Scheduling within the Contract Manufacturing Organization (CMO) environment requires flexibility as most products produced at the Catalent Bloomington site are biologics. These products generally require storage at frozen or refrigerated temperatures and have limited room temperature stability. When the filling date for formulated drug product (DP) is scheduled, all supporting manufacturing activities are then scheduled through back calculation. Data for the time required for drug substances (DS) to thaw and subsequently reach room temperature (15C) is often not known. Additionally, if the filling line is not ready to receive formulated product, the product may have to be placed into refrigerated storage.
Once the line is ready, the product must then be allowed to reach a minimum filling temperature of 15C to avoid adverse filler performance effects due to the input variation in density and viscosity at different temperatures. This equilibration duration does apply to the DP’s Time Out of Refrigeration (TOR) limits and must be carefully managed to ensure the TOR limits are not exceeded. Creating predictive models to give scheduling and manufacturing accurate time frames for DS thaw and DP Equilibration will allow for more efficient scheduling and decreased probability of exceeding the room temperature limits leading to potential adverse effects on product quality.
A technical review of executed batch records spanning from 2013 through 2018 here at the Catalent, Bloomington Site was conducted capturing all recorded DS thawing data. The product specific information gathered included Biologic Characterization and Composition / Concentrations, DS batch size, container type / size and number of units, room temperature, thaw check frequency, and overall thaw time. A regression analysis was applied via Minitab Software to determine which parameters had a statistically significant impact on thaw duration. The model, developed from the 2013 – 2015 data, was then assessed by comparing the outputs to the 2016 – 2018 data.
To predict the Equilibration durations of formulated DP the most commonly utilized holding bag sizes, 50 – 500 L, were filled with water as a representative worst-case due to its high heat capacity compared to most DPs. The bags were then placed in cold storage, within their designated totes or on a holding rack, for twenty-four hours. The surface temperature at three locations on each individual bag was monitored by a k-validator system once the bags were moved to room temperature. Once the bags reached 15C, the bags were placed back in cold storage and the trail was repeated in triplicate.
Through an analysis of variance, four parameters were observed to have a statistically significant effect on thaw duration: DS Batch Size (1 L: p = 004, 5 L: p=0.000), Room Temperature (1 L p = 0.664, 5 L: p=0.001), Biologic concentration (1 L: p=0.009, p = 0.000) and Container size. The regression analysis indicates a direct correlation of Thaw Duration to DS Batch Size and Container Size however, Room Temperature and Biologic concentration exhibit an indirect relationship with Thaw duration. Most of the data gathered applied to monoclonal antibodies in either 1 or 5 Liter bottles therefore two models were created isolating the bottle size. The regression equations, see Figure 1, developed from the remaining parameters from the 2013 – 2015 data and was then applied to observe the fit as compared to the 2016 – 2018 data reference Figure 2.
The regression analysis of the Equilibration study data indicated that each bag regardless of size followed the same minor quadratic trend as the points reached room temperature. The Equilibration duration was observed to be dependent on the size of the bag and by the material of construction of the holding tote. The 500L tote is a stainless-steel vessel which is much more conductive exhibited faster Equilibration times as shown in Table 1. The resulting outputs from the regression analysis are summarized in Table 1 for manufacturing to review.
The two models created provide manufacturing with approximate estimates within a few hours as to the Thaw Durations for incoming DS. This will allow more flexibility and scheduling of formulation activities.
With water representing the worst-case scenario for the DP Equilibration time, we now have a model to better understand when we will exceed a DPs TOR limits and how to adequately plan for the remaining manufacturing activities if the filling line is not ready to commit product.