Discussion
We reported here on the CFD-based HMW prediction model. The model includes two components. The first component is the CFD model for VI simulation, which provides profiles of ApH3.3 andMF0.1N HCl to calculate ILPZ. The second component is the HMW predicting equations from ILPZ. The model was established using a training data set of 6 VI runs and verified with a validation data set of 12 VI runs.
The CFD model for VIA simulation was described in our previous report (Jin et al., 2019). We report here the details of the modeling methodology. Furthermore, in the previous work, the volume fractions of the outflows in the CFD model were fixed, whereVFout1 of 0.1 was used. The model provided acceptable accuracy to simulate VIA (≤ 5% variation between the predictions and the experimental EOVIA MF0.1N HCl ) in SUM-100 and SUM-50 mixers (n = 3) (Jin et al., 2019). Being screened whole spectrum of VFout1 values (0.1 - 0.9), theVFout1 of 0.7, appeared to applicable to broader types/scales of VI mixing vessels, including SUM-100, SUM-50, XDM-200, Applikon 20L, and Appilkon 2L mixers (n = 18).
The CFD model for VIA simulation was modified from the original species transfer model (Spann et al., 2019), of which the tracer was patched to the fluid and distribution of the tracer was then tracked in a duration for mixing time calculation. This bolus addition model is not the case of VI condition at pilot/production scale, where acid is typically continuously added during VIA. To simulate volume expansion due to acid addition during VIA, one thought was to use the gas-liquid two-phase model and patch air in headspace that would enable liquid expansion during simulation. However, the ANSYS FLUENT software is incapable to run the species model coupled with the multiple phase model. To have continuous acid addition in the liquid phase model while avoid volume expansion, two outlets boundary was introduced into the modified model. With this modification, the CFD model is capable to simulate the VIA processing with the continuous acid addition.
This work aims to mitigate the risk of product aggregation in the VI operation. This would ensure the designed protein loading at subsequent chromatographic polishing steps while achieving satisfactory step yield, final product quality, and overall robust downstream performance. A purification platform for mAbs typically includes two chromatographic polishing steps after VI operation (Fahrner et al., 2001; Shukla et al., 2007), e.g., AEX/HIC, AEX/CEX, and etc. (Shukla, Leslie, Wolfe, Mostafa, & Norman, 2017). In the case here, a polishing step yield was positively correlated the protein loading but inversely correlated with HMW%, VIN (data not shown). The reliable control of HMW%, VIN to < 2.5% significantly improved downstream throughput, resulting in substantial reduced cost of goods of the manufacturing.
We demonstrated here on the application of the CFD-based HMW prediction model to assist troubleshooting and guide scale-up of the VI process. Like other common situations during commercial process development, representative scale down model (SDM) was not established yet when the initial scale-up run was operated. The at-scale test using PAE was impractical due to prohibitive study cost and large material requirement (Kateja, Kumar, Sethi, & Rathore, 2018). In this work, the CFD-based HMW prediction model was used to quantify and minimize the localized extreme low-pH zones, especially for large-scale VI operation, to reduce the mAb aggregation risk. Agreed with the model predictions, the validation data set of 12 pilot runs (50-200 L scale) achieved 100% success rate. The results suggested that the CFD-based HMW prediction model may be general applicable to optimize the scale-up parameters of VI process from bench to pilot/production scale for mAbs and potential biologics of other modalities.