Each new year kicks off with the unveiling of new technology trends and ideas designed to enable companies in all industries to improve, expand and explore new opportunities. In the 2020 technology arena, data has emerged as a key player in technology innovation. In essence, data is more prevalent, more robust and is a catalyst for nearly unlimited possibilities in business operations. In particular, the life sciences industry has a lot to gain from data’s rise to prominence.
All accolades aside, having access to vast amounts of data is of little value unless you can sculpt it into a useful asset that allows your organization to fully benefit from its net worth. Just like any other aspect of a business, data needs to be properly managed and nurtured to ensure it sustains a high level of quality and integrity. A new MasterControl trend brief, “2020 Trends in Quality Data Management and Integrity for the Life Sciences,” examines new life sciences industry trends that pivot around data and how they will impact regulated companies.
In August 2019, Switzerland-based health care product company Novartis was scolded by the U.S. Food and Drug Administration (FDA) for dereliction of data integrity practices. Shortly after receiving approval for its breakthrough gene therapy Zolgensma, FDA officials discovered that the company manipulated data from certain preclinical testing processes.1 2019 wasn’t exactly a banner year for data integrity compliance as Novartis was just one of numerous regulated companies cited for data management infractions.
The FDA maintains a hard stance when it comes to ensuring the quality, safety and effectiveness of health care products. Data is a central component of life sciences product development. Therefore, the agency has released numerous guidances and other documents clarifying the requirements for ensuring data integrity. Nevertheless, data integrity violations still account for the bulk of warning letters issued globally.2
The FDA is determined to rectify that scenario. In navigating regulatory pathways, plan on auditors putting your data and data management processes under a microscope. In the meantime, life sciences companies are urged to implement digitized technologies designed to automatically prevent omission, incorrect entry, unauthorized alteration and all other mismanagement of data.
Throughout the manufacturing supply chain, exponential amounts of data is being retrieved from management systems, equipment, personnel, products, organizational communications and much more. Because data is becoming a more valuable asset, it’s critical to have processes and tools in place for mining, processing and analyzing the data obtained from these sources. Many enterprise software systems have been built for this purpose, including:
Enjoying this article? You may also enjoy this Trend Brief:
"2020 Trends in Quality Data Management and Integrity for the Life Sciences"Download Free Trend Brief
These are all powerful, precision tools for their respective business units; however, they don’t all work and play well together. Bottom line, data is scattered across limited-access databases, standalone spreadsheets, emails, etc. Ultimately, the data ends up in multiple formats on paper documents. Hence, more attention is given to the accuracy and completeness of documents instead of the data. At the end of the day, organizations are unable to derive the full value of all the data at their disposal.
Quality is one area that is largely dependent on data. Having a tangible repository of data may satisfy the requirements for compliance, but data packed away in boxes yields only fragmented, inconclusive and delayed insights. This fosters more of a reactive, post-production enforcement of quality rather than a proactive, full-scale continuous improvement effort.
The FDA is taking a genuine interest in a data-centric approach to quality management. For instance, in the pharmaceutical industry, the agency is currently exploring a rating system program based on key metrics (data) gathered from companies. In addition, the program will include incentives for companies to update their quality management and manufacturing technologies and practices.3 Companies will become more productive and profitable while helping to mitigate issues facing the pharma industry, such as drug shortages.
The life sciences industry is still hampered by widespread product recalls, shortages and growing numbers of compliance violations. Meanwhile, significant scientific advancements in areas such as cell and gene therapies are expanding health care capabilities and introducing more patient options. The FDA is eager to expedite the new innovations, which is evidenced by programs such as the 21 Century Cures Act4 and Breakthrough Therapy Designation (BTD)5 designed to address critical and unmet medical needs.
New scientific innovations, along with the FDA’s pledge to help accelerate product approvals are evidence that simply meeting current good manufacturing practices (CGMP) requirements is no longer sufficient. More needs to be done to prevent innovative products from getting tripped up by compliance-related setbacks. That said, the life sciences industry is trending toward a more modernized structure of data, quality and compliance management.
Going forward, expect to see more expectations to integrate predictive analytics, all-hands risk intelligence and forward-looking insights into data management, quality and product manufacturing processes as well as the organization’s daily operations.
In February 2020, icometrix, developer of brain scan data extraction software, received 510(k) clearance for its icobrain ep solution. The technology uses artificial intelligence (AI) to detect subtle abnormalities in the outer layer of the cerebrum in epileptic patients that may go unseen by traditional measures.6
The icobrain ep solution is one of numerous technology products employing AI, machine learning (ML) and natural language processing (NLP) that are receiving regulatory approval for use in health care environments. These breakthrough technologies are redefining the parameters of what is possible in life sciences product innovation.
AI and other advanced technologies are becoming more mainstream in all industries. Companies are finding that turning over repetitive and logistically arduous tasks to automated technology allows people to apply their expertise more to innovation and problem-solving. That concept is one of many reasons why enterprises are incorporating AI into their business models. More organizations are discovering that advanced, data-driven technology is a valuable asset for effective decision-making, strategic planning and continuous improvement.