Establishing equations to predict HMW formation
The HMW prediction equations were then developed using the training data set of 6 VI runs as presented in Table III. Considering that the HMW assay variation could cause a relative standard deviation (RSD) of 25% for samples containing the VIN pool HMW level (HMW%,VIN ) of 1%, the lots selected as the training data set had a criterion of > 2.8%HMW%,VIN in order to ensure a RSD of < 10%. The training data set included VI runs using different agitation speeds and acid addition rates in mixers of various scales and types.
The values of ILPZ and HMWtotal,VIN of the training runs were plotted in Figure 4B, which suggests an exponential correlation between ILPZ and HMWtotal,VIN as defined by Eq. 4. The HMWtotal,VIN was then converted into HMW%,VIN using Eq. 5, where theCPAE and VPAE represent protein concentration (g/L) and volume (L) of the PAE pool, respectively.
\(\text{HMW}_{\text{total.VIN}}=2.364e^{1.179IPTZ}\) (4)
\(\text{HMW}_{\%,VIN}=100\bullet\text{HMW}_{total,VIN}/\left(C_{\text{PAE}}\bullet V_{\text{PAE}}\right)\)(5)
The model predictions and the experimentalHMW%,VIN values of the training runs are presented in Table III. The model predictedHMW%,VIN values ranged from 2.7% of Lot A20_1 to 7.9% of Lot S100_1, which were in good agreement with the experimental HMW%,VIN values (2.9% of Lot A20_1 and 7.1% of Lot S100_1).
Furthermore, the simulation of lot S100_1 not only predicted well theHMW%,VIN value but also demonstrated the time cause of low-pH zone growth as previously shown in Figure 3A, suggesting that product exposure to the localized low-pH zone resulted from poor mixing were the root cause of the HMW formation observed during the VI operation.