21-Sep-2017
AI: The next frontier of innovation in life sciences
Summary
AI: The next frontier of innovation in life sciences
Last Updated: 21-Sep-2017
ProductLife Group’s Marco Anelli looks at the potential of artificial intelligence to change the way life sciences organisations operate.
From discovery and development and throughout the entire product life cycle, artificial intelligence (AI) has already shaken up the life sciences industry by way of innovations such as AI-enabled drugs expected to be brought to market in the near future. But AI has the potential to go beyond that. Given the plethora of data all across the life sciences value chain, the ability to gather, manage, and effectively use intelligence from that data has posed a challenge. AI offers the chance to exploit the data in both structured and unstructured forms.
Data that previously was hard to access or too difficult to analyse can now be exploited for a wide variety of purposes, including identifying potential new indications or unmet needs—even from data collected many years ago. Data leveraged through artificial intelligence could be invaluable in the approval process, in improving adoption rates, and in gathering real-world outcomes. And safety and tolerability issues, too, could be identified and handled before they became real problems.
Many in the field recognise a strong correlation between AI and personalised medicine because clear insights into data will make it easier to determine which sets of patients a drug might be best suited to.
AI and data management
One of the challenges companies typically face—and an issue that will become more important because of the Identification of Medicinal Products (IDMP)—is that data about products gets created, gathered, and stored by multiple different functions in multiple different systems. And accessing that data manually from the summary of product characteristics, clinical reports, and manufacturing reports would require an enormous amount of resources. Therefore, AI is considered to have important potential in the IDMP data-gathering process. Indeed, Gens and Associates found in its 2016 survey entitled Pursuing World Class Regulatory Information Management that about half of the 54 companies surveyed say they’re investigating AI for that data-gathering purpose, and another, third are monitoring what other companies are doing in that area.
The supply chain and AI
Elsewhere, AI is already being put to use in the supply chain. In 2016, Zipline, a drone start-up, began dropping blood products into Rwanda by using AI-controlled drones. Autonomous delivery, made possible by AI, is likely to become more prevalent across all forms of health-care delivery by following models adopted by such companies as Amazon. A few pharmaceutical companies are exploring uses for AI in other capacities. GlaxoSmithKline, for example, has been developing AI-enabled apps that provide information for patients, teaming up with IBM’s Watson for a Q&A feature for its cold and flu medication Theraflu. LEO Pharma’s LEO Science & Tech Hub, which was established to build collaborations with life sciences innovators, is exploring how AI can be applied in the area of drug discovery.
Nevertheless, there are barriers to overcome—perhaps most notably around how well and how quickly the regulatory environment can keep up with AI’s rapid rates of change. Take the development of devices, for example. Self-learning, AI-based devices would continue to learn and adapt, but how well and how fast the regulatory environment is keeping up with those ongoing changes is uncertain. It could potentially take time—and lobbying by the industry—to get regulatory authorities to adapt guidelines to AI.
But as machine learning gains pace and as its potential to speed things up, improve organisations, and remove cost from every part of an organisation becomes clearer, more and more companies will embark on ever more ambitious AI programmes.
About the author
Marco Anelli, MD, is head of the pharmacovigilance advisory and medical affairs practice at ProductLife Group.
From discovery and development and throughout the entire product life cycle, artificial intelligence (AI) has already shaken up the life sciences industry by way of innovations such as AI-enabled drugs expected to be brought to market in the near future. But AI has the potential to go beyond that. Given the plethora of data all across the life sciences value chain, the ability to gather, manage, and effectively use intelligence from that data has posed a challenge. AI offers the chance to exploit the data in both structured and unstructured forms.
Data that previously was hard to access or too difficult to analyse can now be exploited for a wide variety of purposes, including identifying potential new indications or unmet needs—even from data collected many years ago. Data leveraged through artificial intelligence could be invaluable in the approval process, in improving adoption rates, and in gathering real-world outcomes. And safety and tolerability issues, too, could be identified and handled before they became real problems.
Many in the field recognise a strong correlation between AI and personalised medicine because clear insights into data will make it easier to determine which sets of patients a drug might be best suited to.
AI and data management
One of the challenges companies typically face—and an issue that will become more important because of the Identification of Medicinal Products (IDMP)—is that data about products gets created, gathered, and stored by multiple different functions in multiple different systems. And accessing that data manually from the summary of product characteristics, clinical reports, and manufacturing reports would require an enormous amount of resources. Therefore, AI is considered to have important potential in the IDMP data-gathering process. Indeed, Gens and Associates found in its 2016 survey entitled Pursuing World Class Regulatory Information Management that about half of the 54 companies surveyed say they’re investigating AI for that data-gathering purpose, and another, third are monitoring what other companies are doing in that area.
The supply chain and AI
Elsewhere, AI is already being put to use in the supply chain. In 2016, Zipline, a drone start-up, began dropping blood products into Rwanda by using AI-controlled drones. Autonomous delivery, made possible by AI, is likely to become more prevalent across all forms of health-care delivery by following models adopted by such companies as Amazon. A few pharmaceutical companies are exploring uses for AI in other capacities. GlaxoSmithKline, for example, has been developing AI-enabled apps that provide information for patients, teaming up with IBM’s Watson for a Q&A feature for its cold and flu medication Theraflu. LEO Pharma’s LEO Science & Tech Hub, which was established to build collaborations with life sciences innovators, is exploring how AI can be applied in the area of drug discovery.
Nevertheless, there are barriers to overcome—perhaps most notably around how well and how quickly the regulatory environment can keep up with AI’s rapid rates of change. Take the development of devices, for example. Self-learning, AI-based devices would continue to learn and adapt, but how well and how fast the regulatory environment is keeping up with those ongoing changes is uncertain. It could potentially take time—and lobbying by the industry—to get regulatory authorities to adapt guidelines to AI.
But as machine learning gains pace and as its potential to speed things up, improve organisations, and remove cost from every part of an organisation becomes clearer, more and more companies will embark on ever more ambitious AI programmes.
About the author
Marco Anelli, MD, is head of the pharmacovigilance advisory and medical affairs practice at ProductLife Group.
