The pharmaceutical industry faces huge challenges today, as yesterday’s blockbuster drugs come off patent, the pace of mergers and acquisitions accelerates, and companies adjust to the dramatic changes in science and technology that have taken place within the past 10 years.
Where the old business model focused on getting fair returns for the high costs and risks associated with R&D, pharmaceutical manufacturers today must be far more consumer focused. Guiding their efforts are a number of indicators, including the unit price of the active pharmaceutical ingredient (API). Process optimization allows manufacturers to improve API production yields and efficiencies, with positive effects on process and product development, technology transfer to first commercial manufacture and site-to-site technology transfers.
To ensure that the best route to an API is in place whenever a new product is launched, Pfizer prefers to “co-design” commercial manufacturing processes. Within this framework, R&D and manufacturing collaborate closely after potential routes have been identified, to ensure that the best process chemistries are developed so that the commercial process will minimize both manufacturing cost and environmental impact.
As its co-design strategy has evolved, Pfizer has refined its use of vital cost and environmental modeling platforms. As a result, scientists and manufacturing professionals now have access to the detailed data they need for final route selection and optimization. This article will take a brief look at how Pfizer is using modeling to improve process optimization and route selection to improve efficiency, reduce cost and environmental impact, and enhance collaboration throughout R&D and manufacturing.
Methods of Green Manufacturing Metrics and Cost of Goods Analysis
In the past, teams had to access important information from myriad sources, ranging from paper notebooks to individual queries, which limited access to critical data. This information would be distilled into very simple form, based on the consumption and cost of regulatory-compliant starting materials and the primary building blocks of the molecules considered for commercialization.
In addition, R&D and manufacturing each developed different cost models, using customized spreadsheets. On the research side, processing costs were often estimated without knowledge of actual cycle times, based on a cost-per-step per unit mass of product, with some graduation of that cost as a function of annual production volume. This method did not incorporate information about disposal costs or relative production of waste per unit mass of product. Its benefits were clearly seen during the earliest stages in the product lifecycle, for instance, when a choice had to be made between five or more potential routes with little lab or scale-up experience of any of the options.
Though it generated results quickly through spreadsheet analysis, it could not provide sufficiently granular information on itemized costs or environmental metrics to add great value to route selection for long-term manufacture. Over time, detail was added to the cost and environmental models, to improve the depth of results and using primary data to drive commercial route selection. Unfortunately, this was often done via custom spreadsheets developed by each portfolio research project leader and shared with his or her manufacturing colleagues.
Meanwhile, the manufacturing organization was also generating its own version of the cost of manufacturing campaigns, with details that were manufacturing site specific but did not represent the long-term, optimized commercial process. Merging these two different estimates was often difficult, and information transfer between R&D and manufacturing less than optimal.
These approaches featured little if any measure of green manufacturing metrics, while the customized nature of the spreadsheet analyses made them difficult to apply.
Though highly detailed, these spreadsheets were adapted each time a new synthetic step or alternate route was analyzed. Reconfiguring them to fit particular scenarios meant that these platforms were prone to error. The unique structure of each, relative to individual project leaders, meant that the results were difficult for managers, who were considering the entire scope of portfolio development and commercialization, to adequately assess.
As a result, management introduced a standardized template structure upon the research and manufacturing spreadsheet approaches in an attempt to capture stepwise information and various route configurations. For instance, they improved the calculation of processing costs by using generic cycle time estimates for each unit operation of a synthetic step. Total cycle time was then multiplied by a generic cost of multiple-vessel workcenter occupation per volume per hour. While this improved the understanding of route processing costs, it did nothing to improve the understanding of materials and waste impact on the cost of goods or environmental metrics used in route selection.
To improve the data model used in commercial route selection, Pfizer tested an alternative solution for knowledge management, based upon an upgrade in analytical capabilities and the flow of work and information. The analytical capabilities were developed using Aspen Technologies’ Batch Process Developer. Other improvements in information flow made the improved analytics more powerful than the previous workflow could have been. These included the use of electronic laboratory notebooks and electronic materials sourcing databases accessible to everyone on the co-design team.
The electronic lab notebook data capture and workflow meant that the scientists responsible for assembling process development cost and environmental information had a central repository for data. Within that environment, a continuously-updated structured, step recipe model was maintained for the purpose of easing technology transfer and enabling cost and green chemistry metrics analyses. This approach made it easier to collect detailed material consumption data and project stepwise operating cycle times on manufacturing scale. Centralized materials databases with historical price information further improved the level of detail in the materials data used in the co-design analysis.