White Paper 9. HSWG-Brivanib Alaninate: DFF Sensor Identifies Optimal Formulation, Determines Granulation End-Point, and Enables Scale Up

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1 HSWG-Brivanib Alaninate: DFF Sensor Identifies Optimal Formulation, Determines Granulation End-Point, and Enables Scale Up Valery Sheverev and Vadim Stepaniuk, Lenterra, Inc. Ajit Narang, Genentech Inc. Brivanib Alaninate Granulation To delineate the pharmaceutical relevance of measuring wet mass consistency, experiments with the DFF sensor were carried out in a wet formulation of a model drug, brivanib alaninate. Batches were prepared at high drug load (6% w/w of brivanib alaninate) using 5% w/w HPC as a binder, 4% w/w croscarmellose sodium as a disintegrant, 9% w/w microcrystalline cellulose as a filler, and as the granulating fluid. Process design space for this formulation was defined (Badawy et al., ) at 4% v/v fill of the high shear granulator with the variables of impeller tip speed ( m/s, target 4.8 m/s), and amount of used for (55% w/w to 6% w/w, target 58% w/w of granule composition)., and wet massing time ( 5 s, target 3 s). To investigate the effect of substantial changes in process parameters outside the design space, two batches of brivanib alaninate were manufactured with concentration at 48% w/w and 67% w/w of the. All the other process parameters were at the center point of the design space. Two DFF sensors were used simultaneously, one installed through the granulator lid and the other one from the side port as shown in the figure. The formulation in the center point of design space (58% ) was scaled-up from a - liter GEA PharmaConnect granulator to a 6-liter one, maintaining volume fill (4% v/v) and blade tip speed (4.8 m/s) at target parameters identified at the center of the design space. Four batches were manufactured to investigate:. whether the DFF sensor signal of wet mass consistency is able to distinguish granules produced with changes in process parameters;. whether the DFF sensor signal can indicate an optimal of ; 3. whether the DFF sensor signal is able to define a scale independent parameter in the scale-up of the from the -liter to the 6-liter granulator. Granulator volume 48% w/w 58% w/w (design space) 67% w/w L Batch Batch Batch 3 6 L Batch 4 Wet Mass Consistency Plots Wet mass consistency results obtained from DFF sensors are illustrated below with time evolution plotss of force pulse magnitude (FPM). In these plots, the data is overlaid with the top sensor data in lighter color. The duration of addition is shown in shaded blue area and accepted end of wet at the center point of design space, 3 seconds after the end of addition, is shown with a vertical line. The similarity in pattern and profile of plots indicate stability of change in wet mass consistency over time (973) INFO@LENTERRA.COM

2 White Paper L: 58% FPM. N L: 58% Time from start of addiiton, min L: 67%.75 L: 48% as measured either by the side or, even though the amplitude of peaks was higher for the attributable to its proximity to the rotating blades. Expectedly, the signal magnitude increased with increasing concentration at the -liter granulator scale. For all four batches, the DFF signal response also showed a pattern of increase in wet mass consistency during the phase of addition, and a decline during extended wet massing. Such evolution is consistent with the modern model of process (Iveson et. al., ). The FPM evolution for the center point batches ( 58%) manufactured in a -liter and 6-liter granulator show similar shape profile between themselves, but noticeably different from the signals obtained for batches with 48% and 67% of This common shape of 58% signal features two salient points. The first one occurs at -3 seconds after end of addition, exactly at the central point of the design space, after which the FPM signal decreases for about a minute, just to pick up afterwards to reach an absolute maximum at about 3 minutes of for the -liter and 6 minutes for the 6-liter granulator. The other two batches, for 48% and 67%, demonstrate no delayed maximum. The fact that this delayed maximum feature appears only for the central point of the design space is common for both -liter and 6-liter granulators and that it is independent of the sensor placement, indicates that DFF sensor could provide a convenient way of identifying an optimal formulation by observing features on the FPM evolution over extended time. (973) INFO@LENTERRA.COM

3 FPM (moving avarage 3), N FPM (moving avarage 3), N White Paper 9 Granulation The first salient feature of the FPM vs. time dependence for brivanib alaninate formulation at 58% occurs approximately at the end of addition. This point manifests itself as a local maximum for L granulator, and as an inflection point for 6L granulator. Apparently, this point corresponds to transition from the first stage of the process (wetting and nucleation) to the consolidation and coalescence stage (Iveson et. al., ) FPM vs. time, L: 58%, L: 58%, 6L: 58%, 6L: 58% Another salient feature of the FPM vs. time plots for the 58% formulations appears at approximately 3 minutes for the -liter and 6 minutes for the 6-liter granulator. The dependencies demonstrate absolute maxima of FPM at these instants.. s The HSWG processing is typically stopped several seconds to a minute after the end of addition (indication on the plots as s ). This conventional practice ensures complete dispersion of within the powder bed mass and it is generally understood that optimum granule quality is obtained upon wet massing of the powder mass after completion of addition. In the case of brivanib alaninate, the wet massing time of 3 seconds was selected as the center point and to 5 seconds as the range in the process DoE study. However, the optimum duration of wet massing time remains a matter of tradition among different industries and practitioners, and can vary from a few seconds to several minutes. In this context, determination of wet mass consistency peak several minutes after the end of addition by the DFF sensor enables identification of a potential second inflection point after which the granule quality may deteriorate (Narang et al, 5, Iveson et. al, ). Scale independent measure Similarity of FPM signal for the 58 % batches manufactured in a -liter and a 6-liter granulator, respectively, include differences within time-domain and peak-amplitude. The peak amplitude differences are due to different wet-mass pressure between the two granulators at the point of measurement. The time domain differences is due to the difference in frequency of blade rotation (RPM) in two granulators. The first two requirements of the FPM vs. blade number slide sensor, L: 58%, L: 58% slide sensor, 6L: 58%, 6L: 58% 3 blades Blade count (f τ) from end of addition scale up were maintaining volume fill (4% v/v) and blade tip speed (4.8 m/s). Maintaining blade tip speed leads to a smaller shaft rotation speed in a larger granulator. Therefore, the process is inherently slower in a larger granulator. After measuring FPM evolution in both granulators, one can find a coefficient that would allow for identifying the end point in a larger granulator if such a point in time is known for a smaller granulator. 3 (973) INFO@LENTERRA.COM

4 Assuming that the starts at the end of addition (at about 3 min in the FPM vs. Time plots above) we can find the time to the granule deterioration point that is characterized by a delayed maximum on the plots. In other words, we find time delay (τ) between the first and second inflection point, for each granulator separately. Granulator Volume L 6L DFF sensor position Time delay, τ sec top 87 side 93 top 34 side 348 Average τ, sec Ratio τ6/ τ.8 Therefore, in the case of the Brivanib formulation, the end point in a 6L granulator should be selected as the time in a L granulator multiplied by.8, as counted from the end of addition. It is interesting to notice that the blade frequency ratio in both granulators is approximately same as the ratio τ6/ τ. Introducing parameter f τ which is simply number of blades passing the sensor, one can plot FPM vs. f τ (see above). In this scale the delayed maximum appears for both granulator sizes, and for both top and s at approximately 3, blade count, indicating that the time-domain differences in sensor response between the two scales was attributed to differences in number of impeller rotations per unit time as the impeller tip speed was kept constant. This data highlights the fact that keeping the same impeller tip speed is a viable scale-up method for the brivanib alaninate. There could be scale-up methods that are different from that proposed above, since the FPM vs. time curves may vary for different formulations depending upon the API characteristics, drug loading, and the excipients used. But the example of brivanib allaniate analyzed here indicates that use of DFF sensor can help design an appropriate scale up protocol. Discussion and Conclusion Differences in the rate of granule densification across different scales of HSWG presents a significant challenge in the scale-up of wet process for formulations of new drug products. This challenge manifests in the unknown adjustment that may be needed in one or more process parameter (as the is scaled-up) to achieve similar granule attributes at the end of (). Measurement of wet mass consistency using the DFF sensor appears to be a scale-independent attribute. For a brivanib alaninate, the DFF sensor response is able to distinguish between a process variable (% w/w used for ), which does not correlate with changes in granule particle size distribution (Badawy et al., 6), but does correlate with granule densification. Granule densification is a critical material attribute (CMA) that correlates with tablet dissolution as a critical quality attribute (CQA) of the drug product (Badawy et al., ). These data demonstrate the utility of DFF sensor as a real-time in-line PAT probe that responds to a property of the wet mass, which is called wet mass consistency that provides a metric of progress different from the size distribution of the granules and correlates with granule densification (Narang et al, 5). Therefore, DFF sensor can be used as a tool to study the effect of formulation and process parameters during formulation and process development, and to monitor reproducible manufacture of s. Time delay to the peak of the DFF signal response correlated well with the expected processes at different levels and was consistent for the side-sensor as well as the topsensor. Wet mechanistically involves several simultaneous processes that proceed at different rates during different stages of. Three major processes involved in wet include wetting and nucleation, consolidation and granule growth, and attrition and breakage (Iveson et. al., ). For example, at the stop of addition, if the level is lower than optimum (48% w/w), the initial granule growth processes of nucleation and aggregation would be predominant. Similarly, if the level is higher than optimum (67% w/w), latter stage processes of attrition and breakage would be predominant. In both these cases, no further granule densification may be expected at the end of addition. On the other hand, at optimum concentration (58% w/w), various mechanisms will be in an equilibrium and the granule densification would be sustained for a while. In the current set of 4 (973) INFO@LENTERRA.COM

5 experiments, this behavior is evident in the higher delay time to peak at 58% w/w level, compared to 48% w/w or 67% w/w level. Interestingly, the time to peak was delayed even further when the formulation was scaled-up to the 6-liter granulator at the center point of concentration (58% w/w). This delay in the time to peak DFF signal was consistent with reduced number of impeller rotations for a given period of time, when the process is scaled up with constant impeller tip speed. The greater time-to-peak lag at the larger scale indicated slower rate of granule densification at the larger scale. These findings were consistent with the observed changes in granule porosity as a function of time at -liter versus 6-liter scale and the exponential fit to this porosity data (Badawy et al., ). These correlations indicated that the value of the DFF sensor time-to-peak response, τ, as a parameter of interest that can inform the rate of granule densification and robustness of the HSWG process. Although the exact state of and the responsible factor for the peak in the DFF response is not known, it is likely to be the culmination of dominance of growth and consolidation mechanisms (over attrition and breakage) in the powder state. Thus, the peak of DFF sensor may serve as an indicator of desirable ranges of wet massing times that may be acceptable as an end point of the. The time to peak can be utilized to derive parameters that can assist scale up of s. References Badawy, S.I., Narang, A.S., Lamarche, K., Subramanian, G., Varia, S.A., Lin, J., Stevens, T., Shah, P.A., 6. Integrated application of quality-bydesign principles to drug product development: a case study of brivanib alaninate film coated tablets. Journal of Pharmaceutical Sciences, 5 (), 68-8 Badawy, S.I., Narang, A.S., Lamarche, K., Subramanian, G., Varia, S.A.,. Mechanistic basis for the effects of process parameters on quality attributes in high shear wet. International journal of pharmaceutics, 439, Iveson, S.M., Litster, J.D., Hapgood, K., Ennis, B.J.,. Nucleation, growth and breakage phenomena in agitated wet processes: a review. Powder Technol. 7, Narang, A.S., Sheverev, V.A., Stepaniuk, V., Badawy, S., Stevens, T., Macias, K., Wolf, A., Pandey, P., Bindra, D., Varia, S., 5. Real-Time Assessment of Granule Densification in High Shear Wet Granulation and Application to Scale-up of a Placebo and a Brivanib Alaninate Formulation. Journal of pharmaceutical sciences 4, (973) INFO@LENTERRA.COM