یک رویکرد یکپارچه برای مدل سازی برگه پویا و تجزیه و تحلیل حساسیت از یک فرآیند تولید مداوم قرص
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|26596||2012||18 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Computers & Chemical Engineering, Volume 42, 11 July 2012, Pages 30–47
Manufacturing of powder-based products is a focus of increasing research in the recent years. The main reason is the lack of predictive process models connecting process parameters and material properties to product quality attributes. Moreover, the trend towards continuous manufacturing for the production of multiple pharmaceutical products increases the need for model-based process and product design. This work aims to identify the challenges in flowsheet model development and simulation for solid-based pharmaceutical processes and show its application and advantages for the integrated simulation and sensitivity analysis of two tablet manufacturing case studies: direct compaction and dry granulation. The developed flowsheet system involves a combination of hybrid, population balance and data-based models. Results show that feeder refill fluctuations propagate downstream and cause fluctuations in the mixing uniformity of the blend as well as the tablet composition. However, this effect can be mitigated through recycling. Dynamic sensitivity analysis performed on the developed flowsheet, classifies the most significant sources of variability, which are material properties such as mean particle size and bulk density of powders.
Historically, the pharmaceutical industry has been very innovative and successful in the field of new drug discovery and development. However, this has drawn the focus away from the development of efficient manufacturing methods and process understanding (Gernaey and Gani, 2010, Huang et al., 2009, Klatt and Marquardt, 2009 and McKenzie et al., 2006). In addition, one of the fears that the industry is facing today is the significant decrease in profit due to the expiration of important patents and the difficulty of the development of new drugs to replace them. This fact drives the focus towards efficient manufacturing strategies, which would significantly make products competitive in a market where generic manufacturers are also involved. Due to the lack of knowledge of how critical material attributes and process parameters affect end-point product attributes, combined with ineffective control strategies, pharmaceutical manufacturing processes generate products that are often characterized by a relatively large amount of variability that would not be tolerated in other process industries (e.g. petrochemicals or foods) (McKenzie et al., 2006). One additional challenge for the establishment of efficient, controlled, and automated manufacturing methods is the considerable variability in new raw material properties, since any new formulation has unique molecular structure, physico-chemical and biological properties. In addition, the majority of pharmaceutical products (∼80%) are in a solid based form of tablets or capsules, composed from bulk powder materials, which are far more complex and challenging to handle than liquid or gas phase materials. Even though significant progress has been made recently in particle technology research, there is a gap between fundamental science and applied engineering due to the need for integration of multiscale knowledge (Ng, 2002). All of the above reasons have been the source of consensus and legacy based heuristic production strategies, conducted overwhelmingly in batch mode; with product quality being traditionally verified offline through acceptance sampling. This approach has lead to additional sources of variability, which are the effects of the analytical method, and the human factor, since it is common for operators to regulate the process based on their individual knowledge and experience. Recently, the Food and Drug Administration (FDA) has recognized the need for modernizing pharmaceutical manufacturing and has launched an initiative for enhancing process understanding through Quality by Design (QbD) and Process Analytical Technology (PAT) tools (Garcia et al., 2008, Lionberger et al., 2008, Nosal and Schultz, 2008 and Yu, 2008). The major goals of these efforts include the development of scientific mechanistic understanding of a wide range of processes; harmonization of processes and equipment; development of technologies to perform online measurements of critical material properties during processing; performance of real-time control and optimization; minimization of the need for empirical experimentation and finally, exploration of process flexibility or design space (Lepore & Spavins, 2008). To achieve these goals, the industry needs the modeling tools and databases for measuring, controlling and predicting quality and performance. In the last five years, one of the main approaches for modernizing pharmaceutical manufacturing, transition of production from batch to continuous mode, is becoming increasingly more appealing to the industry and regulatory authorities (Betz et al., 2003, Gonnissen et al., 2008, Leuenberger, 2001 and Plumb, 2005). The advantages of this change have been proven very beneficial in many aspects when applied in other fields, such as petrochemicals and specialty chemicals (Gorsek & Glavic, 1997). Firstly, continuous manufacturing allows the use of the same equipment for the production of smaller and larger quantities, which minimizes the need for scale-up studies and the time-to-market significantly (Leuenberger, 2001). At the present time, processes developed in small-scale equipment used for initial clinical studies must be scaled-up (empirically) and subsequently validated experimentally and further optimized, since their operation is always potentially different at the larger scale. This leads to another advantage of continuous integrated manufacturing, which is the minimization of the plant footprint, since the entire continuous process typically fits inside a much smaller space. A well controlled continuous process involves the handling of small aliquots of material throughout the unit operations, increasing the ability to monitor a significant fraction of the process streams, which is impossible in a large-scale batch process. In addition, continuous operations can produce higher throughputs under better control, which implies the optimal use of the invested capital (space, raw materials and equipment), as well as the reduction of waste (Plumb, 2005). Also, in a continuous setting, the human factor is significantly decreased through automation of operation and thus labor costs can be reduced. Finally, risks associated with material handling, such as contamination and undesired segregation and agglomeration are reduced since less time is necessary for filling, emptying and cleaning equipment. A detailed economical analysis and comparison of batch versus continuous operating mode for the production of pharmaceuticals has been performed by Schaber et al. (2011), demonstrating the possibilities and advantages of the latter. However, a switch from the already established batch to continuous operation involves many challenges, and could lead to failure if not performed correctly. Firstly, pharmaceutical substances are highly sensitive to environmental conditions, such as humidity and temperature and a possible larger residence time than required can cause significant material degradation. This can cause dangerous product contaminations and should be avoided. In a batch setting, the residence time is more easily controlled whereas in a continuous setting this is more challenging. Subsequently, in a continuous production, the process does not reach steady state from the beginning, and this may cause off-specification product to be produced during a particular time interval. However, because regulatory authorities require detailed and time-consuming documentation for the establishment of a manufacturing strategy, and because in many cases companies currently have unused capacity, it is still debatable by pharmaceutical companies whether the modification of an already established batch manufacturing procedure to a continuous one is worth the risk and the up-front expense. In addition, one of the most discussed topics involved in the batch-to-continuous transition is the definition of a batch in a continuous setting, since this must be clearly defined due to regulatory aspects. However, the steady-state nature of continuous processing as well as the ability to process less material at a unit time, will facilitate monitoring and efficient control. All of the above together with process knowledge and tools such as the one developed in this work, can be used to identify possible problems ahead of time and be able to predict which fraction of the production to discard. Ultimately, as we move towards better process knowledge and improve the available models and control strategies, more and more perturbations can be handled and alleviated before even affecting the final product quality (feed forward control). In other words, as we move forward, our ability to predict and control a continuous process should be the aspects which will define the conception of a batch in a continuous line. Continuous manufacturing cannot be performed successfully unless each sub-process is well understood in terms of the effect of material properties, operating parameters and environmental conditions on critical product quality attributes. If process understanding is then translated into models, computer aided simulation tools, such as flowsheet modeling, allow the proposed continuous integrated process to be designed, analyzed and optimized. Flowsheet modeling is one of the most influential achievements of computer-aided process systems engineering, which has enabled the design, analysis and optimization of robust processes in the chemical industry. A robust and detailed flowsheet simulation is an approximate representation of the actual plant operation, which also helps in the establishment of successful control strategies that will regulate the process when given a desired set-point change or when a problem occurs during the operation of the integrated process (Ramachandran et al., 2011 and Ramachandran and Chaudhury, 2011). Process control aims to maintain the process in a desired state. Through accurate simulations, one can predict the time interval of the transitional stage during which product has not yet reached desired state, and the control actions to take when the system deviates from the desired state (feedback control) or when perturbations are detected as they enter into the system (feed-forward control). Accurate modeling of the residence time distribution of material in the process also allows discarding a small percentage of faulty product when necessary, which is more profitable than production of failing batches. A developed flowsheet simulation can also facilitate the identification of possible process integration bottlenecks, conflicting design and control objectives, simulation of the effect of recycle streams as well as process start-up and shut-down. For these and other reasons, flowsheet synthesis is an extremely important first step in a wide range of industries, during which the optimal process configuration is decided upon according to the desired design objectives (Biegler, Grossmann, & Westerberg, 1997). This procedure enables the investigation of design alternatives through the formulation of superstructure networks and the solution of mixed integer optimization problems which have operating conditions and design parameters as decision variables (Biegler and Grossmann, 2004, Biegler et al., 1997 and Henao and Maravelias, 2011). In the literature and in industrial practice, flowsheet simulations have helped identify global optimal operating conditions and design configurations that lead to robust, flexible and economically profitable processes. Research in flowsheet building for fluid-based processes common to the chemical industry (i.e. petrochemicals) has become a mature activity, resulting in a variety of state of the art software packages (e.g. ASPEN, gPROMS, CHEMCAD, etc.) that contain all the needed capabilities. Using the developed software is easy since a user can simply ‘drag-and-drop’ the necessary unit operations from established model libraries and connect them appropriately to simulate a specific integrated process. On the contrary, flowsheet models and software for solid based processes are only in a primitive stage for a variety of reasons (Gruhn et al., 2004, Ng and Fung, 2003, Ng, 2002, Werther et al., 2008 and Werther et al., 2009). First of all, due to the predominantly batch configuration in which essentially all solid pharmaceuticals are manufactured, the need for a preliminary flowsheet synthesis step appeared unnecessary. More importantly, the lack of knowledge of the critical material properties, design, and process variables, as well as the lack of unit operation process models have inhibited the development of model-based flowsheet simulators. Due to the high complexity of the raw materials and the lack of standardized procedures, another obstacle is the inexistence of comprehensive material property databases for a wide range of used excipients and APIs. In fact, material characterization of pharmaceutical powders is a non standardized procedure which differs amongst the different companies. Universal material property libraries are vital to model libraries, if one wants to produce a wide ranging multipurpose flowsheet model, independent of specific product applications. Lastly, solid-based flowsheet modeling has been held back due to the lack of software with capabilities for handling dynamic changes of distributed parameters (i.e. particle size distributions). This problem has recently been tackled in incipient form by a variety of software developers such as gPROMs/gSOLIDs and SolidSim (Werther, Toebermann, Rosenkranz, & Gruhn, 2000). In this work, gPROMs is used as a platform for building a flowsheet model for two production schemes for pharmaceutical tablets. gPROMs is an equation-based (Oh and Pantelides, 1996 and Winkel et al., 1995) and dynamic software, which is widely accepted in a variety of fluid based product industries. The specific objectives of this work include (1) model development for a variety of powder unit operations, (2) integration and simulation of the developed models and models obtained from literature in gPROMS and (3) dynamic sensitivity analysis of the developed flowsheet simulation for the identification and quantification of critical sources of uncertainty. The remainder of the paper is organized as follows. Section 2 is a description of the models developed and implemented to describe each unit operation and how these models are integrated to simulate actual production scenarios for direct compaction and dry granulation. The theoretical background of dynamic sensitivity analysis is described in Section 3. Results for the two case studies and sensitivity analysis are discussed in Section 4. Finally, the paper concludes with Section 5 with a discussion of this work and future plans.
نتیجه گیری انگلیسی
This work aims to outline the necessary steps and challenges associated with flowsheet model building for integrated pharmaceutical manufacturing processes. It is evident, that the current state of flowsheet model building in the pharmaceutical industry is very primitive compared to state of the art of fluid-based flowsheet models. This initial approach aims to combine different types of model types, ranging from semi-empirical population balances to empirical models, in order to capture all the effects and interactions of current knowledge of the necessary processes for transforming a mixture of raw powder materials to actual pharmaceutical tablets. Results prove that the developed models capture the expected trends and responses of stream variables during a dynamic operation. Such results can be used to identify promising control strategies through the simulation of dynamic step change experiments. Most importantly, such a simulation can provide insight in capturing the dynamics of a process through accurate prediction of the residence time of powder throughout each process and thus it can be used to estimate the time interval for off-spec product in the case of step-changes or unexpected events upstream as well as the propagation of the variability of the inputs and their effects on tablet properties. Dynamic sensitivity analysis which is enabled through stochastic simulations of the flowsheet model based on a computer experiment can lead to important conclusions about the quantification of uncertainty effects towards specific outputs. The effects of uncertainty of a large number of fluctuating inputs is important since this can lead the process to operate outside the design space where process is outside of the validated operational interval and the product might be out of specifications (OOS). Finally, ranking the relative effect of the uncertain inputs on specific outputs is vital when identifying efficient control strategies, which is a future goal towards the implementation of an efficient continuous tablet manufacturing process. Most importantly, these rankings can help answer a critical question companies currently struggle with: which variables are critical? Future goals include the incorporation of powder material properties into the flowsheet simulations, such as flowability metrics (flow index) and interfacial properties (contact angle), which imply the modification of the developed models in order to take the effects of these variables into account. In addition, significant interactions between ribbon properties (density and thickness) and the input variables of the milling process are currently being researched both computationally and experimentally in order to be able to capture the propagation of effects and the coupled dynamics between these two processes. Finally, since the vast majority of modeling work for powder based processes is in forms of computationally expensive DEM or FEM simulations, incorporation of the predictions of such models into a flowsheet simulation through reduced order models is another future goal. Simulation of start-up and shut-down procedures has been identified to require modifications in the model parameters and will be included in future versions of the developed flowsheet model library.