Manufacturing throughput (the amount of material a plant can produce per year) is affected by process yield and plant run rate. The higher they are, the more a plant can produce per year, requiring fewer lots to meet annual demand. Although a process development team obviously determines the process yield, the team also determines the impact on the run rate of duration and potential implementation complexity of the entire train of unit operations. Thus, an optimized process maximizes plant throughput by maximizing process yield and run rate while assuring product quality and minimizing the cost-per-unit mass of manufactured drug (1). Based on how COGM is defined, we limit ourselves here to considering the impact of plant time and raw material cost. We believe run rate, in addition to yield, should be considered simultaneously during process development to arrive at a process design desirable from a COGM perspective.

Plant cycle time (the time between the start of two consecutive batches) can be affected by process and operating parameters that are often coupled. The longest unit operation tends to define a plant's cycle time. For instance, if a cell culture process takes 10 days to complete and two days to turn around, a plant with a single bioreactor would have a 12-day cycle time. For n reactors furnished, the theoretical minimum cycle time will decrease to 12/n days until the plant is limited by the next unit operation with a cycle time longer than 12/n days. Operating parameters that can affect plant cycle time are often related to shared use of supporting equipment such as clean-in-place (CIP) skids, media, buffer, and in-process pool hold tanks. The emergence of high-titer monoclonal antibody processes also necessitates using chromatography columns in multiple cycles accompanied by increasing buffer consumption, thus imposing additional operating challenges to existing plants (2,3).

PRODUCT FOCUS: PROTEINS, ANTIBODIES, PARENTERAL PRODUCTS

PROCESS FOCUS: UPSTREAM AND DOWNSTREAM PROCESS DESIGN

WHO SHOULD READ: PROCESS DEVELOPMENT AND MANUFACTURING, FACILITY-DESIGN ENGINEERS

KEYWORDS: MANUFACTURING THROUGHPUT, STATIC MODELING, COGM, RAW MATERIALS, RUN RATE, UNIT OPERATIONS, CHROMATOGRAPHY

LEVEL: INTERMEDIATE

A good understanding of the interdependence between process requirements and facility capability during development is critical to maximizing run rate and minimizing COGM in full production. This can be achieved by static or dynamic plant modeling that reflects plant operation and process requirements to a sufficient degree of accuracy.

Case 1: Raw Materials and Unit Operation Affect Run Rate

In general, raw material cost contributes to a higher percentage of COGM as run rate increases. Efforts to reduce that cost, however, must be accompanied by analysis of the entire process to prevent negative impact on run rate (4). This case study shows how selecting a raw material and unit operation with higher unit cost can be justified if the associated run rate is favorable and a comparable step yield can be maintained. Here, the projected annual demand for a drug substance (DS) was up to one metric ton, and one purification step was critical in achieving high yield and throughput for the entire process. However, potentially high raw material cost was associated with that step. After preliminary screening, two options were based on two different purification principles: raw material A (RM A) and raw material B (RM B). The estimated unit cost of RM B was as much as nine times that of RM A. In addition, the mass-based processing capacity of RM A was superior to RM B by a factor of three. Therefore, RM A was a clear winner based on unit raw material cost and mass-based processing capacity. However, the projected operating flow rate for RM B was three times faster than that of RM A. RM B can potentially decrease the process time and overall plant time that directly affect overall COGM. Plant modeling was performed to calculate the COGM difference between the RM choices (Table 1).

Table 1 shows that RM A requires fewer cycles than RM B to process an entire batch of DS because of its higher mass-based processing capacity (~3×). However, the actual time required to process an entire batch would almost double if RM A is selected. This difference comes from the high flow rate capability with RM B. Not only does that speed up batch time, but it also shortens the overall plant time required to meet annual demand by ~13 weeks (Table 1). This provides significant annual COGM savings and frees up capacity to be used by other products in a multiproduct licensed facility.

Another factor to consider in a COGM analysis was annual raw material cost. Here, the campaign is defined as total time needed to produce enough lots to meet annual demand. As stated earlier, the basic unit cost of RM B ($/L) is almost 9× higher than that of RM A. However, the total cost difference between them was only a factor of two instead of nine after accounting for annual requirement. That was possible because RM B has a lifetime of 15 lots compared with RM A's 6.6 lots. Furthermore, after accounting for both total plant time and total raw material cost, the analysis showed that overall COGM is ~30% less using RM B as the raw material of choice for the process, translating into significant annual savings.

This case study demonstrates that minimizing raw material cost alone does not always optimize COGM. Instead, the impact of a raw material on COGM factors, in this case the plant time cost, should be investigated and demonstrated to reach the cost optimum of a process for successful commercialization.