Engineering Research Center for Structured Organic Particulate Systems (ERC-SOPS), Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.

Ravendra Singh

The future continuous pharmaceutical manufacturing is desired to be based on modular plug-and-play concept. In this approach, mobile modules consisting of a group of unit operations need to be developed that can be easily reconfigured to assemble a scalable pharmaceutical manufacturing process for different products. It is also suitable for on-demand urgent manufacturing such as in the case of the COVID-19 pandemic. However, because of different levels of complexity, the development and adaptation of flexible and modular CM plants is still an open area of research (1-3). Therefore, a systematic framework of methods and tools are needed to develop, adapt, and evaluate the flexible modular CM plant for API manufacturing with reduced time and resources. In particular, a digital twin is critical for quick design, adaptation, optimization, scale up, and control of continuous APIs manufacturing process, allowing for virtual modeling of operations without extensive process development.

Advanced Continuous Manufacturing of Drugs

Pharmaceutical Technology Trends in Manufacturing 2023

In this work, a digital twin for continuous APIs manufacturing process using modular components has been developed. This involves building a digital twin model library of the unit operations commonly used in continuous API manufacturing processes that can be easily integrated to develop the integrated digital system for a range of drug manufacturing processes. Such a toolbox will greatly facilitate the implementation of economically competitive API manufacturing processes, significantly enabling reshoring of API manufacturing activities, and will support effective “digital evaluation” of the manufacturing process by regulators. Additionally, the toolbox will greatly facilitate digital process design, digital process implementation, digital scale-up, and process optimization for API manufacturing systems.

Read this article in the Pharmaceutical Technology Trends in Manufacturing 2023 eBook

Ravendra Singh is faculty of the Department of Chemical and Biochemical Engineering at Rutgers University.

When referring to this article, please cite it as R. Singh, "Advanced Continuous Manufacturing of Drugs," 

Pharmaceutical Technology's Trends in Manufacturing

Articles

Advanced continuous manufacturing with modular components is emerging as a preferred platform to produce APIs.

In continuous pharmaceutical manufacturing, real-time assurance of critical quality attributes (CQAs) is highly desired. Among CQAs, control and assurance of tablet potency has been challenging due to lack of real-time measurement sensors tied to optimization programs. A modular self-contained toolbox developed using Python programming has been demonstrated to predict tablet potency based on a residence time distribution (RTD) model and apply a diversion strategy to divert out-of-specification tablets. The modular toolbox can be integrated with any commercially available tools to control tablet potency, can automatically calibrate the RTD model for different formulations and processes, and can be used to predict outlet concentrations, assuming accurately quantified inputs.

Pharmaceutical Technology’s October 2021 APIs, Excipients, and Manufacturing eBook

Read the article:
Predicting Tablet Potency in Continuous Manufacturing

Ravendra Singh is Assistant Research Professor, NSF-ERC C-SOPS, Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey.

When referring to this article, please cite it as R. Singh, “Predicting Tablet Potency in Continuous Manufacturing,” 

Pharmaceutical Technology APIs, Excipients, and Manufacturing 

A modular toolbox enables residence time distribution-based control for continuous pharmaceutical manufacturing. 

Predicting Tablet Potency in Continuous Manufacturing

Pharmaceutical manufacturing is an area with strict regulations requiring precise quality control of drug products. Traditional manufacturing practices have complied with these guidelines for decades. Initiatives have been put forth for advanced manufacturing technologies to be used in the pharmaceutical industry (1),  such as continuous manufacturing of oral solid dosage (OSD) drug products. Continuous manufacturing has the potential for a higher level of quality control than batch manufacturing. Because the process is different from traditional batch operations, however, regulatory requirements, such as traceability, will be achieved in a different way.

	As opposed to traditional batch processes, where drug components are weighed and then added to a batch unit operation such as a large bin blender, continuous processes have a flow of material traveling through them, from one unit operation to the next. In this way, the amount of material to be processed is dependent on the mass flow rate of the material and the total duration of continuous operation. Batch size is not limited by the size of the equipment, as it is in batch processes.  Much work has been done in studying the various areas affecting the performance of continuous manufacturing of OSD drug products, including material characterization (2), facility development (3, 4), modeling for process simulation (5–7), real-time monitoring (8, 9), and process control (10–12). Less attention has been paid, however, to developing the methods and tools through which the materials can be systematically traced during continuous pharmaceutical manufacturing.

	For patient safety and regulatory compliance, the history of each pharmaceutical product should be known and traceable. Traceability is rather straightforward for batch processes. The batch numbers for each of the individual components added to a batch unit operation are recorded. At the completion of this operation, a clear record states the amount of each component added and their respective batch numbers. In a continuous system, there is not such a clear distinction when components change from one batch number to another, specifically how and when the change at the inlet of the process affects the outlet. The literature available on traceability in continuous systems address the challenges in other industries, such as diary and food, mining, and pulp and paper (13–15), and although research has been done in developing efficient lot-tracing systems (16), it has failed to address the challenges specific to the pharmaceutical industry and its continuous processes. This article aims to address these challenges and present a framework for implementation of material traceability applicable to continuous pharmaceutical manufacturing.

 Title 21 presents the following definitions (17):

	Batch: “specific quantity of a drug…intended to have uniform character and quality, … produced according to single manufacturing order”

	Lot: “specific identified portion of a batch, having uniform character and quality, … in the case of … continuous process, it is a specific identified amount produced … that assures its having uniform character and quality … ”

	Lot number: “distinctive combination of letters, numbers, or symbols, or any combination of them, from which complete history of the manufacture, processing, packing, holding, and distribution of a batch or lot of drug product … can be determined.”

	From these definitions, the argument can be made that a continuous production run, in which the critical process parameters (CPPs) remain within a validated range resulting in acceptable tablet critical quality attributes (CQAs), constitutes a single batch. And as batches of tablet components change during this continuous production run, the lot number can distinguish between tablets whose components come from different batches. In the case that one component of the formulation changes the batch number, theoretically, the tablet quality should not change, given that the process conditions are unchanged. For material traceability purposes, however, the tablets containing raw material from one batch and another need to be distinguished. Because this raw material batch change occurs during a single manufacturing order, with no changes to the process conditions, it is, therefore, still the same batch of finished drug product. However, the tablets containing the new raw material batch can be assigned to a separate “lot” within the current tablet batch, making the lot a “specific identified portion of a batch” in which the tablets contain material from a raw material batch different from the previous lot. The idea is that, when tablets are released,  a specific batch number and lot number trace exactly what raw material batches are present in the tablet. 

 graphically illustrates the difference in material traceability between (a) batch and (b) continuous processes. 

	One of the technical challenges that must be understood to implement material traceability in a continuous system is the time it takes for a change in component batch number, which occurs at the powder feeder upstream, to be present in the tablet at the outlet of the system. This problem can be addressed by studying the residence time distribution (RTD) of the continuous system (18). By learning the minimum and maximum residence time of a component traveling through the system, a prediction can be made as to when a change in component batch will reach the outlet and when the previous batch will be fully cleared. There are many documented strategies to acquiring RTD of powder processes (19–21). Experimentally, the RTD is studied by conducting tracer experiments, where a pulse of detectable material is added at the inlet and its impact is measured at the outlet. The shape and characteristics of this outlet distribution contain important information for predicting the behavior of powder during a component batch change.

The results from RTD experiments can be used to begin to understand how material traceability will be possible for continuous pharmaceutical manufacturing. From the RTD, the minimum and maximum residence time of each component, namely the time delay of impact at the outlet and the clearance time of the component, can be determined. The tablets created between these two times will have a mixture of the component’s previous batch and new batch and will be referred to as a “transitional lot.” Because the goal for material traceability is to trace component batch numbers for each lot of tablets, the authors propose assigning a new lot number every time the effect of a component batch change is predicted to affect the tablets at the outlet, whether that be the new batch being present in the tablets, or the old batch no longer being present in the tablets.

	To implement such a strategy, a few specific determinations must be made. One is a feeder refill protocol, which must specify the amount of material per refill as well as what point triggers the refill. Also required is an automated plant, allowing for real-time data flow from unit operations to a centralized control system, which allows all process parameters to be stored for all unit operations. The residence time values must also be determined for each component in the formulation. The final piece of the framework is a software tool that manages all the data associated with material traceability. 

No software tool currently exists for continuous pharmaceutical material traceability; a prototype was developed to illustrate the proof of concept. As shown in 

, the software is responsible for all material traceability functionality and data management. It must receive the new component batch numbers and assign it to the correct component. It must also increment the tablet lot number according to the proper residence time value for the component that is undergoing a batch change. It is also responsible for updating a table summarizing the composition of each tablet lot with the correct batch number for each component. And finally, it is responsible for communicating with the control system, sending the tablet lot number in real-time for data storage.

	One proposed solution to material traceability in continuous manufacturing is to discard tablets once refill occurs, and wait until the maximum residence time to start collecting tablets, thereby, ensuring that the tablets have components with known batch numbers. This strategy is wasteful, potentially resulting in discarding over 10 minutes worth of production, even though,  theoretically, the quality of these “transitional” tablets is not compromised. With a transitional lot assigned to tablets while a component is undergoing a batch change, appropriate testing can be conducted, and those tablets released, not discarded, if properly traced by the proposed framework.

	This systematic framework for implementation of material traceability in continuous pharmaceutical manufacturing process and the corresponding software prototype to automate the procedure are generic and can be applied in any continuous manufacturing process. The developed framework is complementary to the existing control platform and thus should have a broad application in continuous pharmaceutical manufacturing. This framework uses knowledge from the evolving fields in pharmaceutical engineering, such as experimental determination of residence time, predictive models for residence time of individual unit operations based on material properties, and improved unit operations for continuous application, to accomplish this necessary goal of material traceability. Further developments in these evolving fields could strengthen and make this framework more efficient.

	Matthew Billups is a graduate student, and Ravendra Singh* is a research assistant professor, both at the Engineering Research Center for Structured Organic Particulate Systems (ERC-SOPS), Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway.  *

To whom correspondence should be addressed

. Tel. +18484454944, ravendra.singh@rutgers.edu.


	Vol. 42, No. 2
	February 2018
	Pages: 32–35, 59

	When referring to this article, please cite it as M. Billups and R. Singh, "Material Traceability in Continuous Pharmaceutical Tablet Manufacturing," 

A systematic framework and software are needed to implement material traceability in continuous pharmaceutical tablet manufacturing.

Material Traceability in Continuous Pharmaceutical Tablet Manufacturing

	The pharmaceutical industry has recognized the value of implementing a systematic approach to drug product development where quality is built into the product and process. The FDA initiative on quality by design (QbD) promotes the design of the product and manufacturing process using principles of chemistry, engineering, material science, and quality assurance to ensure acceptable and reproducible product quality and performance throughout a product’s lifecycle. Product quality is achieved through design of robust processes that are controlled and optimized using product and process knowledge (1, 2). In the QbD paradigm, mathematical models can potentially be used at every stage of drug product development and manufacturing (3). Modeling can help establish a predictive framework using experimental data and scientific principles to create mathematical representations of the system. Predictive models aid process design by evaluating the impact that operations, equipment, and inputs have on product attributes 

. Predictive models also provide a framework for risk assessment, process control, and optimization, where accurate predictions of the system are required (4).
	


	In this article, the authors focus on the use of flowsheet modeling, a process system engineering tool for the design, development, and integration of pharmaceutical processes. More specifically, they discuss application of flowsheet models for process risk assessment and design of control strategies.
	
	

Process development paradigm and novel methods

 Major components of the QbD approach to development include assessment of process risk and establishment of a design space. Risk is defined as “the combination of the probability of occurrence of harm and the severity of that harm” (5). Risk assessment is a science-based process used in quality risk management to identify and rank parameters (e.g., process, equipment, input materials) with potential to have an impact on product quality. Once the significant parameters are identified, they can be further studied to enhance process understanding, which could lead to the establishment of a design space (5). Design space is defined as “the multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality” (6). In general, a good understanding of potential risks when defining a design space can potentially reduce process uncertainty and increase process sustainability. Such knowledge can be used to establish a quantitative framework to measure how process failures impact product quality and determine a risk mitigation approach to reduce process-derived patient hazards.
	
	

Pharmaceutical process systems engineering. 

As the pharmaceutical industry modernizes its manufacturing practices and increasingly incorporates more efficient processing approaches such as continuous manufacturing, it is important to assess the process design elements that affect product quality for these emerging pharmaceutical manufacturing approaches. In designing continuous flow systems, while the analysis and optimization of individual process equipment remain important, the primary objective is to identify and evaluate design elements that pose a potential risk to product quality for the fully integrated system, leading to effective risk management. It is also important to consider the multivariate nature of such systems in process design (7). Within this context, process systems engineering (PSE) tools have been implemented with the goal of facilitating effective and efficient process design. PSE is the application of computer-aided systematic science and engineering approaches to the modeling, design, analysis, control, optimization, and operation of process systems.  
	
	PSE tools can provide insight to pharmaceutical development as a means of evaluating processes 

 (i.e., using a computer). Mathematical models embedded in the PSE tools can potentially supplement expensive and time-consuming 

 experimentation throughout process development (8). Furthermore, predictive mathematical models, once validated, can be used to study the process dynamics in detail, to help achieve high process efficiency, and to attain the desired product quality. These models can facilitate the design of processes where consistent product quality is achieved at every step of manufacturing within the framework of QbD and process analytical technology (PAT) (9). 

 summarizes the PSE tools (10) and their potential utility in pharmaceutical process development.


	In an integrated process, individual pieces of equipment (i.e., unit operations) are connected in series. In such a process, a train of multiple units, one after the next, is connected with piping to perform powder-to-tablet manufacturing sequentially, without isolation of intermediates. The output of a preceding unit becomes the input of a subsequent one, with material continuously flowing between them. Mathematically, process integration follows the same logic. Individual equipment models are combined by taking the results from a preceding model and using it as the inputs of a subsequent one. The integrated process models are called flowsheet models, as the flow of information between the unit models resembles the flow of material(s) between unit operations. 

 shows an example of a flowsheet model for a continuous direct compression system developed at the Engineering Research Center for Structured Organic Particulate Systems. Several flowsheet modeling software packagies (e.g., ASPEN Plus, ChemCAD, gPROMS) have been effectively demonstrated for predictive modeling and design of fluid-based processes and are already widely used across the chemical and petrochemical industries (11). Flowsheet models have been recently developed for continuous pharmaceutical manufacturing schemes and have been shown to effectively capture integrated process dynamics (3, 12).

Application of flowsheet models for process development


	Flowsheet models, such as the one shown in 

, can be used as a tool for process design, optimization (13, 14), risk assessment (15), control strategy analysis (16, 17), and monitoring of a continuous pharmaceutical process.

 From a design standpoint, flowsheet models can potentially provide great value as a tool to evaluate equipment configurations and manufacturing schemes 

 at a much lower cost than the equivalent experimental investigation. Using flowsheet models, the major routes for drug product manufacturing can be evaluated, and challenges with process scale-up can be anticipated and resolved (9, 12). Flowsheet models for continuous direct compression, dry granulation, and wet granulation, shown in 

, have already been developed and demonstrated for their potential use in process design and optimization (15). Such multiplicity of models leads to a flexible flowsheet modeling platform that can streamline the design process compared to the relatively iterative and expensive, experimental and empirical-based process design approach.  

	Using flowsheet models, process engineers can study the system 

 and obtain information about process conditions that would lie outside the range of acceptable outcomes, and, thus, narrow the scope of subsequent experimental investigations. This information would naturally lead to more focused development efforts, which could eventually lead  to a higher level  of process understanding and establishment of the design space. The reduction in experimentation due to 

 evaluation reduces materials usage (e.g., API, excipients), waste, development time, cost, and personnel exposure, while it can potentially improve product quality. During process optimization, flowsheet modeling can potentially be used to determine optimal values for high-risk process parameters.

 Understanding the impact upstream operations have on the process is one of the critical aspects for assessing risk to product quality.  Flowsheet models can aid risk assessment through the use of sensitivity analysis. This tool elucidates the impact different process variables and parameters have on the overall system performance and product quality. Case studies considering disturbances and their impact on the process performance using sensitivity analysis have been previously discussed (12, 15, 18). A simple example of risk assessment relates to whether a continuous blender can sufficiently reduce the disturbances introduced by the feeders. In the example illustrated in 

, flowsheet models are used to study the scenario addressing the question: “Will the blender keep the concentration within specified limits if one of the feeders overfeeds material for a short time?” The scenario was simulated by modeling the output blender concentration after the feed of API surges, leading to a change in concentration of the material entering the comil (i.e., the conical mill between the feeder and blender) for 10 seconds. For comparison, two impeller configurations (“all blades forward” and “one-third blades back”) for the blender were simulated using pre-existing blender models to demonstrate the application of flowsheet models in the risk assessment.

	The simulation illustrates the impact of the feeder and blender on the API concentration in the blend as a function of time (

). Before the comil, the output of the feeder surged,  causing a 10% increase in the concentration of API, which was well above the (arbitrarily selected) permissible 2% upper specification represented with the green dotted line. After the blender, only one of the blade configurations (i.e., one-third configuration) was capable of mitigating the perturbation sufficiently to bring the output API concentration back within the specified limit. The operational space where the blender, given a blade configuration, would be able to dampen similar process disturbances is shown in 

. This type of analysis allows process engineers to understand the risk associated with each process parameter (e.g., blade configuration). Edges of failure, process sensitivity, and flexibility can also be studied using a similar approach (15).

. In the pharmaceutical industry, it is imperative to assure consistent manufacture of the desired product quality. To achieve this goal, material properties and process parameters need to be maintained within predetermined ranges. Deviations from the established ranges increase the risk of producing poor quality products. A process control system can be implemented to automatically adjust the process in response to disturbances to ensure that the quality attributes consistently conform to the established ranges. The design and implementation of an efficient control system is an interactive procedure that involves identification of critical controlled variables; coupling of the controlled variables with suitable actuators (manipulated variables); selection of monitoring tools; selection of a process-control approach followed by controller tuning; model-based, closed-loop performance assessment; and finally, implementation at the manufacturing plant through the available sensing and control platform integrated with control interfaces (19, 20). Integrated flowsheet models can facilitate the design, implementation, and tuning of process-control systems (21). 

 identification and evaluation of process variability sources (e.g., blending) aid the selection and location of appropriate monitoring and control methods.

	Using the same scenario modeled in the previous section, a case study was conducted to identify a suitable  process control approach for mitigating a deviation in API feed rate. Using the flowsheet models, the process parameters for which adjustment can help mitigate the process deviation (i.e., potential risk) can be identified. The analysis conducted in this case study indicates that reducing the flow rate and, counter-intuitively, the impeller rotational speed can increase the mixing ability of the system. The increased mixing is able to mitigate the simulated disturbance, bringing  the product back within specification as shown in 

. This analysis therefore identifies blade speed and flow rate as potential control variables.

	Flowsheet models can also provide knowledge regarding the process dynamics through estimation of the residence time distribution to determine the adequate in-process measurement frequency for process monitoring and control application. Furthermore, controller tuning and testing can be evaluated using flowsheet simulations prior to being implemented in the manufacturing plant.

Pharmaceutical process development using flowsheet models.

 Given the potential benefits of flowsheet models, a methodology for their use in design, optimization, control, and future process assessment is proposed in 

	The process begins with the characterization of materials, to assess whether the mathematical models for individual equipment are capable of predicting the behavior of the ingredients and intermediates (e.g., blends) in the process. If the material properties are not within a unit’s studied range, an experimental evaluation should then be performed to characterize the powder behavior in the unit, and the resulting information should be incorporated into the model.
	
	Once individual models have been tested, 

 design spaces for these units can be created. Combination of the design spaces of individual unit operations using flowsheet models can then be used to propose a set of manufacturing process conditions that best suits the system. Subsequently, the process design space can be created and optimized. Target operating conditions and process control approaches can then be formulated. Once the process conditions and process control approaches are selected, it is recommended to experimentally verify model predictions. The experimental data collected can be used to further tune and improve model predictions as appropriate. After confirmation of the model’s accuracy, risk assessment and process sensitivity are performed to ensure process robustness. The flowsheet models created during process design and development can then be applied to evaluate strategies for process monitoring and control.

Potential regulatory application of flowsheet models


	Risk-based regulatory approaches increase the efficiency and effectiveness of review and inspection activities by directing resources to focus more on the assessment of high-risk areas for products and processes. Quality risk assessments require product and process knowledge to evaluate potential sources of harm (e.g., failure modes of a process and sources of variability) and probability of detection of problems (5).

	As discussed, continuous processing, compared with batch processing, offers a greater opportunity to develop and better use process models to gain process knowledge, because governing equations can generally be derived based on physical and chemical principles. Integrated process models can support a quantitative initial risk assessment through sensitivity analysis by examining the relative magnitude of the impact of variation in process parameters and/or material attributes on quality attributes.

	As an example, in a published study, the most significant sources of variability for a particular continuous tablet-manufacturing process were found to be the mean particle size and bulk density of the raw materials (12). This type of analysis can then be used to focus the regulatory assessment on whether a proposed control strategy is appropriate for mitigating the identified high-risk areas (e.g., variability in raw material particle size and density). Sensitivity analysis can also be used to guide the evaluation of advanced process control approaches employed by identifying control and manipulated variables that should be incorporated into the control strategy (22). The process control strategy can be further assessed through the use of case studies, such as examining the processes’ ability to mitigate the impact of disturbances (e.g., feeder refills). These case studies can aid regulatory review and inspection activities by identifying types of disturbances that may have a significant impact on product quality.

	The level of detail required for describing a model in a regulatory submission depends on the impact its implementation has in assuring the final product quality. Integrated process models used to support process development and initial risk assessments by industry may be considered “low-impact models” because they are not used to assure the final product quality. Documentation for low-impact models should include a discussion of how the models were used to make decisions during process development. Integrated process models used for operating space determination or process control design may be classified as “medium-impact models.” The International Conference on Harmonization Quality Implementation Working Group Points to Consider gives recommendations on documenting higher impact models (23).    

	The use of models by FDA to support review is not new (e.g., pharmacokinetic and drug adsorption modeling to support regulatory decisions with regards to bioequivalence and quantitative structure activity relationships models to risk-assess the potential toxicity of impurities). It is recognized that, although there have been significant advancements in the modeling and simulation of continuous pharmaceutical manufacturing processes, the technology is not yet sufficiently mature to aid regulatory assessment. To address this gap, FDA has sponsored two grants for the development of process simulation and model tools for the continuous manufacturing of solid oral dosage forms to facilitate the risk assessment of manufacturing process and control strategies. The goal is for these projects to lead to a collaborative platform for process simulation that builds on the process modeling knowledge developed in academia, industry, and regulatory bodies. The use of common risk assessment approaches and tools can facilitate the communication of risk mitigation approaches between industry and regulatory bodies.

design through flowsheet models has had major impact in other industries, and it is reasonable to expect that it will also be transformative for pharmaceutical manufacturing. It could have major impact on the design process through the implementation of better, less wasteful, and smarter process design. It could also facilitate process optimization and process control, while minimizing development time. From a regulatory perspective, use of predictive models can enable a quantitative risk assessment, facilitating the quality assessment of manufacturing processes. It can also support the evaluation of control strategies by demonstrating system capabilities to handle multiple sources of variability, either individually or in combination.

	While the technology is relatively new to pharmaceutical manufacturing, its potential is evident. The authors would welcome an active dialogue on how to accelerate the development of such capabilities across the spectrum of relevant processes.


	This work is supported by the National Science Foundation Engineering Research Center on Structured Organic Particulate Systems, through Grant NSF-ECC 0540855, and FDA.
	
	All figures are courtesy of the authors. 

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	M. Sebastian Escotet-Espinoza is graduate research assistant, Ravendra Singh is assistant research professor, and Maitraye Sen is graduate research assistant, all of the Chemical and Biochemical Engineering Department at Rutgers, The State University of New Jersey, Piscataway, NJ 08854. Thomas O’Connor is chemical engineer, science staff, Sau Lee is acting associate director for science, and Sharmista Chatterjee is acting branch I chief/division 1 of process assessment/Office of Process and Facilities, all from the Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, FDA, 10903 New Hampshire Ave., Silver Spring, MD 20993. Rohit Ramachandran is assistant professor, Marianthi Ierapetritou is professor and chair, and Fernando J. Muzzio* is distinguished professor, all of the Rutgers Chemical and Biochemical Engineering Department. Dr. Muzzio is also director of the Engineering Research Center for Structured Organic Particulate Systems (ERC-SOPS).

	*To whom all correspondence should be addressed.  

 When referring to this article, please cite it as M.S. Escotet-Espinoza, “Flowsheet Models Modernize Pharmaceutical Manufacturing Design and Risk Assessment,” 

In-silico design facilitates process optimization and evaluation of process control strategies.