Speaker | Transcript |
| Pranjal Taskar | All right. Thank you, Peter. So I'm going to get started now. Hello everyone. Today I'm going to talk about my poster. This poster is regarding systematic analysis of targeting, which includes effect of formulation and process variables on final quality attributes of my product. |
| So delving into all the statistical analysis before that, I wanted to give a background about what exactly we're talking about. What is tableting? Tableting is a pharmaceutical process. |
| Looking over in the introduction, I'm going to talk about what tableting is a little bit. It's a pharmaceutical process in which your active ingredient or active moiety (API) |
| is blended with other excipients to form a free flowing good flowing blend and this blend is compressed into our final dosage form, which is a tablet. |
| So in a lot of situations, there are some active moieties or APIs, as we would call it, that have a low bioavailability and that could be due to their crystalline nature. They're just too stable, |
| too rigid in their ways. So our site kind of specializes into making this crystalline API, a little bit more soluble, little bit more reactive |
| amorphous form and it makes it into like a more bioavailable form. |
| And when we do that, we fortified this API by a polymer. |
| This this intermediate that we form is a tablet intermediate called a spray dried intermediate, SDI. |
| And this is what we basically use in our tablets as our active intermediate. |
| But when you look at it, it has poor flow ability and it's extremely fluffy. So when you have to incorporate this API into your tablet, you need to have |
| other pharmaceutical processes involved to make it more streamlined, to make the blend more flowable. So this is what we're going to do. |
| In this study, we are going to identify our critical quality attributes, the variables that matter, |
| or our dependent variables and then we are going to identify variables that impact our critical quality attributes, |
| which are the composition of that tablet of that blend and then different process processing parameters that we used in us in tableting. |
| Which of these are main effects? Are there any interactions? And then we'll use JMP to identify all of these |
| main effect and interaction variables and try to catch out the tableting process basically. So this was the introduction. |
| Moving on to the methods and objectives. So how do we do this? For this study we looked at a placebo formulation. There is no active product or actor moiety and we used a commonly used spray-dried polymer which is hypromellose acetate succinate. |
| We spray dried it and made it into the fluffy |
| blend that it usually is. And Figure 2 talks about our usual granulation tabulating process. So, what, what we do is basically have our spray-dried intermediate (SDI) blended along with other excipients using this blender. |
| We move on to roller compaction, which is densification of this blend using these there are rollers right here and these rollers move slowly to densify |
| the blend which goes into this hopper and you get ribbons out of the roller compactor. Now what you have done is you have made that fluffy material into densified ribbons and you mill it down using a comil. And you get |
| granules. These granules are more dense and they are a lot better flowing than your API or your SDI. |
| So looking at this entire process, there are a lot of variables that go in there that you need to change and look out for. So what are those variables? |
| This diagram over here will identify different kinds of variables, the independent variable variables that go into the formulation and process. so |
| The first variable would be a bit more base formulation related than the...rather than the process related. So it would talk about different types of ??? excipients that are used. |
| And the ratio of these excipients that I used the percent of SDI loading, or active loading, and in our case, the placebo loading. |
| And then the order of addition and the point of addition at where the SDI, or other excipients are loaded into the formulation. |
| And then sorting process related parameters such as ribbon solid fraction, which basically talks about this equipment, the roller compactor and the speed at which the rollers and the spools move. |
| We have also identified independent variables of our critical quality attributes that we look out for, which is bulk density of our blend, |
| Which we look at before and after granulation and you have labeled it bulk density 1 and 2. |
| Hausner ratio, which is again a ratio that depicts the flow of your blend and we also identify that before and after granulation, labeled as Hausner ratio 1 and 2. |
| And the percent of fines that collect...are collected in the roller compaction process. And this is usually monitored after granulation. So all of these points out to talk about |
| basically our method and why we chose our variables. What we did was we had an I optimal, 19-run custom design looking at all of these independent variables impacting on the dependent variables. |
| And the way we analyze this model or the way we constructed effects, was that we looked at the main effect and the second degree interactions and we analyzed the data using the standard least squares personality in the fit model function. |
| So, |
| Identifying the process and the objectives, we will move on to results, but before doing that really quickly, I wanted to look at the JMP window which I have pulled up right now. |
| These different columns are my independent and dependent variables and I'm going to highlight |
| right here, these are the different independent variables that we are going to be looking at. So type of filler, which is the type of inert excipient and we have looked at mannitol and lactose. |
| percent SDI, which is the active or in our case placebo loading, |
| looking at highs and lows away here; and amount intragranular, so the amount of our excipients that we add before the roller compaction versus after the roller compaction and outline here are 75 and 95; |
| and mannitol and lactose, which is a filler to MCC, which is micro course design cellulose ratio. Mannitol lactose are, I would say a little bit more |
| excipient and MCC is more ???, gives more strength to the blend. So we have looked at a ratio of this to see how it impacts our tableting blend overall. And |
| on the right are our responses. |
| Bulk density 1, Hausner ratio 1, which is before granulation. Bulk density 2 and Hausner ratio 2, which is after granulation, and percent fines. |
| So I'm gonna |
| go over here quickly into this window and look at how we created our model, our response variables y, that I just talked about. And then our model effects which are |
| secondary interactions and main effects. Standard least squares. That's what we used and I run the model. |
| This is my effect summary right here and based on this data that we're looking at and prior experience, I'm going to take off |
| the last two effects. |
| Just remove that extra noise and then over here, I have my responses and how the data kind of impacts these responses. |
| It would be just easier if we go down and look at the prediction profiler over here. |
| And how all of these dependent variables are impacted by this. So I think it might just be easier if I pull up my |
| poster and... |
| Alright, so looking at the results over here, what we found out from Figure 3 was that, look, the two fillers lactose had higher bulk density initially, |
| but post ruler compaction, the bulk density two of these fillers dropped and you can see a corresponding increase in the fines. So what we think would have happened is that lactose is more brittle |
| in comparison to mannitol. And this generated all of that attrition and that fines and that impacted the flow, making it less bulky, drop in the bulk density. |
| And the Hausner ratio, a little bit higher with the lactose. So basically, what we're doing is targeting a higher bulk density and we want a lower Hausner because a lower Hausner indicates a better flowing blend. |
| So looking at the data, mannitol had a slight edge over lactose as a filler. And the, the second point would be talking about the solid fraction and overall we saw that there was a slight plateauing effect at around .6 |
| solid fraction. Overall, we see that .7 has the least number of fines, which is why we see a recommended .7 with a maximum and desirability, but |
| the plateau effect in terms of your flow properties (bulk and Hausner) start bottoming out at around .6 and onwards. |
| that having lower SDI in general in the formulation had overall better flow properties. |
| Just because the SDI, it's fluffy and it causes the blend to flow a lot worse. So the design just suggested us to have lower SDI loading. |
| a higher amount of that ingredient of that excipient added in an intragranular fashion |
| than an extragranular, just because it improves your bulk, it has a lower Hausner which means that your blend is flowing smoother. |
| We also observed that mannitol to lactose ratio having more of that critical component was more desirable and I see that because |
| overall, the fines have dropped in the presence of having a little bit more of the mannitol lactose component. And that could be the reason why we are seeing this. |
| We also have in the Figure 4, a couple of surface plots of a few interesting trends that I saw. And in Figure 4A, you can see that having a lower SDI loading |
| and having more amount intragranularly resulted in this hotspot right here of a very high Hausner ratio. So when you add a lot of...when you have a low....I'm sorry...have a higher SDI and |
| higher intragranular had an extremely high Hausner ratio. So what this says is basically when you have more of that fluffy material intragranularly, your flow is going to be bad, but you correspond that |
| after granulation, when you again have more more of your excipient intragranularly and you're targeting a solid fraction of |
| about .6 and about, your bulk density improves. So you're basically post granulation, your blend is getting more denser and this is what these two diagrams talk about. So all of the result points basically talk about these |
| things that I discussed right now. Overall, we conclude from our study that in order to optimize this process and maximize desirability for formulations, |
| 1, |
| a higher ratio intragranularly and a lower SDI loading would be a preferable formulation and targeting a solid fraction of around 0.6 would also be beneficial to the formulation. |
| Thank you very much. I would welcome your questions. |
