Advanced Mammalian Biomanufacturing Innovation Center (AMBIC)
The Advanced Mammalian Biomanufacturing Innovation Center (AMBIC) brings together leading academic and industrial biotechnologists focused on mammalian cell culture manufacturing at a precompetitive research level. AMBIC’s mission is to develop enabling technologies, knowledge, design tools, and methods that apply and integrate high-throughput and genome-based technologies to fast-track advanced biomanufacturing processes. Through systems-level biology analysis, novel cell line development, bioreactor optimization, and advanced analytics, AMBIC provides transformative solutions that can lower biomanufacturing costs and improve bioprocessing efficiency.
Much biomanufacturing involves the use of cells to make medicines, including cancer medicines and vaccines. AMBIC implements engineering innovations to enhance the ability to manufacture these life-extending and life-saving medicines. This enhanced ability will increase the competitiveness of U.S. biomanufacturing, leading to more economic investment by companies in this sector and more jobs for American workers. Most important, advances made possible by AMBIC’s research may ultimately make more biopharmaceuticals available to patients who need them and lower overall health care costs for consumers.
Furthermore, AMBIC maintains a pipeline of educated and motivated students working toward careers in biopharmaceutical manufacturing and development. AMBIC’s principal investigators are committed to increasing the participation of women and underrepresented minorities in STEM disciplines and teaching students from all backgrounds about the exciting opportunities to help others through careers in biotechnology and biomedicine.
- Clemson University
- Johns Hopkins University
- University of Delaware
- University of Massachusetts, Lowell
- University of Maryland, College Park
Dr. Kelvin H. Lee
Dr. Michael J. Betenbaugh
Dr. William Bentley
Dr. Sarah W. Harcum
Dr. Seongkyu Yoon
Research in this area which includes standards, simple fingerprints, raw material issues, regulatory issues, forensic bioprocessing, and clonality addresses important questions arising from new biological insights. These questions must be considered in the context of industry-wide impact, where each company has unique products, processes, and platforms but may be affected by issues common across the industry, or where companies seek to develop industry-wide standards to facilitate regulatory compliance and best practices. Projects include work on leachables and extractables, impurities, sustainability issues, performance standards for disposables, genome analysis and clone characterization, demonstration projects for new technologies, standardization of the Chinese hamster ovary (CHO) genome, and creation of a better CHO.
Industrially relevant biology
This area of research which includes bioinformatics, process and product quality, and different types of genome analysis seeks to understand relevant principles of biology that are important to the biopharma industry. Projects focus on topics such as developing and improving genome analytics and metabolic models, identifying early markers for stability, understanding effects of raw materials on cells, identifying biomarkers for cell behavior, reviewing epigenetic and ncRNA studies, identifying cell characteristics amenable to continuous processing, and identifying hot spots for target integration.
Process monitoring and control
Research in this area which includes analytics, instrumentation, data mining, and modeling seeks to develop new analytical tools, sensors, and equipment, as well as approaches to integrate data from these devices into improved methods for process monitoring and control. Projects focus on topics such as in silico modeling for process integration; real-time genome analysis profiling; statistical process control; sensors for at-line, in-line, and online product assays; measurement and control of variability in raw materials; systems biology models for product quality predictions; predictive feedforward control models for critical quality attributes; and data mining tools for pattern recognition.