How AI Can Help Pharma Risk Managers Beat Their Regulatory Nightmares and Get the Rest They Need
Artificial intelligence is revolutionizing the pharmaceutical industry by empowering companies to dramatically accelerate the development of new drugs while cutting costs by hundreds of millions of dollars.
But the growing use of the technology in the industry is also fast creating new risks and updating old ones.
By enabling scientists to analyze millions of data points with a few clicks of a mouse, AI technologies such as natural language processing and machine learning have the potential to slash the time it takes for companies to comply with the scientific and regulatory requirements necessary to research, test and market new drugs by several years.
This kind of efficiency-boosting potential comes in handy as the industry is pushed by governments and by public opinion to devise quick solutions to the devastating COVID-19 crisis.
“The widespread use of high‐throughput screening in the lab, namely, the technique of testing large collections of compounds using robotics, coupled with AI, will speed up the testing of vaccine candidates to detect an immune response,” said Matthew Clark, the director of Global Markets at brokers La Playa.“The use of computational methods to track and record clinical trials data, and seek out trends in it, will surely speed the process still further.”
COVID-19 and the AI Push
While it can take up to eight or 10 years and hundreds of millions and even a few billion dollars to research, test and start manufacturing a new drug with traditional methods, AI can significantly streamline the process and reduce costs.
The importance of a fast-tracked process to the bottom line of pharma companies is punctuated by estimates that around 90% of the drugs that reach initial trial phases end up failing.
“As many companies are focusing on COVID-19 right now, I believe that it will take one to two years to find a treatment for it,” said Nagesh Jadhav, a digital transformation and innovation leader at consultants Stefanini.
“The pharmaceutical industry is accumulating a huge amount of data about microorganisms and genome sequencing of drug candidates. There are thousands and thousands of drug candidates and it becomes very hard to manually screen them.”
Large groups, such as GlaxoSmithKline, have announced partnerships with AI tech firms to fast-track COVID-19 treatments.
In April, UK-based biotech firm Healx announced it was employing AI to investigating treatments for COVID-19 by looking at combinations of 4,000 different medications available in the market. That implies analyzing 8 million pairs and 10.5 billion drug triples, according to the company.
The sheer amount of work to assess the combinations would be enough to keep a team of scientists busy for a long time. With AI, however, the expectation is that valid conclusions will be reached in a much shorter time.
Successful examples of the application of the technology have already popped up.
In January, Sumitomo Dainippon Pharma, a Japanese drug maker, and Exscientia, a British AI developer, announced they had created a new treatment for obsessive compulsive disorder, making the drug available for phase 1 clinical trials after less than 12 months of AI-based research. Usually, the same work would have taken around four to five years, the companies estimate.
Clinical trials can also benefit from the technology, noted Tom Daniels, the life science practice leader at HDI Global SE.
“AI has the potential to match suitable patients, based on factors such as age, medical history, location or symptoms, to a clinical trial they may be eligible for, thereby streamlining the recruitment and screening phases of a trial,” he said.
“There is also the potential for AI-enabled sensors to be used during a trial to ensure adherence to the study protocol by sensing whether a dose has been missed and issuing an alert. This can ensure trial integrity, improved accuracy and help to explain anomalies in the trial data, which will be presented to the regulator if applying for approval.”
Adding in Machine Learning
Machine learning has also enabled the industry to collate medical information sourced from scientific literature, as scientists rush to find a cure for COVID-19.
One company in particular used its system to search for approved drugs that had the potential to reduce the ability of COVID-19 to infect lung cells, noted Tanya Patel, an underwriter with HDI Global SE.
“This list was whittled down, based upon criteria such as side effects and required dosage, to identify the most promising candidate, an approved rheumatoid arthritis drug, which can now be trialed,” she said.
“At least 11 other molecules have been identified through AI technology as potential candidates by various firms globally.”
Still a Set of Risks to Watch
But deploying powerful new technologies often implies new risks for companies, and the marriage between pharma and AI is no exception to the rule.
Not the least because, to a certain extent, drug makers are treading into a realm that is not really their strongest suit.
“The core expertise of a biopharma company is not AI on itself. Many times they have to contract out a third party product or service as the starting point for how they are going to apply AI to their product development,” said Brad John, the head of life sciences at The Hartford.
“An algorithm and/or the dataset feeding the algorithm can come with bugs, issues or biases that pharmaceutical companies are not always made aware of.”
As other information-based technologies, the quality of the work performed by AI-based systems is only as good as the data fed into them. In an accelerated R&D and trial process, the usual controls that pharma companies employ to find problems in the development of drugs may not be sufficient to spot them on time.
“With AI, companies can potentially develop drugs at a much faster pace. As a result, errors and omissions may not be spotted until much later at the end of the process,” said Narvin Powar, a director at broker PharmaInsure.
Privacy concerns related to patient data are another worry, as the handling of enormous amounts of information from public health systems, hospital databases and clinical trials raise ethical and regulatory issues.
Pharma firms are a trove of riches for hackers, and if these firms are hacked, they can find themselves in violation of regulations such as the U.S. Health Insurance Portability and Accountability Act or Europe’s General Data Protection Regulations, which can result in large fines and reputational damage.
“There is no consistent regulatory framework on how companies must use people’s information,” John pointed out. “What a company does may be acceptable and completely compliant in one jurisdiction, but not in other.”
And then there is the matter of liability, a big concern for pharma companies in any situation, but particularly when factoring in the risk added by partners that work in areas out of the industry’s realm of expertise.
“There may be little clarity about where the liability of the pharmaceutical company stops and where the liability of the AI technology partner starts,” Powar said.
“Although it is possible to deal with it in contracts, there will always be gray areas.”
This is exactly the kind of risk that gets exacerbated during a time of emergency.
In recent months, companies have been pushed to come up with solutions for COVID-19, as media personalities and even presidents have touted the potential of unproven medicines that, if widely used without proper due diligence, could result in lawsuits for their producers.
“Despite the obvious pressure to get treatments to patients as quickly as possible, it is still necessary to properly test or assess any vaccine to ensure it’s safe and effective,” Clark said.
“As ever, regulators have a key role to play to ensure corners can be cut without compromising patient safety.” &