The pharmaceutical sector is advancing at a frenzied pace in 2018 as transformational technology changes and dynamic market forces continue to offer the industry new opportunities and challenges. Some of the top trends affecting pharmaceutical companies were recently explored in a free white paper, “Top 5 Trends in the Pharmaceutical Industry in 2018.”
One of the trends examined in the white paper is the advent of artificial intelligence (AI) and how that nascent technology is not only making substantial inroads in the life sciences generally, but in pharmaceutical manufacturing more specifically.
Artificial intelligence is the concept of using computerized algorithms to process data more efficiently than humans can. The “first wave” of AI involved optimization programs or “knowledge engineering” (1). However, this initial form couldn’t perceive facts, learn new rules or deal well with the unknown. The second wave of AI progressed to where practitioners developed statistical learning programs or “machine learning.” In medicine and the life sciences, evolving pattern-recognition systems are also known as clinical decision support systems and can be used, for example, to analyze ECGs, retinal scans and genetic data. Third-wave AI are termed “hypothesis generation or “contextual normalization’ programs,” and are able to increase the amount of data that can be deconstructed to find meaning. It’s third-wave AI that’s currently being used for drug discovery due to the technology’s penchant for identifying connections that were previously loosely associated by normalizing unrelated contexts.
“This is disruptive because it allows us to simultaneously generate and test novel hypotheses for a variety of life-science use cases,” said Gunjan Bhardwaj, founder and CEO of Innoplexus, an AI service business that specializes in the life science and health care sectors.
Increasingly, pharmaceutical companies are turning to AI to reduce the time and costs involved in formulating new medicines. In July 2017, GlaxoSmithKline announced that it was investing $43 million in AI, and others, like Merck, Johnson & Johnson and Sanofi, are also throwing their lot in with the emerging technology as a way to streamline the drug discovery process (2). According to a January 2018 survey, nearly half of life science professionals are using or experimenting with AI (3).
With AI already playing an important role in other major industries, such as in the development of self-driving cars and facial recognition software, it’s perhaps unsurprising that the life sciences, including pharmaceuticals, would follow suit. More specifically, the goal is to utilize modern supercomputers and machine learning systems to predict how molecules will behave and how likely a compound is to make an effective drug, thus saving companies time and money during development.
“AI has the potential to revolutionize life sciences and healthcare — all the way from early preclinical drug discovery to selecting precision treatments for individual patients,” said Dr. Steve Arlington, president of The Pistoia Alliance, a nonprofit that conducted the AI survey.
Andrew Hopkins, chief executive officer of privately owned Exscientia which is working with GSK on its AI, said the company’s AI system “could deliver drug candidates in roughly one-quarter the time and at one-quarter of the cost of traditional approaches.”
But the excitement over the potential that AI can bring to pharma is also tempered with caution by pharma companies. Despite its enormous potential to revolutionize drug R&D, the technology has yet to firmly demonstrate that it can successfully bring a new molecule from computer screen to lab, to clinic and finally to market.
Still, many pharma industry analysts not only say that AI is up-and-coming, but that it is here to stay.
“Though no AI-driven drug has required regulatory approval yet, experts across the board anticipate that implementing AI will soon be necessary to compete in the industry,” according to an article by WuXi Global Forum Team at PharmExec.com (4). “In the next 10 years, they say AI will be universally integrated into pharma R&D operations. With 90 percent of drug candidates failing to reach approval and the expense of clinical trial failure estimated to be up to $1.4 billion (over half of the average new drug cost), pharma companies are very much incentivized by the prospect of improved drug development success.”
Similarly, Jan Segal, a senior conference producer at NextLevel Pharma, says “What can be automated, will be automated — There are many stages of early drug discovery that are expensive to conduct, take time and are labor demanding. If pharma companies proceed through these various stages faster with automation, they will eventually save money, time and increase rate success.” (5)
In this opportunity-rich environment, pharmaceutical companies will need to create explicit strategies for AI development that will lead to greater efficiencies and cost savings in drug development.
Alex Zhavoronkov, co-founder of Insilico Medicine, recommends that pharmaceutical executives fully absorb the fast-moving landscape as startups and tech firms move on drug discovery applications for AI. He said that drug companies should also seek to develop their own in-house AI expertise and, most importantly, change their relationships with startups and tech businesses from partnerships into acquisitions.
“In 2017, almost every large pharmaceutical announced a partnership with one or more AI startup[s]. The recent acquisition of Flatiron Health heralds the new trend,” Zhavoronkov said. “It is important for executives to explore the strengths and weaknesses in the startup environment.”
He also recommends keeping a close eye on China and forming a more globally strategic perspective on AI.
“China is developing much more rapidly in AI than other countries primarily due to the heavy investments in the field and enormous interest among a very large population,” Zhavoronkov said. “Hiring AI talent in China is now more expensive than in many other areas, and there are multiple pharmaceutical companies looking to incorporate AI into drug discovery.”
As drug manufacturers begin to adopt AI, the technology will provide greater opportunities for improved efficiencies and increased revenue. This underscores the importance and competitive advantages your company can gain by integrating data and processes as a preparatory measure so that you are fully AI-ready. That planning begins with evaluating and upgrading your existing quality management system. In other words, the implementation of AI won’t necessarily be effective as it could be if your systems remain disconnected and your data is siloed. An integrated electronic quality management system (eQMS) will be essential to pharma to provide a unified platform for drug discovery and manufacturing processes, reporting and analytics. A robust, closed-loop eQMS also makes compliance more efficient and can help a drug firm free up staff to focus on key priorities, such as innovation and speed to market. As a leader in the quality management space, MasterControl provides the enterprise solutions pharmaceutical companies will need to fully incorporate and capitalize on the many advantages that AI will offer.
Artificial intelligence holds much promise for pharmaceuticals in the form of accelerated drug development and reduced costs. However, acquiring the expertise and resources to implement these technologies will require careful planning, strategy formation and investment. Above all else, drug company decision-makers need to closely track the pulse of AI progress worldwide so that they can reap the benefits of riding the leading edge of the trend wave as opposed to reacting after competitors gain the advantage.