NoviSystems Launches Data Analytics Solution for More Efficient Drug Manufacturing
RALEIGH, N.C., Nov. 1, 2021 /PRNewswire/ -- NoviSystems Inc., a company that empowers data-driven decisionmaking, has commercially launched Fusion Analytics for Quality Systems, an analytics solution for improving operational performance at pharmaceutical and biopharmaceutical manufacturing facilities in the new era of individualized medicine.
"With the emergence of cell/gene therapy and individualized treatments, Fusion Analytics for Quality Systems is even more essential as bio/pharma manufacturers deal with increasingly complex products, multiple lines and global in-house and outsourced sites," said Michael Kowolenko, Ph.D., founder and chief executive officer of NoviSystems and a former biomanufacturing executive. "Fusion Analytics for Quality Systems leverages a platform that can meet evolving needs in deviation, process and release analytics, leading to further improvements in operational performance."
The product launch comes after NoviSystem engineers developed and validated the solution in collaboration with Thermo Fisher Scientific Corp., a global scientific services company, at 16 of Thermo Fisher's drug-manufacturing sites around the world.
"With Fusion Analytics, our organization has taken a more systematic and data-driven approach to understanding our deviation trends," said Matt Wessel, Ph.D., data scientist in the Pharmaceutical Services Group at Thermo Fisher Scientific. "Fusion Analytics gives us the ability to quickly identify areas of weakness and implement appropriate and meaningful actions to help reduce and eliminate deviations across our global manufacturing network. Fusion Analytics has been a real benefit for our quality teams."
Fusion Analytics for Quality Systems is a targeted application of NoviSystems' Fusion Analytics platform, an integrated software and hardware solution based on IBM's Power9 and Open Shift architectures. The platform abstracts the complexity of natural language processing and machine learning techniques to help users generate actionable, "real world" answers from complex data sets without having to become data scientists.
Fusion Analytics is well suited for complex businesses in health care such as biopharmaceutical and pharmaceutical manufacturers, clinicians specializing in rare diseases, and contract research organizations. It can help them gain insights that save time and money, increasing profitability.
Thermo Fisher Scientific participated in NoviSystems' Proof of Value Program available to all new clients. Demos are available upon request at https://novi.systems/schedule-demo/.
Important tool for individualized medicine
Cell-based gene therapies, including treatments for patients with rare diseases, represent the new frontier of individualized medicine. Loss of biologic material in a bio/pharma facility can mean the difference between life or death for patients.
Multiple systems must therefore be monitored continually for consistent output to meet regulatory specifications and the highest quality standards. From classic chemical synthesis to innovative cell cultures to biobanking, scientists today must assess data in real, or near real time, to determine whether product attributes fall within the licensed design space.
Systems for bio/pharma facilities, equipment and process control -- along with those for laboratory information management, personnel training, and monitoring of environmental exposures -- often do not cross-communicate to identify, investigate and mitigate serious deviations. This can lead to delays with extensive QC reviews, CAPA investigations, and QA approvals required for product lot release.
Data fusion (unifying multiple text and numeric data sources), natural language processing and machine learning can integrate process development, continuous and batch manufacturing, and quality oversight. These tools and techniques can also help create new predictive risk-assessment models.
Service providers in the IT field often focus on creating solutions to minimize or replace skilled personnel. In contrast, the customizable, proprietary dashboards for NoviSystems' Fusion Analytics enable staff members to unify data from multiple sources while maintaining visibility and verifiability throughout the organization.
"Users can focus on their problems and not the technology," Kowolenko said. "There is no need for programming, data science or database administration skills to benefit from NoviSystems products. Anyone familiar with a pivot table in Excel has the data skills necessary to use Fusion Analytics."
With Fusion Analytics for Quality Systems, bio/pharma manufacturers can easily prioritize manufacturing problems, identify emerging trends and compare deviations with the U.S. Food and Drug Administration's published citations.
Annual subscriptions to the solution include hardware, software, training, technical support and consulting.
NoviSystems was spun out of North Carolina State University in 2018 when Kowolenko, a faculty member and director of the university's Institute for Next Generation IT Systems, saw a need for commercial innovations in data integration and analytics for complex businesses, particularly those in health care that are highly regulated and procedure oriented, such as drug manufacturers and hospitals.
His prior experience as a senior vice president for Biogen Idec and Wyeth's (now Pfizer's) Biotechnology and Vaccine Division also convinced him that more could be done to accelerate data-informed decisionmaking by bringing together operational data from multiple quality systems. He and his staff at the biopharmaceutical companies needed better ways to aggregate and filter complex data to ensure a continuous supply of safe drug products while demonstrating an overall state of control and compliance with various multinational regulatory requirements.
Media contacts:
Dr. Mike Kowolenko
Chief Executive Officer
[email protected]
John Bass
Chief Operating Officer
[email protected]
Rick Williams
Acting Chief Commercial Officer
[email protected]
SOURCE NoviSystems, Inc
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