Fine Chemicals

Enhanced information sharing in API manufacture

Dr Eric Cordi of Pfizer Worldwide R&D and Russell Schofield of Aspen Technology look at the benefits of a Co-Design R&D and Manufacturing Approach

Delivery of the highest quality, most cost-effective product portfolio is key to the future success of manufacturers in the global pharmaceuticals industry. Pfizer seeks to address this via enhanced decision-making that is based on the effective integration of business and science. Breakthroughs in collaboration and productivity are ever more critical in increasing the speed of product delivery from the research pipeline.

Guiding these efforts are some key factors, including the unit price of APIs. Process optimisation allows manufacturers to improve API production yields and efficiencies, with positive effects on technology transfer to first commercial manufacture, site-to-site technology transfers and process and product development.

To ensure that the best route to an API is in place when a new product is launched, Pfizer uses a co-design approach in shaping the commercial manufacturing processes. Within this framework, R&D and manufacturing collaborate to achieve process designs that minimise commercial manufacturing cost and environmental impact.

As this strategy has evolved, vital cost and environmental modelling platforms have been refined to improve results. Consequently, scientists and manufacturing professionals can now access the detailed data they need for final route selection and optimisation.

COGS analysis & green manufacturing

Historically, teams accessed key information from a range of sources, including paper lab notebooks, and through repetitive queries, which limited access to critical data. This collection of information was distilled into a summary for analysis, based upon projected annual product demand and the costs of regulatory compliant starting materials as the primary building blocks of the molecules considered for commercialisation.

In the analysis, R&D and manufacturing each used customised spreadsheets to develop their respective cost models. On the research side, processing costs were often incorporated into the analysis without any knowledge of unit cycle times and site operating expenses. Instead, these costs were calculated from the historical average processing cost per unit mass of product, which was multiplied by the number of synthetic steps to the API, with some graduation of that cost as a function of annual production volume.

Figure 1- Examples of graphics in Batch Process Developer

This high-level spreadsheet analysis could not provide sufficiently granular information about itemised costs or environmental metrics to improve decision-making as the route options matured toward selection for long-term manufacture. Further refinements were needed in the cost and environmental spreadsheet analysis to increase the precision and accuracy of estimates across the product portfolio and to drive commercial route selection process from a common basis.

At the same time, the manufacturing organisation generated its own version of the cost of manufacturing campaigns, with site-specific details, but did not represent the long-term, optimised commercial process. Harmonising estimates from research and manufacturing was invariably difficult and information sharing between the two functions was challenging without the benefit of an accessible and harmonised cost and environmental modelling approach.

Over time, a harmonised spreadsheet platform evolved with the increasing detail needed to meet co-design objectives. The calculation of processing costs was improved by using generic cycle time estimates for each unit operation of a synthetic step. Additionally, spreadsheets were manually adapted to accommodate longer-than-usual or converging syntheses.

Reconfiguring these models to fit particular scenarios meant that these platforms were prone to error, however. The unique structure of each analysis also meant that portfolio directors, who evaluate the full scope of product development and commercialisation options, found the results challenging to assess.

New approach

Seeking continuous improvement in the data model used in commercial route selection, Pfizer tested an alternative approach to knowledge management, based on an upgrade in analytical capabilities and enhanced collaboration technologies.

These capabilities were developed using Aspen Technologies' Batch Process Developer, a recipe-based modelling technology for the batch industries which supports process development and the generation of required documentation from early route selection to full scale manufacturing. It facilitates sharing of information across the company by providing a standard approach for creating and managing process information throughout the development workflow.

Other improvements in information flow, including the use of electronic laboratory notebooks, materials sourcing databases and collaborative work platforms that were accessible to everyone on the co-design team, made the improved analytics more powerful than the previous workflow could have been. The electronic lab notebook data capture and workflow meant that scientists responsible for assembling process development cost and environmental information had a central data repository.

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 made it easier to collect detailed material consumption data and project stepwise operating cycle times on a manufacturing scale. Centralised materials databases with historical price information further improved the level of detail in the materials data used in the co-design analysis.

Using this refined cost of goods and environmental analysis, Pfizer captures process development and scale-up history in a single platform rather than in multiple, customised spreadsheets. After choosing a route of interest and displaying it as a synthetic flow document, users edit step inputs within four categories describing each step: properties, materials, processing, and comments.

The properties input includes current and optimised step yields, based on current experience in development and scale-up, plus expected improvements from long-term implementation. The materials section allows input of the full list of starting materials, reagents and solvents. The cost of each material is displayed as a line item from a separate materials database. Next, the user chooses a waste category for each material, in order to generate green manufacturing metrics with the standardised output.

Figure 2 - Sensitivity analysis plots

In the processing section, scientists enter details about equipment fill volume, maximum dilution of the starting material in the vessel and total step cycle time. Lastly, the cost per hour of processing is entered. Once all the steps in one route are populated, the graphical synthetic route is automatically updated with primary information about each step, including the step intermediate name, molecular formula, molecular weight, current or optimised yield, and cost per unit mass of the intermediates and final product of the route.

Pfizer finds this method of entry convenient and transparent to the various users on a project team. Quick data entry into routes under consideration has made a significant difference in co-design discussions between research and manufacturing.

Route modelling analysis

Beyond the primary data displayed directly in the process development software, detailed analysis of any given route can be produced as an Excel report based on a standard template. The co-design team finds this output useful in examining details about materials, processing or waste disposal within a particular step or the entire route. It is from this multi-page Excel report that Pfizer generates numerous graphics (Figure 1) for use in co-design brainstorming sessions.

For example, the waste material summary relies upon information about the fate of each material in the stepwise material tables - waste sources are split by category and total waste is calculated per unit mass of API or product. The division in waste category information enables the project team to easily calculate three primary green manufacturing metrics: the E-factor (waste mass/product mass), the mass intensity (waste mass except water/product mass) and the reaction mass efficiency (mass of product/mass of reagents).

A comprehensive data summary is generated for each route in terms of cost of goods and green metrics. Project team members then have time to assess the data and draw preliminary conclusions before meeting to discuss and route options.

A final component of route selection is a sensitivity analysis of key cost and environmental factors. Changes are made to key inputs in the interface and results are plotted for a visual indication of metrics sensitivity (Figure 2). Such sensitivity plots provide a means of prioritising further process development or optimisation of the chosen route in manufacturing.

Benefits of new approach

Co-design between R&D and manufacturing at Pfizer has been greatly enhanced through this centralised approach. It facilitates decision-making transparency within a project team, enabling members to thoroughly examine each step of each route under consideration, for commercial nomination.

The approach has improved the organisation of data and results, which has driven greater collaboration between divisions involved in route selection. Critically, Pfizer can better select synthetic routes that will have the greatest cost and environmental benefits in commercial production.

Contact:
Russell Schofield
AspenTech Technology
Email: info@aspentech.com
Website: http://www.aspentech.com

 

 

From Online Issue: August 2012