(11h) Utilizing Data Systems and Modeling to Evaluate Equipment Fit and Technical Capabilities in External Drug Substance Manufacturing

Authors: 
Holloway, B., Bristol-Myers Squibb
Nikitczuk, W., Bristol-Myers Squibb
Mack, B. C., Bristol-Myers Squibb
Perkins, D., Bristol-Myers Squibb
Lavender, J., Bristol-Myers Squibb
Wilbert, C., Bristol Myers Squibb
Both clinical and commercial small molecule pharmaceutical intermediates and API are increasingly manufactured at Contract Manufacturing Organizations (CMO’s) relative to internal manufacturing plants. This paradigm shift implies some loss of ability and flexibility to ensure optimal process fit within the plant equipment. Internal manufacturing plants allow for self-selection of equipment for process fit. At an external plant, the CMO selects the equipment for a process. Particularly in the clinical space where processes are new and CMO experience with the processes is low, this leads to a higher risk of poor equipment selection, potentially causing timeline delays, quality insufficiencies, and yield fluctuation. To mitigate this risk, our team developed data systems and a suite of tools to assess process fit and technical capabilities throughout our CMO network. For the data systems, we used a combination of vendor survey and historical operating experience to assemble a consolidated CMO equipment database and a plant capability log. Our suite of tools, including various Dynochem utilities, interactive data visualization, and statistical modeling, leverages our data systems and allows us to cross-reference CMO equipment and capabilities against individual API or intermediate processes. Typical products of this evaluation include proactive equipment fit, decision analysis around vendor selection, and evaluations of equipment impact on process performance. As a result, we can proactively address issues with vendor capabilities and equipment fit, which saves us from delays and cost increases. In this talk, we will describe the systems that we implemented, the workflows that were developed, and several case studies describing how we used these tools to address individual problems associated with technology transfer and scale up in an external manufacturing network for clinical API.