The Excipients Chosen To Formulate
The excipients chosen to formulate a tablet must be selected with care as they can have major implications to final tablets produced. They play a major role in aiding the manufacturing process all the way down to the way the drug is released in the blood stream.
A model-dependent optimisation is a mathematical approach used to produce the optimum formulation. The benefits of this method of optimisation is that it saves time resulting in cheaper costs of production. It also provides a more complete understanding of the formulation and thus the compatibilities of the variables rather than a ‘hit and miss’ approach. Model-dependent optimisation uses a factorial design (see table 1) to conduct many experiments in which the formulation factors are changed and several responses are measured. The tests on the tablets are a measure of the quality of the tablets produced.
The results show both positive and negative correlations with starch and magnesium stearate concentration and tensile strength. The results show that when increasing magnesium stearate concentration there is a fall in tensile strength when 5% and 10% starch was used (see figure 2). This is as expected. As a boundary lubricant, magnesium stearate works by forming a boundary between the powder mix and the die cavity to reduce friction (Aulton and Taylor, 2017). It can also form a boundary between excipients interrupting interparticle bonds thus causing a weaker tablet (Morin and Briens, 2013). In contrary, when 7.5% starch was used there is an increase in crushing strength when magnesium stearate concentration was increased. However, the low R2 value suggests that this conclusion is invalid as it is not a representation of the real data points.
It is more accurate to use tensile strength over hardness as it takes into account the diameter and thickness of the tablet into consideration. The hardness is tested in an automated hardness tester.
The standard deviations were significantly high which means the data is unreliable as there is great variation within results. The reasons for the unexpected results could be down to experimental error. For example, when placing the tablet into the hardness tester it was placed in different orientations. The force applied was subjected to different diameters resulting in different results.
Increasing the concentration of magnesium stearate should in turn decrease tablet hardness and increase friability (Shipar et al., 2014). Tablet hardness and friability are related properties and therefore should show similar trends in results. This was not seen within the results.
All tablets failed the BP friability specification tests as all batches had weight loss greater than 1%. The BP states the tests should be repeated if the batch failed but due to time constraints we were unable to do this. The reasons for the high friability could be due to a low compaction force used when formulating tablets which resulted in weak tablets. Compaction pressure must be high enough to initiate strong interparticulate bonding (Mohan, 2015).
There is no strong correlation shown between starch and magnesium stearate concentration with friability seen in the data (figure 7 and 8). Increasing the magnesium stearate concentration interferes with the interparticulate bonding and therefore result in weaker tablets with higher friability. Increasing starch concentration should also increase friability as it is known that starch has poor compaction properties at higher concentrations (Zhang, Law and Chakrabarti, 2003). The reasons for the inconsistent results may be due to experimental error for example when weighing out the excipients especially the small quantities of magnesium stearate. Also, material could have been lost when transferring from the weighing boat to the bottle.
In order to identify the optimum formulation regression analysis was completed for all four tests carried out. The two most relevant tests were disintegration time and uniformity of weight.
Before a tablet can dissolve it must disintegrate into smaller particles. A tablet must have a suitable disintegration time depending on its use. As paracetamol tablets are used for their analgesic and antipyretic properties producing a tablet that has a slow disintegration time would not be beneficial, fast disintegration times are required. The disintegration rate of a tablet is a good determinant of the dissolution profile and therefore the success of a tablet in producing desired therapeutic effect.
Disintegration testing has its limitations as there is no assurance that the fragmented drug will completely dissolve at an appropriate rate (Al-Gousous and Langguth, 2014). It is not a measure of bioavailability therefore, it is essential to complete other tests such as dissolution.
In figure 9, as starch concentration increased the disintegration time of the tablets did not always show to decrease. Starch is a disintegrant that works by swelling and capillary action (Gohel, n.d.). Capillary action is a mechanism which attracts water into tablet pores breaking the physical bonds between the particles and fragmenting the tablet. Starch is very hygroscopic and therefore absorbs moisture easily causing the tablet to swell forcing the tablet apart (Musa, Gambo and Bhatia, 2017) (Aulton and Taylor, 2017).
When 0.25% and 0.75% magnesium stearate was used, disintegration time increased with increasing starch concentrations. This was not expected. When 1.50% magnesium stearate was used the results show that there was an increase in disintegration time as starch concentration increased. This is the expected result and the most reliable result with the highest R2 value and smallest standard deviations. This tells us that a strong correlation is shown and there is least variation in results.
Perhaps too low concentrations of starch were used as Ingram and Lowenthal found the optimum starch concentration to be up to 15% to be able to see a significant decrease in disintegration time. Starch is the most commonly used disintegrant however, advances to technology have led to the development of superdisintegrants which can be used at lower levels and still have the same effect on disintegration. (Zhang, Law and Chakrabarti, 2003). An improvement to the experiment could be to use a superdisintegrant like croscarmellose.
When carrying out the disintegration test it was noticed that some tablets break up into irregular shapes while some disintegrate as one. By recording the time taken for each tablet to disintegrate and then calculated a mean gave us a more representative disintegration time for the whole batch. Disintegration time of the tablets was not uniform in the batches analysed as some disintegrated rapidly whereas some were more gradual. This indicates there is batch inconsistency or could also be due to practical aspects. For example, when carrying out the experiment some of the fragmented tablets appeared to have been stuck in the mesh or on the disc causing delayed disintegration. This would affect the results obtained as this does not allow the tablet to disintegrate accordingly.
Increasing the concentration of magnesium stearate caused disintegration time to decrease when 7.5% and 10% starch was used as shown in figure 10. This was not the expected result. As magnesium stearate is a hydrophobic lubricant it decreases the wettability of tablets which will increase disintegration time (Shipar et al., 2014). Magnesium stearate also forms a hydrophobic film around the starch particles thus preventing it from acting as a disintegrant. This was not observed within all the results, perhaps the concentration of magnesium stearate was not high enough for this to be seen or was limited by the short mixing time of the magnesium stearate (Kikuta and Kitamori, 2008). This interaction between magnesium stearate and starch can also be masked by the use of discs in the disintegration apparatus which could be the reason for the discrepancies in results (Bolhuis, Smallenbroek and Lerk, 1981). There were large standard deviations which implies there was great variability within the data. When 5% starch was used, increasing magnesium stearate caused disintegration time to increase. This is the expected result. The results also had very low standard deviations and a high R2 value (0.93) which implies the data is reliable with a strong correlation.
A high disintegration time may suggest the tablet is too highly compressed. But this would also result in hard tablets which was not seen within the results. Another improvement for future experiments could be to include a wetting agent like sodium lauryl sulfate which increases water uptake enhancing disintegration which can be used to overcome the effects of magnesium stearate (Augsburger and Hoag, 2007) (Pabari and Ramtoola, 2012).
Uniformity of weight is a good indication of content uniformity due to the different densities of individual excipients (Nelson, Wu and Wymbs, 2005). The tablets produced showed a fairly uniform weight amongst batches. There were however some anomalous results, the tablets selected could have been the first or last tablets to be produced which should have been discarded as the tableting machine is warming up and at the end is running out of powder.
Increasing the concentration of magnesium stearate should increase the coefficient of variation resulting in a greater variation of tablet masses. This is seen within the results (figure 2). Magnesium stearate reduces flowability of the powders by affecting friction through modified particle-particle contact (Morin and Briens, 2013). Powder flow is important during tableting as if must flow uniformly into the tablet dies and with the same amount every time to ensure weight uniformity (Manufacturingchemist.com, 2017) (Tan and Newton, 2017) (Fassihi and Kanfer, 2017) and thus also affecting content uniformity (Swaminathan and O. Kildsig, 2015).
A contour plot shows how the measured dependent variables changes as a function of the independent variables; starch and magnesium stearate concentrations. The contour plots are figure 4 and 12. Upon combining the data from the contour plots the optimum concentration of starch was 8.5%-12% and magnesium stearate was 1.75%-2.35% to give the optimum disintegration time and uniformity of mass. However, the R2 values of the predicted and measured results, as seen in figure 3 and 11, were very low which suggests there is not a strong correlation shown and the model is unreliable. Low R2 values mean that the regression equation has a poor predictive value for the experiment.
Regression analysis has its limitations including its sensitivity to outliers and it also assumes there is a linear relationship between the variables which is not always the case (Barrett, 2012). Coefficient of determination only gives indication that a relationship exists. The reasons for the low R2 values could be down to experimental error, in which the experiment should be repeated to disprove or confirm the results.