Alan Rencher, Vice President, Engineering, MasterControl03.04.21
To continue its ongoing mission of advancing public health, the U.S. Food and Drug Administration (FDA) has always welcomed new discoveries and innovation. Yet in recent years, the rapid pace of progress has forged a sizeable gap between the industry’s progress and the technologies needed to translate them into useful therapies. There is an urgent need to get advanced technologies into the healthcare ecosystem, but all products must first get regulatory approval.
In early 2019, the FDA experienced an influx of investigational new drug (IND) applications. Much of the growth was attributed to a surge in new cell and gene therapy products. Drug delivery system technology has also been on the rise, and nanotechnology is currently being explored as a drug delivery vehicle due to its ability to provide site-specific and target-oriented delivery of precision medicines.1
Combination products using advanced software and interconnectivity are becoming increasingly popular in the point of care and the home healthcare markets. These products are designed to provide a high level of precision with treatment regimens. They also enable healthcare providers to receive and react to data in real time, which greatly enhances many aspects of patient care.
The innovations moving through regulatory pathways are made possible through technologies enabling connectivity, interoperability, and the use of more data. Data is more abundant, more focus-area specific, and has a wide range of life sciences uses. For example, artificial intelligence (AI) and machine learning (ML) technologies are highly data-centric and are becoming more common in mainstream healthcare.
AI is designed to simulate human intelligence processes. It acquires information, determines how to analyze and use the information, and self corrects upon receiving new data. An AI algorithm has a programmed ability to continue learning and adapting based on the information it receives, making it useful for processes that involve analyzing enormous amounts of data. ML is an artificial intelligence technique that can be used to design and train software algorithms to learn and act on data, which is often used for predictive analytics. According to the FDA’s action plan on artificial intelligence and machine learning, “the technologies have the potential to transform healthcare by deriving new and important insights from the vast amount of data generated during the delivery of health care every day.”2
By nature, AI can continually acquire and analyze data and provide useful information for making decisions or solving problems. However, AI is designed to work through every possible route or option to present the best solution. This approach isn’t always practical because it can take a long time to reach a goal. Heuristic learning is a practical method for problem-solving, learning, or discovery. It may not always find the best solution, but it will always find a satisfactory solution in a reasonable amount of time. Used with AI, heuristic learning is a way to inform the algorithm about the direction to a goal, then it estimates the least costly route to a good solution to use in decision-making.
FDA’s TMAP Program Fosters Innovation
In an effort to stay abreast of scientific and technological advancements, the FDA implemented its Technology Modernization Action Plan (TMAP) in 2019. The modernization initiative has been a groundbreaking endeavor not only to facilitate innovation, but to overcome challenges within the regulatory paradigm. The FDA’s multifaceted responsibilities call for the agency to continuously monitor science, technology, and data trends. Oversight of complex, interconnected medical products requires cross-functional expertise from multiple offices within the FDA. That said, the agency’s modernization effort allows more transparency and collaboration across the organization, as well as the overall life sciences community.
To effectively expedite innovative and breakthrough products, the FDA must be more agile and dynamic. Therefore, a top TMAP priority was the migration of the agency’s technology infrastructure to a cloud environment. This helped the organization become more modernized, thus delivering more value to the industry and public. Because data has taken a more central role in medical product development and use, the agency had to better focus on data management.
FDA’s Data Modernization Strategy
The digitization of processes, pervasive use of mobile technologies, and advancements in data gathering and analytics have created new types and more uses for data. A central part of the FDA’s TMAP blueprint is making better use of data, which led to the development of the Data Modernization Action Plan (DMAP). Specifically, this data strategy focuses on the stewardship, security, quality control, analysis, and use of data in developing state-of-the-art products and solutions. At a high level, the DMAP will enable the agency to align technology and innovation across multiple industries, enhance its data practices, and allow efficient collaboration across a growing, diverse workforce. Operating in a cloud environment is crucial to executing DMAP.3
The Necessity of a Data Management Strategy
Understanding the potential of what can be achieved through data is largely the impetus for the FDA’s DMAP strategy. Evolving technologies are ushering in next-generation solutions and more precision processes. Data is a highly integral component of these technologies. Capabilities to track and trace medical and food products can lead to more expedient responsiveness to unplanned events such as pandemics, natural disasters, and global supply chain disruptions.
A significant component of TMAP is data governance, which is a collection of practices and processes that help ensure proper data asset management. For example, the number of projects in the
Center for Drug Evaluation and Research’s data standards portfolio has substantially increased. Having a more structured approach to manage the center’s data initiatives has become imperative.
TMAP/DMAP Progress Report
One year into the TMAP initiative—before, and even during, the COVID-19 pandemic—the FDA has been making some notable progress with its action plans. In October 2020, the agency published a TMAP program update highlighting some of its achievements.
While the FDA’s technology modernization efforts aimed to meet the rapid pace of scientific and technology innovation, they were also established to improve the user experience of its workforce as the agency’s workload increased. The Office of Information Management and Technology has been actively launching a mobile device management program to give staff the necessary tools to work remotely without slowing productivity.
AI technology can rapidly analyze data and automatically identify connections and patterns in data that people or rules-based screening systems can easily overlook. The agency has been leveraging AI to expand its predictive analytics capabilities in a pilot program for screening imported foods as part of its food safety initiative. Noting the amount and diversity of foods—particularly seafood—the United States imports, safety is a top priority.4
According to former FDA Commissioner Stephen Hahn, the pilot program has made a significant improvement in surveillance efforts. In addition to improved food product oversight, the FDA is using ML to gain insight from its stores of collected data to make more informed decisions about which facilities to inspect, what foods are most likely to cause health concerns, and other potential risks. “The results are exciting. This approach has real potential to be a tool that expedites the clearance of lower risk seafood shipments and identifies those that are higher risk,” said Hahn.5
Technologies that were once siloed and independent are now becoming more interoperable. In August 2020, the FDA approved a novel automated insulin delivery and monitoring system. The interconnected, bluetooth-enabled device is a closed-loop system that works by measuring the body’s glucose levels every five minutes and automatically adjusting insulin delivery. The device functions on internet of things technology, which enables medical devices from different manufacturers to be interconnected and effectively communicate with one another.6
Measuring the Progress
Overall, the TMAP and DMAP action plans have enabled the FDA to take a more modernized, proactive approach to pursuing its regulatory mission. The modernization strategies are a work in progress, but the agency has identified specific metrics to measure the success of each strategy. These include a recognizable reduction in regulatory cycles, broader data sharing across the organization and the life sciences industry, and the creation of a culture of empowerment among its workforce. Going forward, the agency plans to continue engaging with industry stakeholders and providing public updates on its activities and accomplishments.
References
Alan Rencher is a results-driven software engineering executive and technology leader with more than 20 years’ experience and expertise in solutions and product development with leading tech firms and multi-billion-dollar companies in various industries. As vice president of engineering at MasterControl, he mentors the software delivery teams to plan and build enterprise-class solutions for the life sciences and works hand-in-hand with product and architecture leadership to set the company’s strategic vision. Alan leads with a development operations mindset to assist software engineers and teams to solve real-world problems that offer customers the most in-demand solutions. Prior to MasterControl, Alan spent time in leadership and architectural roles with companies such as Target, Melaleuca, COPB, Deseret Book, Defense Support Services, and Ameritrade. He holds various engineering and computer science degrees.
In early 2019, the FDA experienced an influx of investigational new drug (IND) applications. Much of the growth was attributed to a surge in new cell and gene therapy products. Drug delivery system technology has also been on the rise, and nanotechnology is currently being explored as a drug delivery vehicle due to its ability to provide site-specific and target-oriented delivery of precision medicines.1
Combination products using advanced software and interconnectivity are becoming increasingly popular in the point of care and the home healthcare markets. These products are designed to provide a high level of precision with treatment regimens. They also enable healthcare providers to receive and react to data in real time, which greatly enhances many aspects of patient care.
The innovations moving through regulatory pathways are made possible through technologies enabling connectivity, interoperability, and the use of more data. Data is more abundant, more focus-area specific, and has a wide range of life sciences uses. For example, artificial intelligence (AI) and machine learning (ML) technologies are highly data-centric and are becoming more common in mainstream healthcare.
AI is designed to simulate human intelligence processes. It acquires information, determines how to analyze and use the information, and self corrects upon receiving new data. An AI algorithm has a programmed ability to continue learning and adapting based on the information it receives, making it useful for processes that involve analyzing enormous amounts of data. ML is an artificial intelligence technique that can be used to design and train software algorithms to learn and act on data, which is often used for predictive analytics. According to the FDA’s action plan on artificial intelligence and machine learning, “the technologies have the potential to transform healthcare by deriving new and important insights from the vast amount of data generated during the delivery of health care every day.”2
By nature, AI can continually acquire and analyze data and provide useful information for making decisions or solving problems. However, AI is designed to work through every possible route or option to present the best solution. This approach isn’t always practical because it can take a long time to reach a goal. Heuristic learning is a practical method for problem-solving, learning, or discovery. It may not always find the best solution, but it will always find a satisfactory solution in a reasonable amount of time. Used with AI, heuristic learning is a way to inform the algorithm about the direction to a goal, then it estimates the least costly route to a good solution to use in decision-making.
FDA’s TMAP Program Fosters Innovation
In an effort to stay abreast of scientific and technological advancements, the FDA implemented its Technology Modernization Action Plan (TMAP) in 2019. The modernization initiative has been a groundbreaking endeavor not only to facilitate innovation, but to overcome challenges within the regulatory paradigm. The FDA’s multifaceted responsibilities call for the agency to continuously monitor science, technology, and data trends. Oversight of complex, interconnected medical products requires cross-functional expertise from multiple offices within the FDA. That said, the agency’s modernization effort allows more transparency and collaboration across the organization, as well as the overall life sciences community.
To effectively expedite innovative and breakthrough products, the FDA must be more agile and dynamic. Therefore, a top TMAP priority was the migration of the agency’s technology infrastructure to a cloud environment. This helped the organization become more modernized, thus delivering more value to the industry and public. Because data has taken a more central role in medical product development and use, the agency had to better focus on data management.
FDA’s Data Modernization Strategy
The digitization of processes, pervasive use of mobile technologies, and advancements in data gathering and analytics have created new types and more uses for data. A central part of the FDA’s TMAP blueprint is making better use of data, which led to the development of the Data Modernization Action Plan (DMAP). Specifically, this data strategy focuses on the stewardship, security, quality control, analysis, and use of data in developing state-of-the-art products and solutions. At a high level, the DMAP will enable the agency to align technology and innovation across multiple industries, enhance its data practices, and allow efficient collaboration across a growing, diverse workforce. Operating in a cloud environment is crucial to executing DMAP.3
The Necessity of a Data Management Strategy
Understanding the potential of what can be achieved through data is largely the impetus for the FDA’s DMAP strategy. Evolving technologies are ushering in next-generation solutions and more precision processes. Data is a highly integral component of these technologies. Capabilities to track and trace medical and food products can lead to more expedient responsiveness to unplanned events such as pandemics, natural disasters, and global supply chain disruptions.
A significant component of TMAP is data governance, which is a collection of practices and processes that help ensure proper data asset management. For example, the number of projects in the
Center for Drug Evaluation and Research’s data standards portfolio has substantially increased. Having a more structured approach to manage the center’s data initiatives has become imperative.
TMAP/DMAP Progress Report
One year into the TMAP initiative—before, and even during, the COVID-19 pandemic—the FDA has been making some notable progress with its action plans. In October 2020, the agency published a TMAP program update highlighting some of its achievements.
While the FDA’s technology modernization efforts aimed to meet the rapid pace of scientific and technology innovation, they were also established to improve the user experience of its workforce as the agency’s workload increased. The Office of Information Management and Technology has been actively launching a mobile device management program to give staff the necessary tools to work remotely without slowing productivity.
AI technology can rapidly analyze data and automatically identify connections and patterns in data that people or rules-based screening systems can easily overlook. The agency has been leveraging AI to expand its predictive analytics capabilities in a pilot program for screening imported foods as part of its food safety initiative. Noting the amount and diversity of foods—particularly seafood—the United States imports, safety is a top priority.4
According to former FDA Commissioner Stephen Hahn, the pilot program has made a significant improvement in surveillance efforts. In addition to improved food product oversight, the FDA is using ML to gain insight from its stores of collected data to make more informed decisions about which facilities to inspect, what foods are most likely to cause health concerns, and other potential risks. “The results are exciting. This approach has real potential to be a tool that expedites the clearance of lower risk seafood shipments and identifies those that are higher risk,” said Hahn.5
Technologies that were once siloed and independent are now becoming more interoperable. In August 2020, the FDA approved a novel automated insulin delivery and monitoring system. The interconnected, bluetooth-enabled device is a closed-loop system that works by measuring the body’s glucose levels every five minutes and automatically adjusting insulin delivery. The device functions on internet of things technology, which enables medical devices from different manufacturers to be interconnected and effectively communicate with one another.6
Measuring the Progress
Overall, the TMAP and DMAP action plans have enabled the FDA to take a more modernized, proactive approach to pursuing its regulatory mission. The modernization strategies are a work in progress, but the agency has identified specific metrics to measure the success of each strategy. These include a recognizable reduction in regulatory cycles, broader data sharing across the organization and the life sciences industry, and the creation of a culture of empowerment among its workforce. Going forward, the agency plans to continue engaging with industry stakeholders and providing public updates on its activities and accomplishments.
References
Alan Rencher is a results-driven software engineering executive and technology leader with more than 20 years’ experience and expertise in solutions and product development with leading tech firms and multi-billion-dollar companies in various industries. As vice president of engineering at MasterControl, he mentors the software delivery teams to plan and build enterprise-class solutions for the life sciences and works hand-in-hand with product and architecture leadership to set the company’s strategic vision. Alan leads with a development operations mindset to assist software engineers and teams to solve real-world problems that offer customers the most in-demand solutions. Prior to MasterControl, Alan spent time in leadership and architectural roles with companies such as Target, Melaleuca, COPB, Deseret Book, Defense Support Services, and Ameritrade. He holds various engineering and computer science degrees.
