The design of experiment is a significant tool that can be employed in various experimental situations. Precisely, with the design of experiment, you can manipulate multiple input factors to determine their effect on the desired output. The design of experiment is used when we suspect that more than one input factor is influencing an output. Moreover, we can use the DOE to confirm the suspected relationship between input and output and develop a predictive equation that would be ideal to perform the what-if analysis. The paper will examine the use of DOE software and method to quantify the effect of changes in the conditions of coating process on the performance and quality of film-coated tablets. The DOE’s application exhibit the potential to allow for rapid optimization of coating performance to meet the ever-changing needs of clients.
The use of aqueous film-coating processes has a significant influence on the quality of the final coated product. For instance, the process influences the coating uniformity, efficiency, surface roughness, content, coated-tablet moisture, and gloss (Rodrigues & Iemma, 2014). With the use of different types of coating process equipment, the aqueous tablet coating formulations must meet the needs of the customers in various application environments. The various tablet coating machines found in the market have different configuration options and controllable process parameters (Rodrigues & Iemma, 2014). It is significant to understand that we can have non-numeric configuration options. For instance, in the situation when we have machines that have been configured with one or more spray guns, the result would be discontinuous effects on the quality and performance of the coated product.
Regarding the consequences, the critical one is that it is impossible for one to define an operating specification that is favorable for all coating conditions. According to Mandenius and Brundin (2008), the existence of broad coating conditions has intense consequence regarding the development of powerful coating formulations. Robust formulations are insensitive to what is regarded as normal coating process variation. With the normal variation, we have the operating tolerance of the controllable process parameters. It is important to extend the normal variation to include uncontrollable environmental factors that would influence the quality and performance of the product. The factors that affect the quality and performance of the product include ambient temperature and humidity (Mandenius & Brundin, 2008).
Due to a wide scope of discontinuous configuration options and operating conditions, it is hard to develop a coating formulation that is adequately robust for all different coating conditions that products might encounter. Therefore, because of this challenge, we shall examine the capability that would help in facilitating the optimization of the quality and performance of coating under any specified set of coating conditions. The need for flexibility and quick re-optimization requires one to understand the effects of the coating process.
Methodology (Design of Experiment)
According to research, process operating conditions influence the quality and performance of the coating. The DOE approach is regarded as a multivariate method to experimenting, and its efficiency is exhibited when one’s goal is to understand the definition of variable effects (Mandenius & Brundin, 2008). The experiment includes the characteristics of six product quality and performance, and they include coating uniformity, surface roughness, gloss, coating process efficiency, process exhaust temperature and percentage of the limit of detection. Next was to identify controllable process parameters that would affect the key response variable. The experiment variable includes pan speed, atomizing air pressure, suspension percentage of solid, fluid delivery spray rate, the number of spray gun and drying air temperature. The operating parameters for this experiment include pan loading, coating level, coating formulation, spraying gun pattern air and process drying air (Rodrigues & Iemma, 2014). With the use of the software navigator wizard, we can develop the correct type of statistical design for the variables and goals.
The optimization goal requires that we quantify all significant variable effects. For instance, the quantification of curvilinear and nonlinear effects would require three experiment levels. The ideal experiment equipment would be Labcoat II Coating machine. In this experiment, each coating trial will use about 300-mg of placebo tablets. Moreover, the experiment will include the use of an aqueous coating formulation with a yellow pigment. When conducting experiment trials, we shall number individual core tablets prior to coating them. Next, we shall dry each tablet core, weigh them and record their weights. Before coating the tablets, we shall add the marked tablet to a 15-kg tablet load. Lastly, we shall sort the tablets after the applying the coating. However, we shall dry to constant weight each marked and coated tablet, and record their weight.
The analysis of tablet moisture content, coating process efficiency and coating uniformity provide the following results. Coating uniformity is significant when the coating applied functions as the primary factor in influencing the release of drugs. For instance, tablets that receive much coating materials will release drugs slowly while those that received too little coating materials will release drugs more quickly. The existence of a wide variation of the coating applied to individual tablets may influence the drug’s dissolution rate (Rodrigues & Iemma, 2014). The pan speed will provide the strongest effect on the coating uniformity while the fluid delivery rate and interaction between the number of guns and the inlet air temperature will provide the second and third strongest effect respectively. The coating process efficiency is an indicator of over-wetting or over-drying (Mandenius & Brundin, 2008). In situations when over-wetting occurs, materials on the surface of the tablet can be transferred to the walls of the coating pan, and this will reduce the efficiency of the coating process.
The process optimization experiment is chosen based on the limited budget. We shall conduct the screening experiment to determine the most significant parameters that influence the quality and performance of the product. We shall not conduct a large experiment that might involve many design parameters so as to limit the budget. The experimental cost will include costs of experiment equipment, labor cost, and data analysis.
|Experiment equipment (Labcoat II Coating machine, 300-mg of placebo tablets )
Labor costs $20/hour (8 hours)
Data analysis costs
Mandenius, C. F., & Brundin, A. (2008). Bioprocess optimization using design‐of‐experiments methodology. Biotechnology progress, 24(6), 1191-1203.
Rodrigues, M. I., & Iemma, A. F. (2014). Experimental design and process optimization. CRC Press.
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