In the pharmaceutical development of tablets, most active substances are difficult to process or dissolve. There are also many process steps and functional components that need to be included to solve all the issues that appear along the way. To narrow the focus, it is important to recognize which of the many potential factors are the most important for the responses of interest.
Definitive screening designs are often considered to be most appropriate for experimentation with four or more factors. Whenever there are available results of experiments that are not part of a specific design, it is good to use tools such as advanced predictive modelling techniques to help capture valuable information. The aim of this project was to apply different analytical techniques to evaluate the effects of input factors on responses. Another goal was to find the balance between the factors that contribute to tablet appearance and mechanical resistance and the factors that enable quick active substance dissolution, which is important for product in-vivo performance. By using a combination of analytical tools, valuable insights were obtained regarding effect of formulation and process factors on tablet characteristics. Optimal settings were then defined to maximise dissolution.
Hello, my name is Tijana Miletić, and I work in Hemofarm, part of STADA Group in product development. Today, I'm going to show you how we use the definitive screening design and advanced predictive modeling as useful tools in product development.
Main goal of product development here was to find optimal formulation and process settings for several quality attributes. At the beginning, we suspected what could be potential effects of formulation and process variables on tablet properties. This is how we selected the factors for our experimental study. But we did not know what would be the actual relationship between these variables for this specific system. Here we use experimental design as a way to extract most information from a limited number of experiments.
Our main challenge in this study was to achieve maximum dissolution while maintaining mechanical resistance of tablets at the same time, and of course, to decide which ranges or which factors we are going to use and which experimental design we are going to select.
Because we suspected that we will have some significant quadratic effects and interactions, we selected the definitive screening design. Overall, impact that we achieved with this study was positive for our product development because we managed to identify most important factors and optimal values to achieve desired responses.
Here, the main response was the solution because it is important in vitro result, which is considered prior going into costly clinical studies. The tablet hardness, on the other hand, is a good indicator of mechanical resistance of tablets, which tablet needs to withstand manufacturing and packaging process.
All in all, moving ahead in product development with the right decisions being made, is something that saves time and resources in all stages of product development. Here we were happy with the value which we achieved because we got some direction in product development. Instead of performing for six factors, full factorial design on 64 runs, we managed to execute the definitive screening design with 13 runs in about four times less time than it would be for 46 runs.
Besides that, we also use some additional experiments and advanced modeling techniques to get even more insights into factor effects and their significance, which in the end resulted in having development goals achieved.
Our main data analysis was execute it with three main activities, which we will present with our poster presentation. We used for this data analysis JMP 17. Here we presented visual data exploration. We also used the platform for Fit definitive screening and for model screening.
At first, we used scatterplot matrix, which provided quick assessment of relationships between multiple variables at the same time. In order to better understand the nature of relationships within our variables, we looked into our models in more detail.
For definitive screening, we recognized that the most significant factor for hardness was compression force, and for this solution, amount of disintegrate and compression force. We did not observe significant quadratic effects, and the only significant interaction was between amount to binder and compression force for response of this integration.
By going back to our visual data exploration, we saw that there is not that much of connection between this integration and the solution, meaning that this result is not that significant, and we will not be able to see if we are going to like our dissolution results, so we did not focus too much on this interaction at the moment. We used the run 14 to evaluate predictability of a model being created here, and we received the 78 versus 80, and 93 versus 94% with dissolution, which is considered to be good match for this type of test. We received exact match for hardness of 54 newtons.
By being happy with this, we then use prediction profiler and by maximization of this ability, meaning we wanted to see how can we maximize the solution, we learned that we need to use 8% of this integrant, 3% of binder, and compression force at the lowest level.
Here we were worried that with lowest compression force, we would compromise mechanical resistance of tablets, knowing that it could lead to lower hardness. Here we used a bigger data set of 27 runs to graphically evaluate if there could be trend there so that we could rely on amount of binder to get a positive effect of tablet hardness.
We also performed the model screening for this response, and we picked Fit least squares method to develop the best models to evaluate possible effects. We reduced models so as to get the highest possible significance of effects. Here it was confirmed that binder could have a positive effect on hardness. We also performed model screening for response dissolution, and Here we presented with two methods and two models, highest possible dissolution prediction, which is in both cases about 87%, and we also confirmed the significance of this integrant amount in compression force for this response.
Here we saw some difference in what would optimal level of lubricant could be, and this is not surprising because we know that the lubricant can have potentially negative effect on dissolution and hardness. But here it seems that this effect is not significant. We could go ahead with higher level in order to decrease risk for sticking or to perform additional experiments to better explore this effect.
By looking at all these insights, we were satisfied with the conclusions that we made. Definitely, key ingredient for desired dissolution was this integrant. We were happy to learn that amount of binder did not have negative impact on the solution, but could have positive impact on tablet hardness. We also learned that glidant does not have significant impact on evaluated responses. Of course, compression force is key process parameter, and it should be carefully set.
Overall, definitive screening design provided the directions in our formulation development, which led to desired results in last time, and we were happy with that. With advanced modeling techniques, we managed to get additional insights. Of course, in order to have better predictability of models or to investigate relationships between variables in more details and with more precision, we would need to generate more data. But based on this experimental study, we got results that made us feel confident enough to go ahead within our product development. That is all that I prepared for today, and thank you for your attention.
