurry stream into a crystals cake. The digital twin includes models for dead-end filtration, cake washing and convective cake drying, based on dynamic multi-component mass, energy and momentum balances. For set of feed conditions and control inputs, the model allows tracking the solvents and impurities content in the cake (critical quality attributes, CQAs) throughout the whole process. The model parameters were identified for the isolation of paracetamol from a multi-component slurry, containing a non-volatile impurity. The calibrated model was used for identifying the probabilistic design space and maximum throughput for the process, expressing the combinations of the carousel feed conditions and control inputs for which the probability of meeting the target CQAs is acceptable.

PharmaPy: An object-oriented tool for the development of hybrid pharmaceutical flowsheets.

Process design and optimization continue to provide computational challenges as the chemical engineering and process optimization communities seek to address more complex and larger scale applications. Software tools for digital design and flowsheet simulation are readily available for traditional chemical processing applications such as in commodity chemicals and hydrocarbon processing; however, tools for pharmaceutical manufacturing are much less well developed. This paper introduces, PharmaPy, a Python-based modelling platform for pharmaceutical manufacturing systems design and optimization. The versatility of the platform is demonstrated in simulation and optimization of both continuous and batch processes. The structure and features of a Python-based modeling platform, PharmaPy are presented. Illustrative examples are shown to highlight key features of the platform and framework.

High-throughput screening of organic reactions in microdroplets using desorption electrospray ionization mass spectrometry (DESI-MS): hardware and software implementation.

This study describes an automated system used for high throughput screening of reaction conditions based on accelerated reactions occurring in small volumes of reagents. Reaction mixtures are prepared in array format using a fluid handling robot and spotted on a flat polytetrafluoroethylene plate at densities up to 6144 per plate. The reaction and analysis steps are performed simultaneously using desorption electrospray ionization (DESI) to release microdroplets containing the reaction mixture from the plate for reaction prior to arrival at a mass spectrometer. Analysis rates are up to 1 reaction mixture per second and data are recorded in real time using an ion trap mass spectrometer. Beacon compounds are used to triangulate position on the plate and this allows tandem mass spectrometry (MS/MS) to be performed on confirm products of interest. Custom software allows the user to control the system. It is also used to receive data from the DESI mass spectrometer to screen the spectra for compounds of interest, to perform MS/MS and to save data. This custom software also communicates with the software controlling the fluid handling robot (Biomek i7) as well as the Beckman software used to prepare reaction mixtures and also the software that controls the solvent used as the DESI spray. Data were recorded for N-alkylation, N-acylation and N-sulfonylation reactions in three 8 hour experiments on successive days to establish the ruggedness and repeatability of the system. Repeatability is high (94-97%) over this period with false negative 6% (depending on noise threshold chosen). Plates containing 384 reaction mixtures are analyzed in 7 min by moving the DESI sprayer in steps under the sprayer instead of continuously.

Design of condition-based maintenance framework for process operations management in pharmaceutical continuous manufacturing.

Continuous manufacturing, an emerging technology in the pharmaceutical industry, has the potential to increase the efficiency, and agility of pharmaceutical manufacturing processes. To realize these potential benefits of continuous operations, effectively managing materials, equipment, analyzers, and data is vital. Developments for continuous pharmaceutical manufacturing have led to novel technologies and methods for processing material, designing and configuring individual equipment and process analyzers, as well as implementing strategies for active process control. However, limited work has been reported on managing abnormal conditions during operations to prevent unplanned deviations and downtime and sustain system capabilities. Moreover, although the sourcing, analysis, and management of real-time data have received growing attention, limited discussion exists on the continued verification of the infrastructure for ensuring reliable operations. Hence, this work introduces condition-based maintenance (CBM) as a general strategy for continually verifying and sustaining advanced pharmaceutical manufacturing systems, with a focus on the continuous manufacture of oral solid drug products (OSD-CM). Frameworks, such as CBM, benefit unified efforts towards continued verification and operational excellence by leveraging process knowledge and the availability of real-time data. A vital implementation consideration for manufacturing operations management applications, such as CBM, is a systems architecture and an enabling infrastructure. This work outlines the systems architecture design for CBM in OSD-CM and highlights sample fault scenarios involving equipment and process analyzers. For illustrative purposes, this work also describes the infrastructure implemented on an OSD-CM testbed, which uses commercially available automation systems and leverages enterprise architecture standards. With the increasing digitalization of manufacturing operations in the pharmaceutical industry, proactively using process data towards modernizing maintenance practices is relevant to a single unit operation as well as to a series of physically integrated unit operations.