Reduction of tablet weight variability by optimizing paddle speed in the forced feeder of a high-speed rotary tablet press

Abstract

Context: Tableting is a complex process due to the large number of process parameters that can be varied. Knowledge and understanding of the influence of these parameters on the final product quality is of great importance for the industry, allowing economic efficiency and parametric release.Objective: The aim of this study was to investigate the influence of paddle speeds and fill depth at different tableting speeds on the weight and weight variability of tablets. Materials and methods:Two excipients possessing different flow behavior, microcrystalline cellulose (MCC) and dibasic calcium phosphate dihydrate (DCP), were selected as model powders. Tablets were manufactured via a high speed rotary tablet press using design of experiments (DoE). During each experiment also the volume of powder in the forced feeder was measured.Results and discussion: Analysis of the DoE revealed that paddle speeds are of minor importance for tablet weight but significantly affect volume of powder inside the feeder in case of powders with excellent flowability (DCP). The opposite effect of paddle speed was observed for fairly flowing powders (MCC). Tableting speed played a role in weight and weight variability, whereas changing fill depth exclusively influenced tablet weight. Conclusion:The DoE approach allowed predicting the optimum combination of process parameters leading to minimum tablet weight variability. Monte Carlo simulations allowed assessing the probability to exceed the acceptable response limits if factor settings were varied around their optimum. This multi-dimensional combination and interaction of input variables leading to response criteria with acceptable probability reflected the design space.

Numerical Analysis of the Die Filling Process Within a Pharmaceutical Tableting Machine

“…This observation is consistent with literature [3,17,19,20]. At higher tableting speed, the die spends less time underneath the force feeder allowing a shorter filling time (at 40 rpm: 0.36 s, at 75 rpm: 0.19 s), thus less tablet mass is obtained.…”

Lubricant sensitivity in function of paddle movement in the forced feeder of a high-speed tablet press

“…A feed frame equipped with 2 paddle wheels, as described by Peeters et al, was utilized. 30 The first paddle wheel, the feeding wheel (further referred to as 'Paddle 1') is composed of eight curved paddles and is located above the overfilling station.…”

“…The fill depth necessary to obtain the desired weight provided info about the fill density and was ranked as Formulation A > B > D > C. As bulk density is dependent on particle size, the higher fill depth of formulation D compared to C is somewhat surprising, since the results of powder flow (Table 5) The formulation without MgSt (Formulation A) has a poor flowability, shown by the large fill depth and its dependence on paddle speed, as fill depth gradually decreases with higher paddle speeds. 30 Also the flow of the mixture subjected for a short period to low shear (Formulation B) depended on the paddle movement. But since the overall fill depth is lower compared to formulation A, the flow can be considered to be improved, due to the presence of MgSt.…”

“…Different mechanisms act simultaneously on the powder bed, which cannot be captured by the existing conventional techniques to measure and quantify powder flow. 30,[44][45][46] Since the capacity of a glidant is mainly expressed in moving material, it is possible that this property is more pronounced in the forced feeder, during actual die filling, then it is during the static flow measurements.…”

Development of a design space and predictive statistical model for capsule filling of low-fill-weight inhalation products

“…Subsequently, the models were refined to include quadratic terms representing two-factor interactions, for which additional experiments were required. This sequential approach, which is used in several areas of pharmaceutical development [17][18][19][20][21] enabled us, first, to identify the main factors that influence the quality of capsules filled with an inhalation product and, second, to minimize the experiments used for developing a predictive model.…”