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Although the roller compaction process appears simple, efforts to quantitatively model the process have proven challenging because of complex material behavior in the feeding and compaction zones. To date, implementation of roller compaction models to experimental work has been limited because these models typically require large experimental data sets or obscure input parameters that are difficult to obtain experimentally. In this work, an alternative approach has been established, expanding upon a widely used roller compaction model, Johanson's model, to enable its incorporation into a daily workflow. The proposed method requires only standard, routinely measured parameters as inputs. An excellent correlation between simulated and experimental results has been achieved for placebo and active blends up to 22% (w/w) drug load. Furthermore, a dimensionless relationship between key process parameters and final compact properties was elucidated. This dimensionless parameter, referred to as the modified Bingham number (Bm *), highlights the importance of balancing yield and viscous stresses during roller compaction to achieve optimal output properties. By maintaining a constant ratio of yield-to-viscous stresses, as indicated by a constant Bm *, consistent products were attained between two scales of operation. Bm * was shown to provide guidance toward determining the design space for formulation development, as well as to facilitate scale-up development.

Roller compaction is a dry granulation process used to convert powder blends into free flowing agglomerates. During scale up or transfer of roller compaction process, it is critical to maintain comparable ribbon densities at each scale in order to achieve similar tensile strengths and subsequently similar particle size distribution of milled material. Similar ribbon densities can be reached by maintaining analogous normal stress applied by the rolls on ribbon for a given gap between rolls. Johanson (1965) developed a model to predict normal stress based on material properties and roll diameter. However, the practical application of Johanson model to estimate normal stress on the ribbon is limited due to its requirement of accurate estimate of nip pressure i.e. pressure at the nip angle. Another weakness of Johanson model is the assumption of a fixed angle of wall friction that leads to use of a fixed nip angle in the model. To overcome the above mentioned limitations, we developed a novel approach using roll force equations based on a modified Johanson model in which the requirement of pressure value at nip angle was eliminated. An instrumented roll on WP120 roller compactor was used to collect normal stress data measured at three locations across the width of a roll (P1, P2, P3), as well as gap and nip angle data on ribbon for placebo and various active blends along with corresponding process parameters. The nip angles were estimated directly using experimental pressure profile data of each run. The roll force equation of Johanson model was validated using normal stress, gap, and nip angle data of the placebo runs. The calculated roll force values compared well with those determined from the roll force equation provided for the Alexanderwerk® WP120 roller compactor. Subsequently, the calculation was reversed to estimate normal stress and corresponding ribbon densities as a function of gap and RFU (roll force per unit roll width). A placebo model was developed and calibrated using a subset of placebo run data obtained on WP120. The roll force values were calculated using vendor supplied equation. The nip angle was expressed as a function of gap and RFU. The nip angle, gap and RFU were used in a new roll force equation to estimate normal stress P2 at the center of the ribbon. Using ratios P1/P2 and P3/P2 from the calibration data set, P1 and P2 were estimated. The ribbon width over which P1, P2, and P3 are effective was determined by minimizing sum square error between the model predicted vs. experimental ribbon densities of the calibration set. The model predicted ribbon densities of the placebo runs compared well with the experimental data. The placebo model also predicted with reasonable accuracy the ribbon densities of active A, B, and C blends prepared at various combinations of process parameters. The placebo model was then used to calculate scale up parameters from WP120 to WP200 roller compactor. While WP120 has a single screw speed, WP200 is equipped with a twin feed screw system. A limited number of roller compaction runs on WP200 was used as a calibration set to determine normal stress profile across ribbon width. The nip angle equation derived from instrumented roll data collected on WP120 was applied to estimate nip angles on WP200 at various processing conditions. The roll force values calculated from vendor supplied equation and the nip angle values were used in roll force equation to estimate normal stress P2 at the tip of the feed screws. Based on feed screw design, it was assumed that the normal stress at the center of the ribbon was equal to those calculated at the tip of the feed screws. The ratio of normal stress at the edge of the ribbon Pe to the normal stress P2 at the feed screw tip was optimized to minimize sum square error between model predicted vs. experimental ribbon densities of the calibration set. The model predicted ribbon densities of the batches prepared on WP200 compared well with the experimental data thus indicating success of the scale up procedure. For the demonstration purpose, the model was also calibrated using instrumented roll data of active C batches. This would be applicable when sufficient amount of API is available or placebo model cannot predict ribbon density of active batches.

Instrumented roll technology on Alexanderwerk WP120 roller compactor was developed and utilized successfully for the measurement of normal stress on ribbon during the process. The effects of process parameters such as roll speed (4-12 rpm), feed screw speed (19-53 rpm), and hydraulic roll pressure (40-70 bar) on normal stress and ribbon density were studied using placebo and active pre-blends. The placebo blend consisted of 1:1 ratio of microcrystalline cellulose PH102 and anhydrous lactose with sodium croscarmellose, colloidal silicon dioxide, and magnesium stearate. The active pre-blends were prepared using various combinations of one active ingredient (3-17%, w/w) and lubricant (0.1-0.9%, w/w) levels with remaining excipients same as placebo. Three force transducers (load cells) were installed linearly along the width of the roll, equidistant from each other with one transducer located in the center. Normal stress values recorded by side sensors and were lower than normal stress values recorded by middle sensor and showed greater variability than middle sensor. Normal stress was found to be directly proportional to hydraulic pressure and inversely to screw to roll speed ratio. For active pre-blends, normal stress was also a function of compressibility. For placebo pre-blends, ribbon density increased as normal stress increased. For active pre-blends, in addition to normal stress, ribbon density was also a function of gap. Models developed using placebo were found to predict ribbon densities of active blends with good accuracy and the prediction error decreased as the drug concentration of active blend decreased. Effective angle of internal friction and compressibility properties of active pre blend may be used as key indicators for predicting ribbon densities of active blend using placebo ribbon density model. Feasibility of on-line prediction of ribbon density during roller compaction was demonstrated using porosity-pressure data of pre-blend and normal stress measurements. Effect of vacuum to de-aerate pre blend prior to entering the nip zone was studied. Varying levels of vacuum for de-aeration of placebo pre blend did not affect the normal stress values. However, turning off vacuum completely caused an increase in normal stress with subsequent decrease in gap. Use of instrumented roll demonstrated potential to reduce the number of DOE runs by enhancing fundamental understanding of relationship between normal stress on ribbon and process parameters.