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Flowsheet Models Modernize Pharmaceutical Manufacturing Design and Risk Assessment – Courtesy(PharmTech)
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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 in silico. 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
Process design and risk assessment. 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 in silico (i.e., using a computer). Mathematical models embedded in the PSE tools can potentially supplement expensive and time-consuming ex-silico 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). Table I summarizes the PSE tools (10) and their potential utility in pharmaceutical process development.

Definition of flowsheet models
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. Figure 1 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 Figure 1, 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.

Figure 1: Flowsheet models for a continuous direct compression system. The output from each unit operation model (represented as a schematic) is the input for the subsequent unit.  These calculations are done automatically and sequentially in a flowsheet model environment (e.g., gPROMS, Process Systems Enterprise).

Process design and optimization. From a design standpoint, flowsheet models can potentially provide great value as a tool to evaluate equipment configurations and manufacturing schemesin silico 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 Figure 2, 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.

Figure 2: Major drug-product manufacturing routes already modeled using flowsheet modeling approaches. While multiple routes are represented in this schematic, a single route would be modeled in each application.

Using flowsheet models, process engineers can study the system in silico 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 in silico 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.

Risk assessment. 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 Figure 3, 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.

Figure 3: A risk assessment case study considers an upstream process deviation at the API feeder. Feeder and blender rates are 30 kg/hr and blender blade speed is 250 rpm. A flowsheet model system is used to evaluate the effect of the blender on mitigating a deviation in the feeder flow (a). The design space for the blending process at two different design blade configurations (all-forward and one-third-back configuration) is evaluated. The operational space for each blade configuration in this blending scenario is shown as the shaded regions (b).

The simulation illustrates the impact of the feeder and blender on the API concentration in the blend as a function of time (Figure 3a). 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 Figure 3b. 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).

Control system design and evaluation. 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). In silico 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 Figure 4. This analysis therefore identifies blade speed and flow rate as potential control variables.

Figure 4: In a control-variable selection and response-design case study, the effect of blender speed on the dilution of feed-rate disturbances is examined using a flowsheet model for a blender with the “all forward” blade configuration. The evaluation leads to the selection of appropriate control action (i.e., reduction of blade speed from 400 to 300 rpm or less) at the blender level, based on the upper bound set by the process.

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 Figure 5.

Figure 5: Process design and development algorithm using flowsheet models as the central method.

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, in silico 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.

In silico 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.

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