Process Development: Maximizing Process Data from Development to Manufacturing - - BioPharm International

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Process Development: Maximizing Process Data from Development to Manufacturing


BioPharm International
Volume 20, Issue 7

PROGRESS TO DATE

The initial focus of the project has been to define a data structure for the general recipe and physical model based on the ISA-88 model, and to populate a test database with data from sample processes and equipment lists. The next stage of the project will involve testing the functions required to transform a general recipe into a master recipe with different scenarios. The consortium has agreed to follow good automated manufacturing practices (GAMP) during the development process, to allow the resulting software to be validated where necessary. The benefits of having a validated knowledge-management model include the potential to extend the data structure and applications into manufacturing and to support scale-up models for regulatory submissions. The validation approach for the project is to consider each component of the knowledge-management model separately, starting with a core database, and then to extend the functionality with additional software applications.

POTENTIAL BENEFITS

An important outcome of the project's requirements-gathering phase was a better understanding of the potential benefits of the knowledge-management model. By allowing key process information to be systematically captured and retained throughout development, the model can enable scientists and engineers to learn more effectively from their collective experiences. A better view of historical performance also enables better predictions of future process performance and costs, and these benefits will be the focus of the next development stage for the project. The following sections describe some benefits identified during the planning phase.

IMPROVE METHODS OF WORKING FOR SCIENTISTS AND ENGINEERS

A simple and intuitive approach to managing data involves separating key process information, such as data about process steps and raw materials, from routine experimental methods and general report information. Storing data and methods separately is a useful design output in itself, and it supports rapid generation of developmental reports while making it easy to search for, access, and interpret previous work.

This approach can reduce the time scientists and engineers spend creating documentation; it also simplifies and accelerates acquisition of data from past studies. For example, a simple data-entry interface can allow process-development scientists to define process parameters, to reference supporting experiments, and to select buffer recipes and operating methods from a predefined standards list. This saves them time when creating process documentation. Once a history of experiments and process definitions is built up over time, the database can also be used as a troubleshooting tool. Engineers can search for information about an unexpected outcome for a particular unit operation to see if the problem has been previously encountered and if any workaround solutions have been suggested. Similarly, operations managers can reference information about maximum allowable hold times for process intermediates during troubleshooting to evaluate whether to proceed with a batch.

PROMOTE STANDARDIZATION AND REDUCE UNCERTAINTY

The increasing popularity of platform processes indicates the value of standardization for bioprocesses. A common data center that provides access to preferred materials and operating methods can promote the development of robust process designs and enable process-development scientists to focus on refining those process sections most likely to impact product quality or process performance. In addition, ensuring that standard representations and terminology are used throughout the process can reduce confusion during technology transfer and improve communication among groups.

MAXIMIZE EFFECTIVE USE OF BIOPROCESS MODELS

Ultimately, the goal of the knowledge-management model is to enable feedback from manufacturing performance to guide process development and create a model that supports continuous improvement and better process understanding. Various modeling tools are commonly used in the industry to gain a better view of the effect of process-development decisions on manufacturability, and the data model should support more effective use of these modeling tools. By enabling assumptions and relationships defined in the model to be compared with historical process data, modeling tools can be continually refined and improved. For example, cost models can assess the impact of process-development decisions on manufacturing costs, and simulation models can assess facility fit and overall manufacturing performance and resource utilization.

CONCLUSION

The planning phase for developing the knowledge-management model indicates that a central repository for process data in a structured format will offer benefits for process development and technology transfer. In addition, the model will support progress toward improved process understanding. The next development phase will focus on creating applications for defining and scaling up bioprocesses based on the general recipe concept.

(Companies interested in participating in this development effort are encouraged to contact Andrew Sinclair at BioPharm Services for further information.)


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