New technology is not always easy to adopt and must be proven in a pilot plant. This may apply to the use of general adsorbents
(such as ion exchangers) rather than biospecific ligand capture or elute technologies; the use of soft gels rather than rigid
matrices; and the use of traditional fixed-column beds in preference to expanded-bed adsorption or multiple-column arrangements
(such as swing-bed or simulated moving-bed chromatography). Membrane chromatography may finally be coming of age and should
have a positive cost impact in removal of low-concentration impurities and contaminants.
Oliver Kaltenbrunner: Biopharmaceutical Costs are Different
Cost estimation for biopharmaceutical processing has its basis in standard chemical engineering practice. First, a model representing
the intended process is created. Second, this model estimates the equipment needed. And third, overall capital cost requirements
are projected based on equipment needs and typical multipliers for piping, instrumentation, construction, and installation.
In addition to fixed capital investments, variable costs are estimated based on raw material needs of the process and typical
labor, laboratory testing, and utilities costs for the particular unit operations. Labor and utility costs are treated as
fixed or variable costs, depending on the particular manufacturing situation. For example, it may be more expensive to shut
down and restart utilities in a regulatory-approved manufacturing site than to keep those utilities running during non-productive
Table 2: Frequently used cost terms
The classical estimation method needs refinement to be suitable for biotechnical processes because it underestimates variable
costs when using a singular metric like direct materials costs, COGS, or unit manufacturing costs. An alternative is to consider
several metrics. Table 2 (presented as an input-output table) summarizes what expenses to include togenerate specific cost
estimates. In this table an input item affects a cost only if "YES" is in its corresponding box. Input and output terms should
not be used interchangeably to avoid complicating the comparison of estimates. Unlike conventional chemical process estimates,
the inventory levels of both work in process and finished goods for biotech products are held significantly higher.
The costing terms in Table 2 work well for estimating costs of a particular production year and are therefore useful to estimate
taxes. However, the results may vary dramatically from year to year. For any multi-year project the timing of expenses can
be critical, and only NPV analysis can capture the true nature of the activities. The comparison of capital-intensive production
versus high operating costs, or of stocking upfront large amounts of expensive raw materials versus large consumption over
the life of the project, can only be assessed when the timing of expenses is considered in more detail.
Another recurrent difficulty is omitting or combining the success rates of process steps. Failure rates of cell culture bioreactors
can be significant and in many cases should not be neglected. But failing a bioreactor production step does not necessarily
fail the downstream process and cannot be lumped into an overall success rate. Reprocessing or waste disposal units in the
process model usually indicate this has been accomplished.
Cost of consumables is one factor that complicates the analysis of typical biotechnological processing considerably and is
very different from standard cost estimating methods.
In contrast to classical chemical processing, consumables like resins and membranes can be a significant portion of the raw
material costs in the production of biopharmaceuticals.
For example, in a typical monoclonal antibody production process, the resin cost for an affinity-capture column can overwhelm
raw material costs. If a monoclonal antibody were to be captured directly on a typical Protein A resin from a 10,000-L cell-culture
broth at a product titer of 1 g/L, resin costs of $4 to $5 million would arise for one packed column. Common sense recommends
reuse of resins despite the complications of reuse validation. Additionally, cycling of columns within one production lot
to increase utilization of expensive consumables seems advisable. Also, the projected inventory of material on hand will add
significantly to the projected process costs. To determine the optimum number of reuses and cycles of a particular resin,
cost of production must be calculated for each scenario under consideration.
Another big problem for establishing a reliable production cost estimate arises from the fact that consumables typically have
both time- and use-dependent expiration criteria. If expiration could be defined just by number of uses, consumables costs
would simply behave as variable costs. If expiration of resins and membranes could be defined just by an expiration time,
they could be depreciated as a fixed asset. However, having criteria for both time and cycles makes their cost contribution
sensitive to the annual production demand. An average production capacity cannot adequately describe production costs for
products ramping up in demand after launch or during conformance lots. During the early production years, a resin may never
experience its maximum number of validated reuses before it expires.
To make a reliable production cost estimate, the entire projected production campaign must be taken into account, because
the use of single-year estimates results in high manufacturing variances. As a result, it is not enough to estimate production
costs based on an average production year. Every year of production must be estimated separately while keeping track of consumables'
cycles and age. This is in stark contrast to traditional chemical engineering cost modeling, where the cost for each unit
produced is virtually identical and process costs can be reasonably estimated without having to draw up detailed production
Estimating has certainly become difficult as consumables costs depend on the demand scenario. Cost estimates must be repeated
for demand scenarios of varying optimism. The complex behavior of consumables costs must be considered when trying to determine
the optimal process scale or number of consumables reuses and cycling within lots.
Unfortunately, commercial software does not allow consideration of this level of complexity. At present, there is no way around
a tedious aggregation of model scenario outputs or the tedious development of custom tools. Eventually, commercial vendors
will acknowledge this as a shortfall of their products and implement solutions that are modified towards the unique circumstances
of cost estimation in biotechnology.
Elements of Biopharmaceutical Production
This article is the third in a series in BioPharm International coordinated by Anurag S. Rathore. This series presents opinions and viewpoints of many industrial experts on issues routinely
faced in the process development and manufacturing of biopharmaceutical products. Earlier articles were: "Process Validation
- How Much to Do and When to Do It," Oct 2002 and "Qualification of a Chromatographic Column - Why and How to Do It," March
1. Agres T. Better future for pharma/biotech? Drug Discovery & Development 2002, Dec 15.
2. Curling J, Baines D. The cost of chromatography. IBC:Production and Economics of Biopharmaceuticals; 2000 Nov 13-15; La
3. Rosenberg, M. Development cost and process economics for biologicals derived from recombinant, natural products fermentation,
and mammalian cell culture. IBC: Production and Economics of Biopharmaceuticals; 2000 Nov 13-15; La Jolla, CA.
4. Sadana A, Beelaram, AM. Efficiency and economics of bioseparation: some case studies, Bioseparation 1994; (4):221.
5. Warner TN, Nochumson S. Rethinking the economics of chromatography. BioPharm International 2003; 16(1).
6. Novais JL, Titchener-Hookner NJ, Hoare M. Economic comparison between conventional and disposables-based technology for
the production of biopharmaceuticals, Biotech. Bioeng. 2001; (75):143.
7. Mullen J. The position of biopharmaceuticals in the future. IBC: Production and Economics of Biopharmaceuticals; 2000
Nov 13-15; La Jolla, CA.
8. Mun J. Applied risk analysis: moving beyond uncertainty. Hoboken (NJ): Wiley Finance; 2003.
9. Myers J. Economic considerations in the development of downstream steps for large scale commercial biopharmaceutical processes.
IBC: Production and Economics of Biopharmaceuticals; 2000 Nov 13-15; La Jolla, CA.
10. Karri S, Davies E, Titchener-Hooker NJ, Washbrook J. Biopharmaceutical process development: part III. J. BioPharm Europe; Sept 2001: 76-82.
11. Pisano GP. The development factory. Boston (MA): Harvard Business School Press; 1997.