Thomas O’Connor is chemical engineer, science staff at the Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, FDA.

Thomas O’Connor

Controlled correspondence provides a crucial communication mechanism between FDA and the generic pharmaceutical industry. Such a mechanism allows the industry to seek for the agency’s input prior to the submission of an abbreviated new drug application (ANDA). On average, FDA has received more than 1000 controlled correspondences every year since the agency began responding to inquiries in 1992.

	Controlled correspondence is defined as “a correspondence submitted to the agency, by or on behalf of a generic drug manufacturer or related industry, requesting information on a specific element of generic drug product development” (1). An effort has been made to data-mine correspondences related to generic-drug chemistry that were submitted in the past 10 years with the goal of identifying major categories of interest and determine areas where FDA can improve communication with the industry. This effort is expected to increase efficiency and thereby assure the fulfillment of the agency’s Generic Drug User Fee Amendments of 2012 (GDUFA) commitment to the industry, as elaborated below.

	Under the GDUFA commitment (2), FDA will respond to:

		70% of controlled correspondence in four months from date of submission in fiscal year (FY) 2015

		70% in two months from date of submission in FY 2016

		90% in two months from data of submission in FY 2017.

	If the controlled correspondence requires input from the clinical division, one additional month will be added to the goals outlined above.

	Several years ago, FDA established a webpage (3) and provided instructions to better guide the industry in submitting controlled correspondence. To further improve the efficiency of processing controlled correspondence, a draft guidance for industry entitled 

Controlled Correspondence Related to Generic Drug Development

 (1), was published in August 2014. In this guidance, controlled correspondence is defined and criteria are given for what correspondence is acceptable for submission to FDA. Pre-ANDA meeting requests,-questions related to pending ANDAs under review, inquiries not directly related to generic-drug development, or general, open-ended, or insufficiently detailed questions will not be considered as controlled correspondence. Requestors inquiring on matters under consideration by FDA (e.g., approval standards for complex products) will be notified, but no conclusive information will be given until the matter is settled by the agency. Moreover, the guidance states that requests for review of clinical protocols for 

 bioequivalence studies will remain excluded from GDUFA goal dates as these recommendations are covered by individual guidances. These well-defined limits are anticipated to reduce the volume of inquiries and allow allocation of FDA resources to providing timely answers to appropriate inquiries.

	This retrospective analysis of chemistry-related controlled correspondence is another tool to aid in improving the agency’s response to the industry. Understanding trends in the content of these correspondences will allow FDA to improve communication, address gaps in guidance interpretation, and inform future guidance development. Increased clarity from FDA will further increase efficiency by reducing the volume of correspondences each year, thereby reducing strain on the agency’s resources.


	The agency’s correspondence database was data-mined to identify chemistry-related correspondence received between January 2004 and May 2014. Preliminary search identified 754 correspondences labeled as chemistry related; among these, 173 were sub-labeled as meeting requests, biowaivers, bioequivalence, and citizen petition. However, they were not included in the further data analysis because they are not considered as chemistry-related correspondences under the new definition. Therefore, the final sample size was 581.

	These correspondences were reviewed and assigned one of 15 categories and a number of relevant keywords. The questions and answers provided by the agency were summarized in a master spreadsheet based on receipt dates. The most frequently asked questions were identified and are discussed in this publication. A Python-based natural language processing tool (4) was then used to count keywords for further analysis of the categories and to generate a lexical dispersion plot to determine the location of each category within the text in a chronological order. In addition, review outcomes and commonly seen issues have been summarized and suggestions for improvement are provided.


	On average, the agency receives approximately 1200 correspondences yearly. Approximately 5-10% are related to generic-drug chemistry. The number of chemistry-related correspondences has increased steadily since 2011, as shown in 

. Topics in these correspondences can be loosely grouped into 11 categories:

	Each correspondence was assigned to one category, and the number of correspondences under each category was counted and summarized in 

. Categories with the highest number of counts include formulation, stability, specifications, and post-approval changes.

	Formulation is a broad category, but generally consists of inquiries about API-related topics such as allowable particle size, salt stoichiometry, or synthetic route deviations from the reference listed drug (RLD); the presentation of the dosage form; and other allowable deviations from the RLD.

	The specifications, post-approval changes, and stability categories were broken down into sub-categories to further elucidate trends.

	Specifications is a broad topic, which includes questions related to residual solvents, identity, limits, drug master file (DMF), test method, validation, and impurity (see 

). While the counts in many of the subcategories were distributed relatively evenly, test method and impurity stood out as major topics. Test method questions were highly varied and case-specific. These questions sought specific recommendations on the procedure and/or data requirements for stability testing; API/excipient characterization and compatibility; in-process testing; quality requirements; and ANDA submission data requirements.

	Inquiries about residual solvents or impurities generally requested advice on the limits or the types of solvents/impurities found with a particular API. The limits category catches all other inquiries regarding limits that are not pertaining to residual solvents or impurities; these questions largely requested advice on the setting of assay limits for the API.

	Inquiries regarding post-approval changes (see 

) revolved almost exclusively around questions about which post-approval reporting category was most appropriate, followed by requests for advice on any other information or data that should be filed with the supplement(s). Questions in this category will no longer be considered as controlled correspondence; the draft Guidance for Industry: Controlled Correspondence Related to Generic Drug Development (August 2014) considers any questions related to a pending or approved ANDA as a review issue and outside the scope of controlled correspondence (1). However, the agency is trying to establish some mechanism to address general questions for approved ANDAs during the post-approval phase.

	Stability questions were divided into subcategories (see 

) and pertained to requirements for batch size and requests for approval of bracketing or matrixing protocols. Batch number questions generally seek to clarify how many primary stability batches are required for supporting the ANDA submission given the requirements set forth in the June 2013 Guidance for Industry, ANDAs: Stability Testing of Drug Substances and Products (5). Because the release of this guidance and the subsequent May 2014 Guidance for Industry, ANDAs: Stability Testing of Drug Substances and Products Questions and Answers (6) generated many questions and represents a relatively major shift in FDA’s approach, further analysis of these controlled correspondences will be elaborated in a future publication.


	The most commonly posed questions across all categories were compiled in 

. The most popular question was about reduced batch size(s): Firms requested for the agency’s concurrence on reduced primary batch sizes compared to the established pilot-scale size according to the International Conference on Harmonisation (ICH) definition. The criteria for an exception to the recommendations regarding minimum size for pilot scale for ANDA submission batch size reduction were established in the agency’s guidance (6):

		The RLD has an orphan drug designation.

		Use of a controlled substance allocated by the Drug Enforcement Administration.

		The test batch size is the same as the commercial batch size with the commitment that a prior approval supplement (PAS) will be provided when there is scale-up.

	FDA did concur with the proposals for reduced batch sizes when they met one of the above criteria; however, in cases where the proposals used the high API cost or other financial/logistical constraints as justifications, the agency normally denied concurrence.

	The second most popular question was regarding multiple API sources. The specific questions were broad, but generally related to whether different routes of synthesis are acceptable from two API sources in the same ANDA submission, whether it is acceptable to have different specifications and test methods for different sources of API, and how many exhibit/stability batches should be manufactured using the API from the alternate source(s). For the first two scenarios, FDA typically concurs with the proposals while recommending sufficient justifications to be included in the ANDA submission(s). Regarding the number of batches, the agency recommends comparative stability and release data from at least one batch of finished drug product produced from each alternate API source. It should be noted that the APIs in discussion are limited to small molecules; recommendations for complex drug substances or products (e.g., peptides, low molecular weight heparins, and biologics) can be different and would require considerations on case-by-case basis.

	Another common question is the reporting mechanism for post-approval changes. Nearly all questions in the post-approval changes category were related to reporting category or what data should be submitted in support of the change. These questions have been addressed using a risk-based -approach following the general principles prescribed by the agency’s guidances on post-approval changes (7, 8).

	Questions on overage were almost exclusively related to whether or not the firm could produce a drug product with an overage of the API. In most circumstances, the overage proposal was not approved. As set forth in the Guidance for Industry: Q8(R2) Pharmaceutical Development (January 2009) (9), the use of an overage to compensate for degradation during manufacture or a product’s shelf life, or to extend shelf life, is discouraged. Many firms claim to have found an overage in the RLD; however, this may not be a sufficient justification for overage because the RLDs may have used a different product design and/or manufacturing processes. Given that only a limited amount of information is typically provided in these correspondences, FDA typically considers the use of overage as a review issue to be evaluated within the full context of the ANDA(s); therefore, the firms should provide sufficient justifications in the ANDA submission(s) if overage is used.

	The fifth most popular question was whether it is acceptable to formulate a tablet or capsule size larger than that of the RLD. It is generally recommended to formulate generic solid oral dosage forms to be similar in size to the RLD to increase patient compliance and reduce the risk of dysphagia. Further details are discussed in the Guidance for Industry: Size, Shape, and Other Physical Attributes of Generic Tablets and Capsules (10). Somewhat similar to the use of overage, the agency typically states in the responses that deviations (especially when larger) should be justified in the ANDA submission. Proper justifications should at least include why a larger size is necessary, and how the firm will circumvent dysphagia and patient-compliance issues. Arguments based on patent infringement or manufacturing limitations are not considered appropriate.


	It is helpful to visualize when a topic becomes popular in time. These time points can signal when guidances have accurately conveyed information or may alert the agency about unclear guidance language or recommendations. A lexical dispersion plot (

) displays words in sequential order within a text to visualize the location of keywords within the text. Each stripe represents the instance of a word (the category) and each row represents the entire text. Simultaneously identifying the location of the years 2004-2014 within the text allowed for correlation between the occurrence of each category to the date that the correspondence was submitted.

	This type of chronological correlation shows a heightened interest in specification, formulation, and overage questions beginning in 2012. Questions regarding post-approval changes have remained fairly steady. The Guidance for Industry: CMC Postapproval Manufacturing Changes to Be Documented in Annual Reports (8) was published in May 2014; correspondences will be monitored using such a chorological correlation to assess how well such a guidance document would guide the industry. 

 shows a sharp increase in stability-related controlled correspondence beginning in late 2012, which loosely correlated with the publication of the stability guidance document (5) and its companion question and answer document (6). Given that many questions were about the number and sizes of primary stability batches, FDA may use this information to guide further policy/guidance development or updates.

Furthermore, the evaluation of the frequency of inquiries on a topic over time can indicate how well the agency’s guidance/policy addressed the related topics. For example, questions regarding acceptable limits for and test methods of residual solvents (see 

) were most popular in 2008 and 2009 and then decreased after 2009, corresponding to the release of the 

Guidance for Industry: Residual Solvents in Drug Products Marketed in the United States

 (11) in November 2009. From 2012, no more residual solvents inquiries are recorded. Similarly, the number of questions on impurities dramatically dropped in 2009 and 2010, concurrent with the publication of two impurity-related guidance documents (12, 13).


	Based on the observations during the review of these correspondences, commonly seen submission issues include:

		Vague, general, or poorly-worded questions

		Imprecise language and/or misused terms

		Questions that make incorrect assumptions

		Too much unnecessary information provided.

	The agency strongly recommends the firms:

		Thoroughly research the question before drafting the controlled correspondence, including visiting the FDA website for published guidances and materials

		Provide relevant information clearly in the letter, including, at a minimum, dosage form

		Do not assume that the agency will correct any incorrect assumptions/information.

	With these recommendations considered, a general workflow for proper assembly of controlled correspondences is provided in 


	Controlled correspondence is a crucial route of communication between FDA and the generic-drug industry to facilitate the development of generic-drug products. Therefore, the agency has agreed to specific program enhancements and performance goals for addressing these correspondences, as set forth in the GDUFA Commitment Letter (2). As part of the effort, this data-mining exercise was conducted to identify gaps and potential continual improvements in chemistry-related correspondences. The findings and observations here can provide valuable reference to both FDA and the generic-drug industry. From these observations, the agency can gain better understanding of how its guidances and policies are received and further gauge the needs and concerns of the industry. Further interpretation of certain guidances and policies are provided here so that the industry can be more efficient in submitting correspondences and developing generic-drug products.

Articles

An analysis of controlled correspondence between generic-drug manufacturers and FDA reveals patterns in questions about specific drug chemistry topics and the response to information provided in FDA guidance documents.

Regulatory Considerations for Controlled Correspondence Related to Generic Drug Chemistry

	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.