Developing a Modular Continuous Drug Product Manufacturing System with Real Time Quality Assurance for Producing Pharmaceutical Mini-Tablets.

The pharmaceutical industry has shown keen interest in developing small-scale modular manufacturing systems for producing medicinal products. These systems offer agile and flexible manufacturing, and are well-suited for use in situations requiring rapid production of drugs such as pandemics and humanitarian disasters. The creation of such systems requires the development of modular facilities for making solid oral drug products. In recent years, however, the development of such facilities has seen limited progress. This study presents a development of a prototype modular system that uses drop on demand (DoD) printing to produce personalized solid oral drug products. The system's operation is demonstrated for manufacturing mini-tablets, a category of pediatric drug products, in continuous and semi-batch modes. In this process, the DoD printer is used to generate molten formulation drops that are solidified into mini-tablets. These dosages are then extracted, washed and dried in a continuous filtration and drying unit which is integrated with the printer. Process monitoring tools are also incorporated in the system to track the critical quality attributes of the product and the critical process parameters of the manufacturing operation in real time. Future areas of innovation are also proposed to improve this prototype unit and to enable the development of advanced drug manufacturing systems based on this platform.

Intramodule pressure profiles and protein accumulation during tangential flow filtration.

Tangential flow filtration (TFF) through a 30 kDa nominal molecular weight cut-off (MWCO) ultrafiltration membrane is widely employed to concentrate purified monoclonal antibodies (mAbs) to levels required for their formulation into injectable biologics. While TFF has been used to remove casein from milk for cheese production for over 35 years, and in pharmaceutical manufacture of biotherapeutic proteins for 20 years, the rapid decline in filtration rate (i.e., flux) at high protein concentrations is a limitation that still needs to be addressed. This is particularly important for mAbs, many of which are 140-160 kDa immunoglobulin G (IgG) type proteins recovered at concentrations of 200 mg/mL or higher. This work reports the direct measurement of local transmembrane pressure drops and off-line confocal imaging of protein accumulation in stagnant regions on the surface of a 30 kDa regenerated cellulose membrane in a flat-sheet configuration widely used in manufacture of biotherapeutic proteins. These first-of-a-kind measurements using 150 kDa bovine IgG show that while axial pressure decreases by 58 psi across a process membrane cassette, the decrease in transmembrane pressure drop is constant at about 1.2 psi/cm along the 20.7 cm length of the membrane. Confocal laser scanning microscopy of the membrane surface at the completion of runs where retentate protein concentration exceeds 200 mg/mL, shows a 50 µm thick protein layer is uniformly deposited. The localized measurements made possible by the modified membrane system confirm the role of protein deposition on limiting ultrafiltration rate and indicate possible targets for improving membrane performance.

Manufacturing pharmaceutical mini-tablets for pediatric patients using drop-on-demand printing.

The pharmaceutical industry has traditionally manufactured medicines in a limited range of dose strengths, with an emphasis on addressing the needs of the largest patient subgroups. This has disadvantaged smaller patient subsets, such as children, who often cannot find drug products in dosage levels suitable for their requirements. In recent years, development of pharmaceutical mini-tablets has emerged as an attractive solution to this problem. These are small-size dosages that offer attractive features such as flexible and personalized drug dosing, ease of swallowing, and tailored drug release, making them an excellent choice for administering medicines to children. This study presents a novel technique for manufacturing pharmaceutical mini-tablets, using a drop-on-demand (DoD) printing system. In this method, a DoD system is used to generate precise droplets of a melt-based formulation, which are then captured and solidified in an inert solvent bath to produce individual mini-tablets. The study also evaluates the performance of this technique for various formulations, and quantifies two critical quality attributes (CQAs) of the resulting mini-tablets: content uniformity and dissolution behavior. The findings demonstrate that the manufactured mini-tablets can meet regulatory specifications for both CQAs, and that their release profiles can be customized by modifying the excipients used. The study also discusses promising areas of application and limitations of this technique.

An integrated data management and informatics framework for continuous drug product manufacturing processes: A case study on two pilot plants.

The pharmaceutical industry continuously looks for ways to improve its development and manufacturing efficiency. In recent years, such efforts have been driven by the transition from batch to continuous manufacturing and digitalization in process development. To facilitate this transition, integrated data management and informatics tools need to be developed and implemented within the framework of Industry 4.0 technology. In this regard, the work aims to guide the data integration development of continuous pharmaceutical manufacturing processes under the Industry 4.0 framework, improving digital maturity and enabling the development of digital twins. This paper demonstrates two instances where a data integration framework has been successfully employed in academic continuous pharmaceutical manufacturing pilot plants. Details of the integration structure and information flows are comprehensively showcased. Approaches to mitigate concerns in incorporating complex data streams, including integrating multiple process analytical technology tools and legacy equipment, connecting cloud data and simulation models, and safeguarding cyber-physical security, are discussed. Critical challenges and opportunities for practical considerations are highlighted.

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.

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.

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.

Reaction kinetics determination and uncertainty analysis for the synthesis of the cancer drug lomustine.

Fast and reliable model development frameworks are required to support current trends in modernization of pharmaceutical processing, promoting the use of digital platforms to assist process design and operation. In this work, we use a parameter estimation framework built into the PharmaPy library to determine rate parameters and uncertainty regions of different mechanistic and semi-empirical kinetic expressions for the synthesis of the drug lomustine. The parameter estimation procedure was complemented by identifiability analysis, resulting in simplified reaction mechanisms. Comparison of parameters and their uncertainty in process design was demonstrated through design space analysis, showing important differences in model prediction and the extent of their corresponding design spaces. The results of this work can serve to analyze lomustine manufacturing processes that include separation and isolation steps, where parametric sensitivity is expected to propagate along the manufacturing line and impact process feasible operation, and attainment of critical quality attributes of the product.

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.

Bayesian statistical approaches to drug product variability assessment and release.

The determination of the variability of critical dosage form attributes has been a challenge in establishing the quality of pharmaceutical products. During the development process knowledge is minimal. Consequently, ad hoc statistical tools such as hypothesis or significance tests, with calibrated decision error rates are often used in an effort to vet CQAs (Critical Quality Attributes) and keep their levels "between the curbs". As progress moves towards product launch, process and mechanistic understanding grows considerably and there are opportunities to leverage that knowledge for predictive modeling. Bayesian models offer a coherent strategy for integrating prior knowledge into both experimental design as well as predictive analysis for optimal risk-based decision making. This is because the Bayesian paradigm, unlike the frequentist paradigm, can assign probabilities to underlying states of nature that directly impact safety and efficacy such as the population distribution of tablet potencies or dissolution profiles in a batch. However, there are challenges and reluctance in switching to a predictive modeling quality framework once regulatory approval has been attained. This paper offers encouragement to make this switch. In this paper, we review a joint Long Island University - Purdue University (LIU-PU) FDA funded project whose purpose was to further integrate the concepts of this adaptive approach to lot release with the rationale and methods for data generation and curation and to extend the testing of this approach. We discuss the utility of the approach in product development. We consider the regulatory compliance implications, with examples, and establish a potential way forward toward implementation of this approach for both industry and regulatory stake-holders.

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 Comprehensive Framework for the Modular Development of Condition Monitoring Systems for a Continuous Dry Granulation Line.

The development of condition monitoring systems often follows a modular scheme where some systems are already embedded in certain equipment by their manufacturers, and some are distributed across various equipment and instruments. This work introduces a framework for guiding the modular development of monitoring systems and integrating them into a comprehensive model that can handle uncertainty of predictions from the constituent modules. Furthermore, this framework improves the robustness of the modular condition monitoring systems as it provides a methodology for maintaining quality assurance and preventing unnecessary shutdowns in the event of some modules going off-line due to condition-based maintenance interventions.

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.

Development of a Virtual Sensor for Real-Time Prediction of Granule Flow Properties.

We report progress of an ongoing work to develop a virtual sensor for flowability, which is a critical tool for enabling real time process monitoring in a granulation line. The sensor is based on camera imaging to measure the size and shape distribution of granules produced by wet granulation. Then, statistical methods were used to correlate them with flowability measurements such as ring shear tests, drained angle of repose, dynamic angle of repose, and tapped density. The virtual sensor addresses the issue with these flowability measurements, which are based on off-line characterization methods that can take hours to perform. With a virtual sensor based on real-time measurement methods, the prediction of granule flowability become faster, allowing for timely decisions regarding process control and the supply chain.

Real-Time Monitoring of Powder Mass Flowrates for Plant-Wide Control of a Continuous Direct Compaction Tablet Manufacturing Process.

While measurement and monitoring of powder/particulate mass flow rate are not essential to the execution of traditional batch pharmaceutical tablet manufacturing, in continuous operation, it is an important additional critical process parameter. It has a key role both in establishing that the process is in a state of control, and as a controlled variable in process control system design. In current continuous tableting line operations, the pharmaceutical community relies on loss-in-weight feeders to monitor and understand upstream powder flow dynamics. However, due to the absence of established sensing technologies for measuring particulate flow rates, the downstream flow of the feeders is monitored and controlled using various indirect strategies. For example, the hopper level of the tablet press is maintained as a controlled process output by adjusting the turret speed of the tablet press, which indirectly controlling the flow rate. This gap in monitoring and control of the critical process flow motivates our investigation of a novel PAT tool, a capacitance-based sensor (ECVT), and its effective integration into the plant-wide control of a direct compaction process. First, the results of stand-alone experimental studies are reported, which confirm that the ECVT sensor can provide real-time measurements of mass flow rate with measurement error within -1.8 ~ 3.3% and with RMSE of 0.1 kg/h over the range of flow rates from 2 to 10 kg/h. The key caveat is that the powder flowability has to be good enough to avoid powder fouling on the transfer line walls. Next, simulation case studies are carried out using a dynamic flowsheet model of a continuous direct compression line implemented in Matlab/Simulink to demonstrate the potential structural and performance advantages in plant-wide process control enabled by mass flow sensing. Finally, experimental studies are performed on a direct compaction pilot plant in which the ECVT sensor is located at the exit of the blender, to confirm that the powder flow can be monitored instantaneously and controlled effectively at the specified setpoint within a plant-wide feedback controller system.

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.

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.

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.