The compelling drivers for the pharmaceutical industry are speed to market and getting the manufacturing process right the first time. However, getting there is a complex challenge, particularly with respect to information management. Getting there involves going through the following stages after the viability of a ınewly discovered molecule is established: laboratory scale, ıpilot plant scale and commercial scale manufacturing. ıDuring this a staggering ıamount of information of different types, ranging from raw data to lab reports to sophisticated math models, is shared and revised by computational tools in each stage. Subsequent to process ıdevelopment, technical specifications and reports ımust be developed to satisfy regulatory requirements. However, due to the Tower of Babel like incompatibility among such diverse types of data, information, and knowledge, an appropriate automated decision support environment to address these needs is very much lacking. Additionally, at present most of the information and knowledge are generated and processed directly by humans. With the onset of information and knowledge explosion, it is clear that we need intelligent software systems to effectively manage and access information for efficient decision making. Proper informatics support to process development is crucial in order ıto achieve speed to market, and getting the process right ıthe first time.

To date the industrial response to all these challenges, however, has been sub-optimal. Even with rapid progress on information integration and sharing in business functions (such as ERP systems) and on plant floor (such as MES systems), the area of process/product development has been largely neglected. Several individual islands of automation exist, but a comprehensive, integrated decision support environment that integrates these islands does not exist. Therefore, practitioners must make do with a limited computer-based assistance to acquire, manage, analyze and interpret complex product and processing information with enormous amounts of human intervention. This increases the inefficiencies, uncertainties, costs, delays, and product quality concerns in all stages of product development. This also hampers the interaction between process development and business or manufacturing functions.

In this paper, we present an overview of our approach to address these challenges. In our work, we start from modeling the data/information/knowledge as well as their flows during decision-making in the entire process development. An ontological informatics infrastructure is developed to support such decision making spanning the entire process, including product portfolio selection, capacity allocation decisions, pilot plant operation, drug product formulation design, process simulation, production planning and scheduling, process safety analysis, and supply chain management for API process as well as drug product development.