Developing a new drug is a satisfying scientific achievement. Bringing that new drug to market and making a profit is essential to the continued existence of a company, and manufacturing costs are crucial to overall profit margins.
This article, the third in the "Elements of Biopharmaceutical Production" series, will discuss various tools available to perform the modeling required to understand, develop, and optimize manufacturing processes, facilities, and operations. Anurag S. Rathore discusses incentives and steps during process development. Then four industry experts present their viewpoints, insights, and experience. Peter Latham and Howard Levine describe modeling tools; John Curling discusses problems of early and late scale-up; and Oliver Kaltenbrunner discusses how to modify classical chemical engineering costing to the realities of biopharmaceuticals.Anurag S. Rathore: Incentives The average cost to bring a new drug to market currently is about $802 million.1 Consequently, drug develo-pers are searching for tools and methods to contain R&D costs without compromising clinical test data. Competition is so intense that FDA is reviewing more than 100 pharmaceutical and biotechnology products that are in phase 3 clinical trials or beyond - the largest number of products so close to approval at one time.1
Production costs loom large in management planning. The optimal time to bring them under control is early in process development while the processes are being designed and various options are still under consideration. Intense product competition, patent expirations, the introduction of more second-generation therapeutics, and pricing constraints are forcing close scrutiny of the economics of bringing drugs to market.
Many pharmaceutical and biotechnology companies are using and developing a variety of sophisticated techniques for evaluating and minimizing the costs of each new therapy with the goal of generating optimal, robust, and economical commercial processes. Performing cost analysis during process development can prove to be extremely helpful for several reasons.
1. When the process for a new therapeutic product is defined during initial process development, use an early cost of goods sold (COGS) analysis to identify manufacturing cost drivers (for example, Protein A and virus removal steps). Then focus your efforts on evaluating new technologies and process options such as increasing lifetimes of the chosen resins or membranes.
2. Take a detailed look at the cycletimes for different unit operations. The process design can be modified to increase productivity. For example, use big bead resins for faster chromatography and appropriately sized equipment and utilities. Also, a model could be used to evaluate the cost difference between a single large bioreactor and multiple small bioreactors.
3. Cost models of existing products can generate useful data for analyses of future products. Even for companies that outsource, models can help predict the production cost and can serve as a starting point for negotiations with contract manufacturers. Several development teams have recently written about their approaches to design more economical processes. If they look interesting, consult the references. Remember, their applicability depends on similarity to your product and constraints specific to your facility. Recent cases include the following:
4. Optimize the facility to minimize downtime and improve efficiency of changeover and maintenance procedures. Some facility changes meeting these objectives include: addition or upgrade of manufacturing area or equipment; upgrade of utility systems; modernization of control systems; and coupling or decoupling of several process steps to improve operational flexibility and maximize equipment utilization.
5. Focus process development efforts on increasing the titer of fermentation and cell cultures.7 A fivefold improvement in titer combined with a 40% improvement in downstream recovery resulted in a sevenfold reduction in processing volume plus a fourfold reduction in COGS and associated capital requirements.
The tools described here are calculation-based models, Monte Carlo simulations, and discrete event simulations. Figure 1 summarizes the best time to use each of these tools during product development.